基于县调系统的短期电力负荷预测
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摘要
县级电网短期负荷预测是电力系统运行调度中一项非常重要的内容,它是保证电力系统安全经济运行和实现电网科学管理及调度的基础,是能量管理系统的组成部分,也是今后进行电网商业化运营所必需的基本内容。
本文首先以课题组开发研究的甘肃某县电力调度自动化项目为例,介绍了县级调度系统的组成和工作原理。然后对县级电网电力负荷预测的现有研究方法进行了综述,分析了县级电网电力负荷特点,并设计了负荷预测的整体方案,主要包括数据预处理、特征参数提取和负荷预测等内容。
在数据预处理中,通过数理统计等方法计算负荷的偏离率来进行负荷异常数据的预处理,对原始数据进行预处理后,负荷的原序列更加趋于合理。在特征参数提取过程中,利用小波分析法有函数局部放大的优点,对神经网络的特征参数进行提取。在负荷预测时,选用了目前最为成熟的BP(back propagation)神经网络预测方案。
在此基础上,对各种方案进行了算法研究,着重分析了神经网络的建模问题,构建了一个三层BP神经网络,确定了BP网络模型中的相关参数。在分析基本BP神经网络的缺陷后,提出了对其改进的OBP(optimization back propagation)算法。
最后通过算例进行了仿真实验,比较两种方案的预测结果可知本文提出的基于神经网络的短期电力负荷预测方法是可行的,也是可靠的。
County power system load forecasting is a important part of the construction of electricity transmit and transforming, it is the basis of ensuring grid safely and economicly run and realizing grid scientific management and dispatching,it is not only the part of energy management system, but also is the contents which grid run commercially need for in the future.
This paper introduced electric power dispatching automation system developped by the research team, and then summarized these present methods in county power system load forecasting, analyzed county power system load's feature, and designed the whole plan .This plan including: data preprocessing, feature parameter extraction, load forecasting,and so on.
In the data preprocessing, through methods such as mathematical statistics deviate from the calculation of the rate of load capacity to carry out abnormal data pretreatment, and then, the raw data sequence become reasonable. In the process of feature parameter extraction ,using wavelet analysis method has the advantage of amplification of a part of function of neural networks to extract the characteristic parameters. In the load forecasting algorithm,BP(back propagation) neural network which is the most mature algorithm at present was selected.
On this basis, I researched various algorithms, and analysised emphatically the problem building neural network model,given the modeling method is more applicable to construct a three-tier BP Neural network, determining parameters used in BP network model. Through analysising the shortcomings of the basic BP neural network, its improvement algorithm called OBP(optimization back propagation) was proposed.
Finally, examples of the simulation experiment to compare results of two plans come to the conclusion: In this paper,the short-term load forecasting method based on neural network is feasible, and also is reliable.
本文首先以课题组开发研究的甘肃某县电力调度自动化项目为例,介绍了县级调度系统的组成和工作原理。然后对县级电网电力负荷预测的现有研究方法进行了综述,分析了县级电网电力负荷特点,并设计了负荷预测的整体方案,主要包括数据预处理、特征参数提取和负荷预测等内容。
在数据预处理中,通过数理统计等方法计算负荷的偏离率来进行负荷异常数据的预处理,对原始数据进行预处理后,负荷的原序列更加趋于合理。在特征参数提取过程中,利用小波分析法有函数局部放大的优点,对神经网络的特征参数进行提取。在负荷预测时,选用了目前最为成熟的BP(back propagation)神经网络预测方案。
在此基础上,对各种方案进行了算法研究,着重分析了神经网络的建模问题,构建了一个三层BP神经网络,确定了BP网络模型中的相关参数。在分析基本BP神经网络的缺陷后,提出了对其改进的OBP(optimization back propagation)算法。
最后通过算例进行了仿真实验,比较两种方案的预测结果可知本文提出的基于神经网络的短期电力负荷预测方法是可行的,也是可靠的。
County power system load forecasting is a important part of the construction of electricity transmit and transforming, it is the basis of ensuring grid safely and economicly run and realizing grid scientific management and dispatching,it is not only the part of energy management system, but also is the contents which grid run commercially need for in the future.
This paper introduced electric power dispatching automation system developped by the research team, and then summarized these present methods in county power system load forecasting, analyzed county power system load's feature, and designed the whole plan .This plan including: data preprocessing, feature parameter extraction, load forecasting,and so on.
In the data preprocessing, through methods such as mathematical statistics deviate from the calculation of the rate of load capacity to carry out abnormal data pretreatment, and then, the raw data sequence become reasonable. In the process of feature parameter extraction ,using wavelet analysis method has the advantage of amplification of a part of function of neural networks to extract the characteristic parameters. In the load forecasting algorithm,BP(back propagation) neural network which is the most mature algorithm at present was selected.
On this basis, I researched various algorithms, and analysised emphatically the problem building neural network model,given the modeling method is more applicable to construct a three-tier BP Neural network, determining parameters used in BP network model. Through analysising the shortcomings of the basic BP neural network, its improvement algorithm called OBP(optimization back propagation) was proposed.
Finally, examples of the simulation experiment to compare results of two plans come to the conclusion: In this paper,the short-term load forecasting method based on neural network is feasible, and also is reliable.
引文
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