摘要
基于分布式控制系统(DCS)设计高效、可靠的风电场有功功率控制系统,采用分层架构优化功率调度与实时控制。引入NNPC与MPC算法提升功率预测精度,结合边缘计算增强数据处理效率。系统采用光纤、5G、工业以太网等高可靠通信技术,实现毫秒级响应,并结合Spark Streaming与Kafka优化数据处理。研究表明,RMSE降至2.8%,功率偏差控制在±1.5%内,提升风机负载优化与电网适应性,为智能风电场建设提供了技术支持。
An efficient and reliable active power control system for wind farms was designed based on a Distributed Control System(DCS),where a hierarchical architecture was adopted to optimize power scheduling and real-time control.The precision of power prediction was enhanced by introducing Neural Network Predictive Control(NNPC)and Model Predictive Control(MPC)algorithms,while edge computing was integrated to improve data processing efficiency.High-reliability communication technologies,including optical fiber,5G,and industrial Ethernet,were employed in the system to achieve millisecond-level response times.Additionally,Spark Streaming and Kafka were utilized to optimize data processing.Research findings demonstrate that the Root Mean Square Error(RMSE)was reduced to 2.8%,with power deviations controlled within±1.5%.This improves wind turbine load optimization and grid adaptability,thereby providing technical support for the construction of intelligent wind farms.
作者
关兆
GUAN Zhao(Heilongjiang New Energy Co.,Ltd.,National Energy Group,Harbin,Heilongjiang 150000,China)
出处
《自动化应用》
2025年第15期146-148,共3页
Automation Application
