Wind and Photovoltaic Generation Scene Division Based on Improved K-means Clustering

doi:10.12096/j.2096-4528.pgt.19090

Abstract

Abstract:

In view of the uncertainty of power generation in renewable energy, especially wind power and photovoltaic power generation, the improved K-means clustering method was used to segment the state of power generation. Firstly, the uncertainty model of wind power and photovoltaic power generation was established, and the appropriate probability density function was used to fit. Then the basic K-means clustering algorithm was improved by combining density clustering and proposed hybrid evaluation function, to solve the problem that the initial clustering center and the number of clusters were difficult to select. The improved K-means clustering was used to cluster the wind and photovoltaic scenes in a certain place, thus transforming the uncertainty problem into a deterministic problem. Finally, the practicability of the proposed method was verified by analyzing an example of scenario division.

Key words: wind power generation, photovoltaic power generation, density clustering, K-means clustering, scenario division

CLC Number:

TK81
TM615

Xuewei SONG, Yuyao LIU. Wind and Photovoltaic Generation Scene Division Based on Improved K-means Clustering[J]. Power Generation Technology, 2020, 41(6): 625-630.

Figures/Tables 6

Fig.1 Basic K-means clustering process

Fig.2 Improved K-means clustering algorithm flow

Tab. 1 Comparison of indicators before and after K-means clustering improvement

指标	CH(+)	DB(-)	迭代次数	时间/s
K-means聚类	4 988.72	2.031 7	18	46
改进K-means聚类	5 366.87	1.726 3	6	22

Tab. 2 Typical day scene probability

场景	概率
1	0.076 8
2	0.052 1
3	0.169 8
4	0.027 4
5	0.257 4
6	0.252 0
7	0.104 1
8	0.060 4

Fig.3 Typical day scene 5

Fig.4 Typical day scene 6

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