Electrical power system robust state estimation method based on self-adaptive kernel density estimation

Abstract

The invention relates to an electrical power system robust state estimation method based on self-adaptive kernel density estimation, comprising the following steps: (1) establishing a state estimation mathematical model based on a self-adaptive kernel density estimation theory; (2) acquiring self-adaptive bandwidth; and (3) according to the state estimation mathematical model in step (1) and the self-adaptive bandwidth obtained in step (2), performing electrical power system robust state estimation. Compared with the prior art, the invention ensures data measurement redundancy and system observability, eliminates residual error contamination and residual error flooding, and further, improves identification accuracy and convergence of the state estimation. The invention has advantages of being strong in robustness, high in computation accuracy, wide in applicability, great in flexibility, and the like.

Description

technical field [0001] The invention relates to a power system state estimation method, in particular to a power system tolerance state estimation method based on adaptive kernel density estimation. Background technique [0002] Power system state estimation is one of the most basic software in EMS. How to identify and eliminate bad data to obtain a state estimation algorithm with strong robustness, high calculation accuracy and fast calculation speed has always been a research topic that has attracted much attention. [0003] For the identification of bad data, the methods can be roughly divided into three categories. The first category may be referred to simply as stepwise elimination. The basic idea is to use the Weighted Least Square method (WLS) to preliminarily complete the state estimation calculation, and then regularize the measurement residuals based on the residual sensitivity matrix, and make the maximum regularized residuals exceed a certain The measurement d...

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Problems solved by technology

This type of method does not have the related problems caused by the discontinuity of the objective function within the domain of the measurement residual error caused by most of the robustness methods above.

Due to the large scale of the actual power system and the large difference in the accuracy of various measuring instruments in the existing power system bad data identification and state estimation methods based on one-dimensional bandwidth equal-scale scaling adjustment strategy, the accuracy of the measuring instruments themselves is relatively large For high bad data, in order to avoid missing identification, it is necessary to use a smaller bandwidth, and for normal measurement with low precision of the measuring instrument, in order to prevent it from being misidentified as bad data, it is necessary to use a larger bandwidth. In this case, it is difficult to select a single suitable bandwidth, which may lead to the failure of the identification of bad data, or the misidentification of normal measurements, and correspondingly, it is impossible to obtain accurate system state estimates, and even iterations do not converge.

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