基于风速局部爬坡误差校正的风电功率优化预测
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摘要
准确的风电功率预测对于电力系统安全稳定运行具有重要意义,滞后性是产生风电功率预测误差的主要原因,尤其是风速变化较快时,滞后性引起的预测误差较大。考虑到风速波动与风电功率的变化息息相关,提出一种基于风速局部爬坡(LR)误差校正的方法来改善预测风速的滞后性,并将校正后的预测风速及历史功率数据作为输入进行风电功率预测。提出利用灰狼优化(GWO)算法对最小二乘支持向量机(LSSVM)的参数进行优化,以提高风电功率预测的准确性。算例结果表明,所提方法能够有效提高风电功率预测精度。
Accurate wind power prediction is significant for secure and stable operation of power system,and the lag is the main reason of wind power prediction error,especially when wind speed changes rapidly,the lag will result in big error. Considering strong relationship between wind speed and wind power,an error correction method based on LR(Local Ramp) of wind speed is proposed to improve the lag,and the predicted wind speed after correction and historical wind power are taken as input for wind power prediction. The parameters of LSSVM(Least Square Support Vector Machine) are optimized by using GWO(Grey Wolf Optimization) algorithm to improve the accuracy of wind power prediction. Case results show that the proposed method can effectively improve the accuracy of wind power prediction.
Accurate wind power prediction is significant for secure and stable operation of power system,and the lag is the main reason of wind power prediction error,especially when wind speed changes rapidly,the lag will result in big error. Considering strong relationship between wind speed and wind power,an error correction method based on LR(Local Ramp) of wind speed is proposed to improve the lag,and the predicted wind speed after correction and historical wind power are taken as input for wind power prediction. The parameters of LSSVM(Least Square Support Vector Machine) are optimized by using GWO(Grey Wolf Optimization) algorithm to improve the accuracy of wind power prediction. Case results show that the proposed method can effectively improve the accuracy of wind power prediction.
引文
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[17]黎静华,文劲宇,程时杰,等.考虑多风电场出力Copula相关关系的场景生成方法[J].中国电机工程学报,2013,33(16):30-36.LI Jinghua,WEN Jingyu,CHENG Shijie,et al. A scene generation method considering copula correlation relationship of multi-wind farms power[J]. Proceedings of the CSEE,2013,33(16):30-36.
[2]肖迁,李文华,李志刚,等.基于改进的小波-BP神经网络的风速和风电功率预测[J].电力系统保护与控制,2014,42(15):80-86.XIAO Qian,LI Wenhua,LI Zhigang,et al. Wind speed and power prediction based on improved wavelet-BP neural network[J].Power System Protection and Control,2014,42(15):80-86.
[3]冬雷,王丽婕,高爽,等.基于混沌时间序列的大型风电场发电功率预测建模与研究[J].电工技术学报,2008,23(12):125-129.DONG Lei,WANG Lijie,GAO Shuang,et al. Modeling and analysis of prediction of wind power generation in the large wind farm based on chaotic time series[J]. Transactions of China Electrotechnical Society,2008,23(12):125-129.
[4]YANG Renfu,LIN Wheimin,TSAI Mingtang,et al. Particle swarm optimisation aided least-square support vector machine for load forecast with spikes[J]. IET Generation,Transmission&Distribution,2016,10(5):1145-1153.
[5]欧阳庭辉,查晓明,秦亮,等.含核函数切换的风电功率短期预测新方法[J].电力自动化设备,2016,36(9):80-86.OUYANG Tinghui,ZHA Xiaoming,QIN Liang,et al. Short-term wind power prediction based on kernel function switching[J]. Electric Power Automation Equipment,2016,36(9):80-86.
[6]程启明,陈路,程尹曼,等.基于EEMD和LS-SVM模型的风电功率短期预测方法[J].电力自动化设备,2018,38(5):27-35.CHENG Qiming,XU Lu,CHENG Yiman,et al. Short-term wind power forecasting method based on EEMD and LS-SVM model[J]. Electric Power Automation Equipment,2018,38(5):27-35.
[7]茆美琴,周松林,苏建徽.基于脊波神经网络的短期风电功率预测[J].电力系统自动化,2011,35(7):70-74.MAO Meiqin,ZHOU Songlin,SU Jianhui. Short-term wind power forecast based on ridgelet neural network[J]. Automation of Electric Power Systems,2011,35(7):70-74.
[8]王丽婕,冬雷,廖晓钟,等.基于小波分析的风电场短期发电功率预测[J].中国电机工程学报,2008,28(29):30-33.WANG Lijie,DONG Lei,LIAO Xiaozhong,et al. Short-term power prediction of a wind farm based on wavelet analysis[J]. Proceedings of the CSEE,2008,28(29):30-33.
[9]杨锡运,孙宝君,张新房,等.基于相似数据的支持向量机短期风速预测仿真研究[J].中国电机工程学报,2012,32(4):35-41.YANG Xiyun,SUN Baojun,ZHANG Xinfang,et al. Short-term wind speed forecasting based on support vector machine with similar data[J]. Proceedings of the CSEE,2012,32(4):35-41.
[10]茆美琴,曹雨,周松林.基于误差叠加修正的改进短期风电功率预测方法[J].电力系统自动化,2013,37(23):34-38.MAO Meiqin,CAO Yu,ZHOU Songlin. Improved short-term wind power forecasting method based on accumulative error correction[J]. Automation of Electric Power Systems,2013,37(23):34-38.
[11] MIRJALILI S,MIRJALILI S,LEWIS A. Grey wolf optimizer[J].Advances in Engineering Software,2014,69:46-61.
[12]TURABIEH M. A hybrid ANN-GWO algorithm for prediction of heart disease[J]. American Journal of Operations Research,2016,6(2):136-146.
[13]GUHA D,ROY P,BANERJEE S. Load frequency control of interconnected power system using grey wolf optimization[J]. Swarm and Evolutionary Computation,2016,27:97-115.
[14]BHATTACHARJEE S,BHATTACHARYA A,SHARMA S. Grey wolf optimisation for optimal sizing of battery energy storage device to minimise operation cost of microgrid[J]. IET Generation,Transmission&Distribution,2016,10(3):625-637.
[15]熊一,査晓明,秦亮,等.风电功率爬坡气象场景分类模型及阈值整定研究[J].电工技术学报,2016,31(19):155-162.XIONG Yi,ZHA Xiaoming,QIN Liang,et al. Study on wind power ramping weather scenario classification model and threshold setting[J]. Transactions of China Electrotechnical Society,2016,31(19):155-162.
[16]雷若冰,徐箭,孙辉,等.基于相关性分析的风电场群风速分布预测方法[J].电力自动化设备,2016,36(5):134-140.LEI Ruobing,XU Jian,SUN Hui,et al. Wind speed distribution forecasting based on correlation analysis forwind farm group[J]. Electric Power Automation Equipment,2016,36(5):134-140.
[17]黎静华,文劲宇,程时杰,等.考虑多风电场出力Copula相关关系的场景生成方法[J].中国电机工程学报,2013,33(16):30-36.LI Jinghua,WEN Jingyu,CHENG Shijie,et al. A scene generation method considering copula correlation relationship of multi-wind farms power[J]. Proceedings of the CSEE,2013,33(16):30-36.