Turn Fault Research Articles

Stator Turn Fault Detection by Relative Voltage Residual Slopes With High-Frequency Injection

Stator Turn Fault Detection by Relative Voltage Residual Slopes With High-Frequency Injection

Stator Turn Fault Diagnosis and Severity Assessment in Converter-Fed Induction Motor Using Flat Diagnosis Structure Based on Deep Learning Approach

This work aims to explore the hybrid deep learning techniques for early interturn fault diagnosis and fault severity assessment in variable frequency induction motor drive. However, the existing literature mostly focuses on identifying the fault patterns, whereas the assessment of fault deterioration before it reaches the extreme degradation state has been less investigated. In this paper, a hybrid structure combining two-dimensional convolution neural network (2DCNN) and long-short term memory/gated recurrent unit (LSTM/GRU) has been modelled as a relatively lightweight flat diagnosis structure (FDS) for early fault identification and its severity assessment simultaneously in converter-fed induction motor. Initially, the hierarchical layers of 2DCNN extract the effective features automatically for fault detection and its severity identification simultaneously. Then, these features are presented as suitable inputs to LSTM/GRU for classification. A comparative evaluation of the proposed hybrid method with the standalone structure, namely 2DCNN, LSTM and SVM proves the advantages of the proposed scheme, and its applicability for both fault diagnosis and fault severity assessment in industrial drive systems, under variable frequencies and load variations.

A Novel Turn Fault Detection Strategy Based on High-Frequency Neutral-to-Ground Voltages

A Novel Turn Fault Detection Strategy Based on High-Frequency Neutral-to-Ground Voltages

An effective torque-based method for automatic turn fault detection and turn fault severity classification in permanent magnet synchronous motor

An effective torque-based method for automatic turn fault detection and turn fault severity classification in permanent magnet synchronous motor

Early detection and localization of stator inter turn faults based on variational mode decomposition and deep learning in induction motor

Early detection and localization of stator inter turn faults based on variational mode decomposition and deep learning in induction motor

Advanced Fault Tolerant 3×3-Phase PM Drive With Concentric Winding for EV Application

Fault tolerant machine drive is essential for EV application to ensure uninterrupted operation during fault event. Among various fault modes, inter-turn fault is the most challenging failure for the PM drive which requires special measures to suppress the excessive fault current. In this paper, a novel concentric winding (CW) is analyzed for a 3×3-phase PM drive to restrict the turn fault current. Compared with the conventional distributed winding (DW), the two sub-coils of each phase occupy the same space angle and both align with the phase axis, and the winding factor of each coil is 0.96 which is 4% lower than the DW. Owing to the two aspects, the coil flux linkages are more effectively nullified after applying winding terminal short circuit (TSC) which indicates lower fault current. The two machines’ healthy performance as well as various fault modes’ behaviors are investigated by detailed simulation and test investigation. The results prove the CW machine has the same healthy characteristics as the DW machine, and the maximum inter-turn fault current is up to 17% lower than the counterpart. In addition, the improved turn fault current suppression capability is achieved simply by re-arranging the coil layout without introducing any extra cost.

Inverter-Embedded Detection of Rotor Winding Faults in Doubly Fed Induction Generators for Wind Energy Applications

Automated and remote testing of wind turbines is highly desirable since they are prone to failure with the harsh operating environment, and are difficult to test due to the remote location and limited accessibility. The turn insulation of the rotor winding is susceptible to failure due to operating stresses. Unlike turn faults in the stator, rotor turn faults do not cause immediate failure due to the low induced voltage in the shorted loop at low slip frequency, and therefore, are difficult to detect during operation. Although this fault progresses slowly, it must be detected to prevent performance degradation due to rotor asymmetry. In this article, a new concept for inverter-embedded testing of doubly fed induction generators (DFIG) used for wind energy applications is presented. The main idea is to inject test voltages into the DFIG rotor winding from the rotor side inverter whenever the wind turbine is stopped, to extract information on the rotor asymmetry. The proposed test concept is a simple off-line test that can be implemented in DFIGs without additional hardware to perform automated and sensitive detection of rotor faults. Experimental results show that faults in the rotor winding can be detected with higher sensitivity compared to on-line methods for improving the reliability of wind turbines.

Prony Method Estimation as a New Approach for Surge Comparison Testing in Turn Insulation Diagnostics for Three Phase Stator Windings

This article presents an evaluation of Prony method estimation and its implementation considerations for surge comparison test application in turn insulation diagnostics for three-phase stator windings. Surge testing diagnostics compares recorded surge voltage signals of motor winding, and a diagnostic is then defined with a defined value of EAR (error area ratio), which evaluates the difference between signals to determine a turn insulation diagnostic. First, an overview of surge testing is presented. Next, the Prony method and the considerations for its implementation in surge testing are described. Then, a numerical simulation is used to define a simulated turn fault surge voltage signal, where its parameters can be obtained with Prony method estimation and compared with EAR to evaluate its performance. Lastly, recorded surge test signals from two tested motors are used to validate Prony method estimation application for surge test diagnostics, where twelve recorded surge signals for no-fault and fault conditions were analyzed. The summary results of the surge signals parameter estimation are presented in the results and discussion section.

Multiple 3-Phase PMA-SynRM With Delta Windings for Enhanced Fault Tolerance

In this article, a novel multiple three-phase PM assisted synchronous reluctance machine with delta windings is proposed for enhanced fault tolerance. The delta connected windings offer an additional degree of freedom of the phase currents. Hence, this machine drive could provide higher torque under phase open-circuit condition. More importantly, the delta windings inherently contain zero sequence current path and could help reducing the turn fault current in the event of the most challenging turn short-circuit fault. Owing to the zero sequence current, the residual flux linkage of the fault turn is more effectively nullified leading to lower fault current. The proposed machine performance together with fault tolerant capability under various fault scenarios are investigated and compared with the star connected winding machine by detailed finite element simulation and experimental tests. It shows that the two machines have the same performance under healthy condition whilst the delta winding machine exhibits higher fault tolerance. These advantages are achieved simply by changing the winding connections without any extra cost. Thus, it is more favorable for the fault tolerant applications.

A Low Coupling Fault Tolerant PMA-SynRM With Mixed-Pitch Segregated Windings

Mutual coupling effect between different winding modules causes undesirable effects for the fault tolerant machine drives, such as current distortion, torque ripple, and increased fault current. In this article, a mixed-pitch segregated winding configuration is presented for a triple modular fault tolerant permanent magnet (PM)-assisted synchronous reluctance machine (PMA-SynRM). It exhibits less mutual coupling effect between the different winding sets, which leads to improved fault tolerance compared to the existing full-pitched segregated windings. The mutual coupling effect is described by deriving the magnetomotive force (MMF) profile, and the fault tolerant performance improvement is proven by detailed finite-element (FE) analysis and prototyping tests in healthy and various fault conditions. The results confirm that the presented winding machine has the same healthy performance as the conventional full-pitched windings, and it operates in a more balanced manner with less current distortion and lower turn fault current. It will become a more competing candidate for the safety critical applications.

Early Stator Fault Detection and Condition Identification in Induction Motor Using Novel Deep Network

Fault diagnosis of induction motor is an important task for both the researchers and industry. The interturn fault is one of the frequented faults and accounts for more than 37% of the failures. Thus, the diagnosis of interturn fault at an early stage has become vital to avoid the catastrophic failures and production losses. In this work, the advantages of deep learning-based methods are explored to detect the interturn fault at an incipient stage. Hybrid architectures has been proposed in this work for incipient inter turn fault diagnosis [i.e., 1D convolution neural network-long short-term memory (1DCNN-LSTM) and 1DCNN-gated recurrent unit (GRU) based methods]. The performance of the hybrid methods has been compared with standalone techniques (1DCNN, LSTM, and GRU), and the results show that the hybrid models (1DCNN-LSTM and 1DCNN-GRU) outperform the individual architectures (in terms of accuracy, sensitivity, and specificity), for fault diagnosis and isolation. The computational time has also been found to be comparable, which is an added advantage for fast diagnosis tasks. The approach combines the feature extraction and classification tasks, to perform early diagnosis, in presence of ambiguous conditions. Here, the incipient diagnosis of fault has been augmented with condition identification i.e., healthy (balanced voltages), healthy (unbalanced voltages), faulty (balanced voltages), and faulty (unbalanced voltages), to effect both early detection and isolation. The performance metrics show the robustness of the proposed method for not only detecting fault, but also identifying such confusing conditions for critical applications, in which induction motors are employed.

Application of machine learning for inter turn fault detection in pumping system

Pump fault diagnosis is essential for the maintenance and safety of the device as it is an important appliance used in various major sectors. Fault diagnosis at the proper time can reduce maintenance costs and save energy. This article uses a Simulink model based on mathematical equations to analyze the effects of parameter estimation of three-phase induction motor-based centrifugal pumps in inter-turn fault conditions. The inter-turn fault causes a massive in, a massive increase in current, which severely affects the parameters of both motor and pump. These have been analyzed by simulation through the Matlab Simulink model. Later, the results are verified by a hardware in loop (HIL) based simulator. In this paper, machine learning (ML) based artificial neural network (ANN) and ANFIS (ANN and Fuzzy) models have been applied for fault detection. ANN and ANFIS-based models provide a satisfactory level of accuracy. These models provide accurate training and testing results. Based on root mean square error (RMSE), R2, prediction accuracy, and mean validation value, these models are compared to find out which is more suitable for this experiment. Various supervised algorithms are compared with ANN, ANFIS, and lastly, found which is the most suitable for this experiment.

Analysis and Simulation of Inter-Turn Fault Of Synchronous Generator Using MATLAB

Abstract: The paper represents a comprehensive analysis of the inter-turn faults of a synchronous generator. In today's power system, we have used three phase synchronous generator to generate electric power, thus we all always try to reduce the loss in order to improve the efficiency of the alternator. We basically focus more on the stator winding faults. Stator winding has unbalanced loading, field failure and stator winding fault (including line to ground fault, line to line fault and double line to ground fault, three phase fault and inter turn fault) Alternator stator inter turn faults are considered to be rare and therefore they are not taken into that serious consideration while designing the protection system for the alternator. But we knew that there could be an inter-turn short circuit fault in the stator winding of the alternator. Early detection of inter turn faults will eliminate damage to the stator core and adjacent coils, reducing repair costs and generator outage times. Since inter turn fault causes imbalance in phase voltage, this concept is discussed in this paperfor inter turn fault detection. The negative sequence voltage of the generator is used as a fault indicator for inter turn fault detection. This new approach is done using MATLAB software. Also, this method works for external as well as internal fault and helps in keeping the generator winding healthy. Keywords: Inter Turn fault, internal and external faults, Internal Negative, Sequence Generator Voltage.

A Negative Sequence Current Phasor Compensation Technique for the Accurate Detection of Stator Shorted Turn Faults in Induction Motors

Stator faults are the most critical faults in induction motors as they develop quickly hence requiring fast online diagnostic methods. A number of online condition monitoring techniques are proposed in the literature to respond to such faults, including the signature analysis of the stator current, vibration monitoring, flux leakage monitoring, negative sequence components of voltage and current and sequence components monitoring based on the identification of asymmetrical behavior in a machine. Negative sequence components of voltage and current and sequence components monitoring are commonly considered for the identification of asymmetrical behavior of induction motors. Negative sequence current analysis is a sensitive technique for the detection of shorted turns as it directly measures the asymmetry produced by the fault. However, the technique is sensitive to other asymmetrical faults and disturbances, which can also produce negative sequence currents. These disturbances include sensor errors, inherent asymmetry and voltage unbalance. This paper provides a comprehensive investigation of the disturbances using a motor model along with experimental measurements under varying load conditions. Then, a new phasor compensation technique is explained to eliminate such disturbances effectively. This technique enables the accurate detection of even relatively small shorted turn faults, even at an early stage. The technique is tested experimentally, and a set of practical results are given to validate the methods developed.

Stator winding fault detection of an Alternator using MATLAB

The paper represents the comprehensive analysis of stator winding faults of an alternator. An alternator is main component of power system which plays a big role in whole system for security and stability. The conventional system only detect symmetrical and unsymmetrical faults, they will not detect inter turn fault. The fault detection of stator winding is an effective and important measure to ensure the safe operation of generator. Early detection of stator winding fault would eliminate damage to stator core & adjacent coils, also reduce repairing cost and outage time of generator. As stator winding fault causes unbalance in phase voltage, this concept is discussed in this paper for the detection of stator winding fault. The negative sequence voltage of generator is used as fault indicator for fault detection. This new method is done using MATLAB software. Also, this method operate for external as well as internal fault and helps the generator winding keep healthy. Keywords— stator winding fault, internal negative sequence voltage, internal and external faults.

Haar-Wavelet-Based Statistical Scanning of Turn Fault in Vehicular Starter Motor

Stator interturn fault is one of the most common faults that affect motor operation. Its failure will not only prevent the vehicle from starting but may also cause other adverse problems. In this letter, an automotive system has first been modeled in MATLAB/Simulink environment. The starter motor used in this system has been simulated to undergo a stepwise interturn fault. For every shorted starter armature coil, the current data were recorded and then wavelet analysis was performed on the gathered data. The wavelet-based parameters variation was determined for every degree of short on the starter stator armature coil. Based on learning, an algorithm has been proposed and then validated with real data to detect the variation in the wavelet parameters and to help determine normal operating conditions, prefault conditions, as well as conditions during fault of the automotive system to help in early detection of starter motor interturn fault. This has been done using a square-shaped Haar wavelet function based on the Hadamard-Haar transform. Such early detection can be very helpful for automotive systems, and if such faults are left unattended, it may result in complete machine failure due to excess heat generation due to the flow of high current in the shorted coils.

High Performance and Strong Fault Tolerant Triple 3-phase PMA-SynRM with Star-delta Windings

Numerous research efforts are devoted to attain high performance and strong fault tolerance for the machine drives in safety critical applications, though the two aspects are conflicted in most cases. In this paper, a novel triple 3-phase PMA-SynRM with star-delta connected windings is proposed. The star-delta winding could produce a higher fundamental magneto-motive force and lower spatial harmonics than the conventional star winding. Therefore, it exhibits higher torque, lower torque ripple and lower iron loss, enhancing the healthy performance. In addition, the coils whose turn fault current is the highest are arranged in the inner delta part which provides circulating current path. It is demonstrated that the proposed winding solution reduces the fault current by 55% and the fault turn copper loss by 65%. Hence, the machines fault tolerance is significantly improved. The machines with the star winding and star-delta winding are studied by detailed finite element calculation and experimental tests under various operation modes. Both the simulation and test results confirm that the proposed star-delta winding machine owns higher performance and stronger fault tolerance than the conventional star winding machine. Thus, it is being a more competitive candidate for the high reliability drives.

Insight to Enhancing the Performance of the Pole Drop Test for Detecting Field Winding Turn Faults in Salient Pole Synchronous Motors

The pole drop test is the most widely applied off-line test for detecting shorted turns in the field winding of salient pole synchronous motors. It is a simple test where the voltage drop (or inductance) of the poles are compared to detect the presence of shorted turns. Although it does not require special test equipment, false indications due to sensitivity problems are common. In this letter, the voltage and inductance distribution between the poles are analyzed under shorted field turn conditions. Some practical insight for enhancing the sensitivity and reliability of the pole drop test are given based on the qualitative analysis and experimental testing on a 30 kVA synchronous motor with shorted field turns. It is shown that the sensitivity of the test is significantly higher when the test is performed with the rotor inserted, which is not reported elsewhere. It is also shown that the reliability of the test can be improved by observing the voltage or inductance in poles adjacent to the pole suspected with shorted turns.

A Study on the Predictive Maintenance Algorithms Considering Load Characteristics of PMSMs to Drive EGR Blowers for Smart Ships

Exhaust gas recirculation (EGR) is a NOx reduction technology that can meet stringent environmental regulatory requirements. EGR blower systems must be used to increase the exhaust gas pressure at a lower rate than the scavenging air pressure. Electric motor drive systems are essential to rotate the EGR blowers. For the effective management of the EGR blower systems in smart ships, there is a growing need for predictive maintenance technology fused with information and communication technology (ICT). Since an electric motor accounts for about 80% of electric loads driven by the EGR, it is essential to apply the predictive maintenance technology of the electric motor to maximize the reliability and operation time of the EGR blower system. Therefore, this paper presents the predictive maintenance algorithm to prevent the stator winding turn faults, which is the most significant cause of the electrical failure of the electric motors. The proposed algorithm predicts the remaining useful life (RUL) by obtaining the winding temperature value by considering the load characteristics of the electric motor. The validity of the proposed algorithm is verified through the simulation results of an EGR blower system model and the experimental results derived from using a test rig.

Condition Monitoring of PMSM and Stator-inter Turn Fault Diagnosis by Advanced Signal Processing Technique

In the present time, Permanent Magnet Synchronous Motors (PMSMs) are extensively used in many industrial applications due to its advantages over conventional synchronous motor. The PMSM is compact and efficient with high dynamic performance, thus having more advantages such as light weight, small size and bulky burden ability. When PMSMs are failed during the operation then large revenue losses occurs for industries. Hence, it is essential to diagnose these faults before occurring, for protection of any industrial plant. In the paper, firstly a comprehensive review of condition monitoring has been done for PMSM faults and their diagnostics techniques. From review, it is found that the stator inter-turn fault diagnosis has been the challenging task for many researchers. Hence, the work has been extended for fault analysis of stator inter-turn under transient conditions, which is effectively analyzed with the help of advanced signal processing technique.