A Review of Fault Diagnosing Methods in Power Transmission Systems

Transient stability is important in power systems. Disturbances like faults need to be segregated to restore transient stability. A comprehensive review of fault diagnosing methods in the power transmission system is presented in this paper. Typically, voltage and current samples are deployed for analysis. Three tasks/topics; fault detection, classification, and location are presented separately to convey a more logical and comprehensive understanding of the concepts. Feature extractions, transformations with dimensionality reduction methods are discussed. Fault classification and location techniques largely use artificial intelligence (AI) and signal processing methods. After the discussion of overall methods and concepts, advancements and future aspects are discussed. Generalized strengths and weaknesses of different AI and machine learning-based algorithms are assessed. A comparison of different fault detection, classification, and location methods is also presented considering features, inputs, complexity, system used and results. This paper may serve as a guideline for the researchers to understand different methods and techniques in this field

A convolutional neural network based deep learning methodology for recognition of partial discharge patterns from high voltage cables

It is a great challenge to differentiate partial discharge (PD) induced by different types of insulation defects in high-voltage cables. Some types of PD signals have very similar characteristics and are specifically difficult to differentiate, even for the most experienced specialists. To overcome the challenge, a convolutional neural network (CNN)-based deep learning methodology for PD pattern recognition is presented in this paper. First, PD testing for five types of artificial defects in ethylene-propylene-rubber cables is carried out in high voltage laboratory to generate signals containing PD data. Second, 3500 sets of PD transient pulses are extracted, and then 33 kinds of PD features are established. The third stage applies a CNN to the data; typical CNN architecture and the key factors which affect the CNN-based pattern recognition accuracy are described. Factors discussed include the number of the network layers, convolutional kernel size, activation function, and pooling method. This paper presents a flowchart of the CNN-based PD pattern recognition method and an evaluation with 3500 sets of PD samples. Finally, the CNN-based pattern recognition results are shown and the proposed method is compared with two more traditional analysis methods, i.e., support vector machine (SVM) and back propagation neural network (BPNN). The results show that the proposed CNN method has higher pattern recognition accuracy than SVM and BPNN, and that the novel method is especially effective for PD type recognition in cases of signals of high similarity, which is applicable for industrial applications

Fault Detection Approaches to Power System: State-of-the-Art Article Reviews for Searching a New Approach in the Future

This paper proposes the state-of-the-art of fault detection approach a power system. Severalarticles presented it in each implementation and method from the last to present (2013). Theadvantage of the approach would be developed to the new detection in the future. Manyinterested topics used for detection of fault in the power system. In this research can beclassified into two types interesting in fault detection. This review of many paper will beused to develop the research or find the new method for an appropriate fault detection in thepower system.DOI:http://dx.doi.org/10.11591/ijece.v3i4.319

A Review on the Classification of Partial Discharges in Medium-Voltage Cables : Detection, Feature Extraction, Artificial Intelligence-Based Classification, and Optimization Techniques

Medium-voltage (MV) cables often experience a shortened lifespan attributed to insulation breakdown resulting from accelerated aging and anomalous operational and environmental stresses. While partial discharge (PD) measurements serve as valuable tools for assessing the insulation state, complexity arises from the presence of diverse discharge sources, making the evaluation of PD data challenging. The reliability of diagnostics for MV cables hinges on the precise interpretation of PD activity. To streamline the repair and maintenance of cables, it becomes crucial to discern and categorize PD types accurately. This paper presents a comprehensive review encompassing the realms of detection, feature extraction, artificial intelligence, and optimization techniques employed in the classification of PD signals/sources. Its exploration encompasses a variety of sensors utilized for PD detection, data processing methodologies for efficient feature extraction, optimization techniques dedicated to selecting optimal features, and artificial intelligence-based approaches for the classification of PD sources. This synthesized review not only serves as a valuable reference for researchers engaged in the application of methods for PD signal classification but also sheds light on potential avenues for future developments of techniques within the context of MV cables.© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

Fault Location in Grid Connected Ungrounded PV Systems Using Wavelets

Solar photovoltaic (PV) power has become one of the major sources of renewable energy worldwide. This thesis develops a wavelet-based fault location method for ungrounded PV farms based on pattern recognition of the high frequency transients due to switching frequencies in the system and which does not need any separate devices for fault location. The solar PV farm used for the simulation studies consists of a large number of PV modules connected to grid-connected inverters through ungrounded DC cables. Manufacturers report that about 1% of installed PV panels fail annually. Detecting phase to ground faults in ungrounded underground DC cables is also difficult and time consuming. Therefore, identifying ground faults is a significant problem in ungrounded PV systems because such earth faults do not provide sufficient fault currents for their detection and location during system operation. If such ground faults are not cleared quickly, a subsequent ground fault on the healthy phase will create a complete short-circuit in the system, which will cause a fire hazard and arc-flashing. Locating such faults with commonly used fault locators requires costly external high frequency signal generators, transducers, relays, and communication devices as well as generally longer lead times to find the fault. This thesis work proposes a novel fault location scheme that overcomes the shortcomings of the currently available methods. In this research, high frequency noise patterns are used to identify the fault location in an ungrounded PV farm. This high frequency noise is generated due to the switching transients of converters combined with parasitic capacitance of PV panels and cables. The pattern recognition approach, using discrete wavelet transform (DWT) multi-resolution analysis (MRA) and artificial neural networks (ANN), is utilized to investigate the proposed method for ungrounded grid integrated PV systems. Detailed time domain electromagnetic simulations of PV systems are done in a real-time environment and the results are analyzed to verify the performance of the fault locator. The fault locator uses a wavelet transform-based digital signal processing technique, which uses the high frequency patterns of the mid-point voltage signal of the converters to analyze the ground fault location. The Daubechies 10 (db10) wavelet and scale 11 are chosen as the appropriate mother wavelet function and decomposition level according to the characteristics of the noise waveform to give the proposed method better performance. In this study, norm values of the measured waveform at different frequency bands give unique features at different fault locations and are used as the feature vectors for pattern recognition. Then, the three-layer feed-forward ANN classifier, which can automatically classify the fault locations according to the extracted features, is investigated. The neural network is trained with the Levenberg-Marquardt back-propagation learning algorithm. The proposed fault locating scheme is tested and verified for different types of faults, such as ground and line-line faults at PV modules and cables of the ungrounded PV system. These faults are simulated in a real-time environment with a digital simulator and the data is then analyzed with wavelets in MATLAB. The test results show that the proposed method achieves 99.177% and 97.851% of fault location accuracy for different faults in DC cables and PV modules, respectively. Finally, the effectiveness and feasibility of the designed fault locator in real field applications is tested under varying fault impedance, power outputs, temperature, PV parasitic elements, and switching frequencies of the converters. The results demonstrate the proposed approach has very accurate and robust performance even with noisy measurements and changes in operating conditions

Faults Detection for Power Systems

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Generative Adversarial Networks Selection Approach for Extremely Imbalanced Fault Diagnosis of Reciprocating Machinery

At present, countless approaches to fault diagnosis in reciprocating machines have been proposed, all considering that the available machinery dataset is in equal proportions for all conditions. However, when the application is closer to reality, the problem of data imbalance is increasingly evident. In this paper, we propose a method for the creation of diagnoses that consider an extreme imbalance in the available data. Our approach first processes the vibration signals of the machine using a wavelet packet transform-based feature-extraction stage. Then, improved generative models are obtained with a dissimilarity-based model selection to artificially balance the dataset. Finally, a Random Forest classifier is created to address the diagnostic task. This methodology provides a considerable improvement with 99% of data imbalance over other approaches reported in the literature, showing performance similar to that obtained with a balanced set of data.National Natural Science Foundation of China, under Grant 51605406National Natural Science Foundation of China under Grant 7180104

Wavelet Transform Based Methods for Fault Detection and Diagnosis of HVDC Transmission Systems

ABSTRACT WAVELET TRANSFORM BASED METHODS FOR FAULT DETECTION AND DIAGNOSIS OF HVDC TRANSMISSION SYSTEMS by Zhonxguan Li The University of Wisconsin-Milwaukee, 2019 Under the Supervision of Professor Lingfeng Wang High-voltage direct current (HVDC) is a key enabler in power system. HVDC offers a most efficient means of transmitting large amount of power. Applications of HVDC can improve the operation security, reliability performance and economy of power systems. Due to factors inside and outside the HVDC system, the system will experience various faults, which have infected HVDC system. VSC-HVDC is a HVDC transmission based on IGBT and PWM. VSC-HVDC direct current transmission has broad application prospects in new energy grid-connected and grid-connected transformation. In this research, aiming at the fault diagnosis of VSC-HVDC, the fault diagnosis and fault detection are studied. In this research, a VSC-HVDC was simulated in MATLAB Simulink, and an adjusted VSC-HVDC model was built. The models were applied to simulate the basic operation of VSC-HVDC and main faults on AC and DC side in the VSC-HVDC system. Take line current on AC or DC side as input data, the result data after wavelet processing was applied in HVDC faults diagnosis. To verify the function of fault detection, DC faults at different locations were set in the adjusted model. Wavelet entropy was applied in fault diagnosis and detection to gather accurate results. According to the simulation results, wavelet transform exhibits a good performance in HVDC fault diagnosis and detection

Wavelet Transform Based Methods for Fault Detection and Diagnosis of HVDC Transmission Systems

ABSTRACT WAVELET TRANSFORM BASED METHODS FOR FAULT DETECTION AND DIAGNOSIS OF HVDC TRANSMISSION SYSTEMS by Zhonxguan Li The University of Wisconsin-Milwaukee, 2019 Under the Supervision of Professor Lingfeng Wang High-voltage direct current (HVDC) is a key enabler in power system. HVDC offers a most efficient means of transmitting large amount of power. Applications of HVDC can improve the operation security, reliability performance and economy of power systems. Due to factors inside and outside the HVDC system, the system will experience various faults, which have infected HVDC system. VSC-HVDC is a HVDC transmission based on IGBT and PWM. VSC-HVDC direct current transmission has broad application prospects in new energy grid-connected and grid-connected transformation. In this research, aiming at the fault diagnosis of VSC-HVDC, the fault diagnosis and fault detection are studied. In this research, a VSC-HVDC was simulated in MATLAB Simulink, and an adjusted VSC-HVDC model was built. The models were applied to simulate the basic operation of VSC-HVDC and main faults on AC and DC side in the VSC-HVDC system. Take line current on AC or DC side as input data, the result data after wavelet processing was applied in HVDC faults diagnosis. To verify the function of fault detection, DC faults at different locations were set in the adjusted model. Wavelet entropy was applied in fault diagnosis and detection to gather accurate results. According to the simulation results, wavelet transform exhibits a good performance in HVDC fault diagnosis and detection

Radial Power Distribution System Fault Classification Model Based on ANFIS

The classification of problems in power systems plays an extremely important part and has evolved into a necessity that is of the utmost importance to the operation of energy grids. For the purpose of fault classification in IEEE 13 node radial distribution systems, this paper makes use of both an Artificial Neural Network (ANN) and a Neural Fuzzy adaptive Inference System (ANFIS). Simulations of the suggested models are carried out in MATLAB/SIMULINK, and fault currents from all three phases are analyzed in order to extract statistical characteristics. Input data vectors include the standard deviation and correlation factors between the currents of any two phases, while output data vectors include the different sorts of faults. The findings demonstrate that the devised method is appropriate for the classification of all symmetrical and unsymmetrical faults