基于RReliefF-BP网络的新型电力系统静态电压稳定在线评估方法

Online Assessment Method for Static Voltage Stability of New Power System Based on RReliefF-BP Network

新型电力系统建设逐步成形,源荷分离趋势愈发显著的同时其随机性也逐渐加剧,电压稳定问题日益突出。新形势下,电网迫切需要一种准确度高、响应速度快、拓展性好的电压稳定评估手段。将静态电压稳定评估问题定义为回归问题,构建人工神经网络进行在线评估。首先通过场景模拟、潮流计算和局部电压稳定指标计算获得训练样本集;然后通过RReliefF方法进行特征排序,剔除权重较低的属性,提升训练效率;接着运用人工神经网络训练得到各关键特征量与电压稳定的映射关系;最后以修改的IEEE39节点系统为例,设置6组对照实验,引入简单线性加权法,计算出关于模型的速度和准确度的综合评价指标,进一步验证所提方法有较为理想的建模速度和较高的准确度,能够应对新形势下的电力系统电压稳定评估要求。

As the construction of new power system gradually takes shape,the trend of source-load separation is becoming more and more obvious and its randomness is increasing,making the problem of voltage stability increasingly prominent.Under the new circumstances,the grid urgently needs a voltage stability assessment method with high accuracy,fast response speed and good extensibility.The static voltage stability assessment problem is defined as regression problem and artificial neural network is constructed to assess the problem online.Firstly,the training sample set is obtained by scenario simulation,power flow calculation and local voltage stability index calculation.Then the RReliefF method is used to sort the features and eliminate the attributes with low weight to improve the training efficiency.Then the mapping relationship between key features and voltage stability is obtained by artificial neural network training.Finally,taking the modified IEEE39-node system as an example,six groups of experiments are set and a simple linear weighting method is introduced to calculate a comprehensive evaluation index about the speed and accuracy of the model to verify that the proposed method has ideal modeling speed and high accuracy,and can meet the requirements of voltage stability assessment of power system under the new situation.