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|Title:||Worst-case identification of reactive power margin and local weakness of power systems||Authors:||Chang, C.S.
Lagrange multiplier method
Relative local weakness
Worst-case reactive margin
|Issue Date:||Feb-1998||Citation:||Chang, C.S.,Huang, J.S. (1998-02). Worst-case identification of reactive power margin and local weakness of power systems. Electric Power Systems Research 44 (2) : 77-83. ScholarBank@NUS Repository.||Abstract:||This paper has the main objectives of evaluating the worst-case VAR margin of power systems and identifying the most vulnerable busbars. One possible method of achieving these objectives is to progressively increase system-wide reactive power (VAR) demands on power systems and to perform loadflow after each VAR increase. The process is continued up to the point where loadflow diverges. This method is inefficient and subjective, and would most likely fail to reach critical stability due to numerical problems. A more sophisticated method is to directly locate critical stability by solving an optimization problem. By evaluating the system VAR margin, traditional optimization approaches usually pre-specifies a disturbance scenario, which distributes the VAR increases for stressing the power system. However, different disturbance scenarios will stress the power system towards different critical points, which will lead to different VAR margins. To estimate the system's capability to withstand VAR disturbance, the worst disturbance scenario should be identified. Traditional optimization approaches did not usually lead to the worst case. Worst-case identification of disturbance scenario is treated in this paper as a separate optimization problem with the VAR disturbance scenario taken as the decision variables. Apart from providing the worst-case VAR margin and the associated disturbance scenario, the proposed method also highlights local weakness of the study power system and relative effectiveness of control measures. The paper presents a systematic method of worst-case identification by incorporating genetic algorithm (GA) and nonlinear programming techniques in two levels. In order to achieve an accurate and reliable estimation, the method performs feasibility checks during optimization on VAR disturbance scenarios, generator reactive limits, and voltage constraints at regulated busbars. © 1998 Elsevier Science S.A.||Source Title:||Electric Power Systems Research||URI:||http://scholarbank.nus.edu.sg/handle/10635/62942||ISSN:||03787796|
|Appears in Collections:||Staff Publications|
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