基于BP神经网络的输电线路舞动预警方法

An early warning method of transmission line galloping based on BP neural network

为了保证在覆冰舞动环境下输电线路的正常运维,将输电线舞动预警问题归结为有监督机器学习方法下的分类预测问题,提出了一种基于BP神经网络的舞动预警方法。通过分析影响舞动的外界气象因素,构建了以风速、风向与线路的夹角、相对湿度以及温度为输入特征量的BP神经网络学习算法,判断是否达到易舞气象条件预测输电线的舞动情况,并采用评价指标评估其预警性能,以便进行模型改进。采用河南电网舞动相关历史数据进行算例分析,验证了所提方法的有效性和实用性。输出的预警结果可为电网运维人员合理制定调度决策提供支持,保证电网安全迎峰度冬。

In order to guarantee the normal operation and maintenance of the transmission line under the icing and galloping conditions, a novel early warning method of transmission line galloping based on BP neural network is proposed through treating it as a problem of classification and prediction under supervised machine learning. Through analyzing the external meteorological factors that influencing galloping, a BP neural network learning algorithm is established by taking wind, inducted angle of wind direction and line, relative humidity, and ambient temperature as input vectors. The galloping probability is predicted by judging whether the prone-galloping weather conditions are satisfied utilizing the proposed method, and its prediction performance is assessed through several test indexes with the purpose of improvement. A case study is presented by adopting historical galloping data of Henan power grid, and the result shows that the proposed method is effective and practical, which can provide support for power system operation staffs to make reasonable decisions as well as ensure the power grid securely tiding over the peak-load during winter.