摘要
风电功率预测对于风电场和电网的安全可靠运行具有重要意义。以某风力发电机为研究对象,根据该风机历史天气信息和风电功率数据,使用遗传算法改进BP神经网络,构建复合型神经网络的风电功率预测系统。运用MATLAB软件对算法进行编程与仿真,仿真结果表明,单一的BP神经网络预测系统波动性较高,精度不足,而复合型的神经网络算法有效地解决了这一问题,改进后的预测系统精度较高、稳定性较强,满足工业生产需求。
Wind power prediction is of great significance for the safe and reliable operation of wind farms and power system.Taking a wind turbine as the research object,according to the historical weather information and power generation data of the turbine,the BP neural network was improved by genetic algorithm,and a composite neural network wind power prediction system was constructed.The arithmetic was programmed and simulated by MATLAB.The simulation results show that the single BP neural network prediction system has high fluctuation and insufficient precision,however,the compound neural network algorithm effectively solves this problem.The improved prediction system has high accuracy and stability,and meets the requirements of industrial production.
作者
王冰冰
赵天乐
WANG Bing-bing;ZHAO Tian-le(School of Automation,Nanjing University of Science and Technology,Nanjing 210094,China)
出处
《电工电气》
2019年第12期16-21,共6页
Electrotechnics Electric
关键词
风电
功率预测
BP神经网络
遗传算法
wind power
power prediction
BP neural network
genetic algorithm
