Probabilistic Small Signal Stability Analysis with Wind Power Based on Maximum Entropy Theory

Abstract

The probability parameters for the small signal stability in a system are studied to quantify the impact of uncertain wind power on system stability. With the increase in the scale of wind power access systems, how to more efficiently and accurately obtain the probability of small signal stability is worthy of further investigation. Therefore, this work proposes an analysis method of probabilistic small signal stability (PSSS) based on maximum entropy (ME) theory for studying power systems with uncertain wind power from doubly fed induction generators (DFIGs). Firstly, the sensitivity of the eigenvalues to the variable power is obtained by linearization. Then, when the probability characteristic for wind is known, the probability characteristic for the system critical eigenvalues is approximated using the ME method and the derived sensitivity. The proposed ME method is used in the analysis of the PSSS for power systems with different wind power penetration scales in case studies. Compared to the probability function for the eigenvalue obtained by the conventional series expansion method, the proposed method has excellent accuracy and sufficient efficiency in analysing the PSSS for a system with uncertain wind power.

Keywords:

• Maximum entropy (ME)
• small signal stability (SSS)
• probabilistic
• doubly fed induction generator (DFIG)
• wind power

Disclosure Statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported in part by National Key Research and Development Program of China under Grant 2019YFE0122600 and Key Program of National Natural Science Foundation of China under Grant 51837007.

Notes on contributors

Ruilin Cao

Ruilin Cao was born in Huaihua, Hunan, China, in 1998. She received the B.S. and M.S. degrees in electrical engineering from Donghua University, Shanghai, China, in 2020 and 2023. Her research interests include power system planning and renewable energy integration.

Jie Xing

Jie Xing received the B.S. and M.S. degrees in electrical engineering from Shandong University, Jinan, China, in 2003 and 2006, respectively, and the Ph.D. degree in electrical engineering from Shanghai Jiao Tong University, Shanghai, China, in 2010. From 2010 to 2018, she was a Senior Engineer with Shanghai Electric Power Design Institute Corporation Ltd. Since 2019, she has been an Associate Professor with the College of Information Science and Technology, Donghua University, Shanghai. Her research interests include planning, stability and control of power system, and renewable energy integration.

Zheng Li

Zheng Li received the B.S. and M.S. degrees in electrical engineering from Southeast University, Nanjing, China, in 1983 and 1986, respectively, and the Ph.D. degree from Donghua University, China, in 1999. From 2005 to 2006, she was with the Department of Aalborg University, Denmark, as a Visiting Researcher. She is currently a Professor with the College of Information Science and Technology, Donghua University, shanghai, China. Her research interests lie in the field of distributed renewable energy system and its integration, energy storage systems, intelligent control system, and hybrid systems control.

Hongyan Ma

Hongyan Ma received the B.S. degree in electrical engineering from Shandong University, Jinan, China, in 2013, and the Ph.D. degree in electrical engineering from Shanghai Jiao Tong University, Shanghai, China, in 2019. She is currently an Assistant Professor with the College of Information Science and Technology, Donghua University, Shanghai, China. Her research interests include power system operation, optimization, and renewable energy integration.