Fault Diagnosis of Sensors for T-type Three-Level Inverter-fed Dual Three-Phase Permanent Magnet Synchronous Motor Drives

AbstractTo improve the reliability of motor system, this paper investigates the sensor fault diagnosis methods for T-type inverter-fed dual three-phase permanent magnet synchronous motor (PMSM) drives. Generally, a T-type three-level inverter-fed dual three-phase motor drive utilizes four phase-current sensors, two direct current (DC)-link voltage sensors and one speed sensor. A series of diagnostic methods have been comprehensively proposed for the three types of sensor faults. Both the sudden error change and gradual error change of sensor faults are considered. Firstly, the diagnosis of speed sensor fault was achieved by monitoring the error between the rotating speed of stator flux and the value from speed sensor. Secondly, the large high-frequency voltage ripple of voltage difference between the estimated voltage and the reference voltage was used to identify the voltage sensor faults, and the faulty voltage sensor was determined according to the deviation of voltage difference. Thirdly, the abnormal current amplitude on harmonic subspace was adopted to identify the current sensor faults, and the faulty current sensor was located by distinguishing the current trajectory on harmonic subspace. The experiments have been taken on a laboratory prototype to verify the effectiveness of the proposed fault diagnosis schemes.

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A Random Forest and Current Fault Texture Feature–Based Method for Current Sensor Fault Diagnosis in Three-Phase PWM VSR

Frontiers in Energy Research ◽

10.3389/fenrg.2021.708456 ◽

2021 ◽

Vol 9 ◽

Author(s):

Lei Kou ◽

Xiao-dong Gong ◽

Yi Zheng ◽

Xiu-hui Ni ◽

Yang Li ◽

...

Keyword(s):

Fault Diagnosis ◽

Random Forest ◽

Texture Feature ◽

Texture Features ◽

Current Sensor ◽

Stable Operation ◽

Sensor Faults ◽

Sensor Fault ◽

Three Phase ◽

Feature Based

Three-phase PWM voltage-source rectifier (VSR) systems have been widely used in various energy conversion systems, where current sensors are the key component for state monitoring and system control. The current sensor faults may bring hidden danger or damage to the whole system; therefore, this paper proposed a random forest (RF) and current fault texture feature–based method for current sensor fault diagnosis in three-phase PWM VSR systems. First, the three-phase alternating currents (ACs) of the three-phase PWM VSR are collected to extract the current fault texture features, and no additional hardware sensors are needed to avoid causing additional unstable factors. Then, the current fault texture features are adopted to train the random forest current sensor fault detection and diagnosis (CSFDD) classifier, which is a data-driven CSFDD classifier. Finally, the effectiveness of the proposed method is verified by simulation experiments. The result shows that the current sensor faults can be detected and located successfully and that it can effectively provide fault locations for maintenance personnel to keep the stable operation of the whole system.

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LPV Model Based Sensor Fault Diagnosis and Isolation for Permanent Magnet Synchronous Generator in Wind Energy Conversion Systems

Applied Sciences ◽

10.3390/app8101816 ◽

2018 ◽

Vol 8 (10) ◽

pp. 1816 ◽

Cited By ~ 5

Author(s):

Zhimin Yang ◽

Yi Chai ◽

Hongpeng Yin ◽

Songbing Tao

Keyword(s):

Fault Diagnosis ◽

Permanent Magnet ◽

Synchronous Generator ◽

Fault Estimation ◽

Linear Matrix ◽

Sensor Faults ◽

Sensor Fault ◽

Permanent Magnet Synchronous Generator ◽

Multiple Sensor ◽

Residual Generator

This paper deals with the current sensor fault diagnosis and isolation (FDI) problem for a permanent magnet synchronous generator (PMSG) based wind system. An observer based scheme is presented to detect and isolate both additive and multiplicative faults in current sensors, under varying torque and speed. This scheme includes a robust residual generator and a fault estimation based isolator. First, the PMSG system model is reformulated as a linear parameter varying (LPV) model by incorporating the electromechanical dynamics into the current dynamics. Then, polytopic decomposition is introduced for H ∞ design of an LPV residual generator and fault estimator in the form of linear matrix inequalities (LMIs). The proposed gain-scheduled FDI is capable of online monitoring three-phase currents and isolating multiple sensor faults by comparing the diagnosis variables with the predefined thresholds. Finally, a MATLAB/SIMULINK model of wind conversion system is established to illustrate FDI performance of the proposed method. The results show that multiple sensor faults are isolated simultaneously with varying input torque and mechanical power.

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Current Sensor Fault Diagnosis Based on Sliding Mode Observer for Permanent Magnet Synchronous Traction Motor

2018 IEEE 27th International Symposium on Industrial Electronics (ISIE) ◽

10.1109/isie.2018.8433824 ◽

2018 ◽

Author(s):

Gang Huang ◽

Edwardo F. Fukushima ◽

Jinhua She ◽

Changfan Zhang

Keyword(s):

Fault Diagnosis ◽

Permanent Magnet ◽

Sliding Mode ◽

Sliding Mode Observer ◽

Current Sensor ◽

Sensor Fault ◽

Traction Motor

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An equivalent-input-disturbance approach for current sensor faults estimation and rejection in permanent magnet synchronous motor drives

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331219869721 ◽

2019 ◽

Vol 42 (3) ◽

pp. 365-373 ◽

Cited By ~ 1

Author(s):

Gang Huang ◽

Zhengtan Li ◽

Edwardo F Fukushima ◽

Changfan Zhang ◽

Jing He

Keyword(s):

Permanent Magnet ◽

Sliding Mode ◽

Permanent Magnet Synchronous Motor ◽

Synchronous Motor ◽

Drive System ◽

Current Sensor ◽

Control Input ◽

Sensor Faults ◽

Input Disturbance ◽

Equivalent Input Disturbance

Current sensor is commonly used in a permanent magnet synchronous motor (PMSM) drive system. Occurrence of unexpected current sensor faults may cause feedback currents deviation and system degradation, which can be extremely detrimental to the safety of the industrial system with PMSM. This paper presents an estimation and rejection strategy of current sensor faults for a PMSM drive system. Sensor faults in current measurement circuits are treated as system disturbances by constructing a new system plant. A sliding mode observer and an improved equivalent-input-disturbance (EID) estimator are designed for the plant based on the EID theory. Accurate estimates of the current sensor equivalent-input-faults are thus obtained readily. Faults rejection is performed by subtracting the equivalent-input-faults from the control input. This allows an existing controller in a PMSM system to continue to function normally even a current sensor fault occurs. An existence analysis and stability proof are also discussed in detail for the system. Finally, different faults examples and a hardware-in-the-loop experiment are given to demonstrate the efficiency of the method.

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