High Impedance Fault Detection in Time-Varying Distributed Generation Systems Using Adaptive Neural Networks

Lucas, Fabricio; Costa, Paula Cristina Pereira da; Batalha, Rose Mary de Souza; Leite, Daniel

doi:10.1109/ijcnn.2018.8489453

Cited by 8 publications

(3 citation statements)

References 28 publications

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“…Unlike the current study, the authors only focused on the classification of faults for a five-terminal transmission system with hybrid energy sources. In [22], the authors described a similar method that was based on WT feature extraction, along with a pair of online adaptive neural networks for detecting and locating high impedance faults in hybrid power generation systems. In [23], the authors proposed a fault-type classification and location scheme based on WT and deep neural networks for microgrid operations.…”

Section: Introductionmentioning

confidence: 99%

Comparison of different regression models to estimate fault location on hybrid power systems

Ekici

Ünal

Ozleyen

2019

IET Generation, Transmission & Distribution

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Section: Introductionmentioning

confidence: 99%

Comparison of different regression models to estimate fault location on hybrid power systems

Ekici

Ünal

Ozleyen

2019

IET Generation, Transmission & Distribution

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“…However, a technique to choose the accurate number of hidden layers and neurons is required to achieve optimal results with less computational time. Moreover, a hybrid approach using MLP-NN and Gaussian process regression (GPR) was implemented by [80,89]. In the subject research, MLP-NN was used to determine the optimum weights and biases for HIF detection and classification, and on the other hand, GPR plays the role of a linear regressor that aims to approximate the fault location in a transmission line.…”

Section: Machine Learningmentioning

confidence: 99%

High-Impedance Fault Diagnosis: A Review

Aljohani

Habiballah

2020

Energies

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High-impedance faults (HIFs) represent one of the biggest challenges in power distribution networks. An HIF occurs when an electrical conductor unintentionally comes into contact with a highly resistive medium, resulting in a fault current lower than 75 amperes in medium-voltage circuits. Under such condition, the fault current is relatively close in value to the normal drawn ampere from the load, resulting in a condition of blindness towards HIFs by conventional overcurrent relays. This paper intends to review the literature related to the HIF phenomenon including models and characteristics. In this work, detection, classification, and location methodologies are reviewed. In addition, diagnosis techniques are categorized, evaluated, and compared with one another. Finally, disadvantages of current approaches and a look ahead to the future of fault diagnosis are discussed.

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“…Tais situações são identificadas e estudadas neste trabalho para validar a robustez do SV proposto. Para remover tais imprecisões que afetam negativamente a qualidade do modelo, o pré-processamento de dadosé fundamental (Lin et al, 2006) (Lucas et al, 2018). SV são frequentemente tratados por métodos estatísticos para extração de características (Dixon, 1999;Lin et al, 2006).…”

Section: Sensor Virtualunclassified

Sensores Virtuais para Detecção de Descargas Parciais em Transformadores de Potência

Barbosa

Leite

Costa

et al. 2019

Anais Do 14º Simpósio Brasileiro De Automação Inteligente

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Partial discharge is a common type of fault in power transformers. It indicates the state of the insulation material. Identifying their existence is not an easy task. Some techniques such as chromatographic analysis of the refreshing oil have been used for offline detection of partial discharges. In this paper we present a Virtual Sensor scheme for online partial-discharge detection. The virtual sensor consists of two steps. First, the pre-processing step consists in cleaning the data, and applying wavelet transforms. Second, a neural classifier, namely, a radialbasis-function neural network (RBF), an adaptive neuro-fuzzy system (ANFIS) or an evolving neuro-fuzzy system (DENFIS) is employed. In this paper partial discharges are simulated using a lumped-parameter model. For performance analysis, disturbances are considered. System results have shown to be feasible and robust. The use of virtual sensors for online detection of partial discharges is encouraging. Resumo: Descarga parcialé um tipo de falta comum em transformadores de potência. Ela indica o estado do material isolante dos equipamentos e sua identificação nãoé uma tarefa fácil. Algumas técnicas como análise cromatográfica doóleo refrigerante têm sido utilizadas na detecção das descargas parciais off-line. Neste artigoé apresentada uma proposta de Sensor Virtual para detecção de descargas parciais on-line. O sensor virtualé constituído de duas seções. Primeiro, o pré-processamento, onde os dados de correntes do transformador são tratados e transformadas wavelet são empregadas. Segundo, o classificador inteligente, constituído por uma rede neural de base radial (RBF), um sistema neuro-fuzzy adaptativo (ANFIS) ou um sistema neuro-fuzzy evolutivo (DENFIS). Neste artigo as descargas parciais foram simuladas utilizando um modelo de parâmetros concentrados. Para análise de desempenho são consideradas situações de distúrbio. Os resultados obtidos mostram-se viáveis, assim como a utilização de sensores virtuais para detecção on-line de descargas parciais.

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High Impedance Fault Detection in Time-Varying Distributed Generation Systems Using Adaptive Neural Networks

Cited by 8 publications

References 28 publications

Comparison of different regression models to estimate fault location on hybrid power systems

Comparison of different regression models to estimate fault location on hybrid power systems

High-Impedance Fault Diagnosis: A Review

Sensores Virtuais para Detecção de Descargas Parciais em Transformadores de Potência

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