| Title |
ANN-Based Diagnostic Method on Multiple Open-Switch Fault for Three-Phase PWM Converters |
| Authors |
김원재(Won-Jae Kim) ; 김상훈(Sang-Hoon Kim) |
| DOI |
https://doi.org/10.5370/KIEE.2021.70.5.764 |
| Keywords |
Adaptive Linear Neuron; Artificial Neural Network; Fault Diagnosis; Open-Switch Fault; Three-Phase PWM Converter; Two-Step Technique |
| Abstract |
For single and double open-switch faults in three-phase PWM (Pulse Width Modulation) converters, there are 21 types of fault modes depending on faulty switches, causing secondary faults in peripherals. In this paper, for diagnosing those fault modes, two-step technique based on ANNs using dc and THDs (Total Harmonics Distortions) of phase currents is proposed. Those dc and THDs are obtained through an ADALINE (Adaptive Linear Neuron) in real-time. In the first step, the ANN categorizes fault modes into six sectors in the three-phase plane. In the second step, the ANN localizes fault modes in each sector. Especially, the fault modes of both switches in the same legs are localized by comparing the number of the sampled zero current of the fault currents.
The proposed method allows a real-time diagnosis with a simple and an high accuracy for the multiple open-switch faults and operates online. Simulations and experiments for a 3.7kW three-phase PWM converter confirmed the validity of the proposed diagnostic method. |