A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 4 Issue 3
Jul.  2017

IEEE/CAA Journal of Automatica Sinica

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    CiteScore: 11.2, Top 5% (Q1)
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Article Contents
Wenying Zhang, Huaguang Zhang, Jinhai Liu, Kai Li, Yang Dongsheng and Tian Hui, "Weather Prediction With Multiclass Support Vector Machines in the Fault Detection of Photovoltaic System," IEEE/CAA J. Autom. Sinica, vol. 4, no. 3, pp. 520-525, July 2017. doi: 10.1109/JAS.2017.7510562
Citation: Wenying Zhang, Huaguang Zhang, Jinhai Liu, Kai Li, Yang Dongsheng and Tian Hui, "Weather Prediction With Multiclass Support Vector Machines in the Fault Detection of Photovoltaic System," IEEE/CAA J. Autom. Sinica, vol. 4, no. 3, pp. 520-525, July 2017. doi: 10.1109/JAS.2017.7510562

Weather Prediction With Multiclass Support Vector Machines in the Fault Detection of Photovoltaic System

doi: 10.1109/JAS.2017.7510562

the National Natural Science Foundation of China 61433004

the National Natural Science Foundation of China 61473069

IAPI Fundamental Research Funds 2013ZCX14

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  • Since the efficiency of photovoltaic (PV) power is closely related to the weather, many PV enterprises install weather instruments to monitor the working state of the PV power system. With the development of the soft measurement technology, the instrumental method seems obsolete and involves high cost. This paper proposes a novel method for predicting the types of weather based on the PV power data and partial meteorological data. By this method, the weather types are deduced by data analysis, instead of weather instrument. A better fault detection is obtained by using the support vector machines (SVM) and comparing the predicted and the actual weather. The model of the weather prediction is established by a direct SVM for training multiclass predictors. Although SVM is suitable for classification, the classified results depend on the type of the kernel, the parameters of the kernel, and the soft margin coefficient, which are difficult to choose. In this paper, these parameters are optimized by particle swarm optimization (PSO) algorithm in anticipation of good prediction results can be achieved. Prediction results show that this method is feasible and effective.


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