S.-Y. Lin and C.-H. Lin (Taiwan)
Distributed Computation, Nonlinear Programming, PC Network
In this paper, we present a distributed constrained state estimator which takes the power flow balance constraints on no generation and no load buses into account so as to improve the accuracy of the estimated states. We propose a method that combines a successive quadratic programming method with a parallel dual-type method to solve the constrained weighted-least-square (CWLS) problem. The novel parallel dual-type method possesses two decomposition effects, which not only make our method being parallel but also greatly increase the computational efficiency of our method. Our method achieves a dramatic speed-up ratio compared with the commercial IMSL subroutine in solving CWLS problems. We employ a previously developed distributed bad data detection and identification scheme as out bad data processing technique and set up a PC network to test our distributed constrained state estimator. The test resutls on the IEEE 118-bus system show that we have achieved a more accurate estimated states than the previously developed distributed unconstrained state estimator.
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