Power system load data identification and recovery method

Abstract

The invention discloses a power system load data identification and recovery method. Firstly, according to user historical load data, the number of clusters and initial cluster centers of sample data are determined on the basis of the hill climbing method; secondly, the final cluster center and the characteristic curve of the historical load data are obtained on the basis of the fuzzy C-means clustering algorithm; thirdly, each kind of load characteristic curve is processed, and the feasible region interval where normal data of the load curve is located is obtained; fourthly, according to correlation coefficients with the load characteristic curves, the category to which a to-be-tested load curve belongs is determined; finally, on the basis of the feasible region interval and the to-be-tested load curve whose category is judged, bad data of to-be-tested load data is identified and corrected. According to the method, the fuzzy C-means algorithm serves as the basis, the hill climbing function method is used, the number of clusters and the initial cluster centers are determined at the same time to improve clustering efficiency, and the initial cluster center determination problem and identification effect judgment randomness problem of bad data are solved.

Description

technical field [0001] The invention relates to a power system, in particular to a method for identifying and restoring load data of the power system. Background technique [0002] With the development of the power system, different types of intelligent measuring equipment are gradually applied in engineering practice, and the load power consumption in the power system will become easy to measure and perceive. Load data is one of the most important basic data of the power system. Whether it is accurate or not can directly affect the credibility of power system state estimation, load forecasting, distribution network optimization, and demand-side management. have a direct impact on operational decisions. However, in practice, due to unknown factors such as faults in measurement tables, faults in external communication lines, external interference, and occasional faults in user electrical equipment, the load data measured by the power system are not all reliable, and will ine...

AI Technical Summary

Problems solved by technology

The method of the present invention is based on the fuzzy C-means algorithm, uses the hill-climbing function method, and simultaneously determines the number of clusters and the cluster center to improve the clustering efficiency, and solves the randomness of determining the initial cluster center and the identification effect of bad data

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