Fault Detection in HVDC System with Gray Wolf Optimization Algorithm Based on Artificial Neural Network

Raad Salih Jawad () and Hafedh Abid
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Raad Salih Jawad: Laboratory of Sciences and Techniques of Automatic Control & Computer Engineering (Lab-STA) Sfax, National School of Engineering of Sfax, University of Sfax, Sfax 3029, Tunisia
Hafedh Abid: Laboratory of Sciences and Techniques of Automatic Control & Computer Engineering (Lab-STA) Sfax, National School of Engineering of Sfax, University of Sfax, Sfax 3029, Tunisia

Energies, 2022, vol. 15, issue 20, 1-17

Abstract: Various methods have been proposed to provide the protection necessitated by the high voltage direct current system. In this field, most of the research is confined to various types of DC and AC line faults and a maximum of two switching converter faults. The main contribution of this study is to use a new method for fault detection in HVDC systems, using the gray wolf optimization method along with artificial neural networks. Under this method, with the help of faulted and non-faulted signals, the features of the voltage and current signals are extracted in a much shorter period of the signal. Subsequently, differences are detected with the help of an artificial neural network. In the studied HVDC system, the behavior of the rectifier, along with its controllers and the required filters are completely modeled. In this study, other methods, such as artificial neural network, radial basis function, learning vector quantization, and self-organizing map, were tested and compared with the proposed method. To demonstrate the performance of the proposed method the accuracy, sensitivity, precision, Jaccard, and F1 score were calculated and obtained as 99.00%, 99.24%, 98.74%, 98.00%, and 98.99%, respectively. Finally, according to the simulation results, it became evident that this method could be a suitable method for fault detection in HVDC systems.

Keywords: artificial neural network; fault detection; HVDC; gray wolf optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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