Logistic Regression Based Arc Fault Detection in Photovoltaic Systems Under Different Conditions

doi:10.1007/s12204-019-2095-1

Journal of Shanghai Jiao Tong University (Science) ›› 2019, Vol. 24 ›› Issue (4): 459-470.doi: 10.1007/s12204-019-2095-1

Logistic Regression Based Arc Fault Detection in Photovoltaic Systems Under Different Conditions

JIA Fan (贾帆), LUO Liwen *(罗利文), GAO Shiyue (高诗悦), YE Jian (叶健)

(1. School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; 2. University of Michigan - Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University, Shanghai 200240, China; 3. Analog Devices, Inc., Shanghai 200021, China)

出版日期:2019-08-01 发布日期:2019-07-29
通讯作者: LUO Liwen *(罗利文) E-mail:lwluo@sjtu.edu.cn

Logistic Regression Based Arc Fault Detection in Photovoltaic Systems Under Different Conditions

JIA Fan (贾帆), LUO Liwen *(罗利文), GAO Shiyue (高诗悦), YE Jian (叶健)

(1. School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; 2. University of Michigan - Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University, Shanghai 200240, China; 3. Analog Devices, Inc., Shanghai 200021, China)

Online:2019-08-01 Published:2019-07-29
Contact: LUO Liwen *(罗利文) E-mail:lwluo@sjtu.edu.cn

摘要/Abstract

摘要： This paper investigates direct current (DC) arc fault detection in photovoltaic system. In order to avoid the risk of fire ignition caused by the arc fault in the photovoltaic power supply, it is urgent to detect the DC arc fault in the photovoltaic system. Once an arc fault is detected, the power supply should be cut off immediately. A lot of field experiments are carried out to obtain the data of arc fault current of the photovoltaic system under different current conditions. Cable length, arc gap, and the effects of different sensors are tested. These three conditions are the most significant features of this paper. Four characteristic variables from both the time domain and the frequency domain are extracted to identify the arc fault. Then the logistic regression method in the field of artificial intelligence and machine learning is originally used to analyze the experimental results of arc fault in the photovoltaic system. The function between the probability of the arc fault and the change of the characteristic variables is obtained. After validating 80 groups of experimental data under different conditions, the accuracy rate of the arc fault detection by this algorithm is proved to reach 100%.

关键词: photovoltaic, arc fault, fast Fourier transform, logistic regression

Abstract: This paper investigates direct current (DC) arc fault detection in photovoltaic system. In order to avoid the risk of fire ignition caused by the arc fault in the photovoltaic power supply, it is urgent to detect the DC arc fault in the photovoltaic system. Once an arc fault is detected, the power supply should be cut off immediately. A lot of field experiments are carried out to obtain the data of arc fault current of the photovoltaic system under different current conditions. Cable length, arc gap, and the effects of different sensors are tested. These three conditions are the most significant features of this paper. Four characteristic variables from both the time domain and the frequency domain are extracted to identify the arc fault. Then the logistic regression method in the field of artificial intelligence and machine learning is originally used to analyze the experimental results of arc fault in the photovoltaic system. The function between the probability of the arc fault and the change of the characteristic variables is obtained. After validating 80 groups of experimental data under different conditions, the accuracy rate of the arc fault detection by this algorithm is proved to reach 100%.

Key words: photovoltaic, arc fault, fast Fourier transform, logistic regression

中图分类号:

TM 914

JIA Fan (贾帆), LUO Liwen *(罗利文), GAO Shiyue (高诗悦), YE Jian (叶健). Logistic Regression Based Arc Fault Detection in Photovoltaic Systems Under Different Conditions[J]. Journal of Shanghai Jiao Tong University (Science), 2019, 24(4): 459-470.

参考文献

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Logistic Regression Based Arc Fault Detection in Photovoltaic Systems Under Different Conditions

Logistic Regression Based Arc Fault Detection in Photovoltaic Systems Under Different Conditions

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