Double-layer spectral clustering method of power load curve considering wavelet entropy dimensionality reduction

Abstract

The invention discloses a double-layer spectral clustering method of a power load curve considering wavelet entropy dimensionality reduction. The method comprises the following steps: obtaining dailyload data of power load, and forming a data set; segmenting the power load data in the data set into q intervals, calculating a wavelet entropy Sq of an original data set within the interval q, and comparing and evaluating a degree of fluctuation of the data according to the calculated wavelet entropy value and a wavelet entropy threshold, wherein the degree of fluctuation greater than the specified threshold is large, and on the contrary, the degree of fluctuation is relatively small; performing statistics on the load number in which the wavelet entropy value is greater than the wavelet entropy threshold within the interval q, and calculating the proportion of the load data to the total load of the power load; dividing the interval with the proportion being greater than the threshold intotwo segments, calculating the wavelet entropy value again, and comparing and evaluating the degree of fluctuation of the data until the proportion of the load with large degree of fluctuation withinthe interval in all load is less than the threshold or the points in the interval cannot be divided equally, and obtaining load curve data with a variable time resolution; and performing double-layerspectral clustering to obtain refined load clusters with similar forms.

Description

technical field [0001] The invention relates to the technical field of power systems, in particular to a double-layer spectrum clustering method for power load curves considering wavelet entropy dimension reduction. Background technique [0002] The construction of the Energy Internet has promoted the development of big data on power distribution and consumption. The continuous accumulation of these energy-consuming data has brought certain difficulties to the implementation of demand response. The massive data of the power grid makes simple statistical analysis of load characteristics meaningless. , it is more unrealistic to adopt different control strategies for each load. It is necessary to classify the loads participating in demand response. The traditional classification method is to divide according to typical industries. However, the load curves of power users in the same industry may vary greatly , the load curves of power loads in different industries may also be ve...

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Problems solved by technology

[0006] In order to solve the deficiencies of the prior art, the present invention provides a two-layer spectrum clustering method for power load curves that takes into account wavelet entropy dimensionality reduction. The present invention overcomes the problems that existing algorithms cannot adapt to the requirements of different demand responses and the data dimension is too high. Realize effective clustering of demand response-oriented user load data, with fast calculation speed, high clustering effectiveness and good algorithm stability

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