Infrared thermography of fault detection in power distribution system equipment using artificial neural network / Nurul Huda Ishak … [et al.]

• Main Authors: Ishak, Nurul Huda, Mohamad Mustafa, Puteri Nur Syahirah, Isa, Iza Sazanita, Md Ramli, Siti Solehah, Ahmad, Nur Darina
• Format: Article
• Language: English
• Published: Universiti Teknologi MARA Cawangan Pulau Pinang 2021
• Subjects: Computer software; Applications of electric power
• Online Access: https://ir.uitm.edu.my/id/eprint/6062/1/6062.pdf; https://ir.uitm.edu.my/id/eprint/6062/; https://uppp.uitm.edu.my/
• Institution: Universiti Teknologi Mara

Repair and maintenance in power distribution is an important factor that affects the continuous productivity services and power efficiency in electrical supply systems. Thermographic inspection has been often used as a maintenance tool, as it allows detection of early-stage failure from the system in electrical distribution. Failure in the system can lead to catastrophic failure like a high-voltage arc fault. The presence of fault is caused by the higher temperature of the instrument that leads to the formation of hot spots. The use of infrared inspection is useful in detecting the hot spot that is hardly noticeable. It helps to overcome the problems that arise during operation and maintenance in the distribution systems. In this research, a fault detection system is proposed with the application of Artificial Neural Network (ANN) in identifying faults on electrical equipment. This method was trained by using the temperature parameter on the IR images taken from TNB Distribution. As a result, it will lead to faults detection. Thus, the purpose of this project is to ensure the correct recommendation of corrective actions in the maintenance procedure of the electrical system. The actions to the detection of faults taken are based on the results of the temperature measured. The neural network training performance for the temperature of hot spot detection was developed with a minimum error of 0.00084165 MSE at epoch 39. The study shows the best-fitting allows detection of early-stage failure. It can be concluded that the current method in conducting the prediction process by using Thermographic inspection is suitable for electrical equipment based on the training result.