Open AccessFault diagnosis of PV array using adaptive network based fuzzy inference system
Author(s) -
Yong Gan,
Zhicong Chen,
Lijun Wu,
Shuying Cheng,
Peijie Lin
Publication year - 2020
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/467/1/012083
Subject(s) - adaptive neuro fuzzy inference system , fault (geology) , artificial neural network , computer science , photovoltaic system , reliability (semiconductor) , inference system , fuzzy logic , artificial intelligence , data mining , real time computing , engineering , fuzzy control system , power (physics) , physics , quantum mechanics , seismology , geology , electrical engineering
A new online intelligent fault diagnosis method is proposed for PV arrays in this paper to improve the reliability and efficiency of PV systems. Firstly, a new seven-dimensional fault feature vector is extracted from the raw data of dynamic operating points of PV arrays including operating voltage, current, irradiance and temperature. Secondly, an optimized adaptive network based fuzzy inference system (ANFIS) is proposed as the fault diagnosis model. Lastly, the feasibility and superiority of the proposed ANFIS based fault diagnosis model are tested by both Simulink based simulation and real fault experiments on a laboratory PV system. Experimental results validate that the proposed ANFIS based method achieves a high performance and is superior to conventional back-propagation neural network (BPNN) based methods. The overall accuracy of the ANFIS based fault diagnosis model on the simulation and experimental dataset is 99.9% and over 97% respectively.