Method for establishing electrical power system clustering load model

Abstract

The invention discloses a method for establishing an electrical power system clustering load model. Timing sequence load curves are firstly sequenced and load duration time curves are obtained, then according to contribution degree of a load level to adequacy index, the load duration curves are divided into three subareas, namely, a high contribution degree, a medium contribution degree and a lower contribution degree, if the subarea is the high contribution degree area, a hierarchical clustering algorithm is adopted to select a clustering center initial value for the high contribution degree area, if the subarea is the medium contribution degree area, a mean value-standard deviation method is adopted to select a clustering center initial value for the medium contribution degree area, and if the subarea is the lower contribution degree area, a clustering center initial value is confirmed according to experience or is confirmed in a random mode for the lower contribution degree area. Improved efficiency index is defined, improved efficiency is regarded as a convergence condition, and then a clustering number in a K-mean clustering algorithm is confirmed. The clustering load model obtained through the above method has high computational accuracy and rapid convergence properties when used in power system adequacy evaluation.

Description

technical field [0001] The invention belongs to the technical field of power system reliability evaluation, and in particular relates to a method for establishing a power system cluster load model. Background technique [0002] The adequacy assessment of the power system can provide a reference for the development planning of the power system and the operation scheduling of the system. According to the different methods of system state extraction, power system adequacy assessment algorithms can be divided into analytical method, state sampling method and state duration sampling method. Among them, the state duration sampling method directly uses the annual time-series load curve as the load model, while the analytical method and the state sampling method use the multi-level load model. The accuracy of the multi-level load model will directly affect the accuracy of the reliability evaluation results. [0003] When forming the cluster load model in the power system adequacy a...

AI Technical Summary

Problems solved by technology

The research results show that if one of the improved algorithms is used alone to establish a clustering model of the annual time-series load of the power system, the resulting clustering load model is not accurate enough when used to evaluate the adequacy of the power system

Although the K-means clustering algorithm based on hierarchical clustering can accurately cluster the load of the high-level part, it also performs a detailed but almost useless clustering of the load of the low-level part, and the actual power load model Usually, the load level of 8760 hours in a year is used as the original data, and the clustering calculation of the 8760 hours of load using the hierarchical clustering-based K-means clustering algorithm will consume a lot of computing time

However, the K-means clustering algorithm based on the mean-standard deviation handles scattered load points roughly when establishing the initial cluster center, and cannot accurately cluster relatively sparse high-level loads or low-level loads, which are high The rough clustering of the load level part will lead to large errors in the calculation results

Aiming at the selection of the number of clusters, the article "Clustering Load Model Based on Dichotomy and Its Application in Power System Reliability Evaluation" in "Power System Protection and Control", Volume 39, Issue 10, 2011, proposes to use the The method is used to quickly determine the number of clusters, and the significance level is used as the evaluation standard of the clustering scheme. It does not take into account the influence of the number of clusters on the calculation speed of the power system adequacy assessment, so the number of clusters obtained is too large, which is not enough. Facilitate quick calculation of adequacy indicators

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