Short-circuit Fault Detection Research Articles

Motor Interturn Short-Circuit Fault Detection Using Leakage Current

Motor Interturn Short-Circuit Fault Detection Using Leakage Current

The Detection of an Inter-Turn Short-Circuit Fault in a Brushless Permanent Magnet Motor with Different Winding Configurations

This article is about the detection of a partial inter-turn short-circuit fault in a brushless motor with permanent magnets (BLPMM). The detection of a single inter-turn short circuit is a difficult issue. The authors of this article tested the sensitivity of the tested powertrain damage detection method. The diagnostic method developed for BLPMM allows for any configuration of the motor winding. A number of analysed configurations have been applied for the combined star–delta connection (YD). For the combined star–delta connection Y/D, the problem of partial short circuit at two locations was analysed. In the first case, this was the short-circuit winding phenomena in the star part (SC1). In the second case, the short circuit was connected in part to the delta (SC2). A mathematical model has been developed that takes into account both the type of connection and the chance of a partial short circuit of the coil. Based on numerical calculations, the sensitivity of diagnostic methods is designated for both cases. Furthermore, the impact of partial short circuits on motor performance was also examined. The effect of a partial inter-turn short-circuit fault on motor parameters was also determined. Laboratory verification was carried out.

Detection of partial shading and short-circuit faults in a pv system using the artificial neural network multilayer perceptron technique

Photovoltaic (PV) systems play a crucial role in renewable energy production but face challenges due to environmental factors such as partial shading and short circuits, which reduce efficiency and reliability. Continuous monitoring and early fault detection are essential to mitigate these issues. In this work, we propose a fault detection method that focuses on identifying partial shading and short-circuit faults using an artificial neural network (ANN), specifically a Multi-Layer Perceptron (MLP). The MLP model is trained to classify these faults under various environmental scenarios, including situations where both faults occur simultaneously. The methodology involves simulating different fault scenarios using MATLAB/Simulink and training the MLP on input parameters such as voltage, current, and power output from the PV system. Results indicate that the proposed approach achieves high fault detection accuracy, minimizing classification errors and ensuring reliable identification of system failures. This research highlights the advantages of integrating ANN-based fault detection methods into PV system monitoring frameworks, enabling real-time diagnostics and proactive maintenance. By improving fault detection accuracy, the proposed method contributes to the operational efficiency, safety, and longevity of PV installations. This approach can be extended to large-scale PV systems, addressing the growing demand for reliable renewable energy solutions globally. The findings demonstrate the potential of ANN-based methods to enhance fault diagnostics, providing a scalable solution for modern PV systems and supporting the global transition to sustainable energy.

An innovative MRAS-based technique for online detection of short-circuit faults in three-phase induction motor windings

An innovative MRAS-based technique for online detection of short-circuit faults in three-phase induction motor windings

Detection of Field Coil Short-Circuit Fault in Synchronous Generators Based on Magnetic Bypass Shunt Effect

Detection of Field Coil Short-Circuit Fault in Synchronous Generators Based on Magnetic Bypass Shunt Effect

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

Short-Circuit Fault Diagnosis on the Windings of Three-Phase Induction Motors through Phasor Analysis and Fuzzy Logic

An induction motor is an electric machine widely used in various industrial and commercial applications due to its efficiency and simple design. In this regard, a methodology based on the electric phasor analysis of line currents and the variations in the phase angles among these line currents is proposed. The values in degrees of the angles between every pair of line currents were introduced to a fuzzy logic algorithm based on the Mamdani model, developed using the Matlab toolbox for detection and isolation of the inter-turn short-circuit faults on the windings of an induction motor. To carry out the analysis, the induction motor was modified in its stator windings to artificially induce short-circuit faults of different magnitudes. The current signals are acquired in real time using a digital platform developed in the Delphi 7 high-level language communicating with a float point unit Digital Signal Processor (DSP) TMS320F28335 by Texas Instruments. The proposed method not only detects the short circuit faults but also isolates the faulty winding.

Prediction of short circuit current of wind turbines based on artificial neural network model

The growth of renewable energy on a global scale is making significant strides in power plants. This is due to the increasing concern about climate change, the rising demand for electricity, and the necessity to reduce reliance on fossil fuels. Ensuring the successful integration of new energy resources into the existing network is just as crucial as it requires the system to be reliable and adaptable. For instance, wind energy, which is one of the renewable sources, has an intermittent nature that necessitates the ability to synchronize its actions to achieve the desired system performance. The objective of this study is to utilize a new neural network system to calculate the short circuit current of power plants. Specifically, the focus is on identifying and categorizing the short circuit faults that occur between the stator coils of the squirrel cage induction generator used in wind power generation. To achieve this, a system was developed to simulate turbine data. Subsequently, four feature extraction techniques and machine learning algorithms were employed to enable early detection of short circuit faults. The numerical results obtained from the simulation demonstrated the high efficiency and accuracy of the proposed model.

Fault Type and Fault Location Detection in Transmission Lines with 6-Convolutional Layered CNN

In this article, we propose a data-driven method for short-circuit fault detection in transmission lines that exploits the capabilities of convolutional neural networks (CNNs). CNNs, a class of deep feedforward neural networks, can autonomously detect different features from data, eliminating the need for manual intervention. To mitigate the effects of noise and increase network robustness, we present a CNN architecture with six convolutional layers. The study uses a single busbar power system model developed with the PSCAD simulation program to evaluate the performance of the proposed method. The proposed CNN method is also compared with machine learning methods such as LSTM, SVM and ELM. Our results show a high success rate of 98.4% across all fault impedances, confirming the effectiveness of the proposed CNN methods in accurately detecting short-circuit faults based on current and voltage measurements.

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.

Controller-in-the-Loop Validation for Virtual Impedance-based Fault Detection Method in Grid-Connected Inverters

Abstract The high penetration of distributed energy resources and grid-connected inverters creates operation and protection challenges in distribution networks which have led to a need for advanced fault protection methods. Therefore, a virtual impedance-based short-circuit fault detection algorithm is proposed in this paper for grid-connected inverters in distribution networks which determines a short-circuit fault through measurement of the spectral change in system impedance at each harmonic frequency. Moreover, a controller-in-the-loop platform is also developed in this paper for verifying the proposed detection algorithm without jeopardizing the operation of distribution networks. By using the developed platform, real-time simulation on both grid-connected inverter and distribution network is carried out. The results have proven the effectiveness of the proposed method and theoretical analysis.

Turn‐to‐turn and phase‐to‐phase short circuit fault detection of wind turbine permanent magnet generator based on equivalent magnetic network modelling by wavelet transform approach

AbstractOne of the most common faults in electric machines is a turn‐to‐turn short circuit (TTSC), which may destroy coil insulation and demagnetise the magnet. In addition, the phase‐to‐phase short circuit (PPSC) fault, which can have even more destructive effects than the TTSC fault, is introduced and analysed. The equivalent magnetic network (EMN) method, with high modelling accuracy and a short computation time, is employed for healthy and faulty machines. The current signal under fault conditions is analysed in the dqo frame, showing the presence of the second harmonic component in its waveform. This fault detection index is processed using the signal processing technique of discrete(wavelet transform (DWT). Besides, energy analysis is used to distinguish TTSC and PPSC faults. Finally, finite element and EMN modelling results are compared with the experimental data of the prototyped permanent magnet generator. The results show that the combination of the proposed EMN method and DWT has very good accuracy and speed. Furthermore, the proposed fault detection method remains unaffected by various linear loads with different power factors.The cover image is based on the article Turn‐to‐turn and phase‐to‐phase short circuit fault detection of wind turbine permanent magnet generator based on equivalent magnetic network modelling by wavelet transform approach by Mehrage Ghods et al., https://doi.org/10.1049/elp2.12452

Current and flux correlation analysis for detection, location, and phase identification of stator inter-turn short-circuit fault in doubly-fed induction generator

This study proposes an innovative method for identifying and locating inter-turn short-circuit faults in doubly-fed induction generators (DFIGs), commonly used in wind-energy production. Our approach uniquely integrates data from both current and magnetic-flux monitoring, offering a complete framework for fault diagnostics. We effectively distinguish between normal and abnormal conditions in the generators by observing the patterns in current and flux waveforms. Normal conditions are marked by uniform waveforms, whereas faults create noticeable imbalances. A key feature of our research is the development of a technique to accurately determine which phase of the generator is affected by the fault, facilitating quicker and more effective repairs. To pinpoint the exact location of the fault, we use a set of four magnetic-flux sensors strategically placed around the generator. These sensors detect variations in magnetic flux, enabling the precise location of the fault. By focusing on these three essential elements—fault detection, phase identification, and exact fault localisation—our proposed algorithm significantly improves the dependability of DFIGs in wind-power applications.

Hybrid Approach for Detection and Diagnosis of Short-Circuit Faults in Power Transmission Lines

In this article, the main problem under investigation is the detection and diagnosis of short-circuit faults in power transmission lines. The proposed fault detection (FDD) approach is mainly based on principal component analysis (PCA). The proposed fault diagnosis/identification (FAI) approach is mainly based on sliding-window versions of the discrete Fourier transform (DFT) and discrete Hilbert transform (DHT). The main contributions of this article are (a) a fault detection approach based on principal component analysis in the two-dimensional scores space; and (b) a rule-based fault identification approach based on human expert knowledge, combined with a probabilistic decision system, which detects variations in the amplitudes and frequencies of current and voltage signals, using DFT and DHT, respectively. Simulation results of power transmission lines in Portugal are presented in order to show the robust and high performance of the proposed FDD approach for different signal-to-noise ratios. The proposed FDD approach, implemented in Python, that can be executed online or offline, can be used to evaluate the stress to which circuit breakers (CBs) are subjected, providing information to supervision- and condition-based monitoring systems in order to improve predictive and preventive maintenance strategies, and it can be applied to high-/medium-voltage power transmission lines as well as to low-voltage electronic transmission systems.

Advanced Detection of Failed LEDs in a Short Circuit for Automotive Lighting Applications.

This paper addresses the issue of LED short-circuit fault detection in signaling and lighting systems in the automotive industry. The conventional diagnostic method commonly implemented in newer vehicles relies on measuring the voltage drop across different LED branches and comparing it with threshold values indicating faults caused by open circuits or LED short circuits. With this algorithm, detecting cases of a few LEDs short-circuited within a branch, particularly a single malfunctioning LED, is particularly challenging. In this work, two easily implementable algorithms are proposed to address this issue within the vehicle's control unit. One is based on a mathematical prediction model, while the other utilizes a neural network. The results obtained offer a 100% LED short-circuit fault detection rate in the majority of analyzed cases, representing a significant improvement over the conventional method, even in scenarios involving a single malfunctioning LED within a branch. Additionally, the neural network-based model can accurately predict the number of failed LEDs.

A novel estimation methodology for multi-resonance equivalent inductance of transformer winding for inter-turn short-circuit fault detection

A novel, indirect measurement technique for estimating equivalent inductance associated with multi-resonance behavior of an isolated transformer winding is reported for both healthy and inter-turn short-circuit fault-condition. For healthy winding, the authors proposed measuring two unique admittance magnitude responses of the winding followed by utilizing their natural frequencies for estimating the said inductance. Here, the one response is for line-end excitation with neutral-end grounded and the other one is for two equal and opposite polarity excitations from two ends of the winding. For inter-turn shorting, driving-point admittance magnitude response with neutral-end open is also utilized. This inductance seems to be one important parameter for verifying the design constraint mainly for impulse behavior as well as for identifying inter-turn shorting of the winding. The proposed method was also verified by experimental measurements on two actual transformer windings; one of which incorporated an iron core within it. All pertinent analytical derivations and the associated results are also included in the paper.

A Search Coil Design Method of PMSM for Detection of Inter-Turn Short-Circuit Fault

Fault diagnosis systems based on search coils are widely used for detecting the inter-turn short-circuit (ITSC) fault, which is one of the most common electrical faults in electrical machines. Although various search coil structures have been proposed, there is not a systematic method proposed for the design of search coils. In addition, there is not a quantitative evaluation standard to evaluate the performances of search coils in each aspect, making it difficult to judge whether the proposed search coil structure is the optimal one. In this paper, the reliability, sensitivity and positioning ability of search coils are derived and evaluated by four defined characteristic indexes. And based on these four characteristic indexes, a systematic search coil design method based on the mutual inductance matrix is also proposed. With the proposed design method, the optimal search coil structures for different permanent magnet synchronous machines (PMSMs) are designed and optimized. It is found that the search coils designed by the proposed method have good performance in reliability, sensitivity and positioning ability. The performances of designed search coils are validated by the finite element analysis (FEA) and experiment.

Universal Short-Circuit and Open-Circuit Fault Protection and Detection for a Power Converter's Power Device

Universal Short-Circuit and Open-Circuit Fault Protection and Detection for a Power Converter's Power Device

Self-Healing Photovoltaic Microconverter With Zero Redundancy and Accurate Low-Cost Fault Detection

This paper studies a zero-redundancy fault-tolerant dc-dc series resonant converter (ZR-SRC) for residential photovoltaic (PV) applications. This converter can handle faulty conditions on-the-fly using topology morphing control without increasing the number of components. Furthermore, it can withstand a single fault in both input and output bridge semiconductors, i. e., up to two semiconductor faults. The proposed dc-dc converter could increase the availability of residential photovoltaic-powered dc microgrids. Comprehensive fault diagnosis and localization algorithms are proposed for MOSFETs on both sides of the ZR-SRC. The paper proposes а short-circuit fault (SCF) detection in the input-side MOSFETs using the existing PV voltage measurements, decreasing the converter cost and size. The open-circuit fault (OCF) in the output-side MOSFETs is detected by sensing the voltage of the bottom two switches sharing the same ground with the dc bus. In addition, the post-fault operation of the converter is thoroughly analyzed. Recommendations for the PV power clipping are given to ensure the safe operation of the converter after a fault. A 300 W experimental setup is built to demonstrate the feasibility and performance of the proposed fault-tolerant converter.

Analysis of PMSM Short-Circuit Detection Systems Using Transfer Learning of Deep Convolutional Networks

Abstract Modern permanent magnet synchronous motor (PMSM) diagnostic systems are now combined with advanced artificial intelligence techniques, such as deep neural networks. However, the design of such systems is mainly focussed on a selected type of damage or motor type with a limited range of rated parameters. The application of the idea of transfer learning (TL) allows the fully automatic extraction of universal fault symptoms, which can be used for various diagnostic tasks. In the research, the possibility of using the TL idea in the implementation of PMSM stator windings fault-detection systems was considered. The method is based on the characteristic symptoms of stator defects determined for another type of motor or mathematical model in the target diagnostic application of PMSM. This paper presents a comparison of PMSM motor inter-turn short circuit fault detection systems using TL of a deep convolutional network. Due to the use of direct phase current signal analysis by the convolutional neural network (CNN), it was possible to ensure high accuracy of fault detection with simultaneously short reaction time to occurring fault. The technique used was based on the use of a weight coefficient matrix of a pre-trained structure, the adaptation of which was carried out for different sources of diagnostic information.