电力工程技术2026，Vol.45Issue(5)：15-26,12.DOI:10.12158/j.2096-3203.2026.05.002
基于深度学习的含风电不确定性的暂态稳定约束最优潮流
Deep learning-based transient stability constrained optimal power flow with wind power uncertainty
摘要
Abstract
A deep learning-based transient stability constrained optimal power flow(TSCOPF)model with wind power uncertainty is proposed to address stability challenges faced by traditional TSCOPF models in power systems with increasing renewable energy integration.A cascaded prediction method combining complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN),empirical wavelet transform(EWT),and long short-term memory(LSTM)networks is firstly developed to enhance wind power prediction accuracy and robustness.An improved multi-layer perceptron(IMLP)model is then employed to establish the mapping relationship between system operating states and transient stability index(TSI)for rapid and accurate transient stability assessment.An improved weIghted mean of vectors(IINFO)algorithm based on Lévy flight is subsequently adopted to solve the TSCOPF optimization problem.Simulation experiments are finally conducted on modified IEEE 39-bus and IEEE 68-bus test systems.Results demonstrate that the proposed cascaded method achieves superior prediction performance compared to traditional methods in wind power forecasting.The proposed TSCOPF model is verified to maintain stable system operation under wind power integration conditions.The improved IINFO algorithm exhibits significantly faster convergence speed and lower optimization costs than other optimization algorithms.关键词
风电不确定性/电力系统/暂态稳定约束最优潮流(TSCOPF)/自适应噪声的完全集合经验模态分解(CEEMDAN)/改进多层感知器(IMLP)/改进向量加权平均(IINFO)算法分类
信息技术与安全科学引用本文复制引用
刘颂凯,成思鋙,苏攀,李彦彰,秦浩,陈常贺..基于深度学习的含风电不确定性的暂态稳定约束最优潮流[J].电力工程技术,2026,45(5):15-26,12.基金项目
国家自然科学基金资助项目(52407118) （52407118）
