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## Empirical Mode Decomposition and Optimization Assisted ANN Based Fault Classification Schemes for Series Capacitor Compensated Transmission Line | ||

Journal of Operation and Automation in Power Engineering | ||

مقالات آماده انتشار، اصلاح شده برای چاپ، انتشار آنلاین از تاریخ 25 شهریور 1402 اصل مقاله (1.88 M) | ||

نوع مقاله: Research paper | ||

شناسه دیجیتال (DOI): 10.22098/joape.2023.12112.1899 | ||

نویسندگان | ||

O. koduri^{*} ^{1}؛ R. Ramachandran^{1}؛ M. Saiveerraju^{2}
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^{1}Department of Electrical Engineering, Faculty of Engineering & Technology, Annamalai University, Annamalainagar, 608002, Tamil Nadu, India. | ||

^{2}Department of Electrical & Electronics Engineering, Sagi Rama Krishnam Raju Engineering College Bhimavaram-534202, Andhra Pradesh, India | ||

چکیده | ||

This paper presents two intelligent classifier schemes for classifying the faults in a series capacitor compensated transmission line (SCCTL). The first proposed intelligent classifier scheme is a particle swarm optimization-assisted artificial neural network (PSO-ANN). The second, proposed one is a teaching-learning optimization-assisted artificial neural network (TLBO-ANN). For each type of fault, the 3-phase current signals are acquired at the sending end and processed through empirical mode decomposition (EMD), to decompose into six intrinsic mode functions. The neighborhood component analysis is used to extract the best feature intrinsic mode functions. From the identified best feature intrinsic mode functions, the energy of each phase of the line is computed. The energy of each phase is fed as inputs for both PSO-ANN and TLBO-ANN classifiers. The practicability of the proposed intelligent classifier schemes has been tested on a 500$\,kV$, 50$\,Hz$, and 300$\,km$ long line with a midpoint series capacitor using MATLAB/Simulink Software. The results demonstrate that the classifier schemes are able to accurately classify faults in less than a half-cycle. Furthermore, the efficacy of the proposed intelligent classifier schemes has been evaluated using Performance Indices including Kappa Statistics, Mean Absolute Error, Root Mean Square Error, Precision, Recall, F-measure, and Receiver Operating Characteristics. From the results of Performance Indices, it is concluded that the proposed TLBO-based artificial neural network classifier outperforms the PSO-based artificial neural network classifier. Finally, the efficacies of proposed intelligent classifier schemes are compared to existing approaches. | ||

کلیدواژهها | ||

Artificial Intelligence؛ Particle swarm optimization-assisted artificial neural network؛ Teaching-learning-optimization-assisted artificial neural network؛ Power System Faults؛ Identification؛ Series capacitor compensation line؛ Signal Processing؛ Empirical mode decomposition | ||

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