Circuits In Stator Windings Research Articles

Detection of Inter-turn Short Circuit in Stator Windings of Electric Machines using Magnetic Symmetry Index and Machine Learning Methods

Detection of Inter-turn Short Circuit in Stator Windings of Electric Machines using Magnetic Symmetry Index and Machine Learning Methods

Diagnostics of inter-turn short circuits in phase stator windings of permanent magnet synchronous machines

BACKGROUND: Currently, electric buses are becoming more widespread in the segment of urban passenger transportation due to the absence of harmful emissions and low noise levels. Permanent magnet synchronous machines (PMSM) are widely used as a traction motor.
 One of the most promising methods of monitoring the technical condition of traction motors is diagnostics using monitoring tools installed on board of a vehicle. The advantages of such methods include the possibility of regular frequent condition checks (as opposed to much rarer diagnostics in stationary maintenance points) and a more rapid response to the development of identified malfunctions, which significantly reduces the cost of maintenance and repair of expensive equipment. The disadvantages include limitations associated with a shortage of layout space for the placement of diagnostic equipment, with the limited capabilities of on-board computer facilities for the transmission and storage of diagnostic information and with the complexity of computational procedures of built-in expert diagnostic systems.
 Concerning the types of operational failures of PMSMs, which are the most common and the most severe in terms of the cost of restoring operability, these are inter-turn short circuits in the stator windings, increased eccentricity of the air gap between the stator and the rotor, bearing damage, as well as damage of the mechanical gearbox coupled with the electric motor.
 AIM: Identification the diagnostic signs of inter-turn short circuits in the phase windings of a permanent magnet synchronous machine, helping to detect these malfunctions on board of route urban vehicles at an early stage.
 METHODS: A mathematical model of a permanent magnet synchronous machine with short-circuited turns of the phase winding, which makes it possible to study the behavior of the motor with a various number of short-circuited turns, has been developed.
 RESULTS: For the first time, the requirements for diagnostic features for the on-board diagnostic system of an urban electric bus, making it possible to detect inter-turn short circuits in the phase windings of traction electric motors at the early stages of the origin of a malfunction, are formulated.
 The main parameters of the process of digital registration of phase currents have been obtained, helping to do measurements and to transmit signals through the onboard information network of the electric bus.
 Using simulation modeling methods, it was found that the peak values of the linear spectrum of the total current vector are stable diagnostic signs that make it possible to detect inter-turn short circuits in the phase windings of traction motors at the early stages of the origin of the malfunction.
 CONCLUSION: The practical value of the study lies in the opportunity of using the proposed methods for diagnosing the technical condition of traction electric drive units in urban passenger electric transport.

Моделювання перехідних процесів в синхронному генераторі при витковому замиканні обмотки статора

The purpose of this study is to develop a mathematical model of a synchronous generator to study its modes of operation in the case of asymmetric damage to the phases of the stator winding, namely twisting, the use of which will make it possible to improve relay protection systems against twisting of the stator winding of a synchronous generator. Transverse differential protection is used to protect synchronous generators from winding circuits. However, such protection can be used only on generators with parallel branches in the phase of the stator winding. In the work, a mathematical model of a synchronous generator was developed, which allows to study the transient processes of the generator in case of the appearance of turn short circuits in the stator winding. This mathematical model is based on differential equations written for the stator phase axes a, b and c. An algorithm for calculating transient processes in a synchronous generator is proposed. In turn-to-turn circuits of the stator winding, which is based on the fourth-order Runge-Kutt numerical integration method. With the help of the developed mathematical model, the values of currents and voltages of the generator were calculated in the case of short-circuits of the stator winding for the TVB-200-2 turbogenerator. The results of mathematical modeling of the transient processes taking place in the TVB-200-2 generator when closing a different number of turns of the stator phase showed that the reduction of the fundamental harmonic voltage at the terminals of the generator in the damaged phase when closing 10 %, 40 %, and 60 % of the winding turns is, respectively, 21 %, 77 % and 89 %. At the same time, there is an increase in the amplitude of the first harmonic of the current of the damaged phase by 3.7 to 7.8 times. The obtained research results make it possible to improve relay protection systems for synchronous generators, namely protection against turn-to-turn circuits in stator windings without parallel branches.

Diagnosis of induction motor faults using the motor current normalized residual harmonic analysis method

Induction motors are susceptible to many types of faults that can become catastrophic and cause production stoppages. To predict emerging faults and avoid an unexpected failure, an induction motor can be diagnosed using the proposed motor current normalized residual harmonic analysis method. This method is simple and efficient. It is based on a more sensitive fault signature and a single frequency of the harmonic for the different faults. It is based also on the analysis of residual harmonics, which can be measured between the normalized linear fast Fourier transform spectrum of the healthy motor current and that of the faulty motor current. The proposed method can mitigate the task of the fault detection system by monitoring the occurrence of a residual harmonic fixed at a precise frequency. This study focuses on the rotor fault of broken bars and inter-turn short circuits in stator windings. In-depth experimental results demonstrate the rapid detection of different faults at the characteristic frequency of 50 Hz. It is not necessary to conduct a wide spectral sweep to search each time for different faults that have variable characteristic frequencies depending on the type of fault. This method can be useful for diagnosing other types of faults such as eccentricities, bearings and magnetic circuits.

Criteria for Detecting Turn-To-Turn Short Circuit in Stator Windings Using Vector Analysis of Electric Motor Phase Currents

At industrial enterprises, seaports, ships, more than 80 % of all electrical engineering equipment is asynchronous motors. In a number of cases, asynchronous motors operate with a sharply variable load, in an aggressive environment; herewith they receive power from the network with deviations of the power quality indicators from the normative ones, which causes their high damageability. Turn circuits in the stator windings account for about 40 % of cases of damage to the insulation of the asynchronous motors windings. Untimely detection of the initial moment of the appearance of defects in the asynchronous motors or disruption of the operating modes of the supply network and current circuits result in emergency situations of the electrical complex, equipment downtime and damage. The article discusses the issues of diagnostics of non-full-phase modes of current circuits, mains voltages and turn circuits in the stator windings of the asynchronous motors that receiving power from the networks in conditions of voltage asymmetry. The analysis of the vector diagrams of currents, voltages and additional phase angles of the phase currents displacement arising from the asymmetry of the mains voltages and turn circuits in the stator windings has been fulfilled. The results obtained made it possible to formulate criteria for identifying the initial moment of the turn circuit and two-phase modes, both in the network and in current circuits, in stationary modes. A method for non-destructive testing of the asynchronous motors state as well as a device for its implementation has been developed. The method is based on comparing the vectors of the measured phase currents of the asynchronous motors with their calculated values. The article presents the results of modeling the change in the vectors of phase currents from the number of closed turns in the winding of the asynchronous motors. The power series of asynchronous motors was established, for which the sensitivity of detecting the initial moment of the turn-to-turn short circuit is maximum. Analytical results of the dependence of the sensitivity coefficient for turn-to-turn short circuit on the absolute increment of the modules of phase currents and the corresponding phase angles have been obtained.

Faults diagnosis in stator windings of high speed solid rotor induction motors using fuzzy neural network

The paper deals with faults diagnosis method proposed to detect the inter-turn and turn to earth short circuit in stator winding of three-phase high-speed solid rotor induction motors. This method based on negative sequence current of motor and fuzzy neural network algorithm. On the basis of analysis of 2-D electromagnet field in the solid rotor the rotor impedance has been derived to develop the solid rotor induction motor equivalent circuit. The motor equivalent circuit is simulated by MATLAB software to study and record the data for training and testing the proposed diagnosis method. The numerical results of proposed approach are evaluated using simulation of a three-phase high-speed solid-rotor induction motor of two-pole, 140 Hz. The results of simulation shows that the proposed diagnosis method is fast and efficient for detecting inter-turn and turn to earth faults in stator winding of high-speed solid-rotor induction motors with different faults conditions

An overview of various faults detection methods in synchronous generators

Synchronous generator (SG) plays a vital and critical role in the power system by supplying electric power to consumers. Various faults in SGs can cause some catastrophic events such as power disruption or blackout. These faults can be classified into two electrical and mechanical faults. Short circuit in stator windings and field winding are electrical fault while bearing, static/dynamic eccentricity, and broken damper bars faults are mechanical one. Unlike the induction machines, there are no much researches in SGs condition monitoring owing to its complex behaviour against the faults. Herein, the SG modelling approaches are presented briefly to elaborate shortcoming and challenging issues in the modelling, and then a comprehensive review of various electrical and mechanical fault detection methods is presented.

Inter-Turns Short Circuits in Stator Winding of Squirrel-Cage Induction Motor

Wydawnictwo SIGMA-NOT wydaje czasopisma fachowe informujące swoich czytelników o najnowszych osiągnięciach naukowych i nowoczesnych rozwiązaniach technicznych w Polsce i na świecie, popularyzuje problemy techniczne oraz poszerza wiedzę i kulturę techniczną.

Diagnosis of Asynchronous Motor with Inter Connective Circuit in Stator Winding

The problems of applying the achievement of general technical diagnostics for the recognition and assessment of the technical state of the stator winding in the process of performing functional processes by an asynchronous motor are considered. It is proposed to use the informative parameters of vibration acceleration obtained from full-scale experiments carried out in idle and load modes of an asynchronous motor for diagnosing an intertuеrn short circuit that often occurs in the stator winding. It is shown that for recognizing a malfunction of the stator winding of an induction motor, mathematical methods of technical diagnostics are effective, in particular, the method of potential functions and the method of diagnostics based on the angular distance in the feature space. The sequence of methods, rapid assessment of the technical condition of the stator winding and the determination of the interturn short circuit at the initial stages of its occurrence during the operation of the induction motor is given.

Induction Motor Fault Classification Based on ROC Curve and t-SNE

This paper proposes a novel fault classification method with application to induction motors, which is based on integrating and combining with receiver operating characteristic (ROC) curve and t-distribution stochastic neighbor embedding (t-SNE). According to the feature selection methods of ReliefF, symmetrical uncertainty (SU), and fast correlation-based filter (FCBF), the significant features were verified. Additionally, support vector machine (SVM), k-nearest neighbor (KNN), and decision tree (DT) are also considered as classifiers to identify the simulation results. To begin with, the current signals obtained from distinctive four topologies of working conditions of the motor, which includes healthy, bearing damage, broken rotor bar, and short circuit in stator windings, respectively. The potential feature set is extracted by using Hilbert-Huang transform (HHT) technique. Then, three feature selection methods are adopted to select three optimal feature subsets from the original feature set. Finally, the classification accuracy (ACC) and ROC curve are used to demonstrate the capability of classifiers’ recognition. The results showed that the optimal feature subsets significantly reduce the number of selected features and improve the classification ACC and area under the curve (AUC) compared with the original feature set. In conclusion, the proposed method can downgrade the data, demonstrate the scatter plot more intuitively, and identify various types of faults, unlike with other fault diagnosis literature.

Development of the software-hardware complex for diagnostics of inter-turn short circuits of stator windings of asynchronous motors

Up to 38% of asynchronous motor (AM) failures are caused by damage to the stator insulation. Despite the wide variety of methods, the reliability of determining the inter-turn short circuits of stator windings under operating voltage still remains at a low level. The existing methods for diagnostics of asynchronous motor failure of the interturn short circuit of stator winding have been examined. New diagnostic methods for individual parameters developed in the course of the previous study are described. A new diagnostic method is formulated, on the basis of which the research work is carried out. The method will allow us to constantly monitor the technical condition of the stator winding, and identify its damage at an early stage of their development. Diagnostics can be performed without dismantling the engine and without removing it from the working process. The microprocessor unit is implemented for diagnostics of the technical condition of AM introducing this method. Moreover, the software and hardware complex is implemented for diagnostics of the technical condition for AM. The results of experiments on a laboratory stand with inter-turn AM closures are presented, which confirm the higher reliability of monitoring.

Induction Motor Fault Classification Based on FCBF-PSO Feature Selection Method

This study proposes a fast correlation-based filter with particle-swarm optimization method. In FCBF–PSO, the weights of the features selected by the fast correlation-based filter are optimized and combined with backpropagation neural network as a classifier to identify the faults of induction motors. Three significant parts were applied to support the FCBF–PSO. First, Hilbert–Huang transforms were used to analyze the current signals of motor normal, bearing damage, broken rotor bars and short circuits in stator windings. Second, ReliefF, symmetrical uncertainty and FCBF three feature-selection methods were applied to select the important features after the feature was captured. Moreover, the accuracy comparison was performed. Third, particle-swarm optimization (PSO) was combined to optimize the selected feature weights which were used to obtain the best solution. The results showed excellent performance of the FCBF–PSO for the induction motor fault classification such as had fewer feature numbers and better identification ability. In addition, the analyzed of the induction motor fault in this study was applied with the different operating environments, namely, SNR = 40 dB, SNR = 30 dB and SNR = 20 dB. The FCBF–PSO proposed by this research could also get the higher accuracy than typical feature-selection methods of ReliefF, SU and FCBF.

Application of Advanced Vibration Monitoring Systems and Long Short-Term Memory Networks for Brushless DC Motor Stator Fault Monitoring and Classification

In this research, electric motors faults and their identification is reviewed. Brushless direct-current (BLDC) motors stator fault identification using long short-term memory neural networks were analyzed. A proposed method of vibration data acquisition using cloud technologies with high accuracy, feature extraction using spectral entropy, and instantaneous frequency and standardization using mean and standard deviation was reviewed. Additionally, model training with raw and standardized data was compared. A total model accuracy of 97.10 percent was achieved. The proposed methods could successfully identify the motor stator status from normal, to loss of stator winding imminent and arcing, and lastly to open circuit in stator winding—motor needing to stop immediately—by using gathered data from real experiments, training the model and testing it theoretically.

Three-phase current sensor for motor protection against turn-to-turn and phase-to-phase short circuits

The main causes of electric motor failures are transients at power supply interruption and short circuit in stator windings. Most of the failures are due to short circuits. Phase-to-phase short circuits refer to the most severe damage of electrical installations, which are also dangerous for other intact electric consumers. Therefore, electrical plants require a high-speed protection against phase-to-phase short circuits in the stator windings and connections with the commutator. The most difficult to detect are turn-to-turn (or inter-turn) faults in stator windings, especially when a small number of winding turns is shorted. Currently, there is no standard way to detect turn-to-turn short circuits in stator windings. It is difficult to detect turn-to-turn fault in magnitude of phase currents, so it is complicated to provide sufficient protection sensitivity. Thus, the development of new protection devices for detect turn-to-turn short circuit is considered relevant. The article presents a three-phase current sensor based on measuring the magnetic field from the three phases simultaneously. This method will identify turn-to-turn and phase-to-phase short circuits by magnetic-field pattern. The presented method increases the protection sensitivity and speed of protection response. The effectiveness of the proposed method is demonstrated by mathematical modeling.

Reference Signal Injection in Induction Motors Drives to Electrical Failures Detection

This paper shows an alternative method to electrical fault diagnosis in three-phase induction motors using reference signal injection. The proposal is based on the insertion of search reference harmonic signals in the supply voltage by the frequency inverter module of the induction machine and the observation of the signals of the electric current of the motor, preprocessed through the FFT and machine signature component analysis. In order to obtain a greater precision of fault classification and state of degradation through intelligent systems, results are validated by different algorithms whose performance are compared in the following methods: k nearest neighbors, naive Bayes, support vector machine, multilayer perceptron and decision tree. The practical results for the tests with controlled simulation of the electrical faults of broken rotor bars and short circuit in stator windings show the capacity that this insertion technique has when it is used as a tool to diagnose three-phase induction motors. The five proposals of intelligent systems result in a classification with success rates of $$93\%$$ to $$100\%$$ in different classes, separated into types of faults and according to the degradation intensity.

Vibration-Signature-Based Inter-Turn Short Circuit Identification in a Three-Phase Induction Motor Using Multiple Hidden Layer Back Propagation Neural Networks

Inter-turn short circuits in stator windings area fairly common fault in induction motors. Early detection of this type of fault will greatly assist in sustaining production processes in manufacture. This paper proposes a method to detect inter-turn short circuits in stator windings at an early stage. The proposed method consists of four steps: (1) preprocessing by decomposing the signal into detail and approximation signals using a wavelet transform, (2) converting the first detail signal into a frequency-based signal using fast Fourier transform, (3) calculating the values of statistical features for the signal in time and frequency domains and (4) identifying faults using a back propagation neural network (BPNN). Using BPNN architecture with 3 hidden layers and 75 neurons per layer, the identified recognition rate was96.67% with a mean square error of 1.39×10-5. The validity of the proposed method is excellent based on a receiver operating characteristic analysis, with a precision level of 94.66%.

Faults Classification Scheme for Three Phase Induction Motor

In every kind of industrial application, the operation of fault detection and diagnosis for induction motors is of paramount importance. Fault diagnosis and detection led to minimize the downtime and improves its reliability and availability of the systems. In this article, a fault classification algorithm based on a robust linear discrimination scheme, for the case of a squirrel–cage three phase induction motor, will be presented. The suggested scheme is based on a novel feature extraction mechanism from the measured magnitude and phase of current park's vector pattern. The proposed classification algorithm is applied to detect of two kinds of induction machine faults, which area) broken rotor bar, and b) short circuit in stator winding. The novel feature generation technique is able to transform the problem of fault detection and diagnosis into a simpler space, where direct robust linear discrimination can be applied for solving the classification problem. And thus a clear classification of the healthy and the faulty cases can be robustly performed, by having the optimal hyper plane. This method can separate the feature current classes in a low dimensional subspace. Robust linear discrimination has been one of the most widely used fault detection methods in real-life applications, as this methodology seeks for directions that are efficient for discrimination and at the same time applies a straight-forward implementation. The efficacy of the proposed scheme will be evaluated based on multiple simulation results in different fault types.

Wide frequency range analysis of the electromagnetic emissions of electrical machine for diagnosis

The paper presents the possibilities to evaluate the health of an electrical machine by analyzing its electromagnetic emissions. Different piece of information can be detected according to the frequency range. First of all, a monitoring system sensitive to the degradation of the turn-to-turn insulation quality of the stator winding is presented; it is based on high frequency electromagnetic emissions. The second part of the paper is based on the medium and low frequency emission analysis. It explains how to detect turn-to-turn short circuits in stator windings or in the excitation coils of synchronous machines.

The detection of inter-turn short circuits in the stator windings of operating motors

This paper develops a winding-function-based method for modeling polyphase cage induction motors with inter-turn short circuits in the machine stator winding. Analytical consideration which sheds light on some components of the stator current spectra of both healthy and faulty machines is developed. It is shown that, as a result of the nature of the cage rotor, no new frequency components of the line current spectra can appear as a consequence of the fault. Only a rise in some of the frequency components which already exist in the line current spectra of a healthy machine can be observed. An experimental setup comprising a 3 kW delta-connected motor loaded by a generator was used to validate this approach. The experimental results obtained clearly validate the analytical and simulation results.