An improved constraint-inference approach for causality exploration of power system transient stability
Abstract Transient 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 fa...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2023-11-01
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| Series: | Protection and Control of Modern Power Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s41601-023-00330-w |
| _version_ | 1797452221883875328 |
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| author | Yibo Zhou Jun An Gang Mu Yan Shi |
| author_facet | Yibo Zhou Jun An Gang Mu Yan Shi |
| author_sort | Yibo Zhou |
| collection | DOAJ |
| description | Abstract Transient 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. |
| first_indexed | 2024-03-09T15:05:40Z |
| format | Article |
| id | doaj.art-2f3efdc954de48fb83190bd04dda1e89 |
| institution | Directory Open Access Journal |
| issn | 2367-2617 2367-0983 |
| language | English |
| last_indexed | 2024-03-09T15:05:40Z |
| publishDate | 2023-11-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Protection and Control of Modern Power Systems |
| spelling | doaj.art-2f3efdc954de48fb83190bd04dda1e892023-11-26T13:39:33ZengSpringerOpenProtection and Control of Modern Power Systems2367-26172367-09832023-11-018111210.1186/s41601-023-00330-wAn improved constraint-inference approach for causality exploration of power system transient stabilityYibo Zhou0Jun An1Gang Mu2Yan Shi3Key Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology, Ministry of Education (Northeast Electric Power University)Key Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology, Ministry of Education (Northeast Electric Power University)Key Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology, Ministry of Education (Northeast Electric Power University)Key Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology, Ministry of Education (Northeast Electric Power University)Abstract Transient 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.https://doi.org/10.1186/s41601-023-00330-wData-drivenCausality exploringTransient stability assessmentCausal structure discoveryCausal effect |
| spellingShingle | Yibo Zhou Jun An Gang Mu Yan Shi An improved constraint-inference approach for causality exploration of power system transient stability Protection and Control of Modern Power Systems Data-driven Causality exploring Transient stability assessment Causal structure discovery Causal effect |
| title | An improved constraint-inference approach for causality exploration of power system transient stability |
| title_full | An improved constraint-inference approach for causality exploration of power system transient stability |
| title_fullStr | An improved constraint-inference approach for causality exploration of power system transient stability |
| title_full_unstemmed | An improved constraint-inference approach for causality exploration of power system transient stability |
| title_short | An improved constraint-inference approach for causality exploration of power system transient stability |
| title_sort | improved constraint inference approach for causality exploration of power system transient stability |
| topic | Data-driven Causality exploring Transient stability assessment Causal structure discovery Causal effect |
| url | https://doi.org/10.1186/s41601-023-00330-w |
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