An arc fault detection system for the household photovoltaic inverter according to the DC bus currents

IEEJ Transactions Elec Engng

Tan

et al. 2018

Self Cite

In a photovoltaic (PV) system, the serial arc is mainly due to the discontinuity in the current‐carrying conductor. Different from the AC arc, the DC arc does not have a periodic zero‐crossing and more easily blossoms into sustained arc, which is more likely to cause accidents. When a serial arc occurs in the DC system, it would lead to a steep drop in current or some unpredictable, irregular change of the current wave. The occurrence of the serial arc can be detected by analyzing the change of amplitude at different frequencies. An arc‐fault detection method based on wavelet packet (WP) and support vector machine (SVM) analysis is proposed in this paper. The threshold to distinguish arcing from normal operation is determined by analyzing the characteristics of the WP coefficients of the DC arc current collected from a real system. Compared to that of the fast Fourier transform (FFT), the effectiveness of the proposed algorithms has been validated with experiments in a 5‐kW grid‐connected PV inverter. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Section: Series DC Arc Characteristic Analysismentioning

confidence: 99%

“…In Ref. , the current signal is transmitted through a band‐pass filter to intercept the characteristic frequency band of the arc. Thus, the noise interference could be suppressed.…”

Section: Introductionmentioning

confidence: 99%

Wavelet packet and support vector machine analysis of series DC ARC fault detection in photovoltaic system

IEEJ Transactions Elec Engng

Tan

et al. 2018

Self Cite

“…Due to the limitations of physical methods, more and more signal processing methods have been used to detect DC serial arc in recent years. In view of different types of arc signals, there are many detection methods, which can mainly be divided into time domain analysis, frequency domain analysis and time-frequency analysis [10][11][12][13][14][15][16]. Shekhar et al [10] adopted power electronic device to detect the change of load-side voltage drop, and defined the operating boundary of the detection algorithm of the DC arc according to the system parameters (grid inductor, resistance and load capacitance) and the threshold, then judged the DC arc in the lines.…”

Section: Introductionmentioning

confidence: 99%

Binary classification model based on machine learning algorithm for the DC serial arc detection in electric vehicle battery system

Guo

et al. 2019

IET Power Electronics

Self Cite

Direct current (DC) serial arc faults usually occur in the damaged insulation lines or line connections, which will cause serious accidents such as fires and explosions. With the rapid increase of electric vehicles, DC serial arc faults are more and more dangerous to battery system. Therefore, a binary classification model based on machine learning algorithm was proposed to detect DC serial arc faults effectively in this study. It was optimised according to the characteristic signals of the arc to be satisfied with different loads for higher detection accuracy and robustness. In the simulative experiments for the power system electric vehicle, while the loads changing to the motor, the resistor or the inverter, it will all reach a highly successful detection rate, respectively.

“…Time-frequency analysis is composed of time-domain analysis, frequency-domain analysis and time-frequency domain analysis, which identifies the arc faults by setting a threshold. Xia et al [3] focused on the high-frequency harmonic parts of 40-100 kHz in arc current. The arc current spectrum of DC arc in household PV system is effectively identified by fast Fourier transform (FFT) and threshold setting.…”

Section: Introductionmentioning

confidence: 99%

Wavelet entropy analysis and machine learning classification model of DC serial arc fault in electric vehicle power system

Liu

et al. 2019

IET Power Electronics

Self Cite

Electric vehicle (EV) power system is flammable and explosive when the direct current (DC) arc occurs at elevated temperature. Thus, DC serial arc real-time monitoring is an insurance to keep away from disaster. In this study, the detection algorithm of DC serial arc is proposed. The wavelet entropy algorithm, the classification model based on support vector machine and logistic regression are analysed separately. The above algorithms are combined to identify the DC serial arc faults effectively under different types of loads in EV power system. The results show that the combined algorithm has a good performance of DC serial arc detection with high accuracy and robustness compared with a simple approach. Meanwhile, the false detection rate of the detection algorithm is close to zero, which could ensure the safety and stable operation of the system.