Abstract
AbstractTransient stability is the key aspect of power system dynamic security assessment, and data-driven methods are becoming alternative measures of assessment. The current data-driven methods only construct correlations between variables while neglecting causal relationships. Therefore, they face problems such as poor robustness, which restrict their practical application. This paper introduces an improved constraint-inference approach for causality exploration of power system transient stability. Firstly, a causal structure discovery method of power system transient stability is proposed based on a PC-IGCI algorithm, which addresses the shortage caused by Markov equivalence and massive variables. Then, a relative average causal effect index is proposed to reveal the relationship between relative intervention strength and causal effects. The results of a case study verify that the proposed method can identify the causal structure between the transient stability variables entirely based on data. In addition, the causal effect sorting between “cause” and “outcome” of transient stability variables is revealed. This paper provides a new approach for data mining to uncover the causal mechanisms between variables in power systems and expand the capabilities of data-driven methods in power system application.
Funder
National Natural Science Foundation of China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Energy Engineering and Power Technology,Safety, Risk, Reliability and Quality
Reference31 articles.
1. Chen, Y., Shen, C., & Wang, J. (2009). Distributed transient stability simulation of power systems based on a Jacobian-Free Newton-GMRES method. IEEE Transactions on Power Systems, 24(1), 146–156.
2. Magnusson, P. C. (1947). The transient-energy method of calculating stability. Transactions of the American Institute of Electrical Engineers, 66(1), 747–755.
3. Xue, Y. (1992). Extended equal area criterion revisited. IEEE Transactions on Power Systems, 7(3), 1012–1022.
4. An, J., Zhang, L., Zhou, Y., & Yu, J. (2022). Transient stability margin prediction under the concept of security region of power systems based on the long short-term memory network and attention mechanism. Frontiers in Energy Research, 10(332), 1–15.
5. Zhang, R. Y., Wu, J. Y., Li, B. Q., & Shoa, M. (2020). Self-adaptive power system transient stability prediction based on transfer learning. Power System Technology, 44(6), 2196–2205.
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