State estimation of a power system using a parameterized potential function for equality constraints.

Abstract

Conventional state estimation methods based on constrained optimization problems with equality and / or inequality constraints rely on penalty-based heuristics that can generate very large weight values, resulting in mal-tuned gain matrices. Disclosed state estimation embodiments transform the constrained optimization problem into an unconstrained convex optimization problem, where violated equality and / or inequality constraints are represented as parameterized potential functions, each including a center of attraction parameter. This unconstrained convex optimization problem can be iteratively formulated using successively updated values ​​of the center of attraction parameter and solved until no equality and / or inequality constraints are violated, generating a final estimated state. This final estimated state may then be used to control a monitored system, such as a power system.

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