摘要
随着智能电网的快速发展,电力调度系统面临海量多源异构数据处理与分析的挑战。针对传统调度方法难以适应新型电网特点的问题,建立了基于数据挖掘与机器学习的调度数据分析模型,开发了包括发电计划优化、网络潮流优化和备用容量优化的调度优化算法。实验数据显示,该方案在电网安全评估、风险预警和调度决策支持等方面取得显著成效,负荷预测准确率提升15%,系统响应时间缩短30%,电网调度效率提高20%。同时,优化后的调度方案能够有效降低系统运行成本,提高电网运行可靠性,为智能电网调度技术的发展提供了新思路。
With the rapid development of smart grids,power dispatch systems are facing the challenge of processing and analyzing massive amounts of heterogeneous data from multiple sources.Aiming at the problem that traditional scheduling methods are difficult to adapt to the characteristics of the new power grid,a scheduling data analysis model based on data mining and machine learning is established,and scheduling optimization algorithms including power generation plan optimization,network flow optimization,and reserve capacity optimization were developed.Experimental data shows that this scheme has achieved significant results in power grid safety assessment,risk warning,and dispatch decision support,with a 15%increase in load forecasting accuracy,a 30%reduction in system response time,and a 20%increase in power grid dispatch efficiency.Meanwhile,the optimized scheduling scheme can effectively reduce system operating costs,improve the reliability of power grid operation,and provide new ideas for the development of smart grid scheduling technology.
作者
白冰
张瑞鑫
BAI Bing;ZHANG Ruixin(Ulanqab Power Supply Branch,Inner Mongolia Electric Power(Group)Co.,Ltd.,Ulanqab,Inner Mongolia 012000,China)
关键词
智能电网
电力调度
数据分析
负荷预测
深度学习
smart grid
electric power dispatching
data analysis
load forecasting
deep learning
