Configurable stochastic model for high impedance faults simulations in electrical distribution systems

Trondoli, Luiz H. P. C.; Lopes, Gleidyane Novais; Vieira, José Carlos M.

doi:10.1016/j.epsr.2021.107686

Cited by 8 publications

(3 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Faced with the massive transmission system equipment alarm information, the maintenance personnel cannot expressly and accurately locate the cause of the fault that generates the alarm, which leads to the inefficiency of fault handling and, in turn, cannot meet the growing demand for intelligent management of the power grid. In the past, the main power system fault reasoning and diagnosis included expert systems (Xiao et al, 2021), configurable rules (Trondoli et al, 2022), fuzzy reasoning (Lokanadham and Subbaiah, 2021), and neural network (Giljum et al, 2021). The research ideas of fault diagnosis are mainly used to sort, train, classify, and identify the historical fault data, and the most important idea is to train the diagnosis model according to the data set so that it can use the data features as much as possible for classification.…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis of the HVDC system based on the CatBoost algorithm using knowledge graphs

Li²,

Chen³

et al. 2023

Front. Energy Res.

View full text Add to dashboard Cite

In order to overcome the difficulty of fault diagnosis in the high-voltage direct current (HVDC) transmission system, a fault diagnosis method based on the categorical boosting (CatBoost) algorithm is proposed in this work. To make the research conform to the actual situation, three kinds of measured fault data in the HVDC system of the Southern Power Grid are selected as the original data set. First, the core role and significance of fault diagnosis in knowledge graphs (KGs) are given, and the characteristics and specific causes of the four fault types are explained in detail. Second, the fault dates are preprocessed and divided into the training data set and the test data set, and the CatBoost algorithm is employed to train and test fault data to realize fault diagnosis. Finally, to verify the progressiveness and effectiveness of the proposed method, the diagnostic results obtained by CatBoost are compared with those obtained by the BP neural network algorithm. The results show that the diagnostic accuracy of the CatBoost algorithm in the three test sets is always higher than that of the BP neural network algorithm; the accuracy rates in the three case studies of the CatBoost algorithm are 94.74%, 100.00%, and 98.21%, respectively, which fully proves that the CatBoost algorithm has a very good fault diagnosis effect on the HVDC system.

show abstract

Section: Introductionmentioning

confidence: 99%

Fault diagnosis of the HVDC system based on the CatBoost algorithm using knowledge graphs

Li²,

Chen³

et al. 2023

Front. Energy Res.

View full text Add to dashboard Cite

show abstract

“…Thus, HIFs are not detected by conventional protection systems [4], compromising the quality of electricity supply and causing high risks such as hazardous shock, fires, and even deaths [2]. HIFs are also considered a threat to facility assets and can cause irreparable damage [5]- [8].…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Networks: Modeling and Comparison to Detect High Impedance Faults

Diefenthäler,

Sausen,

De Campos

et al. 2023

IEEE Access

View full text Add to dashboard Cite

High impedance faults constitute one of the biggest challenges in electrical power systems. In overhead distribution systems, faults are caused by tree branches that touch the electrical grid or by the rupture of energized conductors on low conductivity soils. They are also known as low-current faults, which are not detected by conventional protection systems, compromising the quality of the power supply and causing hazardous risks to the electrical system. This paper aims to address the problem of high impedance faults detection using Artificial Neural Networks: two Multi Layer Perceptron networks, being one Neural Pattern Recognition and another Neural Fitting, and a Convolutional Neural Network. The neural networks are trained and analyzed in scenarios based on a medium-voltage distribution grid model, located in the Basque Country, Spain. The network topologies are implemented, repeatedly trained considering multiple architectures, and validated in other scenarios with different location, time, and duration of the fault using the Matlab software. After, the criteria of accuracy, reliability, security, safety and sensitivity are evaluated. At last, a comparative analysis between them is carried out, and from the results obtained, a superior performance of the Convolutional Neural Network in compared to the Multi Layer Perceptron networks is observed.

show abstract

“…Ainda, considerou-se nesse trabalho um ajuste fixado para a tensão, cujo valor foi de 0, 88 pu. Assim sendo, a estratégia de proteção torna-se hábil em proteger a MR contra faltas que impliquem em tensões de falta menores que esse ajuste, não sendo favorável para a proteção contra faltas com valores elevados de resistência de falta ou ainda faltas de alta impedância, cujos modelos também devem ser específicos para esses tipos de faltas (TRONDOLI et al, 2022). Esse valor específico para 𝑉 𝑠,𝑟 𝑐 foi definido com vistas à IEEE Std.…”

Section: Definição De Condições De Contornounclassified

Nova estratégia de proteção para microrredes utilizando uma abordagem adaptativa com características híbridas de atuação

Barra¹

View full text Add to dashboard Cite

Agradeço primeiramente a Deus, que me permitiu o traçado dessa trajetória e esteve comigo durante todo tempo. Agradeço pelo favor, direção, livramento e cuidado comigo e minha família. A Deus toda a glória! À minha esposa, Caroline, por estar ao meu lado em todos os momentos. Nos dias bons e ruins, sempre me trouxe a motivação que eu precisava para seguir em frente. O seu incentivo e suporte foram fundamentais para que eu conseguisse chegar até aqui.À minha filha, Elisa, que chegou trazendo alegria para a nossa família. O seu nascimento fez com que os dias fossem mais felizes, cheios de cor e vida.Aos meus pais (Sebastião e Geraldina) e irmãos (Janayna e Marcos), que não mediram esforços para que eu concluísse esse ciclo. Sou imensamente grato por toda ajuda e força que me deram.Ao meu orientador, Prof. Denis Coury, por todos os ensinamentos em pesquisa, pelas oportunidades concedidas e por toda orientação ao longo desse período. Foram diversos os aprendizados que me fizeram crescer e ampliar meus horizontes. À Jane, meus sinceros agradecimentos pela paciência nas revisões sempre muito cuidadosas de nossas publicações.Ao Prof. Ricardo Fernandes, que esteve sempre presente durante o desenvolvimento deste trabalho, pelas recomendações e sugestões. A parceria dele foi inestimável no meu desenvolvimento ao longo do doutorado.Aos Profs. José Carlos, Mário Oleskovicz e Eduardo Asada pela parceria no projeto de P&D, publicações e outras oportunidades de trabalhos conjuntos.Aos companheiros da pós-graduação, que me ajudaram de alguma maneira a tornar esse caminho mais amigável, onde posso citar

show abstract

Configurable stochastic model for high impedance faults simulations in electrical distribution systems

Cited by 8 publications

References 24 publications

Fault diagnosis of the HVDC system based on the CatBoost algorithm using knowledge graphs

Fault diagnosis of the HVDC system based on the CatBoost algorithm using knowledge graphs

Artificial Neural Networks: Modeling and Comparison to Detect High Impedance Faults

Nova estratégia de proteção para microrredes utilizando uma abordagem adaptativa com características híbridas de atuação

Contact Info

Product

Resources

About