Inter-turn Short Circuit Fault Diagnosis Research Articles

ResNet-18-Based Interturn Short-Circuit Fault Diagnosis of PMSMs With Consideration of Speed and Current Loop Bandwidths

ResNet-18-Based Interturn Short-Circuit Fault Diagnosis of PMSMs With Consideration of Speed and Current Loop Bandwidths

Rotor speed signature analysis‐based inter‐turn short circuit fault detection for permanent magnet synchronous machines

AbstractA rotor speed signature analysis‐ (RSSA‐)based inter‐turn short circuit (ITSC) fault diagnostic method is proposed, which is robust with regard to speed‐ and current‐loop bandwidths of permanent magnet synchronous machine (PMSM) drive systems. Conventional ITSC fault detection solutions that rely on current signals or extra devices. Thus, an initial step of investigation on the validity of RSSA is taken for residual insulation capacity monitoring of the ITSC fault at the incipient stage. The Vold–Kalman filtering order tracking method is employed for the real‐time extraction of fault features. Besides, the impact of speed‐ and current‐loop controller bandwidths on ITSC fault diagnosis is also analysed and an Adaline estimator is designed to decouple their impacts. Finally, the effectiveness of the proposed method is verified on a faulty PMSM, which exhibits satisfying results in tracking the degradation of residual insulation, even if the phase currents are significantly distorted due to low switching frequency of the inverter.

Inter-turn short circuit and demagnetization fault diagnosis of ship PMSM based on multiscale residual dilated CNN and BiLSTM

Inter-turn short circuit (ITSC) and demagnetization of permanent magnet synchronous motors (PMSMs) can lead to serious ship accidents, timely and accurate fault diagnosis of these faults is very important. A multi-signal fusion fault diagnosis method (MD-CNN-BiLSTM) is proposed based on multi-scale residual dilated convolutional neural network (D-CNN) and bidirectional long and short-term memory (BiLSTM) for PMSM fault diagnosis. This method first takes three-phase current and vibration signals as input; uses a three-column parallel CNN structure with different scales to extract both global signal and local feature. A residual connection in the expanded CNN is then used to eliminate the problems of gradient disappearance or explosion; and finally, BiLSTM is used to further extract features and identify the fault. A 2.2 kW permanent magnet synchronous motor was used to build a fault simulation test rig. The motor stator was rewound to simulate the ITSC fault, and different sizes of permanent magnets were replaced to simulate demagnetization fault. ITSC, demagnetization and their coupled faults were simulated under 10 specific motor speeds and loads respectively. The test proved that the diagnostic accuracy of the proposed method was 4.2% higher than that of ordinary CNN and 29.06% higher than that of BiLSTM. It also had the best diagnostic effect under the noise interference of different intensities. It was verified that the proposed method has good noise interference and strong classification ability.

Digital Twin-driven Hot Spot Capture for Power Transformer Inter-Turn Short Circuit Fault Diagnosis based on Microthermal Field

Digital Twin-driven Hot Spot Capture for Power Transformer Inter-Turn Short Circuit Fault Diagnosis based on Microthermal Field

Diagnosis of Inter-Turn Short-Circuit Incipient Fault in Permanent Magnet Synchronous Motors Using Input Current on the Power Side

Diagnosis of Inter-Turn Short-Circuit Incipient Fault in Permanent Magnet Synchronous Motors Using Input Current on the Power Side

Research on the Vibration Characteristics of Pumped Storage Unit Stator Core Based on Fiber Optic Sensing

Stator core vibration (SCV) response of pumped storage units (PSU) can effectively reflect its operation status and provide reference for excitation winding inter-turn short circuit (ITSC) fault diagnosis. Considering that traditional piezoelectric acceleration sensors (PAS) are difficult to be installed on the stator core surface, this study proposes an online monitoring method based on fiber optic vibration sensing (FOVS). Firstly, this study derives the SCV response equations for the normal and ITSC states of the excitation winding, and obtains the vibration characteristic harmonics corresponding to each state. Secondly, a three-dimensional finite element simulation model of PSU with a generating capacity of 278 MVA is built, and the acceleration amplitude and characteristic frequency harmonics of the stator core are calculated to provide a reference for fiber optic vibration detection. Thirdly, this study focuses on the principle of modulation and demodulation of optical phase, builds an optical frequency-domain reflectometer (OFDR) - based FOVS system, and calibrates its sensitivity, amplitude and frequency characteristics as well as the lower detection limit. Finally, FOVS probes are installed on the surface of synchronous generator stator core to capture the vibration signals under different degrees of ITSC, and compared with the collected signals of PAS. The experimental results prove that the proposed method has obvious advantages in vibration monitoring in the low frequency band below 100 Hz.

Research on Inter-Turn Short-Circuit Fault Diagnosis Method Based on High Frequency Voltage Residual for PMSM

Research on Inter-Turn Short-Circuit Fault Diagnosis Method Based on High Frequency Voltage Residual for PMSM

Stator inter-turn short-circuit fault diagnosis in induction motors applying VI loci-based technique

Due to the susceptibility of low-power three-phase induction motors (IMs) to stator inter-turn short-circuits (ITSC), researchers have been required to develop practical and cost-effective online systems for detecting faults in the stator winding. It is highly recommended to detect faults at a low fault severity level, as the fault current circulating through the shorted turns exponentially increases due to a reduction in insulation resistance and associated insulation degradation. The proposed Voltage–Current (VI) loci-based technique is a non-invasive, less expensive, and fault-sensitive approach that has been scarcely implemented in industries for stator ITSC faults analysis and accurate faulty phase identification, even under low fault severity. The proposed algorithm can compensate for various factors such as unbalanced supply voltages, asymmetries in winding construction, and errors in sensing element calibration. Experimental results presented for a three-phase 1 hp, 415 V SCIM investigate and validate the competency of the proposed work.

PWM Harmonic-Current-Based Interturn Short-Circuit Fault Diagnosis for the Aerospace FTPMSM System Even in the Fault-Tolerant Operation Condition

This paper proposes a new PWM harmonic current based diagnostic method for the interturn short-circuit fault (ITSCF) of fault tolerant permanent magnet synchronous motor (FTPMSM) drives, which is able to detect and locate the fault phase for the system even in fault tolerant operation (FTO) condition. The ITSCF model for the H-bridge drive based FTPMSM system is firstly proposed, while the high-frequency (HF) harmonic current component and impedance are analyzed both in normal and ITSCF conditions. The PWM harmonic current based fault diagnostic approach is then proposed to confirm the fault phase, which consists of the band-pass filter, the periodic root mean square (RMS) calculator and the fault identification. The resulting fault diagnostic method has simple structure, fast fault detection, good robustness and low computational burden, which can achieve the ITSCF for the FTPMSM system both under normal and FTO conditions. The effectiveness of the ITSCF diagnostic method is verified via the six-phase FTPMSM experimental platform, which can provide a guideline for the ITSCF mitigation and FTO of the FTPMSM system.

Fault diagnosis of inter-turn short circuit in turbogenerator rotor windings based on vibration-current signal fusion

The inter-turn short circuit of rotor winding is a common fault of turbogenerator, and fault diagnosis in the early stage is of great significance to ensure the safe and reliable operation of turbogenerator. In this paper, an inter-turn short circuit fault diagnosis model based on vibration-current fusion is proposed by using the real-time state data collected by the distributed control system (DCS) of turbogenerators. Firstly, Pearson correlation coefficient (PCC) analysis method is used to analyze the correlation characteristics between the square of generator excitation current and the shaft radial vibration when the inter-turn short circuit occurs in the rotor winding of the turbogenerator. Then, the method of cooperative gain transformation is adopted to fuse current-vibration correlation coefficient which can amplify the weak correlation between the square of the excitation current and the vibration signal during the early defect, and the residual between the measured and predicted excitation current is combined to further calculate the cooperative gain residual, and an early warning signal can be issued when cooperative gain residual exceeds threshold. Finally, the model is verified by the historical DCS data of a 600 MW large turbogenerator. The results show that the proposed model can sensitively and accurately diagnose the early defects of inter-turn short circuit in turbogenerator rotor windings and ensure the safe operation of the generator.

Online Interturn Short-Circuit Fault Diagnosis in Induction Motors Operating Under Unbalanced Supply Voltage and Load Variations, Using the STLSP Technique

It is well known that the most common reason for electrical machines breakdown is the stator windings’ fault occurrence. Indeed, this type of fault represents almost 40% of faults occurring in induction machines. One of the major causes of stator defects is the occurrence of interturn short-circuit (ITSC) faults that have critical and dangerous effects on the motor itself, as well as on the related electrical equipment. Therefore, early detection and a precise severity estimation of the occurrence of ITSC faults for all working conditions can prevent failure breakdowns and increase reliability and safety of industrial facilities. In this context, this article proposes an efficient online diagnostics method based on calculating and monitoring a pertinent severity factor defined as the ratio between the zero and positive voltage symmetrical components. The online implementation of this method is performed on a LabVIEW environment, using the short-time least square Prony's (STLSP) method. It does not need any estimation of motor parameters and it requires only voltage sensors. Several tests under healthy and faulty conditions are carried out on a three-phase 3-kW induction motor. The obtained results demonstrate the effectiveness of the proposed method for diagnosing the occurrence of ITSC faults with high reliability, fastness, and high precision, even under load variations or unbalanced supply voltage.

Interturn Short-Circuit Fault Diagnosis of Interior Permanent Magnet Synchronous Motor for Electric Vehicle Based on Search Coil

This article proposes an interturn short-circuit fault diagnosis method for permanent magnet synchronous machines for electric vehicles based on search coils (SC) with a unique structure. In this proposed structure, the SCs are uniformly wound in the main flux direction of each phase, and the number of SCs is twice the number of phases, resulting in significant cost savings. Based on this structure, a novel fault feature based on the negative-sequence components of the second carrier frequency sideband harmonics in the voltage signal of the SC is proposed. As a result, this fault characteristic frequency component is not only more obvious than the fundamental component used in the traditional SCs method but also can be transformed to a dc indicator by two coordinate transformations, which is conducive to online implementation in both stationary and nonstationary conditions. Finally, experiments are presented to verify the proposed diagnosis strategy.

On Bayesian Optimization-Based Residual CNN for Estimation of Inter-Turn Short Circuit Fault in PMSM

Interturn short circuit (ITSC) fault diagnosis at its early stage is very essential to improve the security of permanent magnet synchronous motors. In this article, a Bayesian optimization (BO) based residual convolutional neural network (CNN) algorithm for ITSC fault diagnosis was proposed. There are mainly two aspects of the contributions. First, it is a challenge to apply a conventional CNN on time-series signal tasks as they can only look back on history with a liner size, which will limit the network depth. Moreover, to obtain enough characteristics from acquired signals, the proposed algorithm requires adequate network depth, which may result in degradation difficulties. To solve these problems, a residual connection is embedded into the dilated CNN. Second, with the increase of network depth, the possible combination of hyperparameters will grow geometrically, thus the hyperparameters tuning will cost considerable time. To overcome this, the BO algorithm is adopted to select the optimal hyperparameter combination automatically. To demonstrate the effectiveness of the proposed algorithm, a motor fault experiment with various operating conditions was conducted on the motor that can be set for 17 fault severity levels. The test results and comparisons with other five algorithms show the advantage of the proposed algorithm.

State Estimation MRAS and Identification of Stator Winding Phase Fault Detection of the PMSG in Wind Energy Based on the Sliding Mode Control

Abstract This paper proposes a method for the diagnosis of stator inter-turn short-circuit fault for permanent magnet synchronous generators (PMSG). Inter-turn short-circuit currents are among the most critical in PMSG. For safety considerations, a fast detection is required when a fault occurs. This approach uses the parameter estimation of the per-phase stator resistance in closed-loop control of variable speed of wind energy conversion system (WECS). In the presence of an incipient short-circuit fault, the estimation of the resistance of the stator in the d-q reference frame does not make it possible to give the exact information. To solve this problem, a novel fault diagnosis scheme is proposed using parameter estimation of the per-phase stator resistance. The per-phase stator resistance of PMSG is estimated using the MRAS algorithm technique in real time. Based on a faulty PMSG model expressed in Park’s reference frame, the number of short-circuited turns is estimated using MRAS. Fault diagnosis is on line detected by analysing the estimated stator resistance of each phase according to the fault condition. The proposed fault diagnosis scheme is implemented without any extra devices. Moreover, the information on the estimated parameters can be used to improve the control performance. The simulation results demonstrate that the proposed method can estimate the faulty phase.

Machine Learning for Inter-Turn Short-Circuit Fault Diagnosis in Permanent Magnet Synchronous Motors

This work presents an approach to detect the inter-turn short-circuit (ITSC) faults in permanent magnet synchronous motors (PMSMs) using machine learning. PMSMs possess many attractive advantages such as high efficiency and high power density. However, with their widespread applications, preventive fault diagnosis to secure safe operations and reduce maintenance costs becomes increasingly critical. To achieve this objective, in this article, machine learning is employed for fault diagnosis, wherewith minor ITSC faults can be detected in a preventive manner. Therefore, the faulty motors can be replaced early to ensure system healthy operations. Moreover, machine learning is also utilized here to classify fault levels (e.g., minor, moderate, serious faults) for reference to appropriate maintenance actions. The proposed method adopts support vector machines (SVMs) and convolutional neural networks (CNNs) for training the diagnosis model with experimental data collected from tests in the laboratory. In the SVM, appropriate features required for training are selected through analysis with the PMSM mathematical model considering ITSC faults. This allows for more efficient training with much fewer data. In contrast, CNNs are purely a data-driven approach that requires much more data for training. The accuracy of both methods is validated with another set of measured data. It is found that both methods can achieve an accuracy of 99%; however, the SVM requires much less amount of data to achieve the same accuracy. This demonstrates the advantages of the model-aided machine learning method, which would be more suitable for applications where faulty motor data are not easily acquired.

Severity Estimation for Interturn Short-Circuit and Demagnetization Faults through Self-Attention Network.

This study presents a novel interturn short-circuit fault (ISCF) and demagnetization fault (DF) diagnosis strategy based on a self-attention-based severity estimation network (SASEN). We analyze the effects of the ISCF and DF in a permanent-magnet synchronous machine and select appropriate inputs for estimating the fault severities, i.e., a positive-sequence voltage and current and negative-sequence voltage and current. The chosen inputs are fed into the SASEN to estimate fault indicators for quantifying the fault severities of the ISCF and DF. The SASEN comprises an encoder and decoder based on a self-attention module. The self-attention mechanism enhances the high-dimensional feature extraction and regression ability of the network by concentrating on specific sequence representations, thereby supporting the estimation of the fault severities. The proposed strategy can diagnose a hybrid fault in which the ISCF and DF occur simultaneously and does not require the exact model and parameters essential for the existing method for estimating the fault severity. The effectiveness and feasibility of the proposed fault diagnosis strategy are demonstrated through experimental results based on various fault cases and load torque conditions.

Research on Diagnosis and Prediction Method of Stator Interturn Short-Circuit Fault of Traction Motor

The traction motor (TM) is an essential part of the high-speed train, the health condition of which determines the quality and safety of the vehicle. Hence, this study proposed a novel approach to diagnosing and predicting the TM stator interturn short-circuit fault (SISCF). Based on the Park vector (PV) of the stator current, this method could overcome the interference of current sensor errors, null shift, and motor frequency fluctuations in the actual conditions. More specifically, Park’s transformation was used to obtain the PV of the stator current. Then, the PV was fitted to obtain the elliptical trajectory and its parameters from which the negative sequence component of the stator current could be calculated. Finally, the SISCF diagnosis and prediction method were realized by the magnitude and trend of the negative current as well as the inclination of the trajectory ellipse. Furthermore, the performance of the proposed method was validated by a simulation model and a series of experiments. The simulation results were consistent with the experimental results, supporting the validity and correctness of the method proposed in this study.

Inter-Turn Short Circuit Fault Diagnosis of PMSM

Permanent Magnet Synchronous Motor (PMSM) is widely used due to its advantages of high power density, high efficiency and so on. In order to ensure the reliability of a PMSM system, it is extremely vital to accurately diagnose the incipient faults. In this paper, a variety of optimization algorithms are utilized to realize the diagnosis of the faulty position and severity of the inter-turn short-circuit (ITSC) fault, which is one of the most destructive and frequent faults in PMSM. Compared with the existing research results gained by particle swarm optimization algorithms, in this paper, the methods using other optimization algorithms incorporating genetic algorithm, whale optimization algorithm and stochastic parallel gradient descent algorithm (SPGD) can acquire more stable and precise results. In particular, the method based on SPGD can obtain the most desirable performance among the methods mentioned above; that is, the relative error of short-circuit turns ratio is approximately as low as 0.03%. In addition, in the case of asymmetric input three-phase voltage and with the adverse impact of high-order harmonics at different load moments, the fault diagnosis method based on SPGD still maintains relatively satisfactory properties. Finally, the verification on the actual PMSM platform demonstrates that the SPGD can still diagnose the faulty severity.

Research on Inter-Turn Short Circuit Fault Diagnosis of Electromechanical Actuator Based on Transfer Learning and VGG16

In this paper, an inter-turn short-circuit fault of a permanent magnet synchronous motor in an electromechanical actuator is analyzed, and a fault diagnosis method based on transfer learning with a VGG16 convolution network is proposed. First, a 2D finite element model of an inter-turn short circuit fault of a permanent magnet synchronous motor was established in ANSOFT Maxwell, and then a simulation experiment analysis was completed. A three-phase current was chosen as a fault characteristic signal. Second, a fault diagnosis method with a VGG16 deep convolutional neural network and based on transfer learning was designed, and the fine tuning of the hyperparameters of the fault diagnosis model was completed by using grid search and cross verification methods. Finally, based on the transfer learning VGG16 model established in this paper, the inter-turn short circuit fault of a permanent magnetic synchronous machine (PMSM) was diagnosed and verified. The experimental results showed that the proposed convolutional network model based on transfer learning can identify faults effectively and accurately, and has a good engineering guidance significance.

Inter-Turn Short Circuit Fault Diagnosis of Synchronous Generator in Field Winding Based on the Eigenvalues of the State Matrix

Inter-Turn Short Circuit Fault Diagnosis of Synchronous Generator in Field Winding Based on the Eigenvalues of the State Matrix