Power load SOM-FCM hierarchical clustering method

Abstract

The invention discloses a power load SOM-FCM hierarchical clustering method based on load fluctuation feature extraction. The power load SOM-FCM hierarchical clustering method comprises the followingsteps: S1, taking power load active time series data as an input, preprocessing the load data, and obtaining a volatility feature vector; S2, performing first layer coarse clustering on the volatilityfeature vector obtained in the step S1 by using a self-organizing map neural network (SOM) clustering algorithm, and obtaining the clustering result and a weight matrix; and S3, using the coarse clustering result in the step S2 as an input of a self-adaptive fuzzy C-means algorithm (FCM), adding effective function judgment, and finally obtaining the clustering result with the optimal clustering number. The invention adaptively clusters the power load by extracting the load fluctuation feature parameters, solves the clustering problem in the case that the electric load composition is complicated and the number is large, clusters the load from the load curve volatility itself, can easily obtain the analysis data, is simple in calculation, and is easy to transplant.

Description

technical field [0001] The invention relates to the technical field of power system load analysis, in particular to a SOM-FCM hierarchical clustering method for electric loads based on load fluctuation feature extraction. Background technique [0002] For a high proportion of new energy connected to the grid, load participation in frequency regulation and voltage regulation is an important way to improve energy consumption capacity. However, with the rapid development of my country's economy, the grid load is becoming more and more complex, showing various fluctuations. Therefore, when electric loads are included in the demand-side response system, it should also be fully considered that different types of loads are aggregated according to the fluctuation characteristics. On the one hand, it can facilitate the coordinated control of active and reactive power for load fluctuations; The adjustment potential of load participation in demand side response is further excavated fr...

AI Technical Summary

Problems solved by technology

The present invention adopts the SOM neural network to carry out the first-layer rough classification of the fluctuations of different loads, which reduces the dimension of the input space, reduces the input scale of the FCM algorithm, and accelerates the convergence speed. The most appropriate number of clusters effectively solves the problems of large number of samples, unclear number of categories, large amount of calculation, and inaccurate calculation results when clustering electric loads

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