Black-Start Decision Making Based on Affinity Propagation and TOPSIS

Abstract

Finding the optimal black-start alternative plays an important role in speeding up the restoration process of a power system after a complete blackout. In this article, a clustering method, affinity propagation, is adopted to deal with the black-start decision making problem. A novel black-start decision making method based on affinity propagation and TOPSIS is proposed. The standard deviation method is used to calculate the weights of the indexes, and a weighted normalized decision matrix is constructed. Then affinity propagation is used to cluster the black-start schemes of the weighted normalized decision matrix, and the optimal cluster is determined. Finally, for each black-start scheme in the optimal cluster, the relative closeness value is computed according to the TOPSIS method, and the best black-start scheme is selected. Compared with the existing black-start decision making methods, the proposed method can not only rank the black-start schemes but also determine which grade each black-start scheme should belong to. Based on the data of an actual power system, experiments were carried out to evaluate the performance of the proposed method. The results show that the proposed method is effective.

Keywords:

• power system restoration
• black-start
• decision making
• affinity propagation
• TOPSIS

Acknowledgements

We would like to express our acknowledgements to the providers of data of Guangdong power system.

Notes

1 In the experiments, we set the damping factor μ to 0.9.

Additional information

Funding

This work was supported partially by the National Natural Science Foundation of China under the Grant Nos. 71601109 and 71601061, partially by the Project of Philosophy and Social Science of Shanghai under the Grant No. 2016EGL001.

Notes on contributors

Ya-Jun Leng

Ya-Jun Leng received the B.S. degree in Business Administration from Hefei University of Technology, Hefei, China, in 2008, and received the Ph.D. degree in Management Science and Engineering from Hefei University of Technology, Hefei, China, in 2013. Currently he is an Associate Professor in the College of Economics and Management at Shanghai University of Electric Power. His research interests include power system restoration and smart grid operation and control.

Di Wang

Di-Wang received the B.S. degree in 2011 from the Department of Electrical Engineering, Shanghai University of Electric Power, Shanghai, China, where she is currently working toward the M.S. degree. Her research interests include power system stability and power system modeling and simulation.

Shu-Ping Zhao

Shu-Ping Zhao received the M.S. and Ph.D. degrees in Management Science and Engineering from Hefei University of Technology, Hefei, China, in 2011 and 2015, respectively. Currently he is a lecturer in the School of Management at Hefei University of Technology. His main research interest is decision making.