摘要
研究提出基于边缘智能计算的风电场双重预控与风险预警方法,通过构建分层系统架构整合边缘节点实时处理与云端协同决策。技术预控使设备故障预测准确率达到89.7%,管理预控使运维成本大幅降低;风险预警模型预测准确度达92.3%,响应时间缩短至1.2 s。实证表明,该方法降低了85.2%故障发生率,在极端天气事件中成功避免了重大设备损失,为风电场安全高效运行提供了有效支撑。
The study proposes a dual pre-control and risk warning method for wind farm power stations based on edge intelligent computing,integrating real-time processing at edge nodes and collaborative decision-making in the cloud through a hierarchical system architecture.Technical pre-control achieves an equipment failure prediction accuracy of 89.7%,while management pre-control significantly reduces operational costs.The risk warning model attains a prediction accuracy of 92.3%,with response time shortened to 1.2 seconds.Empirical results demonstrate that this method reduces fault occurrence by 85.2%,successfully prevents major equipment losses during extreme weather events,and provides effective support for the safe and efficient operation of wind farms.
出处
《电力系统装备》
2025年第12期153-155,共3页
Electric Power System Equipment
关键词
边缘智能计算
风电场
双重预控
风险预警
故障预测
edge intelligence
wind farm
dual pre-control
risk early warning
fault prediction
