High-Performance Fault Diagnosis in PWM Voltage ... - IEEE Xplore

IEEE TRANSACTIONS ON POWER ELECTRONICS, VOL. 29, NO. 11, NOVEMBER 2014

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High-Performance Fault Diagnosis in PWM Voltage-Source Inverters for Vector-Controlled Induction Motor Drives Jianghan Zhang, Jin Zhao, Dehong Zhou, and Chengguang Huang

Abstract—This paper proposes a simple method for single switch and double switches open-circuit fault diagnosis in pulsewidthmodulated voltage-source inverters (PWM VSIs) for vectorcontrolled induction motor drives, which also applies to secondary open-circuit fault diagnosis. According to the phase angle of one phase current, the repetitive operation process of VSI is evenly divided into six operating stages by certain rules. At each stage, only three of the six power switches exert a vital influence on this operation and the others make a negligible influence. An open-circuit fault of power switches introduces the repetitive current distortions, whose period is identical to that of the three-phase currents. The current distortions appear at faulty stages and disappear at healthy stages. The stage is determined by recalculating the current vector rotating angle. The d- and q-axis current repetitive distortions are applied to the detection of faulty switches due to its simplicity and fair robustness, while the faulty stages are used for the identification of faulty switches. The simulations and experiments are carried out and the results show the effectiveness of the proposed method. Index Terms—Fault diagnosis, induction motor, open-circuit fault, pulsewidth-modulated voltage-source inverter (PWM VSI), vector control.

I. INTRODUCTION N most industrial and manufacturing processes, the electric drive systems are exposed to overloading and hard environmental conditions, which may lead, in addition to the natural aging process, to many faults essentially related to the induction motor or the inverter. These faults can lead in turn to unpredicted downtime or even huge damages to human life. Thus, the demand for reliability and maintainability is growing largely, which has promoted the development of many fault detection and isolation (FDI) methodologies. FDI performs the following three tasks [1]: fault detection; fault identification; and remedial actions, also known as fault isolation. Among these three tasks, the fault detection and identification are considered as a prime

I

Manuscript received September 2, 2013; revised November 19, 2013; accepted January 9, 2014. Date of publication January 17, 2014; date of current version July 8, 2014. This work was supported by the National Natural Science Foundation of China under Grant 61273174 and Grant 61034006. Recommended for publication by Associate Editor J. Hur. The authors are with the School of Automation, Huazhong University of Science and Technology, Wuhan 430074, China (e-mail: jianghanzhang2007@ 163.com; [email protected]; [email protected]; hcgcool@163. com). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TPEL.2014.2301167

Fig. 1.

General configuration of an induction motor drive system.

process for the practical implementation and are often called as fault diagnosis. FDI allows to ensure a safe and continuous operation of the treated system and to guarantee the timely maintenance to the faulty process components. Typically, a motor drive system consists of a microcontroller unit (MCU) for implementing the control algorithms, a power electronic converter such as pulsewidth-modulated voltagesource inverter (PWM VSI), and an induction motor as shown in Fig. 1. Within the considered drive system, faults may occur in the inverter, dc bus, or in the motor itself. The most common inverter topology used in these systems and also in power quality applications is voltage-source inverter. Taking into account their complexity, these power converters are very susceptible to suffer critical failures. In fact, statistic results show that about 38% of the failures in variable speed ac drives in industry are concentrated in power electronics [2]. More recently, an industry-based survey of reliability in power electronic converters also shows that “semiconductor power device’ is the most fragile component, which is followed by “capacitors” and “gate drives” [3]. Generally, power device failures in the inverter can be broadly classified as open-circuit faults and short circuit faults. Presently, the power converters designed for industrial use are often equipped with some diagnostic units that enable protection against disturbances and execute a shutdown in case of severe faults to avoid greater damages. Although the protection against power transistor overcurrent or short circuit by monitoring the transistor collector–emitter voltage has become a standard feature for the inverters, the open-circuit failures are often overlooked yet for its characteristic of slow response. This type of fault may result from the disconnection of the semiconductor

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due to lifting of bonding wires caused by thermic cycling or due to a problem in the gate control signal [4]. This kind of failure does not necessarily cause the system shutdown and can remain undetected for an extended period of time [5]. However, it causes the operating topology imbalance, raises the current pressure of healthy transistors, degrades the performance of the system, and then may lead to a secondary fault in the converter or in the remaining drive components, resulting in the total system shutdown and higher repairing costs, or even personal injury. So this paper presents a simple and effective system approach to the problem of real time detection of the open-circuit faults in inverter switches. Research works have been developed and published in the literature [1], [6]–[30] on the subject of open-circuit fault diagnosis in the motor drive systems. The methods based on voltage, such as the error voltage method which compares the actual voltage with voltage command [1], [6] and lower switch voltage method [7], where the measurement of the voltages is used, show a fast diagnosis performance and thus reduce the time between the fault occurrence and diagnosis. But its major problem is to increase system cost by extra hardware equipment such as voltage sensors and electric circuits. In [10], the authors proposed two methods based on the analysis of the current vector. The first one often called slope method uses the current vector trajectory in the Concordia plane to detect and localize faults on a PWM inverter. The second one often called instantaneous frequency method allows to detect an inverter fault, but not to identify the faulty switch. And the current vector trajectory slope method was extended in [11] by reanalyzing the knowledge of the output inverter currents distribution in α − β frame combined with additional diagnosis variables which use their mean values. The Park current vector method was introduced in [12], [13] by monitoring the average motor supply current trajectory to diagnose the faults in variable speed ac drives. The centroid determination method presented in [9] detects and identifies the faults in an inverter-fed induction motor from the centroid of the three-phase currents transformed from a − b − c to α − β plane. The chief drawback of the earlier mentioned methods is the susceptibility to the load torque. In [8] and [28], the authors suggested the normalized direct component (DC) current method. In order to make the scheme independent from the load, the mean values of the three-phase currents are divided by the absolute value of its own first harmonic coefficients and then compared with a threshold and thus faults are diagnosed. All these suggested current-based methods earlier have shown acceptable performances in current open-loop control scheme such as scalar (V /f ) control, but are not quite effective in current closed-loop control scheme such as vector control. Load current analysis method [14] extended and improved the normalized DC current method by using fuzzy logic symptoms to identify the faulty switch. Other fuzzy diagnosis methods [14], [15] use fuzzy logic to make it effective under current closedloop condition, but still have so many uncertainties about its utility in practical drive systems for the memberships of fuzzy inference are easily influenced by parameters of environment and motor. The artificial intelligent diagnosis methods based on advanced algorithms such as neural network [26], wavelet

Fig. 2.

Ordinary indirect vector control system of an induction motor.

neural network [17], [18], wavelet fuzzy network [16], subtractive clustering method [19], [20], and SVM [21], [27] have also been proposed, but an excessive amount of computation is a major drawback. The authors in [29] detect and identify the faults by incorporating a simple switch control in the conventional method. Recently, model-based [22], [24] and observerbased [23], [25], [30] methods have been addressed with its high performance of immunity to the load torque and appropriate computation. However, robustness on parameter variation is issued due to nonlinear motor model. This paper proposes a simple and low-cost fault diagnosis method for open-circuit faults of a vector-controlled induction motor drive. A deep analysis is given into the principle of vector control and thus the relative features in faulty condition are obtained to detect and identify the faults. The proposed scheme is divided into four parts: 1) error calculation; 2) fault detection; 3) stage determination; and 4) fault identification. The error calculation part generates the residual errors between the actual currents and current commands to represent the current distortions in the d–q frame. Then, the d-axis residual error is processed by a series of algorithm to detect the occurrence of the fault. The stage determination part determines the faulty stages by recalculating the current vector rotating angle and the d- and q-axis current distortions. Then, according to a stageconversion table, the faulty switch is identified. The proposed fault diagnosis is configured without extra hardware equipment and excessive computational effort and thus allows to be embedded into an existing vector-controlled induction motor drive system. To test and validate the proposed method, single switch and double switches open-circuit faults are introduced in the VSI for a vector-controlled induction motor drive. Simulation and experimental results are presented to show the validity of the proposed diagnosis method. II. CHARACTERISTIC ANALYSIS OF THE OPEN-CIRCUIT FAULT IN THE VSI The ultimate object of vector control is to drive the induction motor as a shunt-wound dc motor, i.e., to control the field excitation and the torque-generating current separately [31]. Fig. 2 shows an ordinary indirect vector control system of an induction motor drive without consideration of flux weakening. The stator currents are first decomposed by coordinate transformation

ZHANG et al.: HIGH-PERFORMANCE FAULT DIAGNOSIS IN PWM VOLTAGE-SOURCE INVERTERS

into two synchronously rotating Cartesian vector components id and iq which are controlled independently to control the rotor flux and torque respectively. It can be expressed by the following standard set of equations with d–q axis fixed in the synchronously rotating frame with rotor-flux orientation ⎧ np Lm ⎪ ⎪ ⎨ Te = Lr ψr iq (1) ⎪ Lm ⎪ ⎩ ψr = id Lr /Rr p + 1

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Fig. 3. frame.

Current vector in the d − q frame and its relationship with the α − β

Fig. 4.

Three-phase currents distribution in the power switches.

where id and iq denote d- and q-axis rotor currents, ψr is the rotor flux in rotor reference frame, Te stands for the electromagnetic torque, np is the number of pole pairs, Lm and Lr are the magnetizing inductance and rotor self-inductance, respectively, Rr is the rotor resistance, and p is the differential operator d/dt. The currents id and iq in the d − q frame can be derived from the currents in the α − β frame by the expressions called Park transformation defined as id = iα cos θ + iβ sin θ (2) iq = −iα sin θ + iβ cos θ where θ is the rotor flux angle (also called transform angle, generated from the speed signal and slip signal) and the currents iα and iβ in stationary frame are transformed by Clarke transformation defined as ⎧ 2 1 1 ⎪ ⎪ i i i = − − i α a b c ⎪ ⎪ 3 2 2 ⎨ √ (3) √

⎪ ⎪ 3 3 2 ⎪ ⎪ ib − ic ⎩ iβ = 3 2 2 where ia , ib , and ic stand for the motor phase currents. It is shown that the vector control is based on projections that transform a three-phase time and speed dependent system into a two-coordinate (d- and q-coordinate) time invariant system. These projections lead to a structure similar to a dc machine control. The vector control machine needs two variables as input references: the torque component (i∗q ) and the flux component (i∗d ). As vector control is based on projections, the control structure handles the instantaneous electrical quantities. This makes the control accurate in every working operation (steady states and transient) and independent of the limited bandwidth mathematical model. Without consideration of flux weakening, the amplitude of the rotor flux (ψr ) is maintained at a fixed value and thus there is a linear relationship between torque and torque component (i∗q ). Thereby, the torque can be control by controlling the torque component of stator current vector. With regard to the current, the complex stator current vector is defined by

i s = ia + γib + γ 2 ic = i d + i q = i α + i β

(4)

where γ = ej 2π /3 and γ = ej 4π /3 represent the spatial operator. Considering the d-axis aligned with the rotor flux, Fig. 3 shows the relationship between the α − β reference frame and the d − q frame.

The initial angle (θc ) and the transformation angle (θ) are given by θc = arctan

iq id

(5)

θ = ωt

(6)

where ω denotes the synchronous angular velocity. The current vector rotating angle (δ) can be expressed by δ = ωt + θc .

(7)

And sin δ indicates that current unit vector rotates counterclockwise at speed ω from the initial angle of θc to the β-axis. In a three-phase, three-wire system, the following condition by the law of Kirchhoff is satisfied as: ia + ib + ic = 0.

(8)

Therefore, from equation (3), the phase current ia becomes ia = iα .

(9)

So through Fig. 3, the phase current ia can be expressed as

ia = | i s | cos δ.

(10)

And the phase currents ib and ic can also be acquired in the similar way. As a result, the phase current distribution is a sinusoid in normal and healthy condition, as is shown in Fig. 4. According to the different combinations of the three-phase current flow directions, six operating stages, noted S1–S6 where the dashed lines are their boundary, are defined. According to the current vector rotating angle from 0◦ to 360◦ , each stage from S1 to S6 has 60◦ in a sequence and S1 stands for the

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TABLE I FAULTY SWITCHES IN A SIX-STAGE CONVERSION

Fig. 5.

Switches conduction states when im is positive.

Fig. 6.

Switches conduction states when im is negative.

part from 0◦ to 60◦ . At stage S1, the three-phase current flow directions are positive, negative and positive, respectively. For each stage, although all six switches are alternatively turned on and off by certain rules, only three transistors of them exert a vital influence and the others make a negligible influence, as will be explained in the next paragraphs. For example, at S1, the influential power switches are T 1, T 3 and T 5, the negligible ones are T 2, T 4, and T 6. For the case of positive value of one phase current as Fig. 5 presents the switches conduction states of one inverter leg, current flows into induction motor and the transistors (Tk , Tk +3 ) of the leg m(k = 1, 2, 3, m = a, b, c) are alternatively turned on and off according to the switching pattern (Sk , Sk +3 ), where Sk and Sk +3 are the control signals of the upper and lower transistors of the leg m and they cannot be both “1” at the same time. When Sk = 1 and Sk +3 = 0, the current flows into induction motor through the transistor Tk directly. However, when Sk = 0 and Sk +3 = 1, or Sk = Sk +3 = 0 corresponding to the dead time interval to prevent short circuit fault occurrence, the current remains connected to the negative potential of the dc-link capacitor through the bypass diode Dk +3 . Thus, the lower transistor Tk +3 makes a negligible influence on the phase current during the interval when the phase current ik is positive. The switches conduction states of one inverter leg when the phase current ik is negative are displayed in Fig. 6. With the similar observation, when Sk = 0 and Sk +3 = 1, the current flows out of induction motor through the transistor Tk +3 ; when Sk = 0 or 1 and Sk +3 = 0, the phase current leg becomes connected to the positive potential of the dc-link through the bypass diode Dk . Thus, the upper transistor Tk does no matter to the

phase current during the interval when the phase current ik is negative. Therefore, three operating transistors at each stage are obtained and represented in Fig. 4. In healthy condition, three influential power switches work normally at each stage and all the six power switches alternatively work healthily according to the given commands. However, when an open-circuit fault occurs, the faulty power switches will affect the operating stages where they are influential and three-phase currents get distorted during these faulty stages. So the fault can be identified according to the faulty stages. Table I presents the faulty switches in a six-stage conversion and the defined value of a fault type. For single switch open-circuit fault, such as that T1 fails, the threephase currents at the operating stages where T 1 is influential are distorted. T 1 makes a vital influence at stage S1, S2, and S3 and makes a negligible influence at stage S4, S5, and S6. So the three-phase currents distort at stage S1, S2, and S3 and return to normal at stage S4, S5, and S6. The current distortions last from stage S1 to S3. Therefore, the faulty power switch T 1 can be judged if the current distortions last from stage S1 to S3. For double switches open-circuit fault in different legs such as T 1 and T 2 fail, they together make a vital influence at stage S1, S2, S3, S4, and S5 and make a negligible influence at stage S6. So the current distortions last form stage S1 to S5. The faulty power switches (T 1 and T 2) can be judged if the current distortions last from S1 to S5. However, for the double switches open-circuit fault in the same leg such as T 1 and T 4 fail, they together make a vital influence at all stages. So an additional approach is introduced for such kind of faults, as will be explained in Section III. As Table I shows, the arrow means


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that can be calculated by the d- and q-axis currents. Therefore, faults can be identified according to the faulty stages. A. Error Detection

Fig. 7. Simulation results of the phase current ia and the cosine of the recalculated current vector rotating angle.

the state conversion of the VSI from the start of faulty stage to the end of the faulty stage. As discussed earlier, the currents in a cycle are divided into six stages and the faulty stage can be used to identify the fault. The stage can be expressed by the current vector rotating angle. From 0◦ to 360◦ , each stage from S1 to S6 has 60◦ in a sequence. As the relationship (7) expresses, the current vector rotating angle δ consists of two component (ωt and θc ). In a vector-controlled induction motor drive, ωt = θ whether faults occur in the VSI or not. As long as the induction motor operates, the rotor flux exists whether faults occur or not and its angle is θ. But when the open-circuit faults occur, the initial angle θc does not remain constant and the current vector rotating angle δ no longer changes from 0◦ to 360◦ sequentially. Therefore, the present stage determined by the current vector rotating angle is very likely to be false. So in order to determine the stage correctly, the current vector rotating angle should be calculated by another computational method and the key factor is to restore the initial angle θc . The initial angle θc only associates with id and iq . And the d- and q-axis currents id and iq only relate to the load torque and setting speed theoretically. Therefore, the initial angle θc at faulty stages can be represented by the initial angle at healthy stages. Fig. 7 shows the simulation results of the phase current ia (its value is divided by a constant number for better legibility) and the cosine of the recalculated current vector rotating angle. III. PROPOSED APPROACH The currents in the a–b–c frame are transformed to the d–q frame by using the coordinate transformation of vector control. In normal and healthy condition, the actual d-axis current (id ) remain unchanged for it is the flux component of the stator current. It changes only when faults occur. So the d-axis current distortion is used to detect the occurrence of faults (Fig. 7). When an open-circuit fault occurs, both the d- and q-axis currents change abnormally and show repetitive waveforms. They distort at faulty stages where the faulty power switches make a vital influence and return to normal at healthy stages where the faulty power switches make a negligible influence. The stages can be determined by the current vector rotating angle

In healthy condition, the actual currents can track the current commands. But an open-circuit fault makes the actual currents deviate from the current commands at faulty stages. These current deviations can be considered as “current distortions” induced by the open-circuit fault. Therefore, in healthy condition, the d-axis current distortion Eid between command i∗d and feedback id is nearly zero and the q-axis current distortion Eiq between command i∗q and feedback iq is also nearly zero when the drive system is in stable state. The d- and q-axis current distortions are expressed by Eid = i∗d − id . (11) Eiq = i∗q − iq In order to improve the robustness of the fault diagnosis algorithm, the load torque change and variable speed are taken into consideration for they will also distort the d- and q-axis currents, although the changes of the currents are not repetitive. In healthy condition, when the load torque or the setting speed change, the distortion Eiq does not remain zero for a short time during which the controller is adjusting the drive to make the induction motor work stably. However, in faulty condition, the distortions are zero at healthy stages and nonzero at faulty stages, and thus show the characteristic of periodic variation. So it is easy to distinguish whether the distortions are caused by a change of load and setting speed or open-circuit faults based on the characteristic. In a real situation, since the actual currents are not exactly identical to the corresponding applied current commands, there exist current differences between the commands and feedbacks. Therefore, the threshold value is employed to determine whether the distortions are zero or not and given by 1, if |Eid | ≥ Kd : error (12) boolEid = 0, otherwise : normal 1, if |Eiq | ≥ Kq : error boolEiq = (13) 0, otherwise : normal where Kd and Kq are the selected threshold values and boolEid and boolEiq are the generated Boolean errors for the current distortions of id and iq . Threshold values Kd and Kq are positive values and carefully selected to minimize the possibility of the false alarms mainly caused by the noises of measurement and the regulation of the controller. If they are selected too high, the d- and q-axis current distortions may not be detected. Moreover, if they are too small, the probability of false alarms increases. In (12), if the Boolean error boolEid has the value of 1, it means that there is a fault and the system is in faulty condition. Since the d-axis current distortion is zero or nonzero depending on whether the system works at healthy stage or at faulty stage, the generated Boolean error boolEid shows periodic square waveforms in faulty condition. The error detection in (12) and (13) is a simple and frequently used method, but has the possibility of false detection due to

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the generated Boolean errors jitters caused by the noise and the regulation of the controller. To guarantee the robustness against the false error detection, the proposed method employs the Counting Algorithm that looks for COUNT sequential stable states of the generated Boolean errors, where COUNT is a fault diagnosis constant number ranging from 1 (no jitters at all) to seemly infinity. Generally the algorithm detects a transition and then starts incrementing a counter till the counter reaches a jitter-free count. If the state is not stable, the counter resets to its initial value. The constant COUNT is defined as how long the Boolean errors are continuously generated and given by COU N T = kf T /Ts

(14)

where kf is the sensitivity factor for the fault diagnosis, T denotes the current cycle of induction motor (T = 2π/ω), and Ts stands for the sampling cycle of three-phase currents. If kf is too large, the open-circuit fault may not be detected. If kf is too small, the possibility of false alarms increases. Therefore, the sensitivity factor kf is carefully selected considering the detection time and reliability of fault detection. Then, the algorithm for the fault detection is given by 1, if counter1 ≥ COU N T 1 f lagF ault = (15) 0, otherwise where counter1 is the counter value from the beginning of the error detection in (12) to the arriving at the fault detection constant COU N T 1 (COU N T 1 = kf 1 T /Ts ) and f lagF ault denotes the fault detection flag indicating the fault condition. The error detection counter value counter1 is reset to zero when the Boolean errors are zero. If the error counter value counter1 is longer than the fault detection constant COU N T 1, the fault detection flag f lagF ault is set from low to high. The identification of the faulty switch is started just after the fault detection. The value “1” of boolEiq only implies the faults after the occurrence of the fault has been detected by boolEid . Since the current distortion are zero at healthy stages and are nonzero at faulty stages when faults occur, the generated Boolean errors show periodic square waveforms. The errors boolEid and boolEiq together are applied to determine the stages which can give high reliability boolEi = boolEid |boolEiq .

(16)

The rising edge of boolEi indicates the currents begin to distort while the falling edge indicates they return to normal. The same jitter-free algorithm called counting algorithm as applied to boolEid is used to process boolEi (represented by boolEiF ilter) where counter2 and COUNT2 (COU N T 2 = kf 2 T /Ts ) have identical definitions to counter1 and COU N T 1, respectively. Moreover the rising and falling edge of boolEi are timely stored. Fig. 8 presents the waveforms of processing boolEi, where the waveform edge is actually a matrix storing the rising and falling edge of boolEi. The matrix edge is composed of two numbers, each of which stores the time of the rising and falling edge, respectively. When boolEiF ilter = 0, its first number of edge is updated every time boolEi rises up until boolEiF ilter also rises up. And

Fig. 8.

Process of the Counting Algorithm.

Fig. 9.

Improved stage classification.

when boolEiF ilter = 1, the other number of the matrix is updated every time boolEi falls down until boolEiF ilter also falls down. B. Table Improvement Since the current deviations between the actual currents and the current commands are considered as “current distortions” induced by the open-circuit fault and regulated by the regulators, there is a regulation process at the boundary between faulty stages and healthy stages. The currents distort at faulty stages and return to normal at healthy stages due to the regulation of the regulator. Right after the system crosses the boundary from faulty stages to healthy stages, the current deviations may be still so large that the system may be determined to be still at faulty stages. Then, after a while of the regulation process, the current deviations become small that can be considered as zero, indicating the system is at healthy stages. Thus, in order to avoid the false determination of operating stages, six stages are redefined, noted Stg1 − Stg6 where six longer dashed lines are their boundary in Fig. 9 and the conversion table is redesigned as Table II. Each stage from Stg1 to Stg6 has 60◦ in a sequence


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TABLE II FAULTY SWITCHES AND THE DEFINED TYPE IN A SIX-STAGE CONVERSION

Fig. 10.

Flowchart of the proposed fault diagnosis algorithm.

Fig. 11.

Process of the proposed fault diagnosis algorithm when T 4 fails.

from 0◦ to 360◦ and Stg1 stands for the part from 30◦ to 90◦ . The midpoint of each former stage in Fig. 5 is selected as the boundary of each new stage in Fig. 9. The fast regulation of currents makes redesigned stages and conversion table acceptable. In addition, as shown in Table I, there is another problem that those double transistors faults in the same leg cannot be identified by this method since this kind of fault makes it happen that there is always at least one faulty transistor at each stage. To handle such faults, the following diagnostic signals are introduced: Dn (k) =

2 · |in (k)| |il (k)| + |im (k)|

(17)

where · denotes evaluation of the time average, the subscripts l, m, n represent the phase symbol (a, b, c), l = m = n, and k is the sampling instant. The quantities in (17) are evaluated in a moving window made of N current samples per fundamental period. In particular, the average current is computed according to the following formula: in (k) =

1 N

k

in (j).

(18)

j =k −N +1

A low value of Dn means that the average absolute current in phase n is much lower than the mean value of average absolute currents in the other phases, which indicates lack of current flow in phase n. Table II represents the summary of faulty switches and the defined fault type in a six-stage conversion, where “Addition” denotes the additional requirement. Fig. 10 shows the flowchart of the proposed fault diagnosis algorithm. The fault diagnosis is accomplished in the following procedures: 1) observation of the currents distortions in the

d − q frame; 2) generation of the Boolean errors by the distortion detection; 3) jitter elimination of the Boolean errors; 4) determination of the fault condition by the fault detection time; and 5) identification of fault type. Fig. 11 shows the waveforms for the process of the proposed fault diagnosis algorithm in the case of the fault occurrence of

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Fig. 12. Simulation results of the fault diagnosis in case of an abrupt change of load torque.

Fig. 13. speed.

T 4. When the open-circuit fault occurs to the transistor T 4, the distortions of currents in the d − q frame appear to have large values, and are compared with the selected threshold value as shown in (12) and (13). Then, they are filtered by the counting algorithm expressed in Section III for the detection of the fault and the storage of the rising edge time and the falling edge time. After the fault detection flag f lagF ault is set to high, the faulty stage conversion is determined according to the rising and falling edge time at the recalculated current vector rotating angle and the fault identification f aultT ype is then easily obtained from Table II.

transistor open-circuit fault, a double-transistor fault involving two transistors in the different legs, a double transistors opencircuit fault involving two transistors in the same inverter leg. All the transistor open-circuit faults are performed by inhibiting their respective gate signals while keeping the bypass diodes still connected. The proposed approach is represented by the signal f lagF ault for fault detection and the signal f aultT ype for fault identification.

IV. SIMULATION AND EXPERIMENTAL RESULTS The simulation in the MATLAB/Simulink environment and the experiment were carried out to verify the feasibility of the proposed fault diagnosis method. A rotor-field-oriented vector control strategy was applied to the inverter in order to control a squirrel-cage motor. Some results are presented to evaluate the performance of the proposed fault diagnosis method. In the test, the variable speed and the load torque change are examined in a first step to prove robust to such transient process, and then three distinct faulty operating conditions were investigated: a single-

Simulation results of the fault diagnosis in case of a change of setting

A. Simulation Results The motor used in the simulation was a 5.5-kW squirrelcage motor with 380 V rated voltage, 12.5 A rated current and 1430 r/min rated speed. The PWM VSI was running with a switching frequency of 20 kHz. And the parameters (Kd , Kq , kf 1 , and kf 2 ) of the algorithm are chosen to be 0.5 A, 0.5 A, 0.2, and 0.2 in the tests, respectively. 1) Immunity to an Abrupt Change of Load Torque and Setting Speed: Fig. 12 shows the time-domain waveforms of the motor speed and three-phase currents together with the diagnostic signals in case of a change of load torque (all transistors are healthy in this test). In this evaluation, by introducing an abrupt change of the load torque from no-load to rated load at t = 0.4 s


Fig. 14.

Simulation results of the fault diagnosis when T 4 fails.

and the other way round at t = 0.6 s, the obtained results verify that f lagF ault and f aultT ype remain unchanged despite of the strong load disturbance, proving the algorithm robustness against the load torque change. Fig. 13 shows the waveforms of the motor speed and threephase currents together with the diagnostic signals in case of a change of setting speed (all transistors are healthy in this test). In this evaluation, by changing the setting speed from 500 to 1200 r/min at t = 0.4 s and the other way round at t = 0.6 s, it is observed that the fault detection variable f lagF ault and the fault identification variable f aultT ype remain well and no false alarms are issued by this perturbation, proving the algorithm robustness against the setting speed change. 2) Faults Diagnosis: A single transistor open-circuit fault corresponding to T 4 is shown in Fig. 14, which presents the simulation waveforms of three-phase currents, the motor speed and the diagnostic signals. When the fault occurs at t = 0.5 s, the characteristic variable Eid stays at zero until the distortions come. Then, the fault detection flag f lagF ault is set from low to high at t = 0.5145 s according to the value of filtered Eid and the fault identification is started to identify the faulty switch. In combination with the recalculated current vector rotating angle, the rising edge and falling edge of boolEi are obtained and

Fig. 15.

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Simulation results of the fault diagnosis when T 4 and T 5 fail.

thus the stages where the three-phase currents begin to distort and return to normal are determined. Therefore, according to Table II, f aultT ype = 4, indicating that the open-circuit fault occurs to T 4. A double transistors open-circuit fault corresponding to T 4 and T 5 is shown in Fig. 15, which presents the simulation waveforms of three-phase currents, the motor speed and the diagnostic signals. It is much the same as the single transistors open-circuit fault. After the faults at t = 0.5 s are introduced, the fault detection flag f lagF ault is set from low to high at t = 0.5145 s according to the value of filtered Eid and then the fault identification is started to identify the faulty switch. The recalculated current vector rotating angle together with the rising edge and falling edge of boolEi determine the stages where the three-phase currents begin to distort and return to normal. Therefore, according to Table II, f aultT ype = 16, indicating that the open-circuit fault occurs to T 4 and T 5. A double transistors open-circuit fault corresponding to T 1 and T 4 is shown in Fig. 16, which presents the simulation waveforms of three-phase currents, the motor speed and the diagnostic signals. In the case, the gate signals are removed from transistors T 1 and T 4 simultaneously at t = 0.5 s, resulting in a

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Fig. 18.

Fig. 16.

Experimental setup.

Simulation results of the fault diagnosis when T 1 and T 4 fail. Fig. 19. Experimental results of the fault diagnosis in case of an abrupt change: (a) setting speed; (b) load torque.

Fig. 17.

Simulation results of the fault diagnosis when T 5 fails after T 4.

single phase open-circuit fault of phase a. Once the fault occurs, the fault detection flag f lagF ault is soon set from low to high which starts the fault identification. Due to the fact that the variable boolEi no longer returns to zero, the falling edge of boolEi no longer exists. As a result, the stage where the threephase currents return to normal cannot be calculated. Then, after one synchronous cycle T , diagnostic signals D1 , D2 , D3

are used to identify the faulty phase. According to Table II, f aultT ype = 19, indicating that the open-circuit fault occurs to T 1 and T 4. The proposed method is also applicable when a single transistor fault occurs following by a double transistors fault. As is shown in Fig. 17, T 4 fails at t = 0.4 s, and then T 5 fails, too, at t = 0.6 s. When the first fault was introduced, the fault detection flag f lagF ault was soon set high, indicating the occurrence of faults. Then, the fault identification was started and the faulty switch was identified by the fault indicator f aultT ype showing “4,” which means that T 4 was in trouble. The fault indicator f aultT ype is constantly updated. So when the second different fault occurs, it was also identified soon by f aultT ype becoming “16” indicating that T 4 and T 5 failed. B. Experiment Results The experimental setup, as shown in Fig. 18, consists of a 2.2 kW squirrel-cage induction motor with 380 V rated voltage, 4.9 A rated current and 1430 r/min rated speed, a power converter with a switching frequency of 20 kHz and the dead


Fig. 20.

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Experimental results of the fault diagnosis when three types of faults occur: (a) T 4; (b) T 4 and T 5; (c) T 1 and T 4.

time of 3.2 μs, a control board, a magnetic power brake, and a constant current source. And the parameters (Kd , Kq , kf 1 , and kf 2 ) of the algorithm are chosen to be 0.5 A, 0.5 A, 0.2, and 0.2, respectively, identical to the value in the simulations. The rotor-field-oriented vector control strategy was implemented on a TMS320F2806 signal processor. And the power converter is a three-phase PWM VSI, composed of IPM (PM25RSB120). Three-phase currents (ia , ib , ic ), two synchronously rotating current commands (i∗d , i∗q ), and the transformation angle θ are stored in the RAM of the control board. And then they are taken as the input of the fault diagnosis algorithm implemented in the MATLAB/Simulink. Figs. 19 and 20 show the experimental results of different cases. They all present the domain-time waveforms of threephase currents, the motor speed and the diagnostic signals. Fig. 19 shows the case of immunity to an abrupt change of load torque and setting speed (all transistors are healthy in this test). Fig. 20 shows the experimental waveforms of the fault diagnosis when three different types of faults occur: a single IGBT open-circuit fault corresponding to T 4, a double IGBTs

open-circuit fault corresponding to T 4 and T 5 and a double IGBTs open-circuit fault corresponding to T 1 and T 4. V. CONCLUSION A simple method for single switch and double switches opencircuit fault diagnosis in PWM VSIs for vector-controlled induction motor drives has been proposed in this paper. This method uses just as inputs the three-phase currents which are already available for the main control system, avoiding the use of extra sensors and the subsequent increase of the system complexity and costs. Comparing with previously published methods, the original method relies on the stage conversion. An open-circuit fault of power switches in the VSI changes the corresponding phase currents and introduces the periodic current distortions. When the fault occurs, the distortions are estimated and compared with the threshold value to detect the fault. And the stage is determined according to the recalculated current vector rotating angle. After the fault is detected, the faulty stages are determined in

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combination with the estimated distortions. Therefore, the faults are identified according to the stage conversion table. In comparison to the existing fault diagnosis, the proposed method can not only identify an initial fault, but also can identify a secondary fault fast and correctly, and show fair robustness. Moreover, it can be embedded into the existing vector-controlled induction motor drive software as a subsystem without excessive computation effort. Simulation and experimental results have validated the proposed method and show that the proposed method has good performance and practical value.

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[18] M. Aktas and V. Turkmenoglu, “Wavelet-based switching faults detection in direct torque control induction motor drives,” IET Sci. Meas. Technol., vol. 4, pp. 303–310, Nov. 2010. [19] Y. F. Guan, D. Sun, and Y. K. He, “Mean current vector based online real-time fault diagnosis for voltage source inverter fed induction motor drives,” in Proc. IEEE Int. Elect. Machines Drives Conf., 2007, vol. 1 and 2, pp. 1114–1118. [20] P. Jang-Hwan, K. Dong-Hwa, K. Sung-Suk, L. Dae-Jong, and C. MyungGeun, “C-ANFIS based fault diagnosis for voltage-fed PWM motor drive systems,” in Proc. IEEE Annu. Meeting Fuzzy Inf. Process. NAFIPS, 2004, vol. 1, pp. 379–383. [21] C. Delpha, C. Hao, and D. Diallo, “SVM based diagnosis of inverter fed induction machine drive: A new challenge,” in Proc. 38th Annu. Conf. IEEE Ind. Electron. Soc., 2012, pp. 3931–3936. [22] S. M. Jung, J. S. Park, H. W. Kim, K. Y. Cho, and M. J. Youn, “An MRASbased diagnosis of open-circuit fault in PWM voltage-source inverters for PM synchronous motor drive systems,” IEEE Trans. Power Electron., vol. 28, no. 5, pp. 2514–2526, May 2013. [23] K. Rothenhagen and F. W. Fuchs, “Current sensor fault detection, isolation, and reconfiguration for doubly fed induction generators,” IEEE Trans. Ind. Electron., vol. 56, no. 10, pp. 4239–4245, Oct. 2009. [24] K. H. Kim, D. U. Choi, B. G. Gu, and I. S. Jung, “Fault model and performance evaluation of an inverter-fed permanent magnet synchronous motor under winding shorted turn and inverter switch open,” IET Elect. Power Appl., vol. 4, pp. 214–225, Apr. 2010. [25] D. U. Campos-Delgado and D. R. Espinoza-Trejo, “An observer-based diagnosis scheme for single and simultaneous open-switch faults in induction motor drives,” IEEE Trans. Ind. Electron., vol. 58, no. 2, pp. 671–679, Feb. 2011. [26] B. Fan and J. Niu, “Three-phase SPWM inverter fault diagnosis based on optimized neural networks,” in Proc. 2011 IEEE Power Eng. Autom. Conf., pp. 331–335. [27] N. M. A. Freire, J. O. Estima, and A. J. M. Cardoso, “A voltage-based approach for open-circuit fault diagnosis in voltage-fed SVM motor drives without extra hardware,” in Proc. 2012 XXth Int. Conf. Elect. Mach., 2012, pp. 2378–2383. [28] K. Rothenhagen and F. W. Fuchs, “Performance of diagnosis methods for IGBT open circuit faults in three phase voltage source inverters for AC variable speed drives,” in Proc. 2005 Eur. Conf. Power Electron. Appl., 2005, pp. 1–10. [29] C. Ui-Min, J. Hae-Gwang, L. Kyo-Beum, and F. Blaabjerg, “Method for detecting an open-switch fault in a grid-connected NPC inverter system,” IEEE Trans. Power Electron., vol. 27, no. 6, pp. 2726–2739, Jun. 2012. [30] S. Shuai, P. W. Wheeler, J. C. Clare, and A. J. Watson, “Fault detection for modular multilevel converters based on sliding mode observer,” IEEE Trans. Power Electron., vol. 28, no. 11, pp. 4867–4872, Nov. 2013. [31] T. A. Lipo, Vector Control and Dynamics of Ac Drives, vol. 41. New York, NY, USA: Oxford Univ. Press, 1996. Jianghan Zhang was born in Hubei Province, China, in 1989. He received the B.S. degree in measurement and control technology and instrumentation in 2011 from the Huazhong University of Science and Technology (HUST), Wuhan, China, where he is currently working toward the Postgraduate degree in control science and engineering. His current research interests include power electronics, ac motor drives, and fault diagnosis.

Jin Zhao was born in Hubei Province, China, in 1967. He received the B.E. and Ph.D. degrees from the Department of Control Science and Engineering, Huazhong University of Science and Technology (HUST), Wuhan, China, in 1989 and 1994, respectively. Since 2004, he has been a Full Professor with the Department of Control Science and Engineering, HUST. During 2001–2002, he was a Visiting Scholar in the Power Electronics Research Laboratory, University of Tennessee, Knoxville, USA. He is involved in research and applications of power electronics, electrical drives, fault diagnosis, and intelligent control. He is the author or coauthor of more than 100 technical papers.


Dehong Zhou was born in SiChuan, China, in 1989. He received the B.S. degree in 2012 from the Department of Control Science and Engineering, Huazhong University of Science and Technology (HUST), Wuhan, China, where he is currently working toward the Ph.D. degree in control science and engineering at the School of Automation. His current research interests include power electronics, high-performance ac motor drives, predictive control, and fault tolerant control.

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Chengguang Huang was born in Hubei Province, China, in 1985. He received the B.E. and M.E. degrees in control science and engineering in 2007 and 2009, respectively, from the Department of Control Science and Engineering, Automation Institute, Huazhong University of Science and Technology (HUST), Wuhan, China, where he is currently working toward the Ph.D. degree. His current research interests include fault-tolerant control and ac motor drives.