文章摘要
丁明,张超,王勃,等.基于功率波动过程的风电功率短期预测及误差修正[J].电力系统自动化,2019,43(3):2-9. DOI: 10.7500/AEPS20180322011.
DING Ming,ZHANG Chao,WANG Bo, et al.Short-term Forecasting and Error Correction of Wind Power Based on Power Fluctuation Process[J].Automation of Electric Power Systems,2019,43(3):2-9. DOI: 10.7500/AEPS20180322011.
基于功率波动过程的风电功率短期预测及误差修正
Short-term Forecasting and Error Correction of Wind Power Based on Power Fluctuation Process
DOI:10.7500/AEPS20180322011
关键词: 风电功率预测  波动特性  神经网络  引力搜索  误差修正
KeyWords: wind power prediction  wave characteristics  neural network  gravitational search  error correction
上网日期:2018-12-19
基金项目:国家电网公司科技项目“基于广域时空大数据分析的风电功率预测方法研究与应用”;可再生能源与工业节能安徽省工程实验室开放资助项目(45000-411104/012)
作者单位E-mail
丁明 安徽省新能源利用与节能重点实验室(合肥工业大学), 安徽省合肥市 230009  
张超 安徽省新能源利用与节能重点实验室(合肥工业大学), 安徽省合肥市 230009  
王勃 新能源与储能运行控制国家重点实验室(中国电力科学研究院有限公司), 北京市 100192  
毕锐 安徽省新能源利用与节能重点实验室(合肥工业大学), 安徽省合肥市 230009 13856077386@126.com 
缪乐颖 安徽省新能源利用与节能重点实验室(合肥工业大学), 安徽省合肥市 230009  
车建峰 新能源与储能运行控制国家重点实验室(中国电力科学研究院有限公司), 北京市 100192  
摘要:
      风资源因具有较强的波动性、随机性与间断性等特点而导致风电功率预测精度不高。为减小风电功率波动对电网的冲击,提高电力系统对风电的接受与消纳能力,提出了改进的风电功率短期预测方法与基于波动的误差修正方法。首先将风电功率按不同波动过程进行聚类划分,提取不同波动的特征曲线对功率值进行修正;采用引力搜索算法优化的反向传播神经网络(GSA-BP)作为基本预测方法进行预测;分析不同波动过程下的预测误差表现,建立预测误差与综合气象指标的映射关系。针对不同波动过程建立相应的风电功率误差修正模型,提出了线性模型和GSA-BP非线性模型相结合的方式对预测误差进行修正,最后以功率预测值叠加预测误差修正值作为最终预测结果。该风电功率预测误差修正方法不仅涉及风速风向等常规因素,而且考虑到了风电功率的波动性。
Abstract:
      Wind resources have the characteristics of strong fluctuation, randomness and discontinuity, which lead to the low accuracy of wind power forecasting. In order to reduce the impact of wind power fluctuations on the power grid and improve the ability of power systems to accept and absorb wind power, an improved wind power short-term prediction method and fluctuation based error correction method are proposed. Firstly, the wind power is divided into clusters according to different fluctuation processes. The characteristic curves of different fluctuations are extracted to correct the power values. Secondly, the back propagation neural network optimized by gravitational search algorithm (GSA-BP) is used as the basic prediction method to predict. Then the performance of forecasting errors under different fluctuations is analyzed, and the mapping relationship between forecasting errors and comprehensive meteorological indicators is established. A corresponding wind power error correction model is established for different fluctuation processes. A combination of linear model and GSA-BP nonlinear model is proposed to modify the prediction error. Finally, the power prediction value is added to the prediction error correction value as the final forecast result. The wind power prediction error correction method not only involves conventional factors such as wind speed and direction, but also takes into account the fluctuation of wind power.
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