摘要： 为提高在负荷波动性较大场景下对异常负荷判别的适应性，提出一种适用于电网异常负荷动态判别的卷积神经网络阈值模型。利用时序历史负荷数据训练卷积神经网络模型进行负荷预测，并根据预测负荷值计算电网未来的状态变量数据，通过该状态变量数据源矩阵的构造，依次构建其窗口矩阵、标准矩阵以及样本协方差矩阵，进而设定基于样本协方差矩阵最大特征值的动态阈值，利用该阈值对当前时刻的最大特征值进行越限判定，实现对电网异常负荷的动态判别。借助Matlab R2014a和PST软件工具，在IEEE50机145母线标准系统中进行仿真测试，结果表明，与传统阈值模型相比，该阈值模型在动态电网中对MESCM指标的异常判定适应性更强、准确性更高。

关键词: 卷积神经网络, 动态阈值, 负荷预测, 样本协方差矩阵, 最大特征值, 异常负荷动态判别

Abstract: In order to improve the adaptability to abnormal load judgment in scenarios where loads fluctuate greatly,this paper proposes a threshold model with convolutional neural network suitable for the dynamic judgment of abnormal load of power grid.The Convolutional Neural Network(CNN) model is trained with historical load data in time series for load prediction,and based on the predicted loads,the future state variable data in power grid can be calculated.Based on the construction of the state variable data source matrix,its window matrix,standard matrix and sample covariance matrix are subsequently obtained.Then the dynamic threshold based on the maximum eigenvalue of the sample covariance matrix is set,and the threshold is used for the overdue judgment of the maximum eigenvalue at the current time,so as to implement dynamic judgment of abnormal load in power grid.With the help of software tools including Matlab R2014a and PST,simulation tests are performed on the IEEE50 machine 145 bus standard system.Results show that compared with the traditional threshold model,the proposed threshold model is more adaptable and accurate for the judgment of abnormal MESCM indicators in dynamic power grid.

Key words: Convolutional Neural Network(CNN), dynamic threshold, load forecasting, sample covariance matrix, maximum eigenvalue, dynamic judgment of abnormal load

中图分类号: 

• TP391