基于CA-WOA-BP算法的调度数据网鲁棒性预测  

Robustness Prediction of Dispatching Data Network Based on CA-WOA-BP Algorithm

作　　者：陈斌 李泽科 余斯航 郭久煜 林碧海 刘延华[3] CHEN Bin;LI Zeke;YU Sihang;GUO Jiuyu;LIN Bihai;LIU Yanhua(State Grid Fujian Electric Power Co.,Ltd.,Fuzhou 350003,China;Electric Power Research Institute of State Grid Fujian Electric Power Co.,Ltd.,Fuzhou 350007,China;College of Computer and Data Science,Fuzhou University,Fuzhou 350108,China)

机构地区：[1]国网福建省电力有限公司,福州350003 [2]国网福建省电力有限公司电力科学研究院,福州350007 [3]福州大学计算机与大数据学院,福州350108

出　　处：《南方电网技术》2025年第2期10-18,共9页Southern Power System Technology

基　　金：国家自然科学基金资助项目(62072109);国网福建省电力有限公司科技项目(ERP.52130021004Q)。

摘　　要：对电力网络鲁棒性进行评估与预测,有利于网络管理人员感知网络系统运行现状,及时采取措施应对可能的风险。为此提出了一种基于改进鲸鱼优化算法的电力调度数据网鲁棒性预测模型。首先,构建了电力调度数据网鲁棒性指标体系,并采用字段提取及公式映射等方法,实现了面向指标体系的数据降维处理;此外,进一步研究了基于混沌映射与自适应权重的WOA-BP改进算法(CA-WOA-BP),实现了电力网络鲁棒性预测方法。实验结果表明,与WOABP算法相比,所提出的改进算法加快了预测模型的收敛速度,并克服了陷入局部最优的情况,同时将预测值误差百分比降低了5.3%,有助于用户更准确及时地感知电力调度数据网系统鲁棒性的态势。Evaluation and prediction of power network system robustness are conducive to system managers'ability to perceive the current status of network system operations and take timely measures to cope with potential risks.Therefore,a robustness prediction model for power dispatching data network is proposed based on an improved whale optimization algorithm.Firstly,an evaluation index system for the robustness of the power dispatching data network is constructed and data dimensionality reduction processing is designed based on methods such as field extraction and formula mapping.Moreover,an improved chaotic mapping and adaptive weight-whale optimization algorithm(CA-WOA-BP)is proposed based on chaotic mapping and adaptive weights(WOA-BP)and a power network robustness prediction method is established.Experimental results show that compared with the WOA-BP algorithm,the proposed improved algorithm speeds up the convergence of the prediction model while overcoming the situation of falling into local optima and reduces the absolute error percentage of the predicted values by 5.3%,which helps users to discover the robustness of power dispatching data network systems more accurately and timely.

关 键 词：电力调度数据网 鲸鱼优化算法 混沌映射 自适应权重 网络鲁棒性预测 

分 类 号：TM732[电气工程—电力系统及自动化]