Magnetic actuator driven switchgear is a new medium voltage switchgear technology. In this switchgear, the conventional spring mechanism which is used to operate the circuit breaker is replaced with a magnetic actuator mechanism. The suitability of this technology in the Malaysian utility network specifically in highly loaded areas with frequent switching was assessed via a field evaluation. Preliminary results indicated that magnetic actuator driven switchgear perform commendably on the safety aspect, on-site performance monitoring and online diagnostic test results. However, there are several concerns that need to be addressed such as the ease of installation, substation system requirements, high life cycle cost and reliability of components, before this technology can be used widely.
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.