Fault Detection and Location in Power Transmission Line Using Concurrent Neuro Fuzzy Technique

Eboule, Patrick S. Pouabe; Pretorius, J.H.C.; Mbuli, Nhlanhla; Leke, Collins Achepsah

doi:10.1109/epec.2018.8598311

Cited by 20 publications

(5 citation statements)

References 13 publications

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“…In [27], the prediction of fault occurrence and fault location using the concurrent fuzzy logic (CFL) was employed on data of different cases of simulated transmission lines. A novel technique for detecting fault locations in simulated transmission line was introduced in [28].…”

Section: Literature Reviewmentioning

confidence: 99%

Enhanced Anomaly-Based Fault Detection System in Electrical Power Grids

Elmasry

Wadi

2022

International Transactions on Electrical Energy Systems

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Early and accurate fault detection in electrical power grids is a very essential research area because of its positive influence on network stability and customer satisfaction. Although many electrical fault detection techniques have been introduced during the past decade, the existence of an effective and robust fault detection system is still rare in real-world applications. Moreover, one of the main challenges that delays the progress in this direction is the severe lack of reliable data for system validation. Therefore, this paper proposes a novel anomaly-based electrical fault detection system which is consistent with the concept of faults in the electrical power grids. It benefits from two phases prior to training phase, namely, data preprocessing and pretraining. While the data preprocessing phase executes all elementary operations on the raw data, the pretraining phase selects the optimal hyperparameters of the model using a particle swarm optimization (PSO)-based algorithm. Furthermore, the one-class support vector machines (OC-SVMs) and the principal component analysis (PCA) anomaly-based detection models are exploited to validate the proposed system on the VSB dataset which is a modern and realistic electrical fault detection dataset. Finally, the results are thoroughly discussed using several quantitative and statistical analyses. The experimental results confirm the effectiveness of the proposed system in improving the detection of electrical faults.

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Section: Literature Reviewmentioning

confidence: 99%

Enhanced Anomaly-Based Fault Detection System in Electrical Power Grids

Elmasry

Wadi

2022

International Transactions on Electrical Energy Systems

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“…Transmission lines are a major component in an electric power system. The chances of experiencing faults are higher in transmission lines than any other component in an electric power system because of its exposure to the environment [4]. Faults in a transmission line can be caused by trees, lightning, animals, weather, and faulty equipment.…”

Section: Background Of the Studymentioning

confidence: 99%

ANN Distance Protection for Transmission Lines

Africa¹

2019

IJETER

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When using transmission lines, faults often happen during the flow of signals. One way to protect transmission lines is to use distance protection relays. These types of relays, however, are still affected by fault resistance. Fault conditions often follow a certain pattern. Having a relay that recognizes these patterns can help improve performance. This paper presents a method to implement this using an artificial neural network (ANN). An ANN is one of the many applications of machine learning, which in meaning, is the algorithms that give the ability to computers to obtain knowledge through data and adapt to make certain deductions and choices. This paper will only consider the effect of the fault resistance of a single line-to-ground type of fault. The ANN should be programmed to identify numerous patterns that each correspond to different fault conditions. This will help in identifying and classifying unknown patterns detected in the transmission lines.

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“…To maintain stability and prevent damage to electrical power transmission line devices, these faults must be detected quickly, classified and cleared within a particular time (Sharma et al , 2017). There are several methods for fault detection and classification such as wavelet transform (WT) (Balakrishnan and Sathiyasekar, 2019), artificial neural network (ANN) (Fuada et al , 2020; Upadhyay et al , 2018), fuzzy logic (Bhatnagar and Yadav, 2020), adaptive neuro-fuzzy inference System (Lirouana and Mohammed, 2021), concurrent neuro-fuzzy (Eboule et al , 2018), support vector machine (Coban and Tezcan, 2021), WT and ANN (Gowrishankar et al , 2016; Thwe and Oo, 2016), WT and fuzzy logic (Ray et al , 2016), and there are also various computational models of the ANN that have been used in transmission line system fault detection and classification, such as multi-layer perceptron neural network (MLPNN) (Okojie et al , 2021), Elman recurrent neural network (ERNN) (Aborisade et al , 2021), WT and ERNN (Zakri and Tua, 2020), and radial basis function neural network (RBFNN) (Gupta and Mahanty, 2015). In this study, an ANN was employed for its capability to detect and classify faults in power transmission line systems.…”

Section: Introductionmentioning

confidence: 99%

Shunt faults detection and classification in electrical power transmission line systems based on artificial neural networks

Assadi

Slimane

Hanene

et al. 2023

COMPEL

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Purpose This study aims to focus on an adaptive method for fault detection and classification of fault types that trigger in three-phase transmission lines using artificial neural networks (ANNs). The proposed scheme can detect and classify several types of faults, including line-to-ground, line-to-line, double-line-to-ground, triple-line and triple-line-to-ground faults. Design/methodology/approach The fundamental components of three-phase current and voltage were used as inputs in the ANNs. An analysis of the impact of variations in the fault resistance, fault type and fault inception time was conducted to evaluate the ANNs performance. The survey compares the performance of the multi-layer perceptron neural network (MLPNN) and Elman recurrent neural network trained with the backpropagation learning technique to improve each of the three phases of the fault detection and classification process. A detailed analysis validates the choice of the ANNs architecture based on the variation in the number of hidden neurons in each step. Findings The mean square error, root mean square error, mean absolute error and linear regression are measured to improve the efficiency of the ANN models for both fault detection and classification. The results indicate that the MLPNN can detect and classify faults with a satisfactory performance. Originality/value The smart adaptive scheme is fast and accurate for fault detection and classification in a single circuit transmission line when faced with different conditions and can be useful for transmission line protection schemes.

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Fault Detection and Location in Power Transmission Line Using Concurrent Neuro Fuzzy Technique

Cited by 20 publications

References 13 publications

Enhanced Anomaly-Based Fault Detection System in Electrical Power Grids

Enhanced Anomaly-Based Fault Detection System in Electrical Power Grids

ANN Distance Protection for Transmission Lines

Shunt faults detection and classification in electrical power transmission line systems based on artificial neural networks

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