基于改进卡尔曼滤波算法的短期负荷预测
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摘要
对于电网的安全运行,短期的负荷预测是必不可少的。电力系统的负荷通常是随着时间呈现出一定范围的非线性波动,这里根据电力系统中负荷特性的变化规律,提出了一种通过引入修正因子改进卡尔曼滤波算法的方法,实现了电力短期负荷预测。通过对成都地区的负荷进行短期预测,说明这种方法较传统的卡尔曼滤波具有更高的预测精度,同时与其他的新型智能算法相比,具有收敛速度快、耗时短等优点。通过MATLAB仿真,说明这种改进后的算法对实现短期负荷预测提供了一条新的途径。
For the safe operation of power grid system,the short-term load forecasting is essential. The load of the power system usually fluctuates with time to show a certain nonlinear wave range,according to the variation of load characteristics in power system,a method by introducing Kalman filtering algorithm with a modified factor to achieve the short-term load forecasting. By forecasting short-term load in the Chengdu region,it shows that this method has a higher prediction accuracy compared with the traditional Calman filter. Meanwhile,compared with other new intelligent algorithms,it has the advantages such as fast convergence and short time consumption. The MATLAB simulation shows that the improved algorithm provides a new way for short-term load forecasting.
For the safe operation of power grid system,the short-term load forecasting is essential. The load of the power system usually fluctuates with time to show a certain nonlinear wave range,according to the variation of load characteristics in power system,a method by introducing Kalman filtering algorithm with a modified factor to achieve the short-term load forecasting. By forecasting short-term load in the Chengdu region,it shows that this method has a higher prediction accuracy compared with the traditional Calman filter. Meanwhile,compared with other new intelligent algorithms,it has the advantages such as fast convergence and short time consumption. The MATLAB simulation shows that the improved algorithm provides a new way for short-term load forecasting.
引文
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[13]沈渊彬,刘庆珍.基于卡尔曼滤波-SVR时刻峰值的短期负荷预测[J].电气开关,2016,54(2):35-38.
[14]吉博文,邹红波,何平,等.改进的容积卡尔曼滤波(CKF)算法在短期负荷预测中的应用[J].三峡大学学报(自然科学版),2015,37(5):74-77.
[15]何平,吉培荣,陈军.无迹卡尔曼滤波算法在短期负荷预测中的应用[J].电气开关,2015,53(2):81-85.
[2]师彪,李郁侠,于新花,等.改进粒子群-BP神经网络模型的短期电力负荷预测[J].计算机应用,2009,29(4):1036-1039.
[3]张红,张建红,康岩松.基于GA的改进SVM算法对RBF优化算法在短期负荷预测中的应用[J].长春工程学院学报(自然科学版),2011,12(2):21-25.
[4]董辉.基于优化遗传算法的灰色-RBF神经网络预测模型研究[J].新乡学院学报(自然科学版),2012,29(3):237-240.
[5]张健,刘定一.一种应用PSO优化RBF神经网络的方法[J].计算机仿真,2014,31(11):269-272.
[6]何平,吉培荣,陈军.无迹卡尔曼滤波算法在短期负荷预测中的应用[J].电气开关,2015,53(2):81-85.
[7]范伟,田丽,汪晨,等.基于PSO-ELM模型的短期电力系统负荷预测[J].南阳理工学院学报,2017,9(4):12-15.
[8]王龙,成天乐,陈宇.基于EMD和ELM的短期负荷预测[J].电力科学与工程,2014,30(6):54-58.
[9]苏尤丽.基于人工神经网络的微电网短期负荷预测[J].内蒙古师范大学学报(自然科学汉文版),2016,45(1):43-45.
[10]黄媛玉,毛弋,娄宁娜.基于Kohonen聚类小波包分解和ELM的短期电力负荷预测[J].湖南师范大学自然科学学报,2016,39(4):53-58.
[11]尹新,周野,何怡刚,等.基于一种混合算法模型的短期电力负荷预测[J].计算机仿真,2010,27(10):255-258.
[12]Qingyu Yang,Dou An,Yuanli Cai.A Novel Evolution Kalman Filter Algorithm for Short-Term Climate Prediction[J].Asian Journal of Control,2016,18(1):400-405.
[13]沈渊彬,刘庆珍.基于卡尔曼滤波-SVR时刻峰值的短期负荷预测[J].电气开关,2016,54(2):35-38.
[14]吉博文,邹红波,何平,等.改进的容积卡尔曼滤波(CKF)算法在短期负荷预测中的应用[J].三峡大学学报(自然科学版),2015,37(5):74-77.
[15]何平,吉培荣,陈军.无迹卡尔曼滤波算法在短期负荷预测中的应用[J].电气开关,2015,53(2):81-85.