Inter-turn Short Circuit Research Articles

A Fault Diagnosis Method for Pumped Storage Unit Stator Based on Improved STFT-SVDD Hybrid Algorithm

Stator faults are one of the common issues in pumped storage generators, significantly impacting their performance and safety. To ensure the safe and stable operation of pumped storage generators, a stator fault diagnosis method based on an improved short-time Fourier transform (STFT)-support vector data description (SVDD) hybrid algorithm is proposed. This method establishes a fault model for inter-turn short circuits in the stator windings of pumped storage generators and analyzes the electrical and magnetic states associated with such faults. Based on the three-phase current signals observed during an inter-turn short circuit fault in the stator windings, the three-phase currents are first converted into two-phase currents using the principle of equal magnetic potential. Then, the STFT is applied to transform the time-domain signals of the stator’s two-phase currents into frequency-domain signals, and the resulting fault current spectrum is input into the improved SVDD network for processing. This ultimately outputs the diagnosis result for inter-turn short circuit faults in the stator windings of the pumped storage generator. Experimental results demonstrate that this method can effectively distinguish between normal and faulty states in pumped storage generators, enabling the diagnosis of inter-turn short circuit faults in stator windings with low cross-entropy loss. Through analysis, under small data sample conditions, the accuracy of the proposed method in this paper can be improved by up to 7.2%. In the presence of strong noise interference, the fault diagnosis accuracy of the proposed method remains above 90%, and compared to conventional methods, the fault diagnosis accuracy can be improved by up to 6.9%. This demonstrates that the proposed method possesses excellent noise robustness and small sample learning ability, making it effective in complex, dynamic, and noisy environments.

Mechanism-Based Fault Diagnosis Deep Learning Method for Permanent Magnet Synchronous Motor.

As an important driving device, the permanent magnet synchronous motor (PMSM) plays a critical role in modern industrial fields. Given the harsh working environment, research into accurate PMSM fault diagnosis methods is of practical significance. Time-frequency analysis captures the rich features of PMSM operating conditions, and convolutional neural networks (CNNs) offer excellent feature extraction capabilities. This study proposes an intelligent fault diagnosis method based on continuous wavelet transform (CWT) and CNNs. Initially, a mechanism analysis is conducted on the inter-turn short-circuit and demagnetization faults of PMSMs, identifying and displaying the key feature frequency range in a time-frequency format. Subsequently, a CNN model is developed to extract and classify these time-frequency images. The feature extraction and diagnosis results are visualized with t-distributed stochastic neighbor embedding (t-SNE). The results demonstrate that our method achieves an accuracy rate of over 98.6% for inter-turn short-circuit and demagnetization faults in PMSMs of various severities.

Detection Method for Inter-Turn Short Circuit Faults in Dry-Type Transformers Based on an Improved YOLOv8 Infrared Image Slicing-Aided Hyper-Inference Algorithm

Detection Method for Inter-Turn Short Circuit Faults in Dry-Type Transformers Based on an Improved YOLOv8 Infrared Image Slicing-Aided Hyper-Inference Algorithm

Study on temperature characteristics of inter-turn short circuit in oil-immersed self-cooling transformer windings

Study on temperature characteristics of inter-turn short circuit in oil-immersed self-cooling transformer windings

Comprehensive Analysis on Rotor Vibration Characteristics Based on a Novel Dynamic Stator Interturn Short Circuit Model of Synchronous Generator

Comprehensive Analysis on Rotor Vibration Characteristics Based on a Novel Dynamic Stator Interturn Short Circuit Model of Synchronous Generator

Stator Winding Interturn Short-Circuit Fault Modeling and Detection of Squirrel-Cage Induction Motors

Stator Winding Interturn Short-Circuit Fault Modeling and Detection of Squirrel-Cage Induction Motors

Inter-Turn Short Circuit Diagnosis of Permanent Magnet Synchronous Motor Based on Siamese Convolutional Neural Network Under Small Fault Samples

Inter-Turn Short Circuit Diagnosis of Permanent Magnet Synchronous Motor Based on Siamese Convolutional Neural Network Under Small Fault Samples

A New Modeling Approach and Comprehensive Monitoring of Electrical Faults Through Spectral Analysis in DSIM

The objective of this study is to evaluate the dependability of double-star induction machines by employing meticulous monitoring and intelligent handling of potential electrical malfunctions that may arise during their functioning. Specifically, the research delves into the examination of three distinct fault types: rotor bar breakage, stator phase opening, and inter-turn short circuit. Utilizing a mathematical model developed in the natural reference frame (abc), our aim is to comprehensively delineate both normal and faulty operational scenarios. To categorize and gauge the gravity of identified faults, we employ a methodology rooted in spectral analysis. In order to ensure continuous machine operation, especially in instances of stator phase opening, our proposed mitigation approach entails intentionally opening a secondary phase positioned at a 90-degree angle to the faulty phase. This strategic alteration transforms the machine into a dual two-phase configuration. Through rigorous simulation analyses, our findings underscore the efficacy and pragmatic viability of the devised fault detection technique, offering valuable insights into the domain of electrical machine reliability and fault management.

Active fault-tolerant control for asynchronous machines using EKF-based fault estimation and 3-H-bridge inverter mitigation of ITSCs

This paper introduces an active fault-tolerant control system designed to effectively detect and mitigate inter-turn short-circuit (ITSC) faults in asynchronous machines. Utilizing the extended Kalman filter, the system precisely estimates electrical parameters, including resistances and inductances, crucial for identifying such faults. By integrating these estimations into the control mechanism, the system dynamically detects, isolates, and adjusts control laws based on fault severity assessments. The proposed controller, built upon an accurate three-phase faulty model, not only addresses torque ripple issues but also incorporates a 3-H-bridge inverter to manage current imbalances, particularly in degraded operating conditions. To evaluate its efficacy and robustness, comprehensive numerical simulations were conducted using MATLAB/SIMULINK. Results validate the effectiveness of the proposed control approach in ensuring the uninterrupted safe operation of asynchronous motors, even in the presence of ITSC faults, presenting a promising solution for enhancing fault tolerance in industrial settings. Further exploration is warranted to assess its real-world applicability and potential limitations across diverse operational scenarios. This system’s implementation promises practical benefits by bolstering operational reliability and safety in industrial machinery, contributing to improved workforce safety and reduced downtime, thus offering significant social advantages.

Study on Thermal Characteristics of Winding under Inter-Turn Fault of Oil-Immersed Transformer

The inter-turn fault is a common fault of oil-immersed transformers. Due to the deterioration of inter-turn insulation, its resistance decreases, which leads to an increase in the winding coil current and loss, causing the winding temperature to rise. Therefore, analyzing the electrothermal characteristics of inter-turn short circuit faults in oil-immersed transformers can provide a theoretical basis for the diagnosis of inter-turn faults in transformers. This paper studies the equivalent circuit under inter-turn faults and establishes a field–circuit coupling model to calculate the current characteristics under different inter-turn short circuit faults of transformers. The distribution of coil losses under faults is calculated as thermal source excitations, and a thermal–fluid-coupled calculation model for oil-immersed transformers is established. After the experimental verification, it is found that both the errors between the winding and tank surface temperatures do not exceed the acceptable limits, validating the effectiveness of this model. Using this model to calculate the temperature distribution for various inter-turn short circuit faults in transformers reveals their hot-spot characteristics. The results indicate that under inter-turn fault conditions, the temperature at faulty turns will gradually increase towards a limit, eventually leading to coil damage. This research provides theoretical support for enhancing transformer detection and diagnostic capabilities.

Research on Winding Dynamic Force and Displacement of Winding in Interturn Short Circuit of Stereo Roll Iron Core Transformer

Abstract The short circuit fault between the turns of the transformer winding accounts for 60% ∼ 70% of the whole winding fault, which seriously adversely impacts the regular functioning of the transformer. This study focuses on analysing the dynamic force and displacement characteristics of the winding in a stereo roll iron core transformer when an inter-turn short circuit occurs. First, a stereo roll iron core transformer finite element model in three dimensions is created. The electromagnetic-solid mechanics coupling method is used to figure out and study the transformer winding’s inter-turn short-circuiting current, leaking electromagnetic field, dynamic force, and displacement distributions. Furthermore, an experimental platform is built to simulate the inter-turn short-circuiting of a stereo roll iron core transformer. The displacement in the time domain and the acceleration in the frequency domain of the winding during the short circuit that occurs between turns will be recorded and analyzed. Finally, it is concluded that under normal working conditions, the winding vibration frequency is between 100 ∼ and 500 Hz. After 19 turns of the short circuit, the winding displacement increases by 32% ∼ 60%, and the high-order harmonic content of the winding vibration frequency increases significantly.

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.

Interturn Short Circuit Detection of Pumped Storage Unit Based on Optical Fiber Weak Magnetic Sensing

Interturn Short Circuit Detection of Pumped Storage Unit Based on Optical Fiber Weak Magnetic Sensing

Analysis of interturn short circuit in regulating winding of power transformer based on field-circuit coupling

The interturn short circuit fault is one of the common faults in power transformers. At present, research on interturn short circuit faults focuses on high, medium, and low voltage windings, while there is relatively little research on interturn short circuit in regulating windings. Specifically, there is a lack of reported studies on the transient electromagnetic processes, magnetic field distribution, and electromagnetic force characteristics of interturn short circuits in regulating windings unconnected to the circuit. This study presents an actual fault scenario involving interturn short circuits occurring in the untapped portion of the regulating windings of a specific power transformer. A field-circuit coupled model was established to analyze the transient electromagnetic processes during the fault, and the model’s effectiveness was validated by comparing its results with actual fault recording data. Additionally, the magnetic field distribution and electromagnetic force characteristics during the fault were analyzed, and discussions were carried out regarding various ratios of short-circuit turns in the regulating windings. The results indicate that even when an interturn short circuit occurs in the portion of the regulating winding that is not connected to the circuit, the current in the short-circuited turns can reach several tens of times the rated value. Additionally, the leakage magnetic field and the electromagnetic force experienced by the short-circuited ring also increase significantly. The short-circuit ratio has a significant impact on the current of the short-circuited ring, leakage magnetic field intensity, and electromagnetic force. This study contributes to a better understanding of the impact of interturn short-circuit faults in the untapped portion of the regulating windings, offering crucial technical support for fault diagnosis and prevention of power transformers.

Comprehensive Investigation of Winding Mechanical Properties in Generators under RISC: Impact of Position, Degree, and Eccentricity

This paper provides an in‐depth study of the winding mechanical properties in generators under rotor interturn short circuit (RISC) cases. Unlike previous studies, this research investigates not only the influence of RISC degrees, but also the RISC positions and the eccentricity on the winding mechanical properties. The electromagnetic force (EF) formulas as well as the electromagnetic‐structure coupling model are first proposed to derive the mechanical winding response characteristics, such as the vibration, deformation, the strain, and the stress. Subsequently, both finite element analysis (FEA) and experimental investigations are conducted to validate the theoretical findings. The results reveal that RISC introduces additional harmonics to the EF. Furthermore, as the eccentricity/RISC degree or the distance between the short circuit position and the N‐pole increases, the end winding vibrations become more pronounced. When comparing single eccentricity and RISC faults to cases where both faults occur simultaneously, it is observed that the combined faults have a more detrimental impact on the end winding mechanical properties. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Multiple faults detection in doubly-fed induction generator wind turbine using artificial neural network

The development of fault detection methods in wind turbine (WT), especially for single fault detection, is continuously increasing. However, the rapid growth of fault detection in WT leads to another challenge where multiple faults can occur. The single fault detection method in WT is no longer reliable, especially when multiple faults occur simultaneously. Therefore, multiple faults detection in doubly-fed induction generators (DFIG) WT was proposed using an artificial neural networks (ANN) model. These multiple faults include internal and external stator faults happening simultaneously. Internal stator faults cover inter-turn short circuit faults and open circuit faults, while external stator faults cover loss of excitation and external short circuit faults. The performance of the developed multiple faults detection model was measured using accuracy and the root mean square error (RMSE) value. The results show that the developed model performs well with high accuracy and a low RMSE value. Thus, the developed model can accurately detect the coexistence of multiple faults in DFIG WT.

Inter-turn Short Circuit Fault Diagnosis and Severity Estimation for Wind Turbine Generators

While preventive maintenance is crucial in wind turbine operation, conventional condition monitoring systems face limitations in terms of cost and complexity when compared to innovative signal processing techniques and artificial intelligence. In this paper, a cascading deep learning framework is proposed for the monitoring of generator winding conditions, specifically to promptly detect and identify inter-turn short circuit faults and estimate their severity in real time. This framework encompasses the processing of high-resolution current signal samples, coupled with the extraction of current signal features in both time and frequency domains, achieved through discrete wavelet transform. By leveraging long short-term memory recurrent neural networks, our aim is to establish a cost-efficient and reliable condition monitoring system for wind turbine generators. Numeral experiments show an over 97% accuracy for fault diagnosis and severity estimation. More specifically, with the intrinsic feature provided by wavelet transform, the faults can be 100% identified by the diagnosis model.

A Fault Diagnosis Method Based on an Improved Deep Q-Network for the Interturn Short Circuits of a Permanent Magnet Synchronous Motor

A Fault Diagnosis Method Based on an Improved Deep Q-Network for the Interturn Short Circuits of a Permanent Magnet Synchronous Motor

Research on the diagnosis method of inter-turn short-circuit faults of dry-type air-core reactor winding based on frequency response analysis

Inter-turn short circuit fault (ISCF) is the main fault type of dry-type air-core reactors (DARs), and the reactor burnout event caused by winding ISCF seriously threatens the safe and stable operation of the electric power system. In order to effectively curb the further worsening of ISCF in DARs, a fault detection method based on frequency response analysis was proposed. First, the effect of ISCF on the frequency response curve of the windings was investigated based on the distributed parameter model of DARs. Then, four sets of eigenvalues were proposed for ISCF diagnosis based on the influence law and curve data analysis theory. Finally, based on the eigenvalues, the support vector machine model optimized by the sparrow search algorithm was used to effectively classify the degree of ISCF of the reactor, and the accuracy reached 96%. Furthermore, a test platform was built based on the theoretical analysis for experimental verification, and the test results are consistent with the simulation results. Hence, the method proposed in this paper could effectively classify the ISCF and its severity in DARs. The research in this paper could provide effective guidance for the diagnosis of ISCF in DARs.

Detection of Inter-Turn Short Circuits in Induction Motors Using the Current Space Vector and Machine Learning Classifiers

Electric motors play a fundamental role in various industries, and their relevance is strengthened in the context of the energy transition. Having efficient tools and techniques to detect and diagnose faults in electrical machines is crucial, as is providing early alerts to facilitate prompt decision-making. This study proposes indicators based on the magnitude of the space vector stator current for detecting and diagnosing incipient inter-turn short circuits (ITSCs) in induction motors (IMs). The effectiveness of these indicators was evaluated using four machine learning methods previously documented in the literature: random forests (RFs), support vector machines (SVMs), the k-nearest neighbor (kNN), and feedforward and recurrent neural networks (FNNs and RNNs). This assessment was conducted using experimental data. The results were compared with indicators based on discrete wavelet transform (DWT), demonstrating the viability of the proposed approach, which opens up a way of detecting incipient ITSCs in three-phase IMs. Furthermore, utilizing features derived from the magnitude of the spatial vector led to the successful identification of the phase affected by the fault.