Inter-turn Short-circuit Diagnosis Research Articles

Online control signal-based diagnosis of interturn short circuits of PMSM drive

Online control signal-based diagnosis of interturn short circuits of PMSM drive

Diagnosis of Interturn Short-Circuits in SRMs by High-Frequency Switching of Phases Amid Low-Torque Unaligned Rotor Positions

The inherent problems of torque ripple, noise and vibrations associated with switched reluctance motors (SRMs) are escalated when the machine is subjected to inter-turn short-circuits (ITSCs). Also, leaving it unchecked can ultimately lead to a complete winding short-circuit due to the insulation failure owing to the heat produced by locally generated hotspots. To avoid such ruinous damage, the diagnosis of ITSCs is very critical. This article proposes a method of diagnosing ITSCs by injecting high-frequency signals in the phases in their low torque regions around unaligned rotor positions. The proposed method utilizes the drive power converter to inject and diagnose the ITSCs and thus requires no additional hardware. It has a higher sensitivity even toward minor fault conditions, i.e., as low as ITSC of 4 turns ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$ \approx$</tex-math></inline-formula> 4% ITSC over a phase). The devised fault indicator is immune to load or speed variation and, hence, better detection reliability. Also, the method can diagnose the fault under load and speed transients proving it to be more robust. Experiments are performed on a test rig of customized 8/6 SRM emulating the ITSCs to verify the potency of the proposed scheme.

Research on Fault Diagnosis Algorithm of Ship Electric Propulsion Motor

The permanent magnet synchronous motor (PMSM) has been used in electric propulsion and other fields. However, it is prone to the stator winding inter-turn short-circuit, and if no effective measures are taken, the ship’s power system will be paralyzed. To realize intelligent diagnosis of inter-turn short circuits, this paper proposes an intelligent fault diagnosis method based on improved variational mode decomposition (VMD), multi-scale principal component analysis (PCA) feature extraction, and improved Bi-LSTM. Firstly, the stator current simulation dataset is obtained by using the mathematic model of the inter-turn short-circuit of PMSM, and the parameters of VMD are optimized by the grey wolf algorithm. Then, the data is coarse-grained to obtain multi-scale features, and the main features are selected as the sample data for fault classification by PCA. Subsequently, the Bi-LSTM neural network is used for training and analyzing the data of the sample set and the test set. Finally, the learning rate and the number of hidden-layer nodes of the Bi-LSTM are optimized by the whale algorithm to increase the diagnosis accuracy. Experimental results show that the accuracy of the proposed method for inter-turn short-circuited fault diagnosis is as high as 100%, which confirms the effectiveness of the method.

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.

Diagnosis of Inter-turn Short Circuit of Permanent Magnet Synchronous Motor Based on Deep learning and Small Fault Samples

An efficient and accurate method based on a conditional generative adversarial net (CGAN) and an optimized sparse auto encoder (OSAE) is proposed to detect the inter-turn short circuit (ITSC) problem for permanent magnet synchronous motors (PMSMs). In order to achieve an accurate detection of the ITSC, the CGAN is adopted to augment the few fault samples, and a noise injection strategy is applied to enhance the generalization ability of the network in the framework of the OSAE. Specifically, we made a combination of two types of signals to create a training set that is augmented by the CGAN, and the parameters of the OSAE are determined by the training process of networks. The experimental results indicate that the proposed method for the fault diagnosis of this fault achieves high accuracy 98.9%.

A Stray Magnetic Flux-Based Robust Diagnosis Method for Detection and Location of Interturn Short Circuit Fault in PMSM

Early diagnosis of interturn short circuit (ITSC) fault is essential to prevent catastrophic electrical machine failures. Along with the detection, locating the faulty winding at its incipient stage is also critical to reduce the outage duration and operation losses. This article proposes a practical method to detect the ITSC fault and locate the faulty winding in permanent magnet synchronous motors (PMSMs) through stray magnetic flux monitoring. It is shown that relying on the fundamental stray flux component can potentially underperform at certain operating conditions leading to fault negative error. To compensate this shortcoming, the third harmonic component of stray flux is comprehensively analyzed and proposed as a complementary ITSC fault indicator. Furthermore, its reliability over the wide range of motor operation is verified to eliminate potential fault negative errors. It is also shown that the proposed method is independent of the fault severity which is a major advantage for practical applications. The findings are supported through 2-D-FEA PMSM drive system simulations and experiments under two different fault severity cases.

Inter-turn Short Circuit Diagnosis Using New D-Q Synchronous Min–Max Coordinate System and Linear Discriminant Analysis

In this paper, a direct-quadrature (D-Q) synchronous min–max coordinate system is proposed (as a new method) for diagnosing the occurrence of inter-turn short circuits (ITSC) of three-phase induction motors, and it was found that this method can linearly diagnose such short circuits using only the maximum value of the d-axis current component from the heavy load to the full load. In the diagnosis of ITSC, a method to perform linear discriminant analysis (LDA) efficiently was applied owing to the difficulty of linear separation under light load conditions. In the aforementioned method, time burden is generated because operations are performed for the entire data and between classes. However, the proposed method is useful even when it is applied to the entire load with only the LDA eigenvector of the minimum light load. This is proved by the graphical evaluation of the interaction between the false acceptance rate (FAR) and false recognition rate (FRR), and the results demonstrate that the proposed method is more efficient than existing LDA application methods.

Linear Method for Diagnosis of Inter-Turn Short Circuits in 3-Phase Induction Motors

When a turn-to-turn short fault occurs in an induction motor, it will be accompanied by vibration and heating, which will have adverse effects on the entire power system. Thus, turn-to-turn short fault diagnosis of the stator is required, and major accidents can be prevented if an inter-turn short circuit (ITSC), which is the early stage of a turn-to-turn short, can be detected. This study reinterprets Park’s vector approach using Direct-Quadrature(D-Q) transformation for the linear separation of ITSCs and proposes an ITSC diagnosis method by defining the magnetic flux linkage pulsation and current change in the event of a turn-to-turn short. It is difficult to diagnose because the turn-to-turn short current change in an ITSC is considerably different from the induction motor loss. Hence, it was found through analysis that when the current change is considered through an analysis of the relationship between inductance and the winding number, the ITSC current becomes slightly smaller than the steady-state current. This was verified using the D-Q synchronous reference frame over time. We proposed a linear separation of the ITSC diagnosis from the steady state by considering the minimum values of the pulsating current as feature points.

Interturn Short-Circuit Faults in Permanent Magnet Synchronous Machines: An Extended Review and Comprehensive Analysis

This paper presents an extended review and recent advances in modeling and diagnosis of interturn short circuit (ITSC) faults in permanent magnet synchronous machines, supported with in-depth fault analysis. In the analysis part, the influence of the fault intensity on the machine’s parameters and performance is analyzed at various operating conditions through finite-element analysis, drive system models, and test bench measurements. The presented findings shed light on the fault signatures and factors that are affecting signature dynamics such as the fault intensity, operating conditions, resistance of the short-circuiting path, and controller action. Following the characterization of the ITSC fault, a detailed literature review on fault signature types and state-of-the art diagnosis methods are presented. The corresponding trends, shortcomings of current solutions, and potential research topics are discussed exhaustively.

Diagnosis of Inter-Turn Short Circuit of Synchronous Generator Rotor Winding Based on Volterra Kernel Identification

The inter-turn short circuit is a common fault in the synchronous generator. This fault is not easily detected at early stage. However, with the development of the fault, it will pose a threat to the safe operation of the generator. To detect the inter-turn short circuit of rotor winding, the feasibility of identifying the stator branch characteristics of synchronous generator during inter-turn short circuit was analyzed. In this paper, an on-line fault identification method based on Volterra kernel identification is presented. This method uses the stator branch voltage and stator unbalance branch current collected from the generator as input and output signals of the series model. Recursive batch least squares method is applied to calculate the three kernels of Volterra series. When the generator is in normal state or fault state, the Volterra kernel will change accordingly. Through the identification of the time-domain kernel of the nonlinear transfer model, the inter-turn short circuit fault of the synchronous generator is diagnosed. The correctness and effectiveness of this method is verified by using the data of fault experimental synchronous generator.

Electrical fault diagnosis in induction motors using local extremes analysis

Purpose – The purpose of this paper is to introduce an algorithm based only on local extreme analysis of a time sequence to further the detection and diagnosis of inter-turn short circuits and unbalanced voltage supply using vibration signals. Design/methodology/approach – The upper and lower extreme envelopes from a modulated and oscillatory time sequence present a particular characteristic being of, theoretically, symmetrical versions with regard to amplitude reflection around the time axis. Thus, one may say that they carry the same characteristics in terms of waveforms and, consequently, frequency content. These envelopes can easily be built by an interpolation process of the local extremes, maximums and minimums, from the original time sequence. Similar to modulator signals, they contain more detailed and useful information about the required electrical fault frequencies. Findings – Results show the efficiency of the proposed algorithm and its relevance to detecting and diagnosing faults in induction motors with the advantage of being a technique that is easy to implement in any computational code. Practical implications – A laboratory investigation carried out through an experimental setup for the study of faults, mainly related to the stator winding inter-turn short circuit and voltage phase unbalance, is presented. Originality/value – The main contribution of the work is the presentation of an alternative tool to demodulate signals which may be used in real applications like the detection of faults in three-phase induction machines.

Modern Diagnostics Techniques for Electrical Machines, Power Electronics, and Drives

For the last ten years, at least three different special sections dealing with diagnostics in power electrical engineering have been published in the IEEE transactions on industrial electronics [1]-[5]. All of them had their specificities, but the last ones, starting in 2011, were more connected to relevant events organized on the topic. In fact, these events have been clearly the only international forums fully dedicated to diagnostics techniques in power electrical engineering. For this particular issue, it has been decided to separate the different submissions into six parts: state of the art; general methods; induction machines (IMs); synchronous machines (SMs); . electrical drives; power components and power converters. The second section includes only one state-of-the-art paper, which is dedicated to actual techniques implemented in both industry and research laboratories. The third section includes three papers on diagnostic techniques not specifically aimed at a particular type of machine. The fourth section includes three papers devoted to diagnostics of rotor faults, two dedicated to stator insulation issues, and four papers dealing with mechanical faults diagnosis in IMs. The fifth section includes papers focusing on different types of SMs. The first two papers deal with wound-rotor SMs, the following three papers are dedicated to permanent-magnet radial flux machines, and the last one deals with permanent-magnet axial flux machines. Regarding the types of faults analyzed, there are three papers devoted to the diagnosis of interturn short circuits in the stator windings, i.e., one dedicated to the detection and location of field-winding-to-ground faults and a paper devoted to the diagnosis of static eccentricities. In the sixth section, two papers investigate issues related to faults in drive sensors, and one is devoted to fault detections in the coupling inductors. The last section includes two papers devoted to diagnosis of faults and losses analysis in switching components of power converters.

Techniques for Online Diagnosis of Stator Shorted Turns in Induction Motors

In low voltage induction motors, the first indication of a stator fault is the appearance of an interturn short circuit. For this reason, several attempts to diagnose reliably these short circuits have been made. Although considerable research has been carried out, the diagnosis of interturn short circuits is not a mature technique yet. This article presents and analyzes the online interturn short circuit diagnostic techniques developed so far for line-connected induction motors. By means of a comparative study, their advantages and drawbacks are highlighted.