Grid Fault Diagnosis Based on Information Entropy and Multi-source Information Fusion

In order to solve the problem of misjudgment caused by the traditional power grid fault diagnosis methods, a new fusion diagnosis method is proposed based on the theory of multi-source information fusion. In this method, the fault degree of the power element is deduced by using the Bayesian network. Then, the time-domain singular spectrum entropy, frequency-domain power spectrum entropy and wavelet packet energy spectrum entropy of the electrical signals of each circuit after the failure are extracted, and these three characteristic quantities are taken as the fault support degree of the power components. Finally, the four fault degrees are normalized and classified as four evidence bodies in the D-S evidence theory for multi-feature fusion, which reduces the uncertainty brought by a single feature body. Simulation results show that the proposed method can obtain more reliable diagnosis results compared with the traditional methods

Grid Fault Diagnosis Based on Information Entropy and Multi-source Information Fusion

In order to solve the problem of misjudgment caused by the traditional power grid fault diagnosis methods, a new fusion diagnosis method is proposed based on the theory of multi-source information fusion. In this method, the fault degree of the power element is deduced by using the Bayesian network. Then, the time-domain singular spectrum entropy, frequency-domain power spectrum entropy and wavelet packet energy spectrum entropy of the electrical signals of each circuit after the failure are extracted, and these three characteristic quantities are taken as the fault support degree of the power components. Finally, the four fault degrees are normalized and classified as four evidence bodies in the D-S evidence theory for multi-feature fusion, which reduces the uncertainty brought by a single feature body. Simulation results show that the proposed method can obtain more reliable diagnosis results compared with the traditional methods

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

HVDC Systems Fault Analysis Using Various Signal Processing Techniques

The detection and fast clearance of faults are important for the safe and optimal operation of HVDC systems. In HVDC systems, various types of AC faults (rectifier & inverter side) and DC faults can occur. It is therefore necessary to detect the faults and classify them for better protection and diagnostics purposes. Various techniques for fault detection and classification in HVDC systems using signal processing techniques are presented and investigated in this research work. In this research work, it is shown that the wavelet transformation can effectively detect abrupt changes in system signals which are indicative of a fault. This research has focused on DC faults at various distances along the lines and AC faults on the converter side. The DC line current is chosen as the input to the wavelet transform. The 5th level coefficients have been used to identify the various faults in the LCC-HVDC system. Moreover, the value of these coefficients has been used for the classification of the different faults. For more accurate classification of faults, the wavelet entropy principle is proposed. In LCC-HVDC systems, a different approach for fault identification and classification is proposed. In this investigation an algorithm is developed that provides the trade-off between large input data size and minimal number of neurons in the hidden layer, without compromising the accuracy. The claim is confirmed by the results provided from the investigation for various fault conditions and its corresponding ANN output which confirms the specific fault detection and its classification. A fault identification and classification strategy based on fuzzy logic for VSC–HVDC systems is proposed. Initially, the developed Fuzzy Inference Engine (FIE) detects AC faults occurring in the rectifier side and DC faults on the cable successfully. However, it could not identify the line on which the fault has occurred. Hence, to classify the faults occurring in either AC section or DC section of the HVDC system, the FIE has to be restructured with appropriate data input. Therefore, a FIE which identifies different types of fault and the corresponding line where the fault occurs anywhere in the HVDC system was developed. Initially the developed FIE with three input and seven output parameters results in an accuracy level of 99.47% being achieved. After a modified FIE was developed with five inputs and seven output parameters, 21 types of faults in the VSC HVDC system were successfully classified with 100% accuracy. The FIE was further developed to successfully classify with 100% accuracy faults in Multi-Terminal HVDC systems

Protection of Future Electricity Systems

The electrical energy industry is undergoing dramatic changes: massive deployment of renewables, increasing share of DC networks at transmission and distribution levels, and at the same time, a continuing reduction in conventional synchronous generation, all contribute to a situation where a variety of technical and economic challenges emerge. As the society’s reliance on electrical power continues to increase as a result of international decarbonisation commitments, the need for secure and uninterrupted delivery of electrical energy to all customers has never been greater. Power system protection plays an important enabling role in future decarbonized energy systems. This book includes ten papers covering a wide range of topics related to protection system problems and solutions, such as adaptive protection, protection of HVDC and LVDC systems, unconventional or enhanced protection methods, protection of superconducting transmission cables, and high voltage lightning protection. This volume has been edited by Adam Dyśko, Senior Lecturer at the University of Strathclyde, UK, and Dimitrios Tzelepis, Research Fellow at the University of Strathclyde

Fault Diagnosis of HVDC Systems Using Machine Learning Based Methods

With the development of high-power electronic technology, HVDC system is applied in the power system because of advantages in large-capacity and long-distance transmission, stability, and flexibility. Therefore, as the guarantee of reliable operating of HVDC system, fault diagnosis of the HVDC system is of great significance. In the current variety methods used in fault diagnosis, Machine Learning based methods have become a hotspot. To this end, the performance of several commonly used machine learning classifiers is compared in HVDC system. First of all, nine faults both in AC systems and DC systems of the HVDC system are set in the HVDC model in Simulink. Therefore, 10 operating states corresponding to the faults and normal operating are considered as the output classes of classifier. Seven parameters, such as DC voltage and DC current, are selected as fault feature parameters of each sample. By simulating the HVDC system in 10 operating states (including normal operating state) correspondingly, 20000 samples, each containing seven parameters, be obtained during the fault period. Then, the training sample set and the test sample set are established by 80% and 20% of the whole sample set. Subsequently, Decision Trees, the Support Vector Machine (SVM), K-Nearest Neighborhood Classifier (KNN), Ensemble classifiers, Discriminant Analysis, Backward Propagation Neural Network (BP-NN), long Short-Term Memory Neural Network (LSTM-NN), Extreme Learning Machine (ELM) was trained and tested. The accuracy of testing is used as the performance index of the model. In particular, for BP-NN, the impact of different transfer functions and learning rules combinations on the accuracy of the model was tested. For ELM, the impact of different activation functions on accuracy is tested. The results have shown that ELM and Bagged Trees have the best performance in HVDC fault diagnosis. The accuracy of these two methods are 92.23% and 96.5% respectively. However, in order to achieve better accuracy in ELM model, a large number of hidden layer nodes are set so that training time increases sharply

Fault Diagnosis of HVDC Systems Using Machine Learning Based Methods

With the development of high-power electronic technology, HVDC system is applied in the power system because of advantages in large-capacity and long-distance transmission, stability, and flexibility. Therefore, as the guarantee of reliable operating of HVDC system, fault diagnosis of the HVDC system is of great significance. In the current variety methods used in fault diagnosis, Machine Learning based methods have become a hotspot. To this end, the performance of several commonly used machine learning classifiers is compared in HVDC system. First of all, nine faults both in AC systems and DC systems of the HVDC system are set in the HVDC model in Simulink. Therefore, 10 operating states corresponding to the faults and normal operating are considered as the output classes of classifier. Seven parameters, such as DC voltage and DC current, are selected as fault feature parameters of each sample. By simulating the HVDC system in 10 operating states (including normal operating state) correspondingly, 20000 samples, each containing seven parameters, be obtained during the fault period. Then, the training sample set and the test sample set are established by 80% and 20% of the whole sample set. Subsequently, Decision Trees, the Support Vector Machine (SVM), K-Nearest Neighborhood Classifier (KNN), Ensemble classifiers, Discriminant Analysis, Backward Propagation Neural Network (BP-NN), long Short-Term Memory Neural Network (LSTM-NN), Extreme Learning Machine (ELM) was trained and tested. The accuracy of testing is used as the performance index of the model. In particular, for BP-NN, the impact of different transfer functions and learning rules combinations on the accuracy of the model was tested. For ELM, the impact of different activation functions on accuracy is tested. The results have shown that ELM and Bagged Trees have the best performance in HVDC fault diagnosis. The accuracy of these two methods are 92.23% and 96.5% respectively. However, in order to achieve better accuracy in ELM model, a large number of hidden layer nodes are set so that training time increases sharply

Recent Developments and Challenges on AC Microgrids Fault Detection and Protection Systems–A Review

The protection of AC microgrids (MGs) is an issue of paramount importance to ensure their reliable and safe operation. Designing reliable protection mechanism, however, is not a trivial task, as many practical issues need to be considered. The operation mode of MGs, which can be grid-connected or islanded, employed control strategy and practical limitations of the power electronic converters that are utilized to interface renewable energy sources and the grid, are some of the practical constraints that make fault detection, classification, and coordination in MGs different from legacy grid protection. This article aims to present the state-of-the-art of the latest research and developments, including the challenges and issues in the field of AC MG protection. A broad overview of the available fault detection, fault classification, and fault location techniques for AC MG protection and coordination are presented. Moreover, the available methods are classified, and their advantages and disadvantages are discussed

Sistema de diagnóstico distribuido de fallas basado en redes inalámbricas de sensores

This article presents the development of a distributed fault diagnosis and monitoring system whose remote nodes are responsible for data collection and distributed analysis to identify problems that could lead to critical faults in industrial processes or systems. The developed intelligent remote node was implemented with MCU LPCXpresso54114 connected to a ZigBee protocol wireless sensor network through XBee communication module. The gateway node is a Raspberrry PI with HTTP communication and JSON format to the PI System industrial monitoring system database. Motor Current Signature Analysis (MCSA) was implemented and validated to identify interturn faults of induction motors. The developed platform is a tool to perform comparison and validation of analysis techniques, indicators, and fault classification, because there are different combinations that can be applied to improve diagnosis reliability, fault observability, differentiation between fault conditions, classification accuracy, tolerance to transients, sensitivity, among others.En este artículo presenta el desarrollo de un sistema de monitoreo y diagnóstico distribuido cuyos nodos remotos se encarguen de la recolección de datos y su posterior análisis para la identificación de anomalías que representen fallas críticas para el proceso o sistema industrial. El dispositivo desarrollado como nodo remoto inteligente se implementó con MCU LPCXpresso54114 con conexión a una red inalámbrica de sensores basada en protocolo ZigBee mediante tarjetas de comunicación XBee. El nodo concentrador está compuesto de una tarjeta Raspberrry PI con comunicación mediante protocolo HTTP y formato JSON a la base de datos del sistema de monitoreo industrial PI System. Se implementó y validó el acondicionamiento de señal para la medición de corrientes de estator (MCSA) que permitió identificar fallas entre espiras de motores de inducción tipo jaula de ardilla. La plataforma presentada finalmente es una herramienta para realizar comparación y validación de técnicas de análisis, indicadores y de clasificación de fallas, puesto que existen diversas combinaciones que pueden ser aplicadas con el fin de mejorar la confiabilidad del diagnóstico, la observación de la falla, la diferenciación entre condiciones de falla, la precisión de la clasificación, la tolerancia a transitorios, sensibilidad, entre otros

New protection algorithms for HVDC grids

233 p.Los sistemas HVDC representan una alternativa prometedora para futuras expansiones del sistema eléctrico gracias a las ventajas que presentan en comparación con el transporte convencional en corriente alterna. Además, el interés por desarrollar redes HVDC multiterminales ha crecido en los últimos años, sin embargo, su implementación se ha visto ralentizada debido a la complejidad que presenta la protección ante faltas en estos sistemas. El objetivo principal de esta tesis es proponer un nuevo algoritmo de protección contra faltas, apropiado para dichas redes y capaz de superar las limitaciones presentes en algoritmos existentes. El algoritmo propuesto es un algoritmo de tensión de inductancia basado en el cálculo del ratio entre las medidas de tensión tomadas a ambos lados de la inductancia limitadora y la derivada de dicho ratio. Es capaz de detectar faltas rápidamente y de discriminar de manera selectiva entre faltas dentro y fuera de la zona de protección. También se propone una metodología para la selección del valor umbral necesario para la operación de algoritmos locales. A continuación, sedesarrolla un esquema de protección completo que se compone de protecciones de línea primaria y de respaldo, protección de barra y protección ante fallo del interruptor. Esta última protección es, así mismo, un nuevo algoritmo propuesto en la tesis, que presenta una operación más rápida que algoritmos convencionales de detección de fallo en el interruptor. Finalmente, la operación del esquema de protección propuesto es validada y analizada a través de simulaciones en un modelo de red de cuatro terminales con diferentes escenarios de falta, comparándolo con algoritmos existentes