基于新型电力系统信息模型的重点区域高压输电线路损毁态势感知预警方法研究

Research on awareness warning method for damage situation of key regional high voltage transmission lines based on novel power system information model

由于高压输电线路通常具有复杂的结构和特性,受到自然灾害、设备故障等影响,使得数据采集难度加大,且存在较多噪声,导致线路损毁态势感知预警结果不理想。为此,提出一种基于新型电力系统信息模型的重点区域高压输电线路损毁态势感知预警方法。文章采集重点区域高压输电线路数据,采用最大重叠离散小波变换(maximum overlapping discrete wavelet transform,MODWT)算法滤波处理重点区域高压输电线路数据。采用混合递阶遗传算法优化处理径向基函数(radial basis function,RBF)神经网络结构和参数,将经过滤波处理的重点区域高压输电线路数据作为RBF神经网络的输入,实现重点区域高压输电线路损毁态势感知。通过实验结果表明,所提方法获取的重点区域高压输电线路损毁态势感知预警结果更加符合实际情况,可以为重点区域高压输电线路稳定运行提供有力的支持。

Due to the complex structure and characteristics of high-voltage transmission lines,which are affected by natural disasters and equipment failures,it is more difficult to collect data and there is a lot of noise,which leads to unsatisfactory situation awareness warning results of line damage.Therefore,a novel power system information model based on the key region of high-voltage transmission line damage situation awareness warning method is proposed.The collected high-voltage transmission line data in key areas are processed by maximum overlapping discrete wavelet transform(MODWT)algorithm.A hybrid hierarchical genetic algorithm is used to optimize the structure and parameters of radial basis function(RBF)neural network,and the filtered high-voltage transmission line data in key areas is taken as the input of RBF neural network to realize the damage situation awareness of high-voltage transmission lines in key areas.The experimental results show that the situation awareness warning results obtained by the proposed method are more consistent with the actual situation,and can provide strong support for the stable operation of HVDC transmission lines in key areas.