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Constrained LAV state estimation using penalty functions
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AI
DOI：10.1109/59.575725.png)
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摘要
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Inequality constraints are often needed in optimization problems in order to deal with uncertainty. This paper introduces a simple technique that allows enforcement of inequality constraints in l(1) norm problems without any modifications to existing programs and shows the equivalence of the proposed technique to the theory of exact penalty functions. The solution of l(1) norm problems is required, for example, in implementing LAV (Least Absolute Value) state estimators in electric power systems. The paper shows how LAV state estimators with inequality constraints can be useful for estimating the state of external systems. This is important in a competitive environment where precise information about a utility's neighboring systems may not be available.
Keyword:
WLAV state estimation
interior point methods
inequality constraints
external systems
期刊
IF:
7.2
论文数:
1.1W
被引数:
5.0W
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