一种面向运行可靠性的短期负荷预测方法研究
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摘要
与传统可靠性相比,电力系统运行可靠性反应的是系统短期运行行为对可靠性的影响,需建立一种面向运行可靠性的短期负荷预测模型,既能提供充裕度指标评估所需的概率模型,又能提供系统安全性评估所需的精确模型。因此,文章通过对历史气候数据进行模糊聚类,提取相似日负荷构成样本数据并进行小波分解,利用改进的PSO-RVM算法对各小波分量进行预测和叠加,以得到预测日负荷序列的均值和概率模型,仿真表明该方法预测准确率较高,可为运行可靠性充裕度、安全性评估提供负荷数据支撑。
Compared with the traditional reliability,the operational reliability of power system can reflect the impacts of short term operation on the reliability. So there is the need to establish a prediction model to forecast short-term load oriented for the operational reliability,which can provide the probability model for adequacy indices evaluation,and provide an accurate model needed for the system security assessment. An improved fuzzy clustering is proposed in this paper to extract similar day,and DB4 wavelet is used to decompose the similar day load sequence in 3-layer wavelet. Then,the improved PSO-RVM algorithm has been used to predict wavelet components of forecasted days,and the results of wavelet prediction have been superposed to get the mean value and probability model of load sequences of forecasted days. The simulation results show that the proposed method has better prediction accuracy,which can provide load data support for reliability adequacy and security evaluation of operation.
Compared with the traditional reliability,the operational reliability of power system can reflect the impacts of short term operation on the reliability. So there is the need to establish a prediction model to forecast short-term load oriented for the operational reliability,which can provide the probability model for adequacy indices evaluation,and provide an accurate model needed for the system security assessment. An improved fuzzy clustering is proposed in this paper to extract similar day,and DB4 wavelet is used to decompose the similar day load sequence in 3-layer wavelet. Then,the improved PSO-RVM algorithm has been used to predict wavelet components of forecasted days,and the results of wavelet prediction have been superposed to get the mean value and probability model of load sequences of forecasted days. The simulation results show that the proposed method has better prediction accuracy,which can provide load data support for reliability adequacy and security evaluation of operation.
引文
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[2]鲁国起,张轶,张焰,等.基于二分法的聚类负荷模型及其在电力系统性评估中的应用[J].电力系统保护与控制,2011,39(10):31-36.Lu Guoqi,Zhang Yi,Zhang Yan,et al. Clustering load model based on the dichotomy and its application in power system reliability evaluation[J]. Power System Protection and Control,2011,39(10):31-36.
[3]陈凡,刘海涛,黄正,等.基于改进k-均值聚类的负荷概率模型[J].电力系统保护与控制,2013,41(22):128-132.Chen Fan,Liu Haitao,Huang Zheng,et al. Probabilistic load model based on improved k-means clustering algorithm[J]. Power System Protection and Control,2013,41(22):128-132.
[4]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.Shi Biao,Li Yuxia,Yu Xinhua,et al. Short-term load forecast based on modified particle swarm optimizer and back propagation neural network model[J]. Journal of Computer Applications,2009,29(4):1036-1039.
[5]吴凯,周西峰,郭前岗.基于粒子群神经网络的负荷预测方法研究[J].电测与仪表,2013,50(3):29-32.Wu Kai,Zhou Xifeng,Guo Qiangang. Study of Load Forecasting Method Based on Particle Swarm Optimization Neural Network[J]. Electrical Measurement&Instrumentation,2013,50(3):29-32.
[6]祖向荣,田敏,白焰.基于模糊聚类与函数小波核回归的短期负荷预测方法[J].电力自动化设备,2016,36(10):134-140.Zu Xiangrong,Tian Min,Bai Yan. Short-term load forecasting based on fuzzy clustering and functional wavelet-kernel regression[J]. Electric Power Automation Equipment,2016,36(10):134-140.
[7]吴倩红,高军,侯广松,等.实现影响因素多源异构融合的短期负荷预测支持向量机算法[J].电力系统自动化,2016,40(15):67-72.Wu Qianhong,Gao Jun,Hou Guangsong,et al. Short-term Load Forecasting Support Vector Machine Algorithm Based on Multi-source Heterogeneous Fusion of Load Factors[J]. Automation of Electric Power Systems,2016,40(15):67-72.
[8]曲正伟,张坤,王云静,等.基于PSO-OMP优化的WD-ASD超短期负荷预测[J/OL].电工电能新技术,2016,DOI:10. 12067/ATEEE1608014.
[9] TIPPING M E. Sparse Bayesian learning and the relevance vector machine[J]. Journal of Machine Learing Research,2011,1(3):211-244.
[10]孙志刚,翟玮星,李伟伦,等.基于EMD和相关向量机的短期负荷预[J].电力系统及其自动化学报,2011,23(1):92-96.Sun Zhigang,Zhai Weixing,Li Weilun,et. Short-Term Load Forecasting Based on EMD and RVM[J]. Proceeding of the CSU-EPSA,2011,23(1):92-96.
[11]诸克军,苏顺华,黎金玲.模糊C-均值中的最优聚类与最佳聚类数[J].系统工程理论与实践,2005,(3):52-61.Zhu Kejun,Su Shunhua,Li Jinling. Optimal Number of Clusters and the Best Partition in Fuzzy C-mean[J]. Systems Engineering-Theory&Practice,2005,(3):52-61.