摘要
电力系统暂态态势感知是指通过实时监测电力系统的电压、电流、频率等参数,对电力系统发生的暂态过程进行实时感知和分析,以及预测和评估电力系统的动态状态。提出基于AP聚类算法的电力系统暂态态势感知方法,获取电力系统历史数据中的量测信息和运行信息,用Huber函数对所获取的历史数据进行增强处理。根据所得到的数据估计电力系统状态,结合状态估计结果对电力系统特征电压残差矩阵做归一化处理,得到协方差矩阵,协方差矩阵经过变换后推导出电力系统暂态态势感知样本。结合态势感知样本与AP聚类算法对任务样本和可用样本进行交互与更新,从而确定观测样本所属样本类别,样本类别包括电压骤变、电压暂升、电流骤增、频率骤减等。实验结果表明,所提方法可精准感知到电力系统中的异常信息,电压幅值和电压相角绝对误差值最小为0.001%,并能精准感知到第8个节点的异常情况,感知结果具有可靠性。
Power system transient situation awareness refers to the real-time monitoring of voltage,current,frequency and other parameters of the power system,the real-time awareness and analysis of transient processes occurring in the power system,and the prediction and evaluation of the dynamic state of the power system.This paper proposes a power system transient situation awareness method based on affinity propagation(AP)clustering algorithm to obtain measurement and operational information from the historical data of the power system.The Huber function is used to enhance the obtained historical data.The power system state is estimated based on the obtained data,and the characteristic voltage residual matrix of the power system is normalized by the state estimation results to obtain the covariance matrix.After transformation,the power system transient situation awareness samples is derived from the covariance matrix.By combining situational awareness samples with AP clustering algorithm,task samples and available samples are interacted and updated to determine the sample category to which the observation samples belong.The sample categories including voltage swell,voltage sag,current sag,frequency sag,etc.The testing results show that the proposed method can accurately perceive abnormal information in the power system,with a minimum absolute error value of 0.001%for voltage amplitude and voltage phase angle.It can also accurately perceive the abnormal situation of the 8th node,and the perception results are reliable.
作者
王伟
WANG Wei(Power Energy Division,CISDI Electric Technology Co.,Ltd.,Chongqing 400013,China)
出处
《微型电脑应用》
2025年第7期47-50,55,共5页
Microcomputer Applications
基金
重庆科技厅科技攻关项目(cstc2021jcyjA0352)。
