Effective index FCM and RBF neural network-based substation load characteristic categorization method

Abstract

The invention discloses an effective index FCM and RBF neural network-based substation load characteristic categorization method. The method comprises the following steps that: load constituent ratios of a substation are adopted as characteristic vectors of load characteristic categorization of the substation; clustering analysis is performed on data samples of the load constituent ratios of the substation through using a fuzzy clustering analysis method so as to obtain data categorization results under different numbers of clusters, and an optimal number of clusters is determined through three kinds of clustering effect evaluation indexes, and a fuzzy subordination degree matrix and the clustering center of each category of under the optimal number of clusters are obtained; one group of samples are selected in each clustering category according to a principle of minimum distance, and category numbers corresponding to each group of samples are set, such that a training sample set is formed; a substation load characteristic secondary categorization model is established through adopting an RBF neural network, and the formed training sample set is utilized to train the neural network, and the trained neural network is further utilized to realize the load characteristic categorization of the substation. The effective index FCM and RBF neural network-based substation load characteristic categorization method of the invention has the advantages of simple operation and high accuracy.

Description

technical field [0001] The invention relates to the technical field of load modeling, in particular to a substation load characteristic classification method based on effective indexes FCM and RBF neural network. Background technique [0002] With the continuous development of the social economy and the continuous improvement of the level of science and technology, the scale of the power grid continues to expand, and the structure of the power system becomes increasingly complex, which puts forward higher and higher requirements for the safe, stable and reliable operation of the power system. Therefore, the establishment of an accurate reflection The real-time load model of the entire grid load is very important. [0003] Since the electric load shows regional dispersion in space and random time-varying in time, in order to accurately reflect the load characteristics, it is necessary to establish a large number of complex comprehensive load models. However, if the number of...

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Problems solved by technology

However, if the number of comprehensive load models adopted by the same power grid is too large and the form is too complicated, it will be difficult to have practical value in the engineering field.

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