Fault Detection In Power Systems Research Articles

Nonlinear Optimization for Optimal Fault Detection and Classification in Power Systems Using Novel Semi-Supervised KNN Approach

Nonlinear Optimization for Optimal Fault Detection and Classification in Power Systems Using Novel Semi-Supervised KNN Approach

Establish intelligent fault detection in electrical power system: evolutionary computation in industrial IoT environments

Establish intelligent fault detection in electrical power system: evolutionary computation in industrial IoT environments

Enhanced power system fault detection using quantum‐AI and herd immunity quantum‐AI fault detection with herd immunity optimisation in power systems

Enhanced power system fault detection using quantum‐AI and herd immunity quantum‐AI fault detection with herd immunity optimisation in power systems

Non-intrusive fault detection in shipboard power systems using wavelet graph neural networks

Naval shipboard power systems (SPS) are rapidly embracing electrification, resulting in loads that generate pulsation currents and encounter substantial transients. However, conventional time-based features alone are inadequate for effectively monitoring and safeguarding these loads against faults. This highlights the critical requirement for advanced machine learning based methods to discern and differentiate between the various transient stages within the load profile. In this paper, we propose a Wavelet Graph Neural Network (WGNN) model for non-intrusive fault detection in SPS. The fault detection system leverages the dynamic model of the SPS to train and test performance with varying fault scenarios. The underlying structure and the interdependence among component states in the SPS network are effectively captured using the WGNN model, resulting in accuracies over 99% for intrusive fault detection and 97% for non-intrusive fault detection. The developed WGNN model has also shown to be robust in the presence of pulse loads and noise, achieving an accuracy of over 95%. At the end, a real-time simulation of the proposed method is validated on a hardware-in-the-loop system, guaranteeing the high fidelity and low latency of the proposed approach. These findings validate the effectiveness of the proposed WGNN model for fault detection and real-world applications in SPS.

Convolutional Deep Belief Network Based Expert System for Automated Fault Diagnosis in Hydro Electrical Power Systems

The paper developed an approach for fault diagnosis in Hydro-Electrical Power Systems (HEPS). Using a Renewable Energy System (RES), HEPS has performed a significant part in contributing to addressing the evolving energy demands of the present. Several electro-mechanical elements that collectively comprise the Hydro-Electric (HE) system are susceptible to corrosion from routine usage and unplanned occurrences. Administration and servicing systems that are successful in implementing and achieving these goals are those that regularly track and predict failures. Detect models applied in the past included those that were primarily reactive or reliant on human involvement to identify and analyse abnormalities. The significant multiple variables intricacies that impact successful fault detection are disregarded by these frameworks. The research presented here proposes a Convolutional Deep Belief Network (CDBN) driven Deep Learning (DL) model for successful fault and failure detection in such power systems that address these problems. Applying sample data collected from two Chinese power plants, the proposed framework has been assessed compared to other practical DL algorithms. Different metrics have been employed to determine the effectiveness of the simulations, namely Accuracy, Precision, Recall, and F1-score. These outcomes indicated that the CDBN is capable of predicting unexpected failures. Graphic representations demonstrating control used to measure turbine blade load, vibration level, and generator heat for assessing the replicas.

Fault Detection and Classification in Ring Power System With DG Penetration Using Hybrid CNN-LSTM

Fault Detection and Classification in Ring Power System With DG Penetration Using Hybrid CNN-LSTM

Fault Detection and Location in Power Systems with Large-Scale Photovoltaic Power Plant by Adaptive Distance Relays

Fault Detection and Location in Power Systems with Large-Scale Photovoltaic Power Plant by Adaptive Distance Relays

Clarke Circuit Analysis for Space-Vector Ellipse Characterization of Phase-to-Ground Faults in Three-Phase Radial Systems

This work deals with the technique for fault detection and classification in three-phase power systems based on the elliptical trajectory of the voltage space vector on the complex plane. A new approach is presented leading to the derivation of equivalent circuits directly in the Clarke domain where the space vector is defined. A specific methodology is introduced to manage the asymmetrical behavior of single-phase and double-phase faults. In particular, an in-depth analysis is presented for the single-phase-to-ground fault. The proposed equivalent circuits allow straightforward derivation and interpretation of the voltage ellipse for fault characterization. The analytical results are validated through the simulation of a three-phase radial system.

Learning the geometry of short‐circuit faults in power systems for real‐time fault detection and classification

AbstractGiven the short time intervals in which short‐circuit faults occur in a power system, a certain time delay between the moment of a fault's inception in the system to the moment in which the fault is actually detected is introduced. In this small time margin, the high amplitudes of the fault current can deal significant damage to the power system. A technique to characterise different types of short circuit faults in a power system for real‐time detection, namely AB, BC, CA, ABC, AG, BG and CG faults (and normal operation), is presented based on the geometry of the curve generated by the Clarke transform of the three‐phase voltages of the power system. The process was conducted in real time using the HIL402 system and a Raspberry Pi 3, and all programming done in the Python programming language. It was concluded that the tested types of faults can be accurately characterised using the eigenvalues and eigenvectors of the matrix that characterises an ellipse associated with each fault: eigenvalues can be used to determine the fault inception distance and eigenvectors can be used to determine the type of fault that occurred. Next, the design of a machine learning model was done based on the previously mentioned characterisation technique. The model was embedded into a Raspberry Pi 3, thus enabling fault detection and classification in a base power system in real time. Finally, the accuracy of the model was tested under different measurement conditions, yielding satisfactory results for a selected set of conditions and overcoming the shortcomings presented in the current research, which do not perform detection and classification in real time.

Signal Processing Application Based on a Hybrid Wavelet Transform to Fault Detection and Identification in Power System

The power system is one of the most susceptible systems to failures, which are most frequently caused by transmission line faults. Transmission line failures account for 85% of all power system malfunctions. However, over the last decade, numerous fault detection methods have been developed to ensure the reliability and stability of power systems. A hybrid detection method based on the idea of redundancy property is presented in this paper. Because the continuous wavelet transform itself does not extract fault features for small defects effectively, the stationary wavelet transform approach is employed to assist in their detection. As a result of its ability to decompose the signal into high- and low-frequency components, undecimated reconstruction by using the algebraic summation operation (ASO) is used. This approach creates redundancy, which is useful for the feature extraction of small defects and makes faulty parts more evident. The numerical value of the redundancy ratio’s contribution to the original signal is approximately equal to 36%. Following this method for redundant signal reconstruction, a continuous wavelet transform is used to extract the fault characteristic significantly easier in the time-scale (frequency) domain. Finally, the suggested technique has been demonstrated to be an efficient fault detection and identification tool for use in power systems. In fact, using this advanced signal processing technique will help with early fault detection, which is mainly about predictive maintenance. This application provides more reliable operation conditions.

Optimize single line to ground fault detection in distribution grid power system using artificial bee colony

The most common power system (PS) distribution network fault, single lineto-ground fault (SLGF), causes residual current (I res) to start an electrical arc and high voltage (HV) three times the rated voltage in other healthy phases. HV from capacitive currents (IC) damages cable insulation and PS appliances. Peterson The neutral point coil (PC) reduces (I res) and extinguishes the electric arc, but the fault current (I fault) remains below the protection devices' threshold. Operations and equipment are riskier. PC adaptive eliminates electrical arcs, making the network safer. This paper detects I faults online using Texas instrument validation in MATLAB and adaptive by artificial bee colony (ABC). This paper discusses Texas instrument fault current detection and MATLAB validation. It improves system reliability, device protection, and copper savings by thousands of tons. ABC intelligently optimizes many mathematical problems. ABC with network neural artificial intelligence (AI) improves algorithm performance (artificial bee colony network neural (ABCNN)). This new method may improve distribution network SLGF detection. This first work can work online in electrical power stations by building the (eZdsp F28335-RS232) into the program to send fault signals to the control when SLGF occurs without damaging devices, equipment, cables, or power outages.

A Machine Learning–Based Approach for Fault Detection in Power Systems

Machine learning techniques are becoming popular for monitoring the health and faults of different components in power systems, including transformers, generators, and induction motors. Normally, fault monitoring is performed based on predetermined healthy and faulty data from the corresponding system. The main objective of this study was to recognize the start of a system fault using a Support Vector Machine (SVM) approach. This technique was applied to detect power system instability before entering an unstable condition. Bus voltages, generator angles, and corresponding times before and after faults were used as training data for the SVM to detect abnormal conditions in a system. Therefore, a trained SVM would be able to determine the fault status after providing similar test data once a disturbance has been resolved.

Power system fault detection and notification with Wi-Fi

In this study, a prototype model was designed and implemented for power system failure. A mobile application was developed for notification of faults that may occur in power systems. The line current data was transmitted with Wi-Fi via module on mobile application and so negative impacts of the production sector was eliminated and the ordinary flow of life by minimizing the duration of power outages due to technical failures were prevented. By measuring the current in the prototype, it was possible to determine a short circuit situation, and to warn the operator with the mobile application on the mobile phone, and then to quickly dispatch them to the defective area. Protection in the power system is carried out more healthily with instant data flow with the mobile application

A review on fault diagnosis in TCSC compensated transmission lines using various techniques

This study presents a comparative analysis of fault detection and categorization methods in TCSC (Thyristor Controlled Series Capacitor)-compensated power systems. The analysis explores different techniques for extracting fault features from current data obtained from TCSC-compensated transmission lines. Machine learning algorithms, namely kNearest Neighbor (k-NN) and Support Vector Machine (SVM) are employed to categorize the extracted features, enabling fault detection and identification. To evaluate the efficacy of the proposed method, simulation tests were conducted on a test system, considering various fault scenarios. The simulations accounted for the presence of noise and TCSC correction, reflecting real-world operating conditions. The results demonstrate the efficiency of multiple approaches in accurately and swiftly detecting and classifying faults. By leveraging the capabilities of machine learning algorithms and extracting relevant features from current data, the proposed method offers a promising solution for fault detection and categorization in TCSC-compensated power systems. The ability to operate effectively even in the presence of noise and TCSC correction highlights the robustness and reliability of the proposed approach.

Fault detection in an interconnected power system using optimal number of phasor measurement unit

<p>Fault identification in a power system is crucial. In recent days, there have been multiple microgrids connected to the power system. And if many buses are connected, then there is a need for an increase in the phasor measurement unit. By using an optimization technique, the number of phasor measurement unit PMUs can be reduced by placing them optimally. In this paper, the fault detection algorithm is implemented using a reduced number of PMUs with the help of the particle swarm optimization (PSO) algorithm. The optimal locations of PMUs are identified using the PSO algorithm. Here, the reduction in the count of PMUs and the PMUs are designed in MATLAB as a model. This is done using the Simulink and dashboard block sets. The IEEE 9 and IEEE 30 test systems are used here for the analysis and tests. The IEEE 9 bus system is constructed in simulation and then the PMU is constructed using the data taken from the phasor measurement blocks. This data is used in the dashboard block set to represent the PMU-based fault detection system.</p>

Application of Convolutional and Feedforward Neural Networks for Fault Detection in Particle Accelerator Power Systems

High voltage converter modulators (HVCM) provide power to the accelerating cavities of the Spallation Neutron Source (SNS) facility. HVCMs experience catastrophic failures, which increase the downtime of the SNS and reduce beam time. The faults may occur due to different reasons including failures of the resonant capacitor, core saturation due to the magnetic flux, insulated-gate bipolar transistor (IGBT) failures, and others. We recently have setup a HVCM test stand to develop and test machine learning models for anomaly detection and fault prognostics. In this work, we propose binary classifiers and autoencoder architectures based on convolutional (CNN) and feedforward neural networks (FNN) to facilitate distinguishing normal from faulty waveforms coming from the HVCM during operation. The results indicate that the CNN binary classifier is the best model among the four showing very stable performance in the training and testing sets with impressive precision and recall metrics, reaching up to 99% with a very small uncertainty. The FNN classifier shows the least performance with a large uncertainty in its metrics. The performances of the two autoencoders based on CNN and FNN were in between, showing very good performance nonetheless.

Support vector machine for fast fault detection and classification in modern power systems using quarter cycle data

This work proposes a support vector machine (SVM) based fault detection and classification method for a transmission system. The novelty of this work is that the time and frequency series parameters at both the ends of a line, amounting to 144, computed over the most recent eight samples, are considered the input data for the developed SVM model. With this higher number of features for the SVM model, the power system faults are detected and classified with a quarter cycle data of power frequency. The developed SVM, trained with operating scenarios involving 25,168 faults and no faults conditions with different fault resistances and angles of fault inception, can classify all symmetrical non-symmetrical faults. The algorithm is tested on an IEEE 9-bus system with 32 samples per cycle sampling rate and is evaluated with ten-fold cross-validation and four evaluation metrics. This methodology provides an average accuracy of 99.89 per cent for all fault scenarios despite measurement noise having a signal-to-noise ratio of 30 dB, 35 dB, and 40 dB. The comparative analysis presented in this work reveals that the developed SVM model performs better than five other reported classification algorithms in terms of the accuracy of the classification of faults.

Big Data and Knowledge Graph Based Fault Diagnosis for Electric Power Systems

Fault detection plays an important role in the daily maintenance of power electric system. Big data and knowledge graph (KG) have been proposed by researchers to solve many problems in industrial Internet of Things, which also give lots of potentials in improving the performance of fault detection for electric power systems. In particular, this paper analyzes a distributed knowledge graph framework for fault detection in the electric power systems, where multiple devices train their local detection models used for fault detection assisted with a central server. Each device owns its local data set composed of historical fault information and current device state, which can be used to train a local model for fault detection. To enhance the detection performance, the distributed devices interact with each other in the KG framework, where the devices ought to achieve the regional computation in addition to the model aggregation within a specified latency threshold. Through searching for the vibrant qualities together with determined ability at the devices, we enhance the knowledge graph framework by the optimum variety of energetic devices together with the restriction of latency as well as data transmission. Particularly, two data transmission bandwidth allocation (BA) schemes are developed for the distributed knowledge graph framework, through which scheme I is actually bared after the instantaneous device state information (DSI), and scheme II utilizes particle swarm optimization (PSO) technique along with the statistical DSI. The results of simulation on the examination as well as convergence are lastly demonstrated to show the advantages of the proposed distributed KG framework in the fault detection for the electric power systems.

Simple algorithm for fault detection, classification and direction discrimination in power systems based on digital current signals processing

A power system suffers from unexpected various faults, which needs smart devices for accurate estimates of fault detection, classification and direction discrimination to provide inspection, maintenance, and repair of the actual faults. Signal processing-based protection technique can be used to detect the disturbances of the measured signals. This paper presents a digital protection scheme, based on three indices derived from the three-phase input and output current signals of the protected equipment, to detect current disturbances, classify faults, discriminate fault directions, select faulty phase(s), define current unbalances, and identify CT saturation extent. Moreover, it develops a new closed-tripping characteristic with a quadrilateral form for differentiating the fault location (internal/external) with respect to the element protection zone. A power system simulation is carried out using ATP platform, and the technique performance is validated via MATLAB software. Extensive simulation case studies show that the proposed algorithm provides a fast relay response and high accuracy under various operating and fault conditions. It is able to perform successfully the function of fault diagnosis within one periodic cycle. Besides, it responds very well with regard to dependability, security, stability and sensitivity. In addition to that it has the ability to recognize the saturated and unsaturated secondary current signals, measured using the current transformers, in the cases of internal and external faults.

Series Arc Fault Detection in a Low-Voltage Power System Based on CEEMDAN Decomposition and Sensitive IMF Selection

In the series arc fault detection of a low-voltage distribution network, the features of the fault current signal are easily submerged and arc fault features are difficult to be represented, which greatly increases the difficulty of fault arc detection based on current signals. To solve these problems, a series arc fault detection method combining CEEMDAN decomposition and sensitive IMF selection is proposed. In this paper, the CEEMDAN algorithm is first applied to complete decomposition of the arc current in series faults. Then, 12 feature indicators of the arc current are defined and the frequency band division of the IMF component is realized according to the kurtosis index and energy feature which are more sensitive. The time window-based feature calculation method is proposed to obtain the local features of the time scale of each high-frequency IMF component. Accurate selection of sensitive IMF components is realized by comparing feature indexes such as the variance and root mean square value. Finally, for the current feature set, the second dimension reduction is realized by the subspace transformation algorithm and the series arc fault detection is realized based on the SVM. The actual experiments show that the optimal detection accuracy of the proposed method is 91.67% and the average accuracy of 10 crossvalidation experiments is 88.33%. It shows that the proposed sensitive IMF selection method can effectively capture the fault component signals in the current and the proposed fault feature description method has good representation and discrimination ability.