具有混沌特性的风电功率预测研究
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摘要
风能作为一种可再生的清洁能源,逐渐成为人类社会可持续发展的首选能源,风速的不确定性和随机性,使得风力发电具有不可调度性,因此准确的分析风电特性和预测风电功率对电力系统的发展至关重要.本文首先基于混沌理论对风电功率数据进行相空间重构,使用自相关法及CAO方法分别确定合适的时间延迟和恰当的嵌入维数,从而达到提高相空间重构质量及其预测精度的研究目的;其次依据指数判断重构的相空间是否具有混沌特性,若具有混沌特性,则建立混沌局域预测模型,对风电序列作混沌特性分析,最后对风电数据运用加权一阶局域预测模型进行短期预测.
As a kind of renewable clean energy,wind energy has gradually become the first choice energy for the sustainable development of human society. Wind power generation has a non-schedulable nature due to the uncertainty and randomicity of the wind speed. Hence,it is imperative to analyze the characteristics of wind power and predict wind power for power system operators. Firstly,the phase space reconstruction of wind power data is carried out based on chaos theory,and the autocorrelation method and the CAO method are applied to determine the appropriate time delay and embedding dimension,respectively,so as to improve the reconstruction quality of phase space and its prediction precision. Secondly,whether the reconstructed phase space has chaotic characteristics is determined according to the Lyapunov exponents. If it has chaos characteristic,then the chaotic local forecast model is established and the chaotic characteristics of the wind power sequence are analyzed. Finally,the wind power is forecasted by using the weighted first-order local prediction method.
As a kind of renewable clean energy,wind energy has gradually become the first choice energy for the sustainable development of human society. Wind power generation has a non-schedulable nature due to the uncertainty and randomicity of the wind speed. Hence,it is imperative to analyze the characteristics of wind power and predict wind power for power system operators. Firstly,the phase space reconstruction of wind power data is carried out based on chaos theory,and the autocorrelation method and the CAO method are applied to determine the appropriate time delay and embedding dimension,respectively,so as to improve the reconstruction quality of phase space and its prediction precision. Secondly,whether the reconstructed phase space has chaotic characteristics is determined according to the Lyapunov exponents. If it has chaos characteristic,then the chaotic local forecast model is established and the chaotic characteristics of the wind power sequence are analyzed. Finally,the wind power is forecasted by using the weighted first-order local prediction method.
引文
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[2]方文弟.分布式能源系统微型电网技术[J].四川电力技术,2008,(4):1~9.
[3]范高锋,裴哲义,辛耀中.风电功率预测的发展现状与展望[J].中国电力,2011,44(6):38~50.
[4]谷兴凯,范高锋,王晓蓉,等.风电功率预测技术综述[J].电网技术,2007,31(2):1~6.
[5]修春波,刘向东,张宇河.相空间重构延迟时间与嵌入维数的选择[J].北京理工大学学报,2003,23(2):219~230.
[6]周金勇.混沌时间序列预测模型研究[D].湖北:武汉理工大学,2009.18~29.
[7]山方.心电模型的混沌特性分析[D].吉林:东北大学,2010.26~36.
[8]张淑清,贾健,高敏,等.混沌时间序列重构相空间参数选取研究[J].物理学报,2010,59(3):1577~1581.
[9]杨永锋,仵敏娟,高喆,等.小数据量法计算最大Lyapunov指数的参数选择[J].振动、测试与诊断,2012,32(3):371~375.
[10]张海龙,闵富红,王恩荣.关于Lyapunov指数计算方法的比较[J].南京师范大学学报:工程技术版,2012,12(1):5~15.
[11]郭杰昊,冯冬涵,严正.基于加权一阶局域法的负荷区间预测研究[J].信息技术,2015,12:61~69.
[12]吕金虎,张锁春.加权一阶局域法在电力系统短期负荷预测中的应用[J].控制理论与应用,2002,19(5):767~770.