Intelligent fault diagnosis of photovoltaic arrays based on optimized kernel extreme learning machine and I-V characteristics. (15th October 2017)

• Record Type: Journal Article
• Authors: Chen, Zhicong; Wu, Lijun; Cheng, Shuying; Lin, Peijie; Wu, Yue; Lin, Wencheng
• Is Part Of: Applied energy. Volume 204(2017)
• Page Start: 912
• Page End: 931
• Publication Date: 2017-10-15
• Subjects: Photovoltaic array -- Fault diagnosis -- Optimized kernel extreme learning machine -- I-V characteristics -- Photovoltaic modeling -- Parameter identification; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2017.05.034
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: An improved Simulink based modeling method is proposed for PV modules and arrays. Key points of I-V curves and PV model parameters are used as the feature variables. Kernel extreme learning machine (KELM) is explored for PV arrays fault diagnosis. The parameters of KELM algorithm are optimized by the Nelder-Mead simplex method. The optimized KELM fault diagnosis model achieves high accuracy and reliability. Abstract: Fault diagnosis of photovoltaic (PV) arrays is important for improving the reliability, efficiency and safety of PV power stations, because the PV arrays usually operate in harsh outdoor environment and tend to suffer various faults. Due to the nonlinear output characteristics and varying operating environment of PV arrays, many machine learning based fault diagnosis methods have been proposed. However, there still exist some issues: fault diagnosis performance is still limited due to insufficient monitored information; fault diagnosis models are not efficient to be trained and updated; labeled fault data samples are hard to obtain by field experiments. To address these issues, this paper makes contribution in the following three aspects: (1) based on the key points and model parameters extracted from monitored I-V characteristic curves and environment condition, an effective and efficient feature vector of seven dimensions is proposed as the input of the fault diagnosis model; (2) the emerging kernel based extreme learning machine (KELM), which features extremely fast learning speed and good generalization performance, is utilized to automatically establish the fault diagnosis model. Moreover, the Nelder-Mead Simplex (NMS) optimization method is employed to optimize the KELM parameters which affect the classification performance; (3) an improved accurate Simulink based PV modeling approach is proposed for a laboratory PV array to facilitate the fault simulation and data sample acquisition. Intensive fault experiments are carried out on the both laboratory PV array and the PV model to acquire abundant simulated and experimental fault data samples. The optimized KELM is then applied to train the fault diagnosis model using the data samples. Both the simulation and experimental results show that the optimized KELM based fault diagnosis model can achieve high accuracy, reliability, and good generalization performance.