Inter-turn Short Circuit Fault Research Articles

Impact of a PMSG inter-turn short-circuits fault on a grid-connected variable-speed wind energy conversion system

This paper assesses the effect of an inter-turn short circuit fault in stator windings of a Permanent Magnet Synchronous generator (PMSG)-based variable speed wind energy conversion system (WECS) connected to the grid by a power converters interface. The objective is to assess the impact of the fault on the WECS performances. This study has two tasks. The first one details a dynamic modeling of different subsystems of the WECS. The mathematical model of the defective PMSG is also set up. The second one presents an appropriate control strategy for the power converters interface. The control of the PMSG-side converter aims to ensure the operation in maximum power point tracking. The grid-side control was also established by controlling the active and reactive powers to ensure the operation in unity power factor and keeping the DC bus voltage stable. The performance of the control design was tested under healthy and faulty conditions.

Analysis and fault-tolerant control of inter-turn short-circuit fault for five-phase permanent-magnet synchronous machine

For the demand of high reliability in the aerospace field, multi-phase electric machines have become a research hotspot for the fault-tolerant ability. Inter-turn short-circuit fault is one of the main fault forms in the electric machine. The fault mechanism, diagnosis method and fault-tolerant control strategy of inter-turn short-circuit fault of the open-winding five-phase permanent-magnet synchronous machine that is used as the starter/generator system are studied in this paper. Firstly, the fault mechanism of the inter-turn short-circuit fault (ITSCF) is analyzed. The characteristics of phase voltage, inter-turn short-circuit current and torque ripple are compared using the phase winding open-circuit method and the phase winding short-circuit method. Secondly, a fault diagnosis method based on the information fusion is proposed, which determines the fault phase firstly and then identifies the inter-turn short-circuit ratio. According to the fault mechanism, a fault-tolerant control method is proposed, in which the accurate inter-turn short-circuit ratio and inter-turn short-circuit current are not required. The proposed fault-tolerant control method has similar torque ripple compared with the control method using accurate inter-turn short-circuit ratio and inter-turn short-circuit current. Finally, the effectiveness of the proposed fault-tolerant control method has been verified by experimental results.

The Effect of Stator Inter-Turn Short-Circuit Fault on DFIG Performance Using FEM

Doubly-Fed Induction Generators (DFIG) are operated for wind energy production, and as their capacity is increasing, their safety and reliability become more important. Several faults affect the performance of DFIG. The stator winding Inter-Turn Short-Circuit Fault (ITSCF) is one of the most prevalent electric machine failures. This study examined the stator ITSCF effects on DFIG performance for different cases. The DFIG was designed and engineered using the Finite Element Method (FEM) and the Maxwell software, and was examined in healthy operation and four defective cases with various Numbers of Inter-Turn Short Circuit Faults (NITSCF): 4, 9, 19, and 29. These models allowed the examination of the effects and the comparison of each case separately from the healthy state. The comparison was plotted in Matlab to show the effects of the faults. The novelty of this study was that it investigated the effects of different NITSCF on the performance of DFIG and not only on their effect on the stator current and distribution of magnetic flux density. A better understanding of the short circuit effects on the performance of the DFIG can be exploited for subsequent implementations of early fault detection systems.

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.

Electromagnetic Torque Fluctuating Properties under Dynamic RISC Fault in Turbogenerators

This paper analyzes the electromagnetic torque (EMT) fluctuation characteristics in synchronous generators under rotor interturn short-circuit (DRISC) fault. The novelty of this paper is that the DRISC fault is proposed based on the intermittent interturn short circuit existing in the actual operation and compared with the static rotor interturn short-circuit (SRISC) fault. In the work, by studying the influence of DRISC with different positions and different short-circuit degrees, the fluctuation characteristic of the EMT is analyzed and verified. The results show that when the DRISC5% fails, the location is in slot 3, the amplitude of first harmonic decreases by 7.2%, second harmonic amplitude increases by 33.4%, third harmonic decreases by 4.3%, and fourth harmonic increases by 26.8%. As the degree increased and positioned away from the large tooth of the DRISC, the overall EMT amplitude and reverse pulse increased, first and third harmonics decreased, and second and fourth harmonics increased.

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.

Fault data expansion method of permanent magnet synchronous motor based on Wasserstein-generative adversarial network

Aiming at the characteristics of non-smooth, non-linear, multi-source heterogeneity, low density of value and unevenness of fault data collected by the online monitoring equipment of permanent magnet synchronous motor (PMSM), and the difficulty of fault mechanism analysis, this paper proposes a method of PMSM data expansion based on the improved generative adversarial network. First, use the real fault data of the motor to train the model to obtain a mature and stable generative countermeasure network. Secondly, use the generative countermeasure network model to test the remaining data and generate pseudo samples. Finally, use the two-dimensional data analysis method and the time-domain analysis method to generate validity analysis of samples. Aiming at the characteristics of unbalanced motor data, the data expansion method of inter-turn short-circuit faults is carried out based on the data expansion method of the improved generative countermeasure network, and the two-dimensional data analysis method and the time-domain analysis method are used for analysis. The experimental results show that the improved Wasserstein-Generative Adversarial Network (W-GAN) has a better ability to generate fake data, which provides a data basis for the mechanism analysis and machine fault diagnosis of PMSMs. Data analysis results show that the improved W-GAN effectively solves the problem of poor convergence of GAN.

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.

Multi-Turn Modeling of a Power Transformer under an Inter-Turn Short-Circuit Fault

This paper presents a new method for modeling the steady-state behavior of the currents transmitted between the windings of a transformer subjected to an inter-turn short-circuit fault (ITSC). ITSC is one of the most frequent and most damaging faults in magnetically coupled circuits, which occur in power transformers. Coupled Electromagnetic Circuit (CEMC) Model modeling the reasoning and relationships describing the operating principle of a power transformer through the electrical and magnetic parameters defined by the self and mutual inductances that influence the voltages and currents transmitted between the transformer windings. The representation of the state variable equations of a proposed model of a three-phase multi-turn transformer in healthy mode (no fault) and in degraded mode (with inter-turn short-circuit faults of 10%, 20% and 30%) has been implemented using a program inserted in MATLAB software. The simulations illustrate the evaluation of the primary and secondary currents as well as the voltage drop across a load, and the accuracy of the state model based on the coupled circuits was validated. The results obtained can provide a basis for the design of the short circuit of the multi-turn transformer.

Real-Time Detection of Incipient Inter-Turn Short Circuit and Sensor Faults in Permanent Magnet Synchronous Motor Drives Based on Generalized Likelihood Ratio Test and Structural Analysis.

This paper presents a robust model-based technique to detect multiple faults in permanent magnet synchronous motors (PMSMs), namely inter-turn short circuit (ITSC) and encoder faults. The proposed model is based on a structural analysis, which uses the dynamic mathematical model of a PMSM in an frame to evaluate the system’s structural model in matrix form. The just-determined and over-determined parts of the system are separated by a Dulmage–Mendelsohn decomposition tool. Subsequently, the analytical redundant relations obtained using the over-determined part of the system are used to form smaller redundant testable sub-models based on the number of defined fault terms. Furthermore, four structured residuals are designed based on the acquired redundant sub-models to detect measurement faults in the encoder and ITSC faults, which are applied in different levels of each phase winding. The effectiveness of the proposed detection method is validated by an in-house test setup of an inverter-fed PMSM, where ITSC and encoder faults are applied to the system in different time intervals using controllable relays. Finally, a statistical detector, namely a generalized likelihood ratio test algorithm, is implemented in the decision-making diagnostic system resulting in the ability to detect ITSC faults as small as one single short-circuited turn out of 102, i.e., when less than 1% of the PMSM phase winding is short-circuited.

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.

Research on inter-turn short circuit fault location of SF6 circuit breaker energy storage motor coil based on traveling wave reflection method

—The traveling wave reflection method is proposed to locate the inter-turn short circuit fault of the circuit breaker energy storage motor coil. The capacitance and inductance matrices of the energy storage motor coil are calculated by finite element simulation, and the wave impedance model of the coil is established based on ATP-EMTP. The low-voltage square wave pulse signal is injected at the winding head to detect the characteristic curve of reflected wave when inter-turn short circuit fault occurs. The variation law of generalized fractal dimension of reflection wave characteristic curve with short circuit position is analyzed. The short-circuit position is evaluated based on BP neural network, and the evaluation accuracy is 100 %, indicating that this method is feasible.

Insulation failure evaluation of SF6 circuit breaker relay coil based on high-frequency equivalent model

Abstract In order to evaluate the insulation failure of SF6 circuit breaker relay coil, the finite element model of the relay coil was established, and the capacitance inductance matrix was calculated by simulation calculation, and the high-frequency equivalent circuit model of the coil was established. Inject low-voltage square wave pulse signal at the input end of the circuit, and the response curve is detected at the end of the circuit, and the characteristic curve is obtained by combining the response curve under the condition of inter-turn short circuit fault and no fault. The wavelet packet - energy spectrum analysis shows that the energy proportion of each frequency band of the characteristic curve changes when the short-circuit position between turns is different. Taking energy spectrum as input and combining with extreme learning machine to locate inter-turn short circuit fault, the simulation sample analysis shows that the correct rate of fault location is 93.3%.

Research on Fault Location of SF6 Circuit Breaker Relay Coil Based on Traveling-Wave Reflection Method

—The traveling wave-reflection method is proposed to locate the short circuit fault of the opening/closing coil of the circuit breaker, the capacitance and inductance matrix of the opening/closing coil are calculated by finite element simulation, and the wave impedance model of the coil is established based on ATP-EMTP. A low-voltage square wave pulse signal is injected into the head end of the winding to detect the characteristic curve of the reflected wave when an inter-turn short-circuit fault occurs. The generalized fractal dimension of reflection wave characteristic curve was calculated and its variation as the position of short circuit change was analysed. Compared with box dimension, the variation of information dimension and correlation dimension are more obvious, thus information dimension and correlation dimension are selected as inpus, and the evaluation model of inter-turn short circuit position is established based on probabilistic neural network.

Drive Integrated Start-Up and Online ITSC Fault Monitoring Through Spatial Inductance Profiling

Fault diagnosis and condition monitoring of electric machines using readily available drive sensors are highly critical in safety-critical applications. This article presents an accurate method to detect stator interturn short-circuit (ITSC) fault using the spatial inductance profile, which can be implemented both at start-up (standstill) or in real time. For this purpose, ac motor drive spatially scans the motor at each start-up in milliseconds by injecting high-frequency signals to obtain spatial inductance profile, analyze the fault status, and locate faulty phase. Using the start-up inductance profiling as reference, online detection version is implanted successfully, which is essential for real-time condition monitoring. This approach is highly sensitive ITSC faults, agnostic to temperature variations, and takes inverter nonlinearities into account caused by dead time and voltage drop across the power devices. To demonstrate the efficacy and practicability of the proposed algorithm, it is experimentally verified on two PMSMs, interior permanent magnet synchronous motor (IPMSM) and an induction motor, all rewounded to mimic ITSC faults. It is shown that the algorithm can detect ITSC faults very accurately in the order of milliseconds even if the number of shorted turns is very low.

Diagnosis of Interturn Short Circuit of Permanent Magnet Synchronous Motor Based on Stacked Normalized Sparse Autoencoder

Regarding the problems such as the traditional auto-encoders’ tendency to learn the similar features during the feature extraction and limited capability of feature learning in the shallow network models, the paper puts forward an interturn short circuit diagnosis method for Permanent Magnet Synchronous Motor (PMSM) based on the Stacked Normalized Sparse Autoencoder (SNSAE). The method is to guarantee the representativeness of the extracted features, acquire stronger anti-interference capability, and is more suitable for training diversified and large-capacity mechanical equipment failure data. Four types of Inter-Turn Short-Circuit (ITSC) fault data were collected on the PMSM fault diagnosis experimental platform for testing. The experimental outcome suggests that the SNSAE algorithm can effectively extract the fault features, and the fault diagnosis effect is better than the traditional intelligent diagnosis method, and its diagnosis accuracy is as high as 98.91%.

Observer-Based Active Fault-Tolerant Control of an Asymmetric Twin Wind Turbine

This paper deals with the design of an active fault-tolerant control based on observers for a twin wind turbine, consisting of two wind turbines mounted on the same tower. We consider an asymmetric conditions case, when only one turbine is affected by an inter-turn short circuit fault of a permanent magnet synchronous machine. A diagnosis design is developed which combines the fault estimation method together with an active fault-tolerant control. The main advantage of the proposed method is to detect and correct the considered fault in a short time in order that the twin wind turbine behaves as it does in the healthy case.

Inter-turn short-circuit fault diagnosis using robust adaptive parameter estimation

This paper presents a robust adaptive parameter estimation procedure, applied to stator inter-turn short-circuit (ITSC) fault detection in induction motors (IM). Using a fault state space model, it is proposed the application of an adaptive parameter estimation methodology, used to identify a parameter related to the failure severity. A linear parameter varying (LPV) robust H∞ filtering technique, obtained by linear matrix inequalities (LMI), is applied to estimate the unavailable states, being the proposed method robust to external disturbances, and also adequate to online applications. The proposed framework is validated through simulations and experimental tests — conducted in a modified IM that allows the emulation of ITSC faults. Tests are performed under several operational conditions, including different levels of fault severity, load torque and voltage unbalance, demonstrating the validity of the ITSC fault detection approach.

A comparative study for stator winding inter-turn short-circuit fault detection based on harmonic analysis of induction machine signatures

This article deals with inter-turn short-circuit fault detection in stator windings of squirrel cage induction machines. The main aim is to perform harmonic analysis of different electrical signatures namely the stator phase current, external magnetic flux and electromagnetic torque at different levels of mechanical load in order to develop an efficient fault detection approach of this kind of defect in induction machines. The proposed approach is based on the analysis of saturation related harmonics at rank 3k1fs, where k1 is an odd number, and magnetomotive force (MMF)-related harmonics at rank (6k2±1)fs, with k2=1,2,… in stator phase current and stray flux and harmonics at rank 2k2fs in electromagnetic torque. The amplitudes of these last harmonics in healthy condition are compared with 3% power supply unbalance, 16.6% (40 turns) and 33% (80 turns) levels of inter-turn short-circuit fault in frequency range from 0 Hz to 2500 Hz under different levels of mechanical load. Besides, the stand-still test is also investigated in this work. Simulation study is carried out based on 2.2kW squirrel cage IM using finite element method (FEM). This method provides accurate and inexpensive tool for evaluating the performance of induction machine under healthy and faulty conditions. The obtained results demonstrate that the stray flux is the most sensitive signature to the stator winding inter-turn short-circuit fault, and it is robust against the power supply unbalance in comparison with both stator current and electromagnetic torque.

Optical FBG-T Based Fault Detection Technique for EV Induction Machines

Electric vehicles (EV) represent a key technology to achieve a low-carbon transportation objective, whist induction motors are one of the promising topologies. The reliability of these machines is crucial to minimize the downtime, cost and unwanted human lives. Although several techniques are utilized in the condition monitoring and fault detection of electrical machines, there is still no single technique that provides an all-round solution to fault detection in these machines and thus hybrid techniques are used widely. This paper presents a novel non-invasive optical fiber technique in condition monitoring of induction machines and in the process detecting inter-turn short circuit faults. Owing to optical fiber’s immunity to magnetic flux, a composite FBG-T sensor formed by bonding a giant magnetostrictive transducer, Terfenol-D, onto a fiber Bragg grating is utilized to sense machines’ stray flux as a signature to determine the internal winding condition of the machines. A tri-axial auto datalogging flux meter was used to obtain the stray magnetic flux and test results obtained via LabView were analyzed in MatLab. Experimental and numerical results agree with each other and how that the FBG-T sensor accurately and reliably detected the short-circuit faults. Bragg shifts observed under short-circuit faults were in 100s of picometre range under various operating frequencies compared to the mid-10s of picometre obtained under healthy machine condition. These provide much promise for future EVs.