Bad data identification and estimation method for power grid

Abstract

The invention relates to a bad data identification and estimation method for a power grid. The method comprises the following steps: reading a power grid model and a plurality of measurement sections; performing state estimation on the measurement sections one by one; performing suspicious parameter or measurement detection; performing parameter or measurement error cooperative identification; and performing parameter estimation. The invention provides an overall error decline index based novel method, so that not only can effective identification and estimation of a single measurement or parameter error be realized but also cooperative identification of measurement errors and parameter errors can be realized efficiently and accurately when the measurement errors and the parameter errors exist at the same time, the process is free of repeated iterations, the calculation amount is reduced, and the efficiency and accuracy of parameter identification and estimation are improved.

Description

technical field [0001] The invention relates to the field of power grids, in particular to a method for identifying and estimating bad data of a power grid. Background technique [0002] Power grid operation measurement data is the technical basis for power grid risk assessment, fault diagnosis and dispatching decisions. [0003] The regularized Lagrange multiplier method that has emerged in recent years represents the highest level of identification at present. This method can effectively distinguish the source of measurement residuals and realize the identification of bad data (measurement errors). However, this method needs to calculate the diagonal elements of the Lagrange multiplier covariance matrix, which is very time-consuming and cannot meet the needs of practical applications in large-scale power systems. On the other hand, in the case of multiple measurement errors and parameter errors, the method needs to repeat the state estimation calculation and parameter est...

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Problems solved by technology

However, this method needs to calculate the diagonal elements of the Lagrange multiplier covariance matrix, which is very time-consuming and cannot meet the needs of practical applications in large-scale power systems

On the other hand, in the case of multiple measurement errors and parameter errors, the method needs to repeat the state estimation calculation and parameter estimation calculation many times, which is inefficient and affects the practicability of the method

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