Determining Disturbance Recovery Conditions by Inverse Sensitivity Minimization
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
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2025
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| _version_ | 1866913646677852160 |
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| author | Fisher, Michael W. Hiskens, Ian A. |
| author_facet | Fisher, Michael W. Hiskens, Ian A. |
| contents | Power systems naturally experience disturbances, some of which can damage equipment and disrupt consumers. It is important to quickly assess the likely consequences of credible disturbances and take preventive action, if necessary. However, assessing the impact of potential disturbances is challenging because many of the influential factors, such as loading patterns, controller settings and load dynamics, are not precisely known. To address this issue, the paper introduces the concept of parameter-space recovery regions. For each disturbance, the corresponding recovery region is the region of parameter space for which the system will recover to the desired operating point. The boundary of the recovery region establishes the separation between parameter values that result in trouble-free recovery and those that incur undesirable non-recovery. The safety margin for a given set of parameter values is defined as the smallest distance (in parameter space) between the given values and the recovery boundary. Novel numerical algorithms with theoretical guarantees are presented for efficiently computing recovery boundaries and safety margins. Unlike prior methods, which tend to be overly conservative and restricted to low dimensional parameter space, these methods compute safety margins to arbitrary user-specified accuracy and do so efficiently in high dimensional parameter space. The efficacy of the methods is demonstrated using the IEEE 39-bus benchmark power system, where safety margins are computed for cases that consider up to 86 parameters, and reveal unexpected safety implications that would not have been observed otherwise. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_07516 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Determining Disturbance Recovery Conditions by Inverse Sensitivity Minimization Fisher, Michael W. Hiskens, Ian A. Systems and Control 93D09, 93D99, 37N99 Power systems naturally experience disturbances, some of which can damage equipment and disrupt consumers. It is important to quickly assess the likely consequences of credible disturbances and take preventive action, if necessary. However, assessing the impact of potential disturbances is challenging because many of the influential factors, such as loading patterns, controller settings and load dynamics, are not precisely known. To address this issue, the paper introduces the concept of parameter-space recovery regions. For each disturbance, the corresponding recovery region is the region of parameter space for which the system will recover to the desired operating point. The boundary of the recovery region establishes the separation between parameter values that result in trouble-free recovery and those that incur undesirable non-recovery. The safety margin for a given set of parameter values is defined as the smallest distance (in parameter space) between the given values and the recovery boundary. Novel numerical algorithms with theoretical guarantees are presented for efficiently computing recovery boundaries and safety margins. Unlike prior methods, which tend to be overly conservative and restricted to low dimensional parameter space, these methods compute safety margins to arbitrary user-specified accuracy and do so efficiently in high dimensional parameter space. The efficacy of the methods is demonstrated using the IEEE 39-bus benchmark power system, where safety margins are computed for cases that consider up to 86 parameters, and reveal unexpected safety implications that would not have been observed otherwise. |
| title | Determining Disturbance Recovery Conditions by Inverse Sensitivity Minimization |
| topic | Systems and Control 93D09, 93D99, 37N99 |
| url | https://arxiv.org/abs/2501.07516 |