Illias, Hazlee Azil and Lim, Ming Ming and Abu Bakar, Ab Halim and Mokhlis, Hazlie and Ishak, Sanuri and Amir, Mohd Dzaki Mohd (2021) Classification of abnormal location in medium voltage switchgears using hybrid gravitational search algorithm-artificial intelligence. PLoS ONE, 16 (7). ISSN 1932-6203, DOI https://doi.org/10.1371/journal.pone.0253967.
Full text not available from this repository.Abstract
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.
| Item Type: | Article |
|---|---|
| Funders: | Ministry of Education, Malaysia (H-16001-D00048), Universiti Malaya (GPF077A-2018) (IIRG001B2020IISS) (LL046-2019) |
| Uncontrolled Keywords: | Power system networks; Gravitational search algorithm; Artificial intelligence (AI) techniques; Artificial neural network (ANN) |
| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) |
| Divisions: | Faculty of Engineering |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 16 Mar 2022 01:37 |
| Last Modified: | 16 Mar 2022 01:37 |
| URI: | http://eprints.um.edu.my/id/eprint/28252 |
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