Identifying the dominant operating variables to evaluate the cascading failure potential in the power system by theory of mutual information

• Presenting an adaptive scheme of estimating the potential of cascading failure as a result of transmission line outages through large power systems. • Developing a set of dominant operating variables to identify cascading failures caused by line outages. • Evaluating the line information consisting of dynamic securities and sensitivities in which the lines with the potential of cascading failures are identified online. • Developing mutual information theory based on the Least Square Error (LSE) method to estimate power system cascading failures without acting any line outages. In this study, the potential of cascading failure as a result of transmission line outages through large power systems is evaluated. Transmission lines are more dispose of the outage resulting from different fault occurrences and extend the uncontrolled failures compared to other power system devices. In this case, power system dynamic security must evaluate the possibility of cascading failures using a power system control center (PSCC) in order to limit the spread of outages caused by line failures. For this issue, using the PSCC online information consisting of power system operating variables, it is possible to present a variables-based scheme to estimate power system cascading failures concerning fault events. In order to cover the issue, based on developing a set of dominant operating variables (DOVs), this paper presents an online scheme of identifying cascading failures caused by line outages in the presence of line operational diversities and communication restrictions. In this scenario, during each time interval, by evaluating the proposed DOVs, the line information consisting of dynamic securities and sensitivities to cascading failure are investigated which the lines with the potential of cascading failures are identified online. Proper DOVs are determined for this issue by studying a variety of mathematical formulations according to theory of mutual information (MI) and measuring the entropy among the variables. In a real-time environment, by using the proposed DOVs and substituting them through online boundary equations based on the Least Square Error (LSE) method, the potentials of power system cascading failures without acting any line outage are provided. The effectiveness of the suggested technique is evaluated using a typical IEEE-39 bus and IEEE-118 bus, and proper results with high accuracy are achieved by evaluating the system potential concerning large cascading failures.

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Publication Details

Journal
International Journal of Electrical Power & Energy Systems
Published
2024-08-20
DOI
https://doi.org/10.1016/j.ijepes.2024.110154
Citations
3
Primary Topic
Power System Optimization and Stability
Type
article
Field-Weighted Citation Impact
0.63
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article

Identifying the dominant operating variables to evaluate the cascading failure potential in the power system by theory of mutual information

Soheil Ranjbar, Morteza Abedi
3 citations
International Journal of Electrical Power & Energy Systems
Power System Optimization and Stability
0.63
article

Identifying the dominant operating variables to evaluate the cascading failure potential in the power system by theory of mutual information

Soheil Ranjbar, Morteza Abedi
article en
3 citations

Abstract

• Presenting an adaptive scheme of estimating the potential of cascading failure as a result of transmission line outages through large power systems. • Developing a set of dominant operating variables to identify cascading failures caused by line outages. • Evaluating the line information consisting of dynamic securities and sensitivities in which the lines with the potential of cascading failures are identified online. • Developing mutual information theory based on the Least Square Error (LSE) method to estimate power system cascading failures without acting any line outages. In this study, the potential of cascading failure as a result of transmission line outages through large power systems is evaluated. Transmission lines are more dispose of the outage resulting from different fault occurrences and extend the uncontrolled failures compared to other power system devices. In this case, power system dynamic security must evaluate the possibility of cascading failures using a power system control center (PSCC) in order to limit the spread of outages caused by line failures. For this issue, using the PSCC online information consisting of power system operating variables, it is possible to present a variables-based scheme to estimate power system cascading failures concerning fault events. In order to cover the issue, based on developing a set of dominant operating variables (DOVs), this paper presents an online scheme of identifying cascading failures caused by line outages in the presence of line operational diversities and communication restrictions. In this scenario, during each time interval, by evaluating the proposed DOVs, the line information consisting of dynamic securities and sensitivities to cascading failure are investigated which the lines with the potential of cascading failures are identified online. Proper DOVs are determined for this issue by studying a variety of mathematical formulations according to theory of mutual information (MI) and measuring the entropy among the variables. In a real-time environment, by using the proposed DOVs and substituting them through online boundary equations based on the Least Square Error (LSE) method, the potentials of power system cascading failures without acting any line outage are provided. The effectiveness of the suggested technique is evaluated using a typical IEEE-39 bus and IEEE-118 bus, and proper results with high accuracy are achieved by evaluating the system potential concerning large cascading failures.

International Journal of Electrical Power & Energy SystemsVol. 161
Technical and Vocational University (IR), Iranshahr University (IR)
Climate action
Openalex Percentile: Top 31%
Power System Optimization and Stability
0.63
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