基于梯度提升决策树静态电压稳定裕度评估

肖繁; 王涛; 饶渝泽; 张宇帆; 艾芊; 周友斌

肖繁,王涛,饶渝泽,张宇帆,艾芊,周友斌.基于梯度提升决策树静态电压稳定裕度评估[J].电测与仪表,2020,57(20):39-45.
Xiao Fan,Wang Tao,Rao Yuze,Zhang Yufan,Ai Qian,Zhou Youbin.Static Voltage Stability Margin Prediction based on Gradient Boosting Regression Tree[J].Electrical Measurement & Instrumentation,2020,57(20):39-45.

基于梯度提升决策树静态电压稳定裕度评估

Static Voltage Stability Margin Prediction based on Gradient Boosting Regression Tree

DOI：10.19753/j.issn1001-1390.2020.20.006

中文关键词: 梯度提升决策树静态电压稳定裕度最优潮流无功补偿测量不确定性 PMU布点

英文关键词:gradient boosting regression tree, static voltage stability margin, optimal power flow, reactive power compensation, measurement uncertainty, PMU placement

基金项目:国家电网公司科技项目(52153218000H)

作者中文名	作者英文名	单位
肖繁	Xiao Fan	国网湖北省电力有限公司电力科学研究院
王涛	Wang Tao	国网湖北省电力有限公司电力科学研究院
饶渝泽	Rao Yuze	国网湖北省电力有限公司电力科学研究院
张宇帆	Zhang Yufan	上海交通大学电子信息与电气工程学院
艾芊	Ai Qian	上海交通大学电子信息与电气工程学院
周友斌	Zhou Youbin	国网湖北省电力有限公司电力科学研究院

摘要点击次数: 2050

中文摘要:

随着特高压直流输电的发展,电网负荷集中地区无功缺乏的问题日益突出,系统电压稳定与无功功率密切相关,有必要研究电压稳定裕度的在线监测问题,以期为无功补偿装置的协调配合提供参考。基于PMU提供的数据资源,提出一种基于梯度提升决策树的电压稳定裕度在线监测方法,该方法分为离线运行点数据库的建立以及实时评估两部分。通过模拟负荷的变化,求解最优潮流模型,得到各个发电机出力状态以及对应的电压稳定裕度从而形成离线运行点数据库,并在线下对梯度提升决策树进行训练,在线做出电压稳定裕度的评估。实验结果表明,所提方法对裕度的预测准确度高于基于支持向量机回归模型,且时间可以满足实时性的要求。在测量不确定性场景下,当信噪比大于40dB时,所提方法应对噪声干扰具有较强的鲁棒性。并且基于梯度提升决策树的特征选择能力,可以为PMU的布点提供一定依据,从而实现系统可靠性和经济性的平衡。

英文摘要:

With the development of UHVDC transmission, the problem of reactive power shortage in load concentration areas is becoming prominent. The system voltage stability is closely related to reactive power. It is necessary to study the online voltage stability margin prediction in order to provide basis for reactive power compensation. Based on the data resources provided by PMU, an online method for voltage stability margin prediction based on gradient boosting regression tree (GBRT) is proposed. The method is divided into two parts: the establishment of offline operation points database and real-time evaluation. By simulating the load change, the optimal power flow model is solved, and the output state of each generator and the corresponding voltage stability margin are obtained, and the GBRT is trained offline. The experimental results show that the proposed method has higher prediction accuracy than support vector machine, and the time can meet the requirements of real-time prediction. Under the measurement uncertainty, when the signal-to-noise ratio is greater than 40dB, the proposed method is robust to measurement uncertainty. And based on the feature selection ability of the GBRT, it can provide a basis for the placement of the PMU, thereby achieving a balance between system reliability and economics.

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