Indian Journal of Science and Technology
Year: 2016, Volume: 9, Issue: 48, Pages: 1-9
Najlan Ismail, Farah Hani Nordin, Ammar Ahmed Alkahtani and Z.A.M Sharrif
Center of Signal Processing and Control System (SPaCS), College of Engineering, Universiti Tenaga Nasional, Putrajaya Campus, Jalan IKRAM-UNITEN, 43000 Kajang, Selangor, Malaysia; na[email protected], [email protected], [email protected], [email protected]
With the increasing usage of solar Photovoltaic (PV) system in Malaysia, the condition aspect of solar PV system especially inverter system needs to be given full attention. Detection of faults at earlier stage is very important in order to avoid the extended period of down-time caused by inverter failure. Thus, this paper aims to detect the source of the incipient faults produced by a single phase inverter of a PV system. The incipient faults were generated by modifying the pulse wave control signal. A total of 100 incipient faults and one set of normal condition waveform are collected at the output of the single phase square wave inverter. These waveforms are then used to train the feed-forward backpropagation neural network. One hidden layer feed-forward backpropagation neural network of 9 neurons was trained and MSE of 6.13 × 10-4 was obtained. It was shown that the trained feed-forward backpropagation neural network was able to detect which IGBT of the single phase square wave inverter produced the incipient faults.
Keywords: Fault Detection, Incipient Faults Detection, Neural Network, PV Inverter, Solar System
Subscribe now for latest articles and news.