Robust optimisation-based state estimation considering parameter errors for systems observed by phasor measurement units
This study presents a new robust state estimation (SE) approach for power systems that are monitored by synchronised phasor measurements. The proposed robust SE utilises the least absolute value (LAV) of residuals to determine the system state. Although conventional deterministic LAV-based SE is rob...
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| Vydané v: | IET generation, transmission & distribution Ročník 12; číslo 8; s. 1915 - 1921 |
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| Hlavní autori: | , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
The Institution of Engineering and Technology
30.04.2018
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| Predmet: | |
| ISSN: | 1751-8687, 1751-8695 |
| On-line prístup: | Získať plný text |
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| Shrnutí: | This study presents a new robust state estimation (SE) approach for power systems that are monitored by synchronised phasor measurements. The proposed robust SE utilises the least absolute value (LAV) of residuals to determine the system state. Although conventional deterministic LAV-based SE is robust against bad measurements, it is vulnerable with respect to the network parameters’ errors. In other words, the conventional LAV-SE method is not robust against the network parameters’ errors and these errors can make the SE results inaccurate. The proposed robust SE aims at solving this problem of the conventional LAV-SE. Robust optimisation, strong duality theorem, and Big-M linearisation technique have been used to construct the proposed SE approach, which is robust against the network parameters’ errors. Additionally, the proposed robust SE is finally formulated as a tractable mixed-integer linear programming optimisation problem. Comprehensive numerical experiments and a probabilistic evaluation approach are presented to evaluate the effectiveness of the proposed robust SE compared to the conventional deterministic SE in the presence of the uncertainty source of network parameters’ errors. |
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| ISSN: | 1751-8687 1751-8695 |
| DOI: | 10.1049/iet-gtd.2017.1128 |