HTML. Hyper-Text Markup Language. KSEB. Kerala State Electricity Board. LDC ...... form of HTML elements enclosed in angle brackets (like ). HTML ...
Electricity Asset Management System using Web GIS Final Report Submitted by
SUNDARA BHARATHI
in partial fulfilment of the requirements of the award of the degree of
MASTER OF TECHNOLOGY in GEOINFORMATICS
Department of Earth and Space Sciences INDIAN INSTITUTE OF SPACE SCIENCE AND TECHNOLOGY Thiruvananthapuram – 695547 May 2015
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CERTIFICATE
This is to certify that the thesis titled Electricity Asset Management System using Web GIS, submitted by Sundara Bharathi to the Indian Institute of Space Science and Technology, Thiruvananthapuram, for the award of the Degree of Master of Technology in Geoinformatics, is a bona fide record of the re-search work done by him under our supervision. The contents of this thesis, in full or in parts, have not been submitted to any other Institute or University for the award of any degree or diploma.
Dr. Gnanappazham L. Assistant Professor Department of Earth and Space Sciences, IIST
Dr. Chandrasekar A. Head of Department Department of Earth and Space Sciences, IIST
Place: Thiruvananthapuram May 2015
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DECLARATION
I declare that this thesis titled Electricity Asset Management System using Web GIS submitted in fulfilment of the Degree of Master of Technology in Geoinformatics is a record of original work carried out by me under the supervision of Dr. Gnanappazham L., and has not formed the basis for the award of any degree, diploma, associate-ship, fellowship or other titles in this or any other Institution or University of higher learning. In keeping with the ethical practice in reporting scientific information, due acknowledgments have been made wherever the findings of others have been cited.
Sundara Bharathi SC13M020
Place: Thiruvananthapuram May 2015
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ACKNOWLEDGEMENT
I would like to express my sincere gratitude and grateful acknowledgement to Dr. Gnanappazham L. for her excellent guidance, constant supervision and encouragement throughout the project work. I am also grateful to Mr. Biju S. S., Assistant Executive Engineer, Power System Engineering, Office of the Director (Transmission & System Operation), KSEB and other officials of KSEB for their continued support by providing real-time data, support and guidance. I take this opportunity to thank Mr. Raja Shekhar S. S., Scientist SE, ASDCIG, NRSC, Hyderabad for his continued support, help and guidance related to open source softwares and to setup application framework. I thank Arun Prasad Kumar, PhD scholar for discussions during project work. Most importantly, I would like to show my sincere gratitude towards my parents and my sister who supported and encouraged me in every stage of life.
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ABSTRACT
As the power system in India is shifting towards smart grid operations, the Phasor Measurement Units (PMUs) are playing vital role in advanced telemetry operations for effective power monitoring and control. PMUs are used to measure phasor values at a rate of 25 to 30 samples per second and the measured data are transmitted to Load Despatch Centre (LDC) to have a better overview of real time security parameters and to improve operational system security (Srivastava et al., 2013). PMUs are much costlier and it is required to find optimum number and locations of PMUs giving complete observability for the entire power system network rather than installing a PMU in each substation. However, optimum PMUs are not sufficient for reliable data transfer to happen since the transmitters that transmit PMU data have a coverage limit (i.e., Transmitters can reliably transmit the data only up to a certain distance) and geographic features exist in the Line Of Sight (LOS) between transmitter and subsequent receiver may be hindering. Therefore, it is essential to establish a reliable data-link among the PMUs (transmitters and receivers) considering above limits. Such reliable data-links are established by installing repeaters in between the PMUs which receive and transmit signal bypassing the obstacles. It is cost-effective to install the repeaters in optimum numbers and locations between PMUs, taking geographic undulations into consideration. In this thesis work, Geographic Information System (GIS) based terrain analysis of finding optimum number and locations of repeaters between PMUs was attempted to establish reliable and economic communication operations in Kerala State Electricity Board (KSEB). Apart from mere numbers and tables from the PMU data obtained from substations, it is quintessential to visualize the data in a spatial context. It not only provides the state of the system but also aids the decision support system with abundant information in an easy and ingenious manner. Since the power industries also involve huge and exorbitant assets, it is beneficial to have a vigil on them with a spatial context to optimally manage the same. This thesis work also analyses the impact of GIS integration with the Transmission Asset Management System (TRAMS) of Kerala State Electricity Board (KSEB). As a consequence, a Web GIS application is developed and made use for the KSEB personals using OpenGeo Suite package.
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TABLE OF CONTENTS
CERTIFICATE ........................................................................................................................ 1 DECLARATION...................................................................................................................... 2 ACKNOWLEDGEMENTS .................................................................................................... 3 ABSTRACT .............................................................................................................................. 4 LIST OF TABLES ................................................................................................................... 8 LIST OF FIGURES ................................................................................................................. 9 ABBREVIATIONS ................................................................................................................ 10 1. INTRODUCTION ............................................................................................................. 11
1.1. GIS in Power Industry .................................................................................................. 11 1.2. Phasor Measurement Unit (PMU) ................................................................................ 12 1.3. PMUs in Indian power grid .......................................................................................... 12 1.4. Transmission System in KSEB – state of affairs ......................................................... 13
2. LITERATURE REVIEW ................................................................................................. 15
2.1. Need for optimum PMUs in Indian Power Grid .......................................................... 15 2.2. Optimization Techniques and PMUs .......................................................................... 16 2.3. GIS for Place-based problems ...................................................................................... 17 2.4. Potential of GIS in Smart Grid Operations ................................................................. 17 2.4.1. Integration of Electric Distribution System with GIS ....................................... 19 2.4.2. Role of GIS in Transmission System ................................................................. 19
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3. OPTIMUM LOCATIONS FOR REPEATER PLACEMENT ..................................... 21
3.1. Data used ...................................................................................................................... 21 3.1.1. Elevation Data .................................................................................................... 21 3.1.2. KSEB Data ......................................................................................................... 21 3.2. Software used ............................................................................................................... 24 3.3. Methodology ............................................................................................................... 24 3.3.1.Optimisation technique used .............................................................................. 24 3.3.2. Geospatial analysis............................................................................................. 28 3.4. Results for Optimum Repeater Locations ................................................................... 33
4. GIS BASED TRANSMISSION ASSET MANAGEMENT SYSTEM ......................... 34
4.1. Software Used ............................................................................................................... 35 4.2. Web GIS-TRAMS ........................................................................................................ 37 4.2.1. Get substation info ............................................................................................. 40 4.2.2. Generate Load Profile ........................................................................................ 44 4.2.3. Generate Regional Load Profile......................................................................... 45 4.2.4. Generate Voltage Profile.................................................................................... 46
REFERENCE ......................................................................................................................... 48
5. APPENDIX ........................................................................................................................ 50
5.1. Substation Visits .......................................................................................................... 50 5.2. TRAMS database ........................................................................................................ 50 5.3. Web GIS - TRAMS ...................................................................................................... 51 5.4. Analysis Functionalities .............................................................................................. 53 5.4.1. Get substation info ............................................................................................ 53
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5.4.2. Generate Load Profile ....................................................................................... 54 5.4.3. Generate Regional Load Profile......................................................................... 55 5.4.4. Generate Voltage Profile.................................................................................... 57
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LIST OF TABLES Table 1: Optimum PMU locations ........................................................................................... 25 Table 2: Repeater Distance Analysis between Kundara and Trivandrum ............................... 31 Table 3: Optimum Repeater Locations Analysis for KSEB .................................................... 33
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LIST OF FIGURES Figure 1: Edison's original service territory (Source: Bill Meehan, 2013b) ............................ 11 Figure 2: Transmission substations of KSEB .......................................................................... 22 Figure 3 Digital Elevation Model ............................................................................................ 23 Figure 4: Substation data with RDBMS model ....................................................................... 24 Figure 5: Optimum PMUs selected including LDC (Kalamassery) ........................................ 26 Figure 6: Process Flow Chart ................................................................................................... 27 Figure 7: Visibility region of Kundara..................................................................................... 29 Figure 8: Visibility region of Trivandrum ............................................................................... 30 Figure 9: Intersection of Visibility regions of Kundara and Trivandrum ................................ 32 Figure 10: OpenGeo Suite Architecure, Source: (Open Source Community, 2010) ............... 35 Figure 11: Web GIS TRAMS Overview ................................................................................. 39 Figure 12: Links shown on substation feature info render ...................................................... 41 Figure 13: Substation information render ................................................................................ 42 Figure 14: Load flow trend generated for the selected substation ........................................... 43 Figure 15: Load Profile of KSEB dated 06-11-2014 and time: 22:00 ..................................... 44 Figure 16: Regional Load Profile on 06-11-2014 at 22:00 ...................................................... 45 Figure 17: Voltage Profile generated on 06-11-2014 at 22:00 ................................................ 47
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ABBREVIATIONS A-CAMS
Asset Commissioning and Maintenance System
DEM
Digital Elevation Model
DMS
Distribution Management System
EMS
Electricity Management System
ESRI
Environmental Systems Research Institute
HTML
Hyper-Text Markup Language
KSEB
Kerala State Electricity Board
LDC
Load Dispatch Centre
LOS
Line Of Sight
MoR
Monthly operating Review
PMU
Phasor Measurement Unit
SCADA
Supervisory Control And Data Acquisition
SLD
Style Layer Descriptor
SoS
Station operating Statistics
SQL
Structured Query Language
TIN
Triangulated Irregular Network
TRAMS
TRansmission Asset Management System
WAMS
Wide Area Monitoring System
XML
Extensible Markup Language
SRTM
Shuttle Radar Topography Mission
AC
Alternating Current
NOTATIONS km
kilometer
m
meter
V
volt
A
ampere
MVA
Mega Volt Ampere
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1. INTRODUCTION 1.1.
GIS in Power Industry Right from the time when the transformer was invented, the electric utility operators
had to map their systems for managing their assets involved in transmission of power over long distances. Even during those times, location was very important in every aspect of the system. (Figure 1) shows an early map of Edison’s original service territory (Bill Meehan, 2013b).
Figure 1: Edison's original service territory (Source: Bill Meehan, 2013b) In this modern era, a fully grown power industry comprises every smart technique to hold the pulse of its consumers without regret. Today, the power industries have their own control and monitoring stations equipped with advanced telemetry operations managed through computers with the help of software. There is no wonder, if such softwares are made integrated with GIS modules. According to Bill Meehan (2013a), the challenges faced by power industries and utility operators are many. These challenges are exponentially growing and will grow due to
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the recent advancement in renewable energy technologies such as solar power, wind power and so on. With the advent of such technology, the existing system models may not be able to withstand the pressure and it is so delicate. For example, if an outage happens because of a tree falling over power lines in a remote location, it is hard to find exactly where the problem is and if the remote location is hard to access, it becomes even harder to resolve the problem in a short span of time. With such challenges ahead, Bill Meehan (2013a) foresees a model to answer the questions such as where does the problem exist and at an advanced level, what is causing the problem. Thus GIS comes into play to answer such questions by integrating all the systems involved in a power industry. Following the black-out that happened in the United States in 2003, there is growing number of countries implementing GIS enabled Smart Grid Operations. The black-out lasted for more than a day which is considered to be the major black out in the history of the United States because of a petty reason that trees fallen over the power lines and the worst case is that it took days to find that cause. This is exactly why GIS is integrated with Power Systems not to resolve the problem directly but to have better overview of where and what is happening. The developed countries such as the United States, Japan, Germany, etc., are place holders of such well-built smart grid with GIS integrated visualization system where they are able to monitor and overview the state of the system in a much better way.
1.2.
Phasor Measurement Unit (PMU) Phasor is a complex number that represents both the magnitude and phase angle of
the sine waves found in AC system and they can be measured precisely by PMUs. PMUs are referred as Synchro-phasors since they measure and timestamp the data with the help of GPS time and thus synchronous measurement is possible with a group of PMUs. Thus comprehensive view of the entire grid at a central location is possible using PMUs as mentioned by (Srivastava et al., 2013). PMUs are widely used in developed countries for monitoring the power system security and health parameters.
1.3.
PMUs in Indian power grid Indian power grid is one of the largest power systems in the world and comprises of
five regions of operation namely Northern Region (NR), Southern Region (SR), Western Region (WR), Eastern Region (ER) and North – Eastern Region (NER). The recent major black out happened in Northern Region along with West, East and North-Eastern Regions in the month of July, 2012 resulted in complete analysis of the state of the system along with the
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cause of the blackout. A report was submitted by the Task Force who investigated on the matter which speaks about Telemetry/Phasor Measurement in one of its chapters. In that, it is deliberated about the enhancement of data acquisition through PMUs based Wide Area Monitoring System (WAMS). According to Srivastava et al. (2013), PMU has a prime advantage over conventional technology by measuring at high speed typically 25 or 50 samples per second when compared to its counterpart which measures one in every 4 to 10 seconds. The device PMU measures and timestamps the phasor values at any instant of time with the help of GPS and hence synchronous measurement is possible across the entire network, i.e., a group of PMUs and so PMUs are called Synchro-phasors. It manipulates and sends a large amount of data in terms of terabytes to the nearest monitoring station through signal of a certain frequency allocated to the concerned power industry. According to Pathirikkat Gopakumar et al. (2013), it was required only 58 PMUs in place of 208 PMUs and thus the total cost estimated went down to USD 1.74 million from USD 6.24 million. Hence, it is important to find optimal number of PMUs to serve the purpose of observing all the substations in the network instead of placing a PMU in every substation. The data measured at these PMUs obtained at the Load Despatch Centre (LDC) gives a better overview of real time security parameters and expected to improve operational system security significantly as mentioned in the report (Srivastava et al., 2013). In this scenario, the data-link should be strongly established between subsequent PMUs to ensure reliable data transfer and thus reliability of the entire system. For such reliability, the major hinders will be lack of Line Of Sight (LOS) from one PMU to another due to undulating topography of the terrain in LOS, the limitations of the hardware devices used (usually the range of coverage of transmitters). With this back ground, the present study focuses on following objectives.
1. Identifying the optimum number and locations of PMUs using Linear Optimisation for the state of Kerala. 2. Identifying the optimum locations for installing intermediate towers (repeaters) between subsequent PMUs using GIS terrain analysis in case of lack of Line of Sight so as to reduce the overall cost of implementation.
1.4.
Transmission System in KSEB – state of affairs: Today’s electric utility comprises integrated systems such as Distribution
Management System (DMS), Transmission Asset Management System (TRAMS), Outage Management System (OMS), Supervisory Control And Data Acquisition (SCADA),
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Electricity Management System (EMS) and Network Planning System (NPS) etc. Each system has its own significance in the proper functioning of Power System every day and during contingencies. These Systems have to communicate each other for their proper functioning and in need of an accurate common Network Topology. Since these systems have spatial information in common, the GIS plays a vital role in pulling all these systems together and keep the operations intact in one monitor and precisely in one mouse click at a very advanced level.
Kerala State Electricity Board (KSEB) forms a part of Southern Grid of India and is also responsible for supplying power from large industries (around cities such as Cochin, Calicut, Thiruvananthapuram, etc.) to a population of about 33.8 million people. The real time data are obtained through RTUs (conventional measurement devices unlike PMUs) via 2.3GHz signal. KSEB has developed an application called Power System Engineering Module (PSEM) envisaging the following components to monitor and manage the system in real time. •
Transmission Asset Management System (TRAMS) incorporating the asset details of all substations under KSEB and also tracks the equipment repair, movement and decommissioning status, etc.
•
Monthly Operating Review (MoR) furnishing the information such as major breakdowns, failures, switching status of various feeders and other equipments, and station operating parameters such as peak loading, metering parameters, etc.
•
Station Operating Statistics (SoS) logging the operational statistics and also capturing scheduled and unscheduled interruption duration in detail.
•
Asset Commissioning and Maintenance System (A-CAMS) handled by Station-inCharge for recording the equipment commissioning test data, for scheduling and tracking equipment maintenance, logging maintenance test results, etc.
The current state of affairs of the above modules is that they all are separate and have no spatial relation in it. It is essential to visualize the TRAMS integrated with the other systems in geospatial environment and that becomes soon the need of the hour. Having seen the state of the existing system at KSEB, this thesis work focuses on • Developing a GIS based web application for TRAMS. • Integrating GIS with power system analysis toolkits and analysing its impact on KSEB operations.
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2. LITERATURE REVIEW Need for optimum PMUs in Indian Power Grid
2.1.
Srivastava et al. (2013) investigated the contingency occurred in the month of July, 2012 in Indian Power Grid. The Task Force assigned to investigate on the matter is of the view that a pragmatic approach in ensuring data availability is needed. It has deliberated upon the benefits of the scheme for enhancement of data acquisition through PMUs based Wide Area Monitoring System (WAMS), employing PMUs. While mentioning about improving Telemetry and Communication for power systems operation and control, it emerged that there is a need for understanding the benefits and development of applications related to PMUs based monitoring system. The following explains the need for PMUs: •
PMUs or Synchro-phasor technology has the capability of measuring the system in real time most effectively and efficiently.
•
PMUs and WAMS would be helpful in better visualization of the system and utilization of existing transmission assets with reliability and security.
•
PMU measurements are taken at high speed typically 25 or 50 samples per second – compared to one every4 to 10 seconds using conventional technology.
As per Srivastava et al. (2013), it is easy for the load dispatchers to have a better overview of real time security parameters and that the operational system security is expected to improve significantly if the systems are implemented and the applications using the data are developed. The case studies performed by Pathirikkat Gopakumar et al. (2013), outbursts the fact that installing PMUs in every substation is not needed to have complete observability of the power system in a region. The cost benefit of establishing PMUs in the optimum locations compared with establishing PMUs in all the buses is widely discussed with appropriate reference to the market value of a PMU by (Pathirikkat Gopakumar et al., 2013). The author concludes that the whole SRIPG, having 208 buses, required only 58 PMUs for its complete observability by optimal PMU placement. By doing so, the total PMU cost by optimal placement was estimated as USD 1.74 million instead of USD6.24 million which means that USD 4.5 million could be saved by optimal PMU placement.
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Optimization Techniques and PMUs:
2.2.
As seen before, it is essential to find optimum number and locations of PMUs for complete observability of an electrical network. In order to find optimum PMUs, there are various optimization techniques available in the world of mathematics. A few of the optimization techniques seen widely used for the purpose are Integer Linear Programming, Genetic Algorithm, Randomized Greedy Algorithm (RGAM), Neural Networks, etc. In RGAM, Initial PMUs are placed randomly in any bus and it is so flexible to the network topology changes. According to Debomita Ghosh et al. (2013), RGAM has a prime advantage over its counterparts as it has no any stringent initial conditions. It is very useful in the case of dynamically changing scenarios. The power grid is dynamic in nature and thus it is suitable for such problems. When a PMU is installed in an electric bus, it is expected to measure both voltage phasor and the current phasors of all the lines concurring to this bus irrespective of the number of current lines. But in real, PMUs have limitations for number of lines that it can measure current phasors and hence it is not practical to adjust the number of lines of every bus as told by (Marin et al., 2003). The work done by Marin et al. (2003) involving Genetic Algorithm for optimisation presents two results •
Minimum set of PMUs guaranteeing the complete observability as a function of the number of measurements that a PMU can measure.
•
Minimal number of measurements that a PMU must measure in order to guarantee the observability with the least number of PMUs. According to Marin et al. (2003), it is shown that 7 PMUs are needed for a 30 bus
network with a limitation of 3 current phasor measurements per each PMU. It is good to go with heuristic optimisation approach in the cases of complex and large networks. It is much easier and straight forward to use linear approaches in case of small networks of size less than 30. Thus the method of Binary Integer Linear Programming is used for the purpose of finding optimum PMU locations in a network of 22 bus system of Kerala region by (Pathirikkat Gopakumar et al., 2013). According to Pathirikkat Gopakumar et al. (2013), the optimum placements were estimated based on a fundamental concept of observability which refers to the ability of a system(in this case the number and locations of PMUs) to observe the entire
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network completely. i.e., if a PMU is connected to a bus(A), the voltage in the immediately connected bus(B) could be estimated by Ohm’s law with the current measured in the feeder connecting A and B. 2.3.
GIS for Place-based problems In all the place-based scenarios, where suitability of locations is studied, GIS had
given convincing solutions with a much lesser effort. Finding suitable place for retail outlet is one such problem carried out by (Ismail Onden et al., 2012) with the integration of Network Analyses and Spatial Analyses. The study by Ismail Onden et al. (2012) is focused on using spatial tool’s closeness and density analysis abilities to find the attractiveness levels of the study area for new retail store. Total coverage of the attractiveness levels of each retail store alternative is determined using Network Analyses. Then the two results are integrated by Integer Programming for retail location selection. This methodology could be extended or applied to any such location selection problems where only the weights should be given meticulously. In this analysis done by Ismail Onden et al. (2012), maximising total coverage of attractiveness of a retail alternative forms the first objective function and minimising the cost forms the second objective function. Similarly, Bukhari et al. (2010) have explored multi criteria evaluation model with the help of GIS to find the suitable locations for schools in Mukim Batu, the study area. Multiple criteria such as Proximity to industrial area, Distance from commercial area, Proximity to main road, Sound level, Slope, Height, Proximity to electrical transmission line and various other factors are considered and all these criteria were input as GIS files in the personal geodatabase. A GIS model is developed including all these constraints and factors to find the suitability of locations for schools. By this project, Bukhari et al. (2010) have integrated the values of decision makers and stakeholders with the logical and scientific foundation for site selection problems.
2.4.
Potential of GIS in Smart Grid Operations In today’s world, GIS plays a vital role in aiding decision support system related to
every field of work. As India is gearing up towards smart grid operations, GIS finds its way through many ways. One such application is finding optimum locations for PMUs. Debomita Ghosh et al. (2013) identified the optimum locations of PMUs using RGAM integrated with GIS analysis. The reliability of the system is analysed through GIS analysis for different configurations of the intermediate towers and the results for Eastern Power Grid Region are
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published along with the case studies. The GIS functionalities such as Line Of Sight (LOS) and Haversine Formula for geographic distance were handled in the study. According to Debomita Ghosh et al. (2013), the intermediate towers are required to be placed if the geographic distance is greater than that of the Line Of Sight distance. The tower height was considered as 100m above ground and the number of intermediate towers was identified by finding the ratio of absolute difference between the geographic distance and LOS distance to the coverage distance of an intermediate tower. Among a region of PMUs, based on the least number of intermediate towers required, one PMU is chosen as the main PMU and the rest as auxiliary PMUs. Then the probabilities of failure for different types of configurations of intermediate towers were estimated using Poisson distribution. From the case studies of Debomita Ghosh et al. (2013), it is observed that Parallel Series configuration of intermediate towers as the most reliable path for continuous and uninterrupted data exchange.
Apart from GIS analysis for place-based problems related to PMUs, the GIS is also used to render the real time measurements with a spatial context in the form of an interactive map. According to Bill Meehan (2013a), the GIS can act as the base system for the entire power system comprising various other systems such as Transmission System, Distribution System, Outage Management System, SCADA, and so on.
Bill Meehan (2013b) depicted the state of affairs of Power System existed before the integration of GIS and envisaged the future impacts of GIS in Power System operations. In order to overcome the upcoming challenges facing the growing population and demand for power, the author foresees the industry and provides a solution. Thus the integration of GIS for power industries came into existence and it was not just a progress but a leap of faith and success as envisaged by the author. In the chapter ‘From legacy to breakthrough’, the author portrays the scarcity for fossil fuels, growing demand for energy, modern renewable energy revolution and its consequences. It brings to notice that, the system that was present at the time of writing the book was not smart enough to tackle the storm of issues that were on the way. Finally, solutions through GIS were suggested by integrating various Systems associated with the power system such as Electric Management System, Distribution Management System, Outage Management System and etc. in order to change the fate of power system operations. Thus the concept of enterprise GIS was introduced in the electric distribution industry based on a sound and thorough data model. The author highlights the need for an integrated information system to keep in track each and every part of the entire
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system and thereby to help operators and decision makers for easy operation to meet the everchanging business environment and growing demand & challenges.
2.4.1. Integration of Electric Distribution System with GIS: Philip Hartley (2005) intended to explore the data exchange between a Geographic Information System and a Power System Analysis toolset. This thesis documents a specific application of GIS to common power system engineering practices, whereby information inherent to a GIS model provides the necessary input data to run a load-flow analysis within an external engineering software package. The four reasons why GIS is a capable source of data to feed engineering analysis in many utilities mentioned by L.V. Trussel were quoted here as: 1) GIS is commercially available in industry standard software (like AutoCAD®); 2) GIS can manage a broad swath of information across department lines; 3) The use of XML and open source database technologies enables sharing; 4) Reporting can be accomplished through standard web-interfaces.
In addition to feeding the load-flow model, the engineering results were returned into the spatial (graphical) mapping display within the GIS. This exercise showcased the interoperability of GIS and highlighted the flexibility it possesses to extend into the technical realm of engineering. 2.4.2. Role of GIS in Transmission System Bill Meehan (2013a) describes how geospatial technology can be applied to all aspects of the electric utility business from Smart Grid to generation to transmission to distribution to the retail supply of electricity to customers in the form of a modern enterprise geographic information system (GIS). Not only technical details of GIS enabled electric system are described but also the way it works in real business world is envisaged. Bill Meehan (2013a) in chapter-3 describes in particular about Electric Transmission and GIS. Today, any electrical network with a voltage level of 100,000V and above is called as Transmission network. The chapter revolves around the blackout that happened in the U.S. at 2003 due to the event when two small trees came in close contact with the transmission lines. According to the author, actually, it was not the trees causing the blackout, but the lack of solid situational awareness, tools for collaboration, and up-to-date data probably
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contributed just as much to the blackout. GIS does not replace the existing SCADA system in any way. It provides the missing spatial context and facilitates the workflow for transmission line repair. It talks about how GIS impacts transmission asset management by taking into various geographical data into account starting from consumer locations to vegetation map and about various technologies used along with such as LiDAR.
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3. OPTIMUM LOCATIONS FOR REPEATER PLACEMENT Kerala resides over the Western Ghats of India which is one of the toughest terrains in the country. Though it is a habitat for diverse species, the human species contributes to the major population in the state of about 33.8 million. It is one of the industrial hubs in the country. It is a host of a few hydro power generators and other such non-reliable source of electricity. Being the most consuming state of the country, Kerala consumes electric power from the neighbouring states.
Kerala State Electricity Board (KSEB) is responsible for maintaining the power system of Kerala comprising 4 substations of 400kv, 21 substations of 220kv, 133 substations of 110kv and 81 substations of 66kv. In KSEB, signal of 2.3 GHz is used for communication network and relies on conventional measuring technology of Real Time Units (RTUs). They have dedicated communication towers of about 100m tall at all the major transmission substations. The transmission system where optimum PMUs have to be located involves about 21 substations (Figure 2).
3.1.
Data used
3.1.1. Elevation data The Digital Elevation Model (DEM) data collected from Shuttle Radar Topographic Mission (SRTM) are obtained as separate tile images. The SRTM DEM is 90 m resolution data which is sufficient for the analysis carried out in this thesis work. The DEMs are mosaicked and clipped to get the DEM data only for Kerala administrative boundary (Figure 3) and then is converted to Triangulated Irregular Network (TIN) file and used for 3D analysis such as visibility, Line Of Sight. 3.1.2. KSEB data Spatial Data The spatial data for substations and the feeders are collected from KSEB. The spatial data are all provided with the unique key identifier for each substation point feature and each feeder line feature. This unique identifier is used to link the spatial data with that of the nonspatial data for power system analysis toolkit development.
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Figure 2: Transmission substations of KSEB
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Figure 3: Digital Elevation Model
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Relational Database Management System (RDBMS) KSEB owns a database of all the information related to substations, feeders and all the equipments involved in power system operation and control. The data are based on the relational database model. The non-spatial data comprising unique identifiers for all the equipments and components are linked to a substation through substationcode (Figure 4). This substationcode is the common key between the spatial and non-spatial data provided by KSEB.
Figure 4: Substation data with RDBMS model 3.2.
Software used There are more number of GIS tools available in market in today’s world. ArcGIS
being the conventional tool for GIS analysis at both academic and enterprise level. ArcGIS is a proprietary software owned by ESRI. Nowadays, open-sources are fluencing the research environment with a handful combination of high performance and flexible tools which are more stable better than the propreitary softwares. Such are the tools like QGIS, PostGIS, GDAL, OSGEO, OpenGeo Suite, and so on. There are even enterprise editions for some of the mentioned open source tools. In this thesis, ArcGIS, QGIS, PostGIS and OpenGeo Suite are handled at required times.
3.3.
Methodology
3.3.1. Optimisation technique used Linear Programming is a straight-forward optimisation technique. Integer Programming is widely used inthe cases where the network is simple comprising less number of nodes around 100. For networks of larger size, the heuristic approach of optimisation will give better results. Binary Integer Linear Programming (BILP) is a Linear optimization techniques where there are only two states of the system either one or zero. In this case, placing a PMU at a bus is considered one and zero otherwise. Initially, it is assumed that PMU is placed in all the nodes of the network and thus the vector ‘X’ having all ones at the start. The connection matrix is formed based on the condition that if two nodes are connected, it is one and zero otherwise.
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Thus the connection matrix is formed as T = [1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0; 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
1 1 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 1 0 1 1 1 1 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 1 1 0 1 1 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 1 0 1 1 0 1 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1 1 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 1 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1
0; 0; 0; 0; 0; 0; 0; 0; 0; 0; 0; 0; 0; 0; 0; 0; 0; 0; 0; 1; 1;];
t i, j 1, if i and j are connected Assuming there are ' N' no. of buses and all are installed with PMUs. Cost of installation is ' x k ' where k N. N
min x k
Objective function is
k 1
constraint is
TPMUX b PMU ,
X x1 , x 2 , x 3 ,..., x N , xi 0,1 PMU placement variable , T
b PMU 1,1,1,...,1Nxi T
The optimum locations of PMUs are found to be (Figure 5) Table 1: Optimum PMU locations No.
Name
1
New Pallom
2
Idukki
3
Areekode
4
Kanhirode
5
Kundara
6
Trichur
7
Trivandrum
26
Figure 5: Optimum PMUs selected including LDC (Kalamassery)
27
Figure 6: Process Flow chart
28
3.3.2. Geospatial analysis Visibility map Line Of Sight in GIS is a phrase used to describe the unimpeded view or access from one point to another point across a terrain or surface. Visibility analysis is a spatial analysis of the portions of a line that are visible as opposed to not visible from a starting point. A viewshed identifies the cells in an input raster that can be seen from one or more observation locations. Each cell in the output raster receives a value that indicates how many observer points can be seen from each location. The altitude values of the substations are all offset by 100m to include the height of the tower (Debomita Ghosh et al., 2013). The connection matrix is written based on the Single Line Diagram for the state of Kerala (Pathirikkat Gopakumar et al., 2013). It is due to following reasons, the repeater locations are highly preferred to be existing substation locations. •
Land is generally costly and acquisition for industry needs makes it much more costly.
•
Allocating a separate security system for maintenance work.
•
If the place is too remote, sending service personals will become difficult. Because of these reasons, it is quintessential to identify KSEB substations located in
such visible regions of both the substations. In order to find substations lying in the visible regions, the substations are buffered to 200m to form polygon features. The reason for choosing 200m buffer is because transmission substations usually cover a square area of about 0.25km2 to 1 km2. P lacing the repeater anywhere within this buffer at a height of 100m will serve the purpose. While proceeding to select two PMUs, the PMU which is close to the other PMU is considered for finding the repeaters in-between. For example, Kundara and Trivandrum are substation s in the southern part of Kerala where Kundara is in between New Pallom and Trivandrum. Since the coverage is of only 50 km from a substation, the signal from Trivandrum to Kundara cannot reach reliably. It is much worse from Trivandrum to New Pallom. Therefore Kundara and Trivandrum are considered for analysis (Fig: 7 to Fig: 9)
29
Figure 7: Visibility region of Kundara
30
Figure 8: Visibility region of Trivandrum
31
It is not enough to identify the substations lying in the intersecting visible regions but also to find the one optimum repeater location if more than one are found in the region. Thus the optimum location is found by comparing the distance of each location from each PMU location. The one which is located closer to the centre of the line joining the two PMU locations is chosen to be the optimum repeater location. Thus table 1 shows Parippally 110kv substation as the most suitable location for placing repeater between Trivandrum and Kundara. The substation corresponding to minimum of absolute difference from the below table gives the optimum location. Table 2: Repeater Distance Analysis between Kundara and Trivandrum
110 kV substations
Distance from Tvm (km)
Distance from
Absolute Difference in distance
Kundara
(km)
(km)
Ambalapuram
39.62
8.58
31.04
Parippally
23.35
17.77
5.58
Attingal
9.59
32.07
22.48
32
Figure 9: Intersection of Visibility regions of Kundara and Trivandrum
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Results for Optimum Repeater Locations:
3.4.
Similarly, the optimum locations for repeater placement for the entire state of Kerala are identified to be the following substations. Table 3: Optimum Repeater Locations Analysis for KSEB S. No 1
2
3
4
4
5
6
From -> To substations Trivandrum -> Kundara
Substations suitable for placing repeaters
Ambalapuram Parippally Attingal Kundara -> New Edathura Pallom Mavelikkara Kayamkulam New Pallom -> Aroor Kalamassery Chellanam Chengalam Kandanad Kanjikuzhy Panangad Thaikkattusery Vaikom Idukki -> New Since direct visibility Pallom exists within 50km, repeaters not requierd. Trichur -> Kandassankadavu Kalamassery Ollur Valappad Viyyur Areekode -> No substations lie in the Kanjikode intersecting visible region. Intersecting visible regions lie in Chittur and Alathur taluks of Palghat district. Areekode -> No substations lie in the Trichur intersecting visible region. Intersecting visible region lies in Perinthalmanna taluk of Malappuram district.
Abs Difference in distance (km) 31.04 5.58 22.48 25.58 0.131 6.68 23.16 19.18 41.19 24.19 20.61 28.4 0.22 6.49
KSEB repeaters
Parippally Kayamkulam (app. 8km from Mavelikkara)
Vaikom (app 5km from Thaikkattusery)
NA
30.40 34.72 18.51 46.07 NA
NA
NA
NA
NA
The results are 100 % accurate when verified with locations identified using above method by KSEB.
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4. GIS BASED TRANSMISSION ASSET MANAGEMENT SYSTEM The transmission system forms a complex network of connections between where power is generated and where it is delivered. The demand and production meets at the transmission system. Since the power loss is minimum at high voltages and it is cost effective, the Transmission System in general involves the equipments which handle voltage level above 100kv. The various components of Transmission System involves •
Transmission Lines which deliver bulk power from Generating substations to UHT, EHT, HT/110kv substations;
•
110kv/66, 33, 11kv substations which deliver power from the transmission system to the distribution system including industrial loads;
•
Phase shifting transformers, Auto-transformers in substations for configuring the transmission system;
•
The links using HVDC transmission and its related AC components;
•
Switch gears, circuit breakers, feeders, earth materials etc.;
•
Supporting structures for the transmission system;
•
The authorities responsible;
•
Load dispatchers and Load Dispatch Centres;
•
Land parcels owned by transmission system;
The power industries need to know the locations of each and every component, their relationship to one another along with their surroundings. It needs a convenient way of looking at it to support decision making process. That is what exactly done by GIS. It can be integrated in such a manner that right from site planning to end-consumer satisfaction, everything can be analysed spatially and the right decisions can be made at the right moment. GIS just complements all the systems such as design systems, security systems and SCADA and doesn’t replace them.
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4.1.
Software Used
OpenSource: According to Pickle (2012), open source software is made of technically advanced, high performing, flexible and stable modules in the world. Open Sources can be modified by the developers based on their requirements and are not so abstract. They are transparent and most powerful tools. OpenGeo Suite Architecture The OpenGeo Suite is one of the open source GIS packages having required components to create a web GIS application. User interface framework: GeoExt / ExtJS&OpenLayers Application cache: GeoWebCache tile cache Application server: GeoServer map/feature server Storage: PostGIS/PostgreSQL spatial database
Figure 10: OpenGeo Suite Architecure, Source: (Open Source Community, 2010)
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Hyper Text Markup Language (HTML) HTML is the standard markup language used to create web pages. It is written in the form of HTML elements enclosed in angle brackets (like ). HTML tags most commonly come in pairs lke
and
.Web browsers can read HTML files and render them into visible or audible web pages. Browsers do not display the HTML tags and scripts, but use them to interpret the content of the page. (Anonymous2, 2015) Extensible Markup Language (XML) It is a markup language designed to describe data. It is a software- and hardwareindependent tool for carrying information. Unlike HTML, these are not used to display the data and the XML tags are not predefined and it must be defined by the developer. Piece of softwares are writen to carry the information wrapped in XML tags and make use of them. (Anonymous3, 2015)
Styled Layer Descriptor(SLD) Geospatial data has no intrinsic visual component. In order to see data, it must be styled. Styling specifies color, thickness, and other visible attributes used to render data on a map. In Geoserver, the styling is accomplished using SLD and SLD is an XML-based markup language. (Anonymous1, 2015)
Javascript Javascript is the most widely used and most reviled programming language on the modern web. It interacts with HTML and renders excellent graphics in object oriented fashion. Extjs and GeoExtjs are javascript libraries available for graphics designing in web applications and in general GIS based web applications along with OpenLayers.js which is used to render geospatial data in browsers.
GeoServer GeoServer is a java-based software server that allows users to view and edit geospatial data.Using open standards set forth by the Open Geospatial Consortium (OGC), GeoServer allows for great flexibility in map creation and data sharing. (Anonymous6, 2015)
37
PostGIS PostGIS is a spatial database extender for PostgreSQL object-relational database. It adds support for geographic objects allowing location queries to be run in SQL.In addition to basic location awareness, PostGIS offers many features rarely found in other competing spatial databases such as Oracle Locator/Spatial and SQL Server.PostGIS is released under the GNU General Public License (GPLv2). PostGIS is developed by a group of contributors led by a Project Steering Committee. (Anonymous4, 2015)
PHP PHP (recursive acronym for PHP: Hypertext Preprocessor) is a widely-used open source general-purpose scripting language that is especially suited for web development and can be embedded into HTML.PHP development is mainly focused on server side scripting and much of its syntax is borrowed from C, Java and Perl with a couple of unique PHPspecific functions thrown in. It is used to generate dynamic web pages quickly. (Anonymous5, 2015)
4.2.
Web GIS-TRAMS: A detailed attention is given towards the construction of GISmodels for
Transmission System. The substation locations as point features, feeders as line features are imported in PostGIS and then added into map view by OpenLayers through GeoServer. The base layer being the continent map. The functionalities include: •
Render substation information
•
Render Load Flow Trend for a given date and time
•
Generate Load Profile on a particular date and time
•
Generate Voltage Profile on a particular date and time
•
Generate Regional Load Profile on a given date and time
The styles for the imported features are applied through SLDs created one for each feature. Once the user is logged in, the user views the application as shown in the (Figure 11). The tools are in the top panel of the map viewer. Left panel contains the legend tree panel which lists the layers displayed in the map viewer. The user can select/deselect the layers if
38
needed to enable/disable displaying the layers in the viewer. The one in the bottom right is the scale tool which displays the scale which the map is viewed. The basic tools include zoom-in, zoom-out, zoom-to-extent, map and print. In map tool, the map shown can be published for the selected layers and rendered with tools of the user’s interest. The published map can be printed any time for any purpose it is meant for. For quick rendering, save map can also be used to bookmark the url. The Query tool is used to perform queries over the selected layer from the table shown at the bottom of the viewer.
Apart from basic functionalities provided in the Open-Source tools, the specific functionalities required for KSEB as mentioned above are implemented writing functions in GeoExpolorer.js. The functions are written as plugins calling the PHP server codes using urls and rendering the data into OpenLayers appropriately. Each functionality is accessed through specified icons and these icons are added along with the basic tools in top panel.
39
Figure 11: Web GIS TRAMS Overview
40
4.2.1. Get substation info: This feature is similar to get feature info. This should enable the features clickable to render the information about the substation selected. The information rendered will give a complete detail of the assets corresponding to the selected substation if the user is able to drill down the content. And hence, all the contents corresponding to a selected substation are displayed in an accordion layout to the right side panel of the viewer. Through this accordion layout, each component information can be drilled down to a limit the user requires. After setting the button on, clicking on 220kv substation features throws a popup with two links (Figure 12). •
View info
•
View Load Flow Trend If the user wants to view the asset information of a substation in detail and the
components associated with that substation, then the right side panel is rendered with detailed information on click of first link (Figure 13). If the user wants to view the trend of load flow in a particular substation, then the user can go for it with the second link provided to render a line chart for load flow (Figure 14). These are the major functionalities related to asset management of transmission system. These functionalities can be enhanced by including more options such as selecting particular dates and time to view the load trend and also implementing further more query functionalities such as generating historical trail of a tranformer, fetching equipments of a same make in the entire KSEB operations. These results will give a better visualisation of where things are and what actually happens to them.
41
Figure 12: Links shown on substation feature info render
42
Figure 13: Substation information render
43
Figure 14: Load flow trend generated for the selected substation
44
4.2.2. Generate Load Profile: GIS can be integrated with Power System Anaysis functions for better understanding of real time scenario. In a substation, the load delivered keeps on changing every time and it is essential for the operators or load despatchers sitting at load despatch centers to view whether the substation is over-loaded or under-loaded. Accordingly, the loads will be despatched. It will help the decision support personals to presume whats going on where and accordingly make plans such as proposing substations, feeders in the appropritate areas etc.
Figure 15: Load Profile of KSEB dated 06-11-2014 and time: 22:00 The legend is shown in the left panel. Green indicating optimally loaded substations, yellow – undr-loaded substations and red (not shown here) – indicating over-loaded substations. The load is the power in MVA calculated from non-spatial data and compared against the capacity of the substation here the maximum power that can be supplied by 11kv feeders to the substation. By default, if it exceeds 90% of the capacity, it is indicated as overloaded.
45
Between 45% to 90% is termed to be optimally loaded and when the substations consume less than 45% of the capacity, then they are termed as under loaded substations (Figure 15).
4.2.3. Generate Regional Load Profile: As per KSEB operations, Kerala state is divided into three regions namely Northern region comprising Kasaragod, Cannanore, Kozhikode, Wayanad, Malappuram and Palgha; Central region comprising Trichur, Ernakulama and Idukki; Southern region comprising Kottayam, Pattanamtitta, Quilon, Trivandrum and Alappuzha; In this scenario, if the power analysis is viewed in regional perspective (Figure 16), it will add more value to the information and decisions.
Figure 16: Regional Load Profile on 06-11-2014 at 22:00 The spatial information in such cases might lead to analyse and verify the decisions such as proposed substations are in the region of over loaded substations or not.
46
4.2.4. Generate Voltage Profile: Similar to load profile, the profile with respect to voltage at any instant of time can also be viewed to realize whether any substation is experiencing high voltage or under voltage. The optimum voltage level should be within +7% to -7% of actual voltage. If the voltage is below 7% of the actual, then it means it is experiencing under voltage condition and if above 7%, then the substation is termed to be experiencing high voltage. Due to advancement in the grid technology with respect renewable energy sources, the power grid is taking a paradigm shift decentralised power distribution and power transfer is happening in both the directions. This might lead to stress in the grids for which the power grids should be well equipped to manage the dynamicity. In such cases, it is important to analyse the voltage profile in geographic perspective.
For example, if the geographic information of the consmers and their installed renewable energy power sources are mapped to the GIS-TRAMS, then the grid stress can be well visualized in a map and decisions can be made with a spatial context. This leads to an effective and more efficient way of handling the assets of TRAMS. The generated voltage profile shown (Figure 17) for a particular date of ‘06-11-2014’ and time of ‘22.00’ with a buffered region of 150m around the substations. This can be extended to generate voltage profiles district-wise and taluk-wise and the coverage region of each substation can be accurately viewed with its current, voltage status.
47
Figure 17: Voltage Profile generated on 06-11-2014 at 22:00
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REFERENCE 1. Anonymous1 (2015). http://docs.geoserver.org/2.6.x/en/user/styling/sldintroduction.html. (n.d.). Retrieved from http://docs.geoserver.org/. 2. Anonymous2 (2015). http://en.wikipedia.org/wiki/HTML. (n.d.). Retrieved from http://en.wikipedia.org. 3. Anonymous3 (2015). http://www.w3schools.com/xml/. (n.d.). Retrieved from http://www.w3schools.com. 4. Anonymous4 (2015). Project Steering Committee, http://postgis.net/. Retrieved from http://postgis.net/ 5. Anonymous5 (2015). PHP Group (2015). http://php.net/manual/en/intro-whatis.php. Retrieved from http://php.net. 6. Bill Meehan (2013a). GIS Enhanced Electric Utility Performance. ESRI. 7. Bill Meehan (2013b). Modeling Electric Distribution with GIS by ESRI. ESRI. 8. Bukhari Z., Rodzi A.M., Noordin A. (2010). Spatial multi-criteria decision analysis for safe school site selection. International Geoinformatics Research and Development Journal. 9. Anonymous6 (2015). Geoserver Open Source Community, http://geoserver.org/about/. Retrieved from http://geoserver.org. 10. Srivastava D. K., Agrawal V. K., Sehgal Y. K., Pahwa P. K., Prof. Kulkarni A., Dr. Singh S. N., Asthana A. K., Arunabha Basu, Rajiv Krishnan, Ramakrishna V. (2013). Report of the Task Force on Power System Analysis Under Contingencies. New Delhi: Ministry of Power, India. 11. DebomitaGhosh, Ghose T., Mohanta D. K. (2013). Reliability analysis of a geographic information system-aided optimal phasor measurement unit location for smart grid operation. Risk and Reliability, 450-458. 12. Marın F. J., Garcia-Lagos F., Joya G., Sandoval F. (2003, September 18). Genetic algorithms for optimal placement of phasor measurement units in electrical networks. 13. Ismail Onden, Ceyda Gungor Sen, Alper Sen. (2012). Integration of Integer Programming with GIS Analyzing Abilities for Determining the Convenience Levels of Retail Stores. Procedia - Social and Behavioural Sciences, 1144-1149. 14. Open Source Community (2010). TheOpenGeo Suite Enterprise Edition. OpenGeo Suite.
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15. Pathirikkat Gopakumar, Sutya Chandra G., Jaya Bharata Reddy M., Dusmata Kumar Mohanta. (2013). Optimal placement of PMUs for the smart grid implementation in Indian power grid - A case study. Higher Energy Press and Springer-Verlag Berlin Heidelberg. 16. Pickle, Eddie. (2012). The Benefits of Open Source/OpenGeoSuite. FOSS4G. Washington, DC: FOSS4G North America Conference. 17. Smith, Philips Hartley (2005). Electrical Distribution Modeling: An Integration of Engineering Analysis and Geographic Information Systems. Blacksburg, Virginia: Virginia Polytechnic Institute and State University.
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5. APPENDIX 5.1.
Substation Visits: As part of data collection, visited a few substations of 220kv, 110kv and 66kv in the
state of Kerala to collect the data of Telemetry system in substations. As per the information, the following are observed: There are indigenous communication towers of 100m tall (on an average) with antennas having coverage of about 40km to 50km. The frequency of the signal used for communication is 2.3GHz. The substations that are visited include Pothencode 220kv substation, Paruthippara 110kv substation and 66kv substation at Thiruvananthapuram. These substations are seen with Real Time Unit (RTU) which are used for measuring the real time electricity parameters. These substations are monitored and controlled remotely using a software application from Gas Insulated Switch Gear (GIS) substation control room. Further the substations transmit the data to Control Center located at Kalamassery. The transmitters transmitting signal from one station to another had Line Of Sight issues and thus are not reliable. This motivated to analyse the geographic terrain to propose the appropriate sites for placing the repeaters for reliable communication to happen. The GIS data of substations and feeders are collected from KSEB. To perform geographic analysis using GIS tools for finding appropriate sites for repeater placement, the terrain data of Kerala is downloaded from http://srtm.csi.cgiar.org/. The SRTM DEM data that are downloaded are of 90m which is sufficient for the purpose. 5.2.
TRAMS database: The TRAMS involve all the electric components corresponding to Transmission
System of KSEB. The data of all the components are available in the PostgreSQL database. All the components corresponding to a substation are linked to the substation data through substationcode. The components are such as Transformers, Switch Gears, Circuit Breakers, Feeders and so on and each component is referred with a unique id. Figures 1, 2 shows the data format of KSEB supplied data. Further to the static data, the dynamic data are obtained through tables such as sosondemand, morfeeders, evefeeders etc. The PostGIS query pseudocodes for the above mentioned is as below:
51
Procedure:
PostGIS queries
Create view -> visibility
Create or Replace with select
Fetch visible polygons where gridcode=1
where conditional
Find the intersection of visibility of two observers
ST_Intersects (obs1, obs2)
Create view for buffering 110kv points for 500m
ST_Buffer (110kv_points, 0.005)
Find 110kv substations in intersecting region
ST_Intersects (obs1_2, 110_bufer)
Find optimum repeater
min (abs (x.dist – y.dist) ) where x.dist = ST_Distance (obs1, 110_intersect) y.dist = ST_Distance (obs2, 110_intersect)
5.3.
Web GIS – TRAMS: The map layers of substations, feeders, taluks, districts, etc. are stylized through
SLDs via GeoServer. A sample SLD for stylzing the substation point feature is shown in the next page.
52
SLD for 220kv substation: Default Point A boring default style A sample style that just prints out a purple square Rule 1 RedSquare A red fill with an 11 pixel size circle #00B32D 10 name #000000
53
5.4.
Analysis Functionalities:
The following are the pseudocodes for implementing the functionalities analysed in GISTRAMS. 5.4.1. Get Substation info:
Add Get Substation info icon in panel toolbar. Onclick of icon, enable features clickable. Set the format of the x-type to ‘grid’ To render the json as name-value pair in the popup accordion, Configure the CustomRenderer, If name is ‘code’ Add a link with text ‘View code info’ End If name is ‘load_trend’ Add a link with text ‘View code Load Flow Trend’ End On click of ‘View code info’ link, call a function fillPanel(code); On click of ‘View code Load Flow Trend’, call a function renderChart(code);
In fillPanel (code) Declare array table with values (Substation, Transformers, Battery_and_battery_charger, Electric_bus,
Circuit_breaker,
Voltage_Transformers,
ccwt,
Earth_Mat,
Capacitor_Bank,
EHT_Consumers,
Isolators,
Current_Transformers, Lightning_Arresters,
Switch_Gears)
While table count is less than or equal to table length Create a JsonStore Get data from PostGIS via PHP Create a Grid Panel with two columns namely ‘Name’ and ‘Value’ Fill the panel with the store. End Expand the right side panel.
54
PHP connection:
If name is equal to table_input Write the appropriate select table query into a string End Do pg_query Declare Json_array with two attributes ‘success and ‘Data While pg_fetch_assoc (query_result) While itemnamein query_result length Name =itemname Value = Item(itemname) Push key_value pair into the json_array attribute Data End End Encode in json and send to javascript.
In renderChart(code)
Create a jsonStore to store the json returned by genchart.php Create a Ext chart object with XField as ‘date’ and YField as ‘load’ Create a GeoExt window Load the chart item inside window Render the window in the viewer
5.4.2. Generate Load Profile:
Add Generte Load Profile icon in the toolbar. Onclick of the toolbar , call the plugin GenerateLoadProfile In GenerateLoadProfile() Define a OpenLayers.Vector protocol with loadprofile.php url Define filters with conditions If load is greater than 90% of capacity Set value as overloaded
55
Set style of symbolizer as red End If load is greater than 45% of capacity and less than 90% of capacity Set value as optimally loaded Set style of the symbolizer as green End If load is less than 45% of capacity Set value as underloaded Set style of the symbolizer as yellow End Add the vector layer to map and render with legend. PHP connection: Fetch current and voltage values of 11kv feeders corresponding to each substation Calculate power in MVA by taking the product of square root of 3, voltage and current divided by 1000 (since current values are in amperes). Fetch the sum of transformer rating as capacity where secondary of transformer is 11kv for each substation. Fetch the geom values of the corresponding substations Push the load, capacity and geom into array and encode into json Echo the json 5.4.3. Generate Regional Load Profile: Add Generte Regional Load Profile icon in the toolbar. Onclick of the toolbar , call the plugin GenerateRegionalLoadProfile
56
In GenerateRegionalLoadProfile() Define a OpenLayers.Vector protocol with regional_load.php url Define filters with conditions If load is greater than 90% of capacity Set value as overloaded Set style of symbolizer as red End If load is greater than 45% of capacity and less than 90% of capacity Set value as optimally loaded Set style of the symbolizer as green End If load is less than 45% of capacity Set value as underloaded Set style of the symbolizer as yellow End Add the vector layer to map and render with legend. PHP connection: Fetch current and voltage values of 11kv feeders corresponding to each substation Calculate power in MVA by taking the product of square root of 3, voltage and current divided by 1000 (since current values are in amperes). Fetch the sum of transformer rating as capacity where secondary of transformer is 11kv for each substation. Fetch the geom values of the corresponding substations
57
Add the load and capacity values for substations within in the corresponding region Push the load, capacity and geom into array and encode into json and echo json. 5.4.4. Generate Voltage Profile: Add Generte Voltage Profile icon in the toolbar. Onclick of the toolbar , call the plugin GenerateVoltageProfile In GenerateVoltageProfile() Define a OpenLayers.Vector protocol with voltageprofile.php url Define filters with conditions If load is greater than 90% of capacity Set value as overloaded Set style of symbolizer as red End If load is greater than 45% of capacity and less than 90% of capacity Set value as optimally loaded Set style of the symbolizer as green End If load is less than 45% of capacity Set value as underloaded Set style of the symbolizer as yellow End Add the vector layer to map and render with legend.
58
PHP connection: Fetch current and voltage values of 11kv feeders corresponding to each substation Calculate power in MVA by taking the product of square root of 3, voltage and current divided by 1000 (since current values are in amperes). Fetch the sum of transformer rating as capacity where secondary of transformer is 11kv for each substation. Fetch the geom values of the corresponding substations Push the load, capacity and geom into array and encode into json Echo the json
