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| Fault diagnosis system of photovoltaic array based on LoRa and adaptive network based fuzzy inference system |
| Gan Yutao1, Wu Zhenhui2, Chen Zhicong1, Wu Lijun1, Cheng Shuying1 |
1. College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108;
2. State Grid Fuzhou Power Supply Company, Fuzhou 350004 |
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Abstract A new online intelligent fault diagnosis system is designed for PV arrays in this paper to improve the reliability and efficiency of PV systems. First, use Hall voltage and current sensors to collect PV array maximum power points as raw data, which is transmitted to the diagnostic center remotely via LoRa, and then the new seven-dimensional fault feature vector is extracted from the collected raw data. 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 97.0% respectively.
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Received: 22 February 2020
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| Cite this article: |
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Gan Yutao,Wu Zhenhui,Chen Zhicong等. Fault diagnosis system of photovoltaic array based on LoRa and adaptive network based fuzzy inference system[J]. Electrical Engineering, 2020, 21(8): 80-86.
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| URL: |
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http://dqjs.cesmedia.cn/EN/Y2020/V21/I8/80
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2020, Vol. 21 
