Najlan Ismail ¹, Farah Hani Nordin ¹, Ammar Ahmed Alkahtani ¹, Z.A.M Sharrif ¹

Affiliations
1 Center of Signal Processing and Control System (SPaCS), College of Engineering, Universiti Tenaga Nasional, Putrajaya Campus, Jalan IKRAM-UNITEN, 43000 Kajang, Selangor, MY

Abstract

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

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Najlan Ismail ¹, Farah Hani Nordin ¹, Z. A. M. Sharrif ¹

Affiliations
1 Electronics and Communication Engineering Department, College of Engineering, Universiti Tenaga Nasional, Putrajaya Campus, Jalan Ikram-Uniten, 43000, Kajang, Selangor, MY

Abstract

Solar Photovoltaic (PV) system consists of three main parts which are PV array, PV inverter and utility grid. From the three, PV inverter is considered the weakest link in the solar PV system. Insulated Gate Bipolar Transistor (IGBT) is the most critical component in an inverter and is often blamed for the failure of inverters. If the incipient faults of the IGBT can be detected, the breakdown possibility of the solar PV system can be improved. However, before the incipient faults can be detected, it needs to be first generated before further analysis and improvements can be made. This paper proposes a process on how to generate the incipient faults which are caused by the short-circuit fault of a single phase PWM inverter. The single phase PWM inverter consists of four IGBTs and there is a total of six parameters that need to be observed for each IGBT. The parameter that could cause short-circuit fault to the IGBTs is identified by modifying the parameters of IGBT one at a time. The response at the inverter output is observed and recorded after each modification to the parameter value is done. From the results, it shows that parameter Threshold voltage (V_ge(th)) is identified to be able to generate the short-circuit incipient faults. For the application of detection the incipient faults using neural network, a total of 100 short-circuit incipient faults and one set of normal condition waveform are collected at the output of the single phase PWM inverter. These waveforms are then used to train the feedforward backpropagation neural network. One hidden layer feedforward backpropagation neural network of 7 neurons was trained and MSE of was obtained. It was shown that the trained feedforward backpropagation neural network was able to detect which IGBT component of the single phase PWM inverter produced the short-circuit incipient faults.

Keywords

Incipient Faults, Inverter, Neural Network, Solar PV System, IGBT.

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