Open AccessANN design of multiple open‐switch fault diagnosis for three‐phase PWM converters
Author(s) -
Kim WonJae,
Kim SangHoon
Publication year - 2020
Publication title -
iet power electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.637
H-Index - 77
eISSN - 1755-4543
pISSN - 1755-4535
DOI - 10.1049/iet-pel.2020.0795
Subject(s) - converters , pulse width modulation , harmonics , fault (geology) , control theory (sociology) , harmonic , artificial neural network , electronic engineering , computer science , three phase , topology (electrical circuits) , engineering , voltage , physics , electrical engineering , artificial intelligence , acoustics , control (management) , seismology , geology
In this paper, Artificial Neural Networks (ANNs) for diagnosing multiple open‐switch faults in three‐phase PWM (Pulse Width Modulation) converters are designed. For single and double open‐switch faults in the converters, there are 21 types of fault modes, causing distorted phase currents. Since these abnormal currents can induce secondary faults in peripherals, the open‐fault diagnosis is essentially required. In this paper, a two‐step technique based on ANNs is utilized for the diagnosis of the multiple open‐switch fault. First, dc and harmonic components of the currents are extracted by using an ADALINE (Adaptive Linear Neuron). In the first step, the ANN categorizes fault modes into six sectors by using dc components in the three‐phase plane. In the second step, the ANN localizes fault modes by using dc components and the ratio of d – q axes currents THDs (Total Harmonics Distortions) in each sector. Especially, both switches open‐faults in the same legs are localised by counting the sampled zero current of the fault currents. The proposed two‐step technique allows a simple design of the ANNs for the diagnosis, and a short execution time about 22 s. Simulations and experiments for a 3.7 kW three‐phase PWM converter confirmed the validity of the proposed diagnostic method.