On-line fault diagnosis of multi-phase drives using self-recurrent wavelet neural networks with adaptive learning rates

Torabi, Niloofar; Sundaram, Vivek M.; Toliyat, H.A.

doi:10.1109/apec.2017.7930751

Cited by 14 publications

(16 citation statements)

References 17 publications

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“…An artificial neural network (ANN) trained by the features extracted from motor currents and the DC-link current has been applied to detect and identify fault locations [17]. An adaptive self-recurrent wavelet ANN has provided an estimation of a nonlinear model for generating the appropriate fault indicators [18].…”

Section: Introductionmentioning

confidence: 99%

Fault Detection Method for Permanent Magnet Synchronous Generator Wind Energy Converters Using Correlation Features Among Three-phase Currents

Tan

Zhang

Zhou

2020

Journal of Modern Power Systems and Clean Energy

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In a permanent magnet synchronous generator (PMSG) system, conversion systems are major points of failure that create expensive and time-consuming problems. Fault detection is usually used to achieve a steady system. This paper presents a full analysis of a PMSG system for wind turbines (WT) and proposes a fault detection method using correlation features. The proposed method is motivated by the balance among the three-phase currents both before and after an opencircuit fault occurs in a converter of the PMSG system. It is unnecessary to analyze the output waveforms of a converter during fault detection. In this study, two correlation features of stator currents, the mean and covariation, are extracted to train an artificial neural network (ANN), thereby enhancing the performance of the proposed method under different wind speed conditions. Moreover, additional sensors and the collection of a massive amount of data are not required. Model simulations of an ideal inverter and a PMSG system are conducted using PSCAD software. The simulation results show that the proposed method can detect the locations of faulty switches with a diagnostic rate greater than 99.4% for the ideal inverter, and the PMSG drives settings at different wind speeds. Index Terms-Permanent magnet synchronous generator (PMSG), artificial neural network (ANN), mixed logical dynamical (MLD) theory, conversion system.

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Section: Introductionmentioning

confidence: 99%

Fault Detection Method for Permanent Magnet Synchronous Generator Wind Energy Converters Using Correlation Features Among Three-phase Currents

Tan

Zhang

Zhou

2020

Journal of Modern Power Systems and Clean Energy

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“…Dybkowski et al (2014) proposed a method for detecting position sensor errors in induction motors [10]. Most published methods related to error detection apply to induction motors [11][12][13][14][15][16][17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

“…Most of the detection methods use current input only. For example, in Torabi et al (2017) and Consoli et al (2010), the method of detecting position errors used a current sensor to detect the stator current [18], [25]. The method proposed in the present paper uses input from current and voltage sensors.…”

Section: Introductionmentioning

confidence: 99%

A Synchronization Loss Detection Method for PMSM Speed Sensorless Control

et al. 2020

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Permanent Magnet Synchronous Motor (PMSM) is an AC motor in which the rotor must operate at synchronous speed in all load conditions. If the motor mechanical load increases, the motor can lose synchronization, stopping the motor. In sensorless control systems, i.e., those without speed sensors, the speed is estimated from the stator current using the Model Reference Adaptive System (MRAS) algorithm. Because such systems therefore cannot detect the loss of synchronization, it is necessary to design a synchronization loss detection system. Here, another speed estimation calculated from the stator currents and voltages is introduced. The speed is called a calculated speed. In the normal condition (synchronous condition), estimated speed and calculated speed will be approximately equal. However, when synchronization loss occurs, these two speed values diverge. On the basis of this phenomenon, a synchronization loss detection algorithm and method are developed. The algorithm’s speed-delta boundary values and detection period must be determined. The greater the setpoint speed, the higher the speed-delta boundary values but the smaller the detection period. The experiments confirm that the proposed algorithm is able to effectively detect the occurrence of synchronization loss.

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“…However, this traditional fault diagnosis procedure is prone to erroneous fault point detections, causing unnecessary waste of labor and time for component repair and replacement [6][7][8]. To improve the reliability of equipment using motor drive systems, researchers have invested considerable effort in exploring fault detection mechanisms [9][10][11][12][13][14][15][16][17][18][19] and fault-tolerant control strategies [20][21][22][23] for multilevel inverters in order to enable early fault detection when inverter power semiconductor components fail; this can thus maintain the inverter operation and minimize damage.…”

Section: Introductionmentioning

confidence: 99%

“…Through such neural connections, data models can be updated using backpropagation, and hidden layers can reduce the dependence of machine learning algorithms on feature engineering. However, a large body of data is required for neural network learning to make accurate judgments, and the corresponding operating time is long [14][15][16][17]. Therefore, developing new smart fault diagnosis systems with high accuracy, easy implementation, and fast response is imperative.…”

Section: Introductionmentioning

confidence: 99%

Smart Fault-Tolerant Control System Based on Chaos Theory and Extension Theory for Locating Faults in a Three-Level T-Type Inverter

Chao

Chang

2019

Applied Sciences

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This study proposes a smart fault-tolerant control system based on the theory of Lorenz chaotic system and extension theory for locating faults and executing tolerant control in a three-level T-type inverter. First, the system constantly monitors the fault states of the 12 power transistor switches of the three-level T-type inverter; if a power transistor fails, the corresponding output phase voltage waveform is converted by a Lorenz chaotic system. Chaos eye coordinates are then extracted from a scatter diagram of chaotic dynamic states and considered as fault characteristics. The system then executes fault diagnosis based on extension theory. The fault characteristic value is used as the input signal for correlation analysis; thus, the faulty power transistor can be located and the fault diagnosis can be achieved for the inverter. The fault-tolerant control system can maintain the three-phase balanced output of the three-level T-type inverter, thereby improving the reliability of the motor drive system. The feasibility of the proposed smart fault-tolerant control system was assessed by conducting simulations in this study, and the results verified its feasibility. Accordingly, after the occurrence of the fault in power switches, the balanced three-phase output line voltage remained unchanged, and the quality of the output voltage was not reduced by using the integration of the proposed fault diagnosis system and fault-tolerant control system for a three-level T-type Inverter.

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On-line fault diagnosis of multi-phase drives using self-recurrent wavelet neural networks with adaptive learning rates

Cited by 14 publications

References 17 publications

Fault Detection Method for Permanent Magnet Synchronous Generator Wind Energy Converters Using Correlation Features Among Three-phase Currents

Fault Detection Method for Permanent Magnet Synchronous Generator Wind Energy Converters Using Correlation Features Among Three-phase Currents

A Synchronization Loss Detection Method for PMSM Speed Sensorless Control

Smart Fault-Tolerant Control System Based on Chaos Theory and Extension Theory for Locating Faults in a Three-Level T-Type Inverter

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