Model based online detection of inter-turn short circuit faults in PMSM drives under non-stationary conditions

Kiselev, Aleksej; Kuznietsov, Alexander; Leidhold, Roberto

doi:10.1109/cpe.2017.7915199

Cited by 13 publications

(9 citation statements)

References 9 publications

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“…Therefore, in comparing the method proposed here with other methods in the literature, the studies made for the FHCT in the other motors operating in nonstationary conditions are considered. The obtained results are coherent with the results found in the literature [16], [23], [24], [34], [44]. In [16], where VKF-OT-based winding fault diagnosis was performed at a PMSM operating under dynamic conditions, the changes in third harmonics of the current signals for the healthy state, light fault state, and severe fault state are 0.004, 0.0125, and 0.0275 dB, respectively (under ramp conditions with a variable speed of 3500-5000 rpm).…”

Section: B Sensitivity Analysis Resultssupporting

confidence: 92%

“…This result shows that this proposed new diagnostic method can successfully diagnose incipient interturn faults. Compared with the changes of energy in the 6 th order in Figure 14, the changes of dq-axis space vector in [23] are similar results for incipient inter-turn diagnostics. In addition, the results in Figure 14 are very close to those given in [45] for diagnosing an incipient winding failure of an induction motor.…”

Section: Figure 14 Gabor-ot Results For Testsupporting

confidence: 65%

“…The model-based diagnostic approach that provides high sensitivity and accuracy in detecting incipient faults, such as winding faults, broken rotor bar faults, etc., is often used to diagnose faults in motors working under dynamic conditions [23], [43], [45]. To compare the fault diagnosis method proposed here with model-based methods, the incipient winding failure test (Test 3) was performed in the experimental studies.…”

Section: B Sensitivity Analysis Resultsmentioning

confidence: 99%

“…For the fault detection in a transient motor current signal, these problems can be solved by analyzing only the faultrelated harmonic component of the signal, instead of analyzing a whole spectrum band [13], [20]- [22]. In recent studies, based on FHC tracking (FHCT) for fault diagnosis of other rotating electric machines operating under nonstationary conditions, model-based methods [23], Vold-Kalman filtering order tracking (VKF-OT) [16], frequency-tracking algorithm [24], etc. have been used.…”

Section: Introductionmentioning

confidence: 99%

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Diagnosis of Inter-Turn Faults Based on Fault Harmonic Component Tracking in LSPMSMs Working Under Nonstationary Conditions

Doğan

Tetik

2021

IEEE Access

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Inter-turn faults are one of the most important issues in line-start permanent magnet synchronous motors (LSPMSMs). To date, steady-state operating conditions of LSPMSMs have been analyzed in studies focusing on diagnosing this fault. However, no studies have been conducted for nonstationary operating conditions, such as variable speed or variable load conditions, which are common in the industry. This paper presents a novel approach for fault harmonic component (FHC) tracking based on transient-MCSA to diagnose inter-turn faults in an LSPMSM operating under nonstationary conditions. First, the inter-turn failure model of LSPMS was analyzed to show the fault effect on motor current, and the most dominant inter-turn FHC in the order and frequency domains were determined. Then, using Gabor-OT, interturn FHC signals were extracted from motor current signals in the frequency domain and reconstructed in the time domain. Intrinsic mode functions (IMFs) were calculated by decomposing the reconstructed FHC signals (RFHCSs) via ensemble empirical mode decomposition. Using the energies of RFHCSs, calculated based on Gabor coefficients, and the Kullback-Leibler divergences of the selected IMFs, diagnosis of the inter-turn faults and detection of the severity of the faults was performed. The results are in agreement with the results in the literature, thus showing that the proposed method is a successful and useful means for detecting interturn faults in LSPMSMs.

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Section: B Sensitivity Analysis Resultssupporting

confidence: 92%

Section: Figure 14 Gabor-ot Results For Testsupporting

confidence: 65%

Section: B Sensitivity Analysis Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Diagnosis of Inter-Turn Faults Based on Fault Harmonic Component Tracking in LSPMSMs Working Under Nonstationary Conditions

Doğan

Tetik

2021

IEEE Access

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“…The early fault detection helps in reducing the machine's maintenance cost and time. Therefore, research effort on the PMSM fault diagnosis has been on the rise [3].…”

Section: Introductionmentioning

confidence: 99%

Modeling and Diagnosis of the Inter-Turn Short Circuit Fault for the Sensorless Input-Output Linearization Control of the PMSM

Maanani

Menacer

2019

Period. Polytech. Elec. Eng. Comp. Sci.

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The purpose of this paper is the inter-turn short circuit fault Modeling and detection for the sensorless input-output linearization control of the permanent magnet synchronous motor (PMSM) based on the Extended Kalman Filter observer (EKF). The fault detection procedures are based through the estimation of the stator resistance variation by the Extended Kalman Filter observer and the Fast Fourier Transformer (FFT) for the stationary state, and the Discrete Wavelet Transform (DWT) analysis of the electrical characteristics of the PMSM, for the non-stationary state. However, the FFT spectral analysis and the DWT is a useful solution to ensure that the variation of the stator resistance estimation is caused by the inter-turn short circuit fault. The effectiveness of the sensorless control and the fault detection techniques are presented in a simulation in MATLAB/Simulink environment.

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A simple signal extraction‐based online real‐time diagnosis approach for interturn short‐circuit fault of permanent magnet motor

Hsu

Hsieh

2023

Circuit Theory & Apps

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Summary A fast and reliable interturn short circuit (ITSC) diagnosis system is necessary to secure safe operations of permanent magnet synchronous motor (PMSM) for its widespread applications in industrial automation and electric vehicles. This paper develops a simple online real‐time technique based on signal extraction to detect the ITSC fault in the PMSM. Some popular diagnosis methods involving machine learning or instantaneous power information may require large computing resources, data storage, or extra sensing devices for data collection. This makes it difficult to fulfill online and self‐detection of ITSC faults in motor drive systems. This paper makes use of the second‐order harmonic components in dq‐axis current for diagnosis when they occur due to ITSC faults. Through the developed amplitude‐tracking algorithm, the diagnosis can be implemented by using a common digital signal processor without complex algorithms and additional hardware or sensors. Therefore, it is suitable for low‐cost and remote applications where self‐detection is needed. The proposed method is validated by finite element analysis and measurements with a hardware‐in‐the‐loop toolkit. It is found that the method successfully detects a short circuit fault with only one out of 54 turns in one phase of the motor (1.9%).

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Model based online detection of inter-turn short circuit faults in PMSM drives under non-stationary conditions

Cited by 13 publications

References 9 publications

Diagnosis of Inter-Turn Faults Based on Fault Harmonic Component Tracking in LSPMSMs Working Under Nonstationary Conditions

Diagnosis of Inter-Turn Faults Based on Fault Harmonic Component Tracking in LSPMSMs Working Under Nonstationary Conditions

Modeling and Diagnosis of the Inter-Turn Short Circuit Fault for the Sensorless Input-Output Linearization Control of the PMSM

A simple signal extraction‐based online real‐time diagnosis approach for interturn short‐circuit fault of permanent magnet motor

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