分工况下风电机组各变量相关性研究

doi:10.3969/j.issn.2097-0706.2022.12.007

摘要/Abstract

摘要：

随着发电技术的成熟和政府政策的支持，近年来海上风电产业飞速发展。为对风电机组状态进行更好的监控并预测可能出现的大功率，研究了风电机组多个变量间的相关性。首先在全工况下采集广东某海上风电场数据采集与监视控制（SCADA）系统的数据，探究了该海上风电场风电机组重要相关状态变量的Pearson相关系数、Spearman相关系数和Copula熵，研究其在变量相关性描述方面的应用。然后，通过对相关状态变量散点图的直观观察和数理性分析，获得了三者在描述变量相关方面的一致性。考虑到不同工况对风电机组运行特性的影响及Copula熵在变量相关性研究方面的优越性，提出了一种基于K-means的工况辨识方法，根据风电机组运行特性构造出衍生状态特征——风速差分。分析在K-means算法划分工况下，各工况特征状态变量的相关性。分析结果表明，各工况下风电机组运行特性差异较大，特征变量相关性各不相同，需分情况进行分类分析。

Abstract:

Due to the support of government policies and the maturity of power generation technology， the offshore wind power industry has developed rapidly in recent years. In order to monitor the states of wind turbines and predict the possible high power， the correlations between wind turbine variables are studied. The SCADA system data of an offshore wind farm in Guangdong under all working conditions were collected firstly，to explore the Pearson correlation coefficient， Spearman rank correlation coefficient and Copula entropy of the important state variables of the offshore wind turbines and their applications in variable correlation description. By observing the scatter diagrams of the correlation state variables and analyzing the correlation coefficients numerically， the consistency of the three coefficients in describing the correlation of variables is obtained. Considering the influence of different operating conditions on wind turbine operating characteristics and the superiority of Copula entropy in variable correlation study， a working condition identification method based on K-means is proposed， and wind speed difference，a derived state variable varying with wind turbine operating characteristics，is presented. The correlations of characteristic state variables under working conditions classified by K-means algorithm are analyzed. The results show that the operating characteristics of wind turbines under various working conditions were greatly different， and the correlations of characteristic variables are varied， which should to be analyzed by case.

Key words: wind turbine, variable correlation, Copulas entropy, offshore wind power, clean energy

中图分类号:

TK89

崔双双, 孙单勋. 分工况下风电机组各变量相关性研究[J]. 综合智慧能源, 2022, 44(12): 49-55.

CUI Shuangshuang, SUN Shanxun. Study on the correlation of wind turbine variables under different conditions[J]. Integrated Intelligent Energy, 2022, 44(12): 49-55.

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状态参数	相关系数
状态参数	Pearson系数	Spearman系数	Copula熵
风速	0.964 072	0.987 202	0.994 263
风速差分	0.163 991	0.203 150	-0.080 865
轴箱温度	0.590 713	0.520 675	0.118 063
发电机绕组温度	0.962 151	0.946 291	1.152 315
桨叶角度	0.155 169	0.487 964	-0.088 160
扭缆角度	-0.245 722	-0.287 857	-0.064 423

变量	Copula熵
变量	工况1	工况2	工况3	工况4	工况5
风速	0.970 337	0.927 549	1.082 895	1.131 050	0.263 833
风速差分	-0.072 644	-0.032 224	-0.073 496	-0.083 175	-0.117 486
轴箱温度	-0.079 670	-0.091 030	0.151 742	0.086 197	-0.072 402
桨叶角度	-0.058 844	-0.083 841	-0.097 249	-0.111 900	0.232 762
扭缆角度	-0.078 972	-0.154 075	-0.121 333	-0.083 636	-0.093 983
发电机绕组温度	0.239 652	0.180 823	0.726 258	0.744 076	0.554 680