电力系统自动化2025，Vol.49Issue(12)：79-90,12.DOI:10.7500/AEPS20240717004
数据驱动型振荡模式预测方案及谐振抑制分析
Data-driven Oscillation Mode Prediction Scheme and Resonance Mitigation Analysis
摘要
Abstract
To assess the small-signal stability of new power system rapidly and mitigate potential mode resonances,a data-driven oscillation mode prediction(DOMP)scheme based on measurement signals and operation scenario information is proposed.First,based on system oscillation modes of the multi-channel measurement signal identification system in historical operating scenarios,the issue of data sources during model training is resolved.Then,based on deep extreme learning machine algorithm,a system oscillation mode prediction model is established,which takes scenario information from historical data as input and identification results of oscillation modes as output to train and evaluate the accuracy of the prediction model,thereby improving the prediction accuracy of the DOMP scheme.Based on the mode prediction results of DOMP,the system control parameters are optimized to avoid mode resonances during operation,and improve the small-signal stability of the system.Through the IEEE 39-bus model,it is verified that the proposed DOMP scheme can quickly and accurately predict the oscillation mode of the system in future scenarios based on scenario information,and then mitigate the mode resonance generated during system operation through parameter optimization to improve system stability.关键词
振荡模式/预测/模型训练/小干扰稳定性/数据驱动/深度极限学习机/谐振抑制Key words
oscillation mode/prediction/model training/small-signal stability/data-driven/deep extreme learning machine/resonance mitigation引用本文复制引用
丁炅,朱介北,张淼,边翊楠,俞露杰,贾宏杰..数据驱动型振荡模式预测方案及谐振抑制分析[J].电力系统自动化,2025,49(12):79-90,12.基金项目
国家自然科学基金联合基金项目资助(U23B20123). This work is supported by National Natural Science Foundation of China(No.U23B20123). （U23B20123）
