Circuit Breaker Fault Diagnosis Method Based on Improved One-Dimensional Convolutional Neural Network

Aiming at the problems of manual feature extraction and poor generalization ability of model in traditional circuit breaker fault diagnosis technology, a circuit breaker fault diagnosis method based on improved one-dimensional convolutional neural network is proposed. Firstly, the input feature sequence is adaptively weighted by self-attention mechanism to highlight the weight of important information; Secondly, 1 1 convolution layer and global average pooling layer are used to replace the full connection layer, which reduces the model training parameters, improves the training efficiency and prevents the phenomenon of over-fitting. Aiming at the problem of small number of data samples, the data is enhanced by Generative Adversarial Network. After adding the generated data to the original data, the accuracy of fault identification is further improved. The experimental results show that this method can effectively and accurately identify different fault types of circuit breaker, and verify the feasibility of its engineering application

Miss SAIGON - Missing Signal Appraising in Globally Optimized Networks

Desde a implementação de sistemas elétricos de transporte e distribuição de energia, os operadores de redes sempre focaram-se no conhecimento da rede, quer seja por implementação de sensores de medição e telemetria para medição local e comunicação com centros de controlo, quer por estimação de eventuais estados da rede através de modelização matemática do comportamento da mesma. Com o surgimento dos primeiros Estimadores de Estados, também surge o problema de estimação de topologia no qual é necessário conhecer a topologia correta da rede para executar tais Estimadores de Estados.Por vezes nos centros de controlo, o conhecimento da rede é reduzido, seja pela falha de intercomunicação ou por ausência de medidores locais. Tendo em consideração o vasto historial de uma rede e os estados que as variáveis de medição podem tomar, é possível construir um processador de topologia de modo a determinar o estado de um interruptor, ou seja, se a linha encontra-se aberta ou fechada. Nesta dissertação é demonstrado todos os procedimentos efetuados na formulação de um processador de topologia baseado numa ferramenta de Deep Learning - CNN (Convolutional Neural Networks) - de modo a determinar o estado desse mesmo interruptor em várias situações exemplo com uma boa taxa de acerto mesmo em situações limite de ausência de informação.A topologia interna de uma subestação também é alvo de estudo, no qual, na ausência de medições internas é possível caracterizar o arranjo interno dos interruptores que a constituem. Esta dissertação pretende fornecer uma abordagem do problema de estimação de topologia em cenários reais de fraco conhecimento da rede fornecendo uma boa alternativa aos modelos tradicionais já existentes

Advancements in Arc Fault Detection for Electrical Distribution Systems: A Comprehensive Review from Artificial Intelligence Perspective

This comprehensive review paper provides a thorough examination of current advancements and research in the field of arc fault detection for electrical distribution systems. The increasing demand for electricity, coupled with the increasing utilization of renewable energy sources, has necessitated vigilance in safeguarding electrical distribution systems against arc faults. Such faults could lead to catastrophic accidents, including fires, equipment damage, loss of human life, and other critical issues. To mitigate these risks, this review article focuses on the identification and early detection of arc faults, with a particular emphasis on the vital role of artificial intelligence (AI) in the detection and prediction of arc faults. The paper explores a wide range of methodologies for arc fault detection and highlights the superior performance of AI-based methods in accurately identifying arc faults when compared to other approaches. A thorough evaluation of existing methodologies is conducted by categorizing them into distinct groups, which provides a structured framework for understanding the current state of arc fault detection techniques. This categorization serves as a foundation for identifying the existing constraints and future research avenues in the domain of arc fault detection for electrical distribution systems. This review paper provides the state of the art in arc fault detection, aiming to enhance safety and reliability in electrical distribution systems and guide future research efforts

Fault Management in DC Microgrids:A Review of Challenges, Countermeasures, and Future Research Trends

The significant benefits of DC microgrids have instigated extensive efforts to be an alternative network as compared to conventional AC power networks. Although their deployment is ever-growing, multiple challenges still occurred for the protection of DC microgrids to efficiently design, control, and operate the system for the islanded mode and grid-tied mode. Therefore, there are extensive research activities underway to tackle these issues. The challenge arises from the sudden exponential increase in DC fault current, which must be extinguished in the absence of the naturally occurring zero crossings, potentially leading to sustained arcs. This paper presents cut-age and state-of-the-art issues concerning the fault management of DC microgrids. It provides an account of research in areas related to fault management of DC microgrids, including fault detection, location, identification, isolation, and reconfiguration. In each area, a comprehensive review has been carried out to identify the fault management of DC microgrids. Finally, future trends and challenges regarding fault management in DC-microgrids are also discussed

A review of networked microgrid protection: Architectures, challenges, solutions, and future trends

The design and selection of advanced protection schemes have become essential for the reliable and secure operation of networked microgrids. Various protection schemes that allow the correct operation of microgrids have been proposed for individual systems in different topologies and connections. Nevertheless, the protection schemes for networked microgrids are still in development, and further research is required to design and operate advanced protection in interconnected systems. The interconnection of these microgrids in different nodes with various interconnection technologies increases the fault occurrence and complicates the protection operation. This paper aims to point out the challenges in developing protection for networked microgrids, potential solutions, and research areas that need to be addressed for their development. First, this article presents a systematic analysis of the different microgrid clusters proposed since 2016, including several architectures of networked microgrids, operation modes, components, and utilization of renewable sources, which have not been widely explored in previous review papers. Second, the paper presents a discussion on the protection systems currently available for microgrid clusters, current challenges, and solutions that have been proposed for these systems. Finally, it discusses the trend of protection schemes in networked microgrids and presents some conclusions related to implementation

Arc fault protections for aeronautic applications: a review identifying the effects, detection methods, current progress, limitations, future challenges, and research needs

©2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Arc faults are serious discharges, damaging insulation systems and triggering electrical fires. This is a transversal topic, affecting from residential to aeronautic applications. Current commercial aircrafts are being progressively equipped with arc fault protections. With the development of more electric aircrafts (MEA), future airliners will require more electrical power to enhance fuel economy, save weight and reduce emissions. The ultimate goal of MEAs is electrical propulsion, where fault management devices will have a leading role, because aircraft safety is of utmost importance. Therefore, current fault management devices must evolve to fulfill the safety requirements of electrical propelled aircrafts. To deal with the increased electrical power generation, the distribution voltage must be raised, thus leading to new electrical fault types, in particular arc tracking and series arcing, which are further promoted by the harsh environments typical of aircraft systems, i.e., low pressure, extreme humidity and a wide range of temperatures. Therefore, the development of specific electrical protections which are able to protect against these fault types is a must. This paper reviews the state-of-the-art of electrical protections for aeronautic applications, identifying the current status and progress, their drawbacks and limitations, the future challenges and research needs to fulfill the future requirements of MEAs, with a special emphasis on series arc faults due to arc tracking, because of difficulty in detecting such low-energy faults in the early stage and the importance and harmful effects of tracking activity in cabling insulation systems. This technological and scientific review is based on a deep analysis of research and conference papers, official reports, white papers and international regulations.This research was partially funded by the Ministerio de Ciencia e Innovación de España, grant number PID2020-114240RB-I00 and by the Generalitat de Catalunya, grant number 2017 SGR 967.Peer ReviewedPostprint (author's final draft

Fast and accurate fault detection and classification in transmission lines using extreme learning machine

To provide stability and a continuous supply of power, the detection and classification of faults in the transmission lines (TLs) are crucial in this modern age. It is required to remove a faulty section from a healthy section to provide safety and to minimize power loss due to the fault. In the contemporary world, machine learning (ML) is extensively used in every aspect of life. In this study, a spontaneous fault detection (FD) and fault classification (FC) system based on ML has been proposed. MATLAB Simulink was employed to simulate two different TLs and to generate normal and fault data (Per unit voltage and current) of ten different types. TL-1 consisted of a single generator and a single load whereas TL-2 consisted of two generators and three loads. Upon normalizing the data, an extreme learning machine (ELM) algorithm was used as the classifier. Two different ELM models were developed for FD and FC purposes through training. The method achieved fault classification accuracies of 99.18% and 99.09% for the TL-1 and TL-2 respectively. On the other hand, fault detection accuracies of 99.53% and 99.60% were achieved for the TL-1 and TL-2. The proposed ELM model compared to a traditional artificial neural network (ANN) model demonstrated relatively a shorter processing time and reduced computational complexity. In addition, the proposed method outperformed the existing state-of-the-art methods