期刊
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
卷 564, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.physa.2020.125540
关键词
Power network; Robustness; Topological model; Artificial flow; Direct current power flow
资金
- National Natural Science Foundations of China [61503166, 61773186]
- Science and Technology Plan Project of Xuzhou, China [KC16SG253]
This paper proposes a robust analysis framework based on complex network theory to explore the robustness of power systems. By establishing different models, the method evaluates the structural and functional robustness of power systems under various fault scenarios. The results demonstrate the effectiveness of the proposed method in analyzing power system robustness and have been validated through case studies.
The growing importance of power systems in the development of modern society has increasingly focused the attention on the various dangers to which these systems are exposed. This paper proposes a robust analysis framework based on complex network theory with the aim of exploring the robustness of the power system from a methodological perspective. The analysis framework establishes three models: a purely topological model, an artificial flow model, and a direct current power flow model to analyze the power system structure and functional robustness. We present different analysis metrics under different models, simulate three fault scenarios, and conduct an evaluation and analysis. The validity of the evaluation analysis was further verified by adopting IEEE300 and two randomly generated 1000-node network models that meet the characteristics of small world and scale, respectively, for detailed robustness analysis. The results show that the proposed method can effectively analyze a power system from the perspectives of pure topology, artificial flow, and direct current power flow. The case analysis based on the IEEE300 network and systems with different network characteristics proves that the framework is effective for the evaluation of power systems with different characteristics. (C) 2020 Elsevier B.V. All rights reserved.
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