Classification of abnormal location in medium voltage switchgears using hybrid gravitational search algorithm-artificial intelligence

• Published in: PloS one Vol. 16; no. 7; p. e0253967
• Main Authors: Illias, Hazlee Azil, Lim, Ming Ming, Abu Bakar, Ab Halim, Mokhlis, Hazlie, Ishak, Sanuri, Amir, Mohd Dzaki Mohd
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.07.2021; Public Library of Science (PLoS)
• Subjects: Accuracy; Algorithms; Analysis; Artificial Intelligence; Artificial neural networks; Biology and Life Sciences; Classification; Classifiers; Computer and Information Sciences; Computer applications; Convergence; Electric potential; Electric Power Supplies - standards; Electrical engineering; Evaluation; Failure analysis; Fault diagnosis; Gravitation; Gravity; Heuristic methods; Humans; Hybrid systems; Neural networks; Neural Networks, Computer; Optimization; Optimization techniques; Physical Sciences; Research and Analysis Methods; Search algorithms; Sensors; Sound; Support Vector Machine; Support vector machines; Temperature; Ultrasonic imaging; Voltage; Yield; Malaysia; Kuala Lumpur Malaysia; Selangor Malaysia; Malaya
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0253967

In power system networks, automatic fault diagnosis techniques of switchgears with high accuracy and less time consuming are important. In this work, classification of abnormal location in switchgears is proposed using hybrid gravitational search algorithm (GSA)-artificial intelligence (AI) techniques. The measurement data were obtained from ultrasound, transient earth voltage, temperature and sound sensors. The AI classifiers used include artificial neural network (ANN) and support vector machine (SVM). The performance of both classifiers was optimized by an optimization technique, GSA. The advantages of GSA classification on AI in classifying the abnormal location in switchgears are easy implementation, fast convergence and low computational cost. For performance comparison, several well-known metaheuristic techniques were also applied on the AI classifiers. From the comparison between ANN and SVM without optimization by GSA, SVM yields 2% higher accuracy than ANN. However, ANN yields slightly higher accuracy than SVM after combining with GSA, which is in the range of 97%-99% compared to 95%-97% for SVM. On the other hand, GSA-SVM converges faster than GSA-ANN. Overall, it was found that combination of both AI classifiers with GSA yields better results than several well-known metaheuristic techniques.