摘要
随着“双碳”目标的提出,高比例可再生能源和高比例电力电子设备正成为电力系统发展的重要趋势和关键特征,其间歇性、不确定性使电力系统实时运行状态辨识面临严峻挑战。为此,该文提出一种基于粒子群优化和卷积神经网络(particle swarm optimization and convolutional neural network,PSO-CNN)的高精度电力系统实时运行状态辨识方法。首先,该方法同时考虑电力系统安全域与稳定域下的暂态问题,适用于暂态稳定故障前、故障中及故障后多场景的电力系统运行状态辨识。其次,为确保样本数据中新能源机组出力方式的全面性,采用拉丁超立方抽样方法对精细化仿真数据采样,考虑到实际电力系统中存在状态类别极端不平衡问题,引入PSO算法调节模型不同类别损失函数权重以提高模型对极端不均衡样本的辨识效果。最后,分别在IEEE39节点系统及某省级电网系统中对所提方法进行评估,实验结果证明了所提状态辨识方法的有效性及鲁棒性。
With the proposal of the"dual-carbon"goal,there has become an important trend of the high penetration access of the renewable energy and the wide applications of the power electronic equipment.However,their intermittences and uncertainties are posing severe challenges to the real-time operation state identification of the power system.A novel method of the real-time operation state identification for a electric power system based on the particle swarm optimization and the convolutional neural network(PSO-CNN)is proposed.Firstly,considering the transient problems under both the safety domain and the stability domain of the power system,this method is suitable for the identification of a power system operation status in multiple scenarios before,during and after a transient stable fault.Secondly,in order to ensure the comprehensiveness of the output modes of the new energy unit in the sample data,the Latin supercube sampling is used to select the refined simulation data.Regarding of the problem of extreme imbalance in the sample classification in the actual data and simulation data of the power system,the PSO algorithm is used to adjust the loss function weights for different status classifications to improve the state identification effect.Finally,taking the IEEE39 system and an actual provincial power grid system as examples,the effectiveness and robustness of the proposed state identification method is verified.
作者
杨晶
赵津蔓
孟润泉
张东霞
李柏堉
武宇翔
YANG Jing;ZHAO Jinman;MENG Runquan;ZHANG Dongxia;LI Baiyu;WU Yuxiang(Shanxi Key Lab of Power System Operation and Control(Taiyuan University of Technology),Taiyuan 030024,Shanxi Province,China;China Electric Power Research Institute,Haidian District,Beijing 100192,China)
出处
《电网技术》
EI
CSCD
北大核心
2024年第1期315-324,共10页
Power System Technology
关键词
电力系统运行状态辨识
粒子群优化算法
深度学习
power system operation state identification
particle swarm optimization algorithm
deep learning
