摘要
文章提出了一种基于人工智能的弹性配电网主动预防调度技术,结合长短期记忆网络(Long Short-Term Memory,LSTM)算法进行故障预测,并利用粒子群优化算法动态调整调度策略。实验结果表明,该方法在降低配电网运行风险的同时,提高了系统的稳定性和经济性。通过对不同场景的对比分析,证明了该方法在电网调度中的有效性和可行性。
This article proposes an active preventive scheduling technology for elastic distribution networks based on artificial intelligence.It combines the Long Short Term Memory(LSTM)algorithm for fault prediction and dynamically adjusts scheduling strategies using particle swarm optimization algorithm.The experimental results show that this method reduces the operational risks of the distribution network while improving the stability and economy of the system.Through comparative analysis of different scenarios,the effectiveness and feasibility of this method in power grid scheduling have been demonstrated.
作者
关润洁
刘崇林
GUAN Runjie;LIU Chonglin(Xingtai Urban Power supplyBranch,State Grid Hebei Electric Power Company,Xingtai,Hebei 054700,China;Weixian Power Supply Branch,State Grid Hebei Electric Power Company,Xingtai Hebei 054700,China)
出处
《长江信息通信》
2025年第5期114-116,共3页
Changjiang Information & Communications
关键词
人工智能
弹性配电网
调度优化
LSTM
粒子群优化算法
Artificial Intelligence
elastic distribution network
scheduling optimization
LSTM
Particle Swarm Optimization Algorithm
