An intelligent fault diagnosis method for PV arrays based on an improved rotation forest algorithm. (February 2019)

• Record Type: Journal Article
• Authors: Han, Fuchang; Chen, Zhicong; Wu, Lijun; Long, Chao; Yu, Jinling; Lin, Peijie; Cheng, Shuying
• Is Part Of: Energy procedia. Volume 158(2019)
• Page Start: 6132
• Page End: 6138
• Publication Date: 2019-02
• Subjects: PV arrays -- Fault diagnosis -- ReliefF algorithm -- Rotation Forest -- Extreme learning machine; Power resources -- Congresses; Power resources -- Periodicals; Power resources; Conference proceedings; Periodicals; 333.7905
• DOI: 10.1016/j.egypro.2019.01.498
• Languages: English
• ISSNs: 1876-6102

Abstract

Abstract: With the exponential growth of global photovoltaic (PV) power capacity, it is essential to monitor, detect and diagnose the faults in PV arrays for optimal operation. This paper presents an improved rotation forest (RoF) algorithm classifiers ensemble hybridized with extreme learning machine (ELM) for fault diagnosis of PV arrays, which mainly consists of feature selection and classification. In the feature selection step, all the attributes are ranked by the ReliefF algorithm and the top-ranked attributes are chosen to create the new training data subset. In the classification step, the base classifier decision tree of the RoF is replaced by the extreme learning machine to form a new hybrid RoF-ELM ensemble classifier. In the RoF-ELM algorithm, the feature space is first split into several subspaces and the best number of feature subsets is found through the traversal search method. Then, the bootstrap algorithm is employed to carry out bootstrap resampling for each feature subspace, and the principal component analysis (PCA) is then used to transform the resampled samples. Finally, the ELM base classifier is exploited to build each classification model and the final decision is determined by the simple voting approach. By combining the RoF ensemble method with the ELM classifier, the proposed RoF-ELM algorithm not only overcomes the overfitting problem of the basic RoF algorithm, but also improves the generalization ability of the basic ELM. In order to experimentally verify the proposed approach, different types and levels of faults have been created in a laboratory small scale grid-connected PV power system to obtain the fault data samples. Experimental results demonstrate that the RoF-ELM can achieve higher diagnosis accuracy and reliability compared to the basic RoF and ELM algorithms.