Implementing and Testing for Pattern Recognition of Partial Discharge Signals Using Artificial Neural Network

Abstract

Artificial Intelligence (AI) has reached many life aspects, including in power engineering field. In power engineering itself, there is a challenge in improving the transmission system quality to make it more reliable. Partial Discharge as one of the main problem that makes the High Voltage Apparatus face the possibility of breakdown. AI would have a low error rate compared to humans and also has incredible precision, accuracy, and speed. Artificial Neural Network (ANN) one of AI types is an adaptive non-linear programming meaning ANN is very suitable for use on sensitive, non-fixed and dynamic systems such as PD signals. This study will analyze the performance of the implementation of artificial neural networks to recognize the types of Partial Discharge (PD) of experimental result gained by author. Important information in the process of pattern recognition and assessment of PD signals is the phase pattern, the amount of charge (q), the number of PD signal appearances, the amplitude max PD, and the min PD amplitude. The phase pattern and the amount of charge (q) can represent the pattern of the PD signal, while the max amplitude, min amplitude, and the number of appearances of the PD signal (n) can represent the level of PD signal vulnerability. These five quantities will be used as the component of artificial neural networks. Then the network that has been created will be used for the process of pattern recognition and assessment of PD signals in the application made.