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基于改进KNN算法的风电功率实时预测研究
引用本文:杨 茂,贾云彭,穆 钢,严干贵,刘佳.基于改进KNN算法的风电功率实时预测研究[J].电测与仪表,2014,51(24).
作者姓名:杨 茂  贾云彭  穆 钢  严干贵  刘佳
作者单位:1. 东北电力大学电气工程学院,吉林吉林,132012
2. 泰安东平供电公司,山东泰安,271500
基金项目:国家重点基础研究发展计划项目(973计划)(2013CB228201);国家自然科学基金资助项目(51307017);吉林省科技发展计划项目(20140520129JH);吉林省教育厅“十二五”科学技术研究项目(吉教科合字[2014]第474号);吉林市科技发展计划资助项目
摘    要:大规模风电并入电网将对电网的规划建设、分析控制以及电能质量等方面产生显著的影响,高精度的超短期风电功率预测可以对含大规模风电电力系统的安全调度和稳定运行提供可靠的依据。文章对风电功率的超短期预测方法进行了研究,以混沌理论为基础,对相空间重构参数进行了计算,提出了基于改进KNN(KNearest Neighbor)算法的风电功率实时预测方法,并且应用多个评价指标来对预测结果进行评价,以吉林西部某风电场实测数据为例,验证了模型的有效性。

关 键 词:风力发电  功率预测  混沌时间序列  相空间重构  C-C方法  KNN算法
收稿时间:2014/7/11 0:00:00
修稿时间:2014/7/11 0:00:00

Wind Power Real-time Prediction Research Based on the Improved KNN algorithm
YANG Mao,JIA Yun-peng,MU Gang,YAN Gan-gui and Liujia.Wind Power Real-time Prediction Research Based on the Improved KNN algorithm[J].Electrical Measurement & Instrumentation,2014,51(24).
Authors:YANG Mao  JIA Yun-peng  MU Gang  YAN Gan-gui and Liujia
Affiliation:School of Electrical Engineering,Northeast Dianli University,School of Electrical Engineering,Northeast Dianli University,School of Electrical Engineering,Northeast Dianli University,School of Electrical Engineering,Northeast Dianli University,Taian Dongping power supply company
Abstract:Integration of large-scale wind-farm in power grid will impact grid planning, construction and energy quality. To improve power system dispatching and safe-stable operation of power grid containing a lot of wind power generating units, accurate short-term wind power forecasting is significance. This paper proposed a method of wind power short-term multi-step prediction research based on the improved KNN algorithm. Optimal parameters for phase space reconstruction and prediction method were studied, and the application of multiple evaluation index to evaluate the forecast results, it verified the effeteness of the model.
Keywords:wind power generation  power prediction  chaotic time series  phase space reconstruction  C-C method  KNN algorithm
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