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

Wenying Zhang; Huaguang Zhang; Jinhai Liu; Kai Li; Yang Dongsheng; Tian Hui

doi:10.1109/JAS.2017.7510562

Volume 4 Issue 3

Jul. 2017

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2017 > 4(3): 520-525

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

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

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

doi: 10.1109/JAS.2017.7510562

1.
College of Information, Northeastern University, Shenyang 110819, China
2.
College of Information, Northeastern University, Shenyang 110819, China

Funds:

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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Author Bio:
Wenying Zhang received the Ph.D degree in systems engineering from Harbin Engineering University, in 2013. She is an Associate Professor in Harbin Engineering University. She is currently working as a post-doctoral fellow of Northeastern University and East-West Control Technology (Shenyang) CO. Ltd. Her research interests include intelligent control and data processing. (e-mail: shuxue2005@163.com)

Huaguang Zhang received the B.S. and M.S. degrees in control engineering from the Northeast Dianli University of China, Jilin City, China, in 1982 and 1985, respectively, and the Ph.D. degree in thermal power engineering and automation from Southeast University, Nanjing, China, in 1991. He joined the Department of Automatic Control, Northeastern University, Shenyang, China, in 1992, as a post-doctoral fellow, for two years. Since 1994, he has been a Professor and the head of the Institute of Electric Automation, School of Information Science and Engineering, Northeastern University. His current research interests include fuzzy control, stochastic system control, neural networks-based control, nonlinear control, and their applications. He has authored and co-authored over 280 journal and conference papers, 6 monographs, and co-invented 90 patents. (e-mail: hgzhang@ieee.org)

Jinhai Liu received the B.S. degree in automation from Harbin Institute of Technology, Harbin, China, in 2002. He received the M.S. degree in power electronics and power transmission, Ph.D. degree in control theory and control engineering from Northeastern University, Shenyang, China, in 2005 and 2009. Dr. Liu is now an Associate Professor at Northeastern University, and is a member of the IEEE. His research interests include data-driven fault diagnosis, neural network and safety technology of long pipeline. (e-mail: 18755208@qq.com)

Kai Li received the bachelor degree in control technology and instrument from Northeastern University at Qinghuangdao, China in 2014. He is now a graduate student at the School of Information Science and Engineering, Northeastern University, China. His research interests include computer vision, perception and localization, lidar mapping. (e-mail: 1091791674@qq.com)

Dongsheng Yang received the B.S., M.S., and Ph. D. degrees from Northeastern University, China, in 1999, 2004, and 2007, respectively. Since 2004, he has been with Northeastern University. His research interests include fuzzy control and fuzzy systems, chaotic control, and complex systems. (e-mail: 11478458@qq.com)

Hui Tian received the M.S. degree in control theory from Shaanxi Normal University, Xi'an, China, in 2006. He is pursuing his Ph.D. degree at Northeastern University, China. His research interests include logical dynamic systems, networked evolutionary games, neural networks, and multi-agent systems. (e-mail: 376841561@qq.com)
Corresponding author: Huaguang Zhang, e-mail:hgzhang@ieee.org
Received Date: 2015-11-12
Accepted Date: 2016-04-09

Abstract

Abstract

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.
- Fault detection,
- multiclass support vector machines,
- photovoltaic power system,
- particle swarm optimization (PSO),
- weather prediction

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References

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

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