摘要
随着智能电网的发展,电力企业对关口日电量及基于关口日电量的各种统计数据的准确性要求不断提高,为保证电能计量系统日电量的正确性,快速定位错误数据,提出一种基于测量不确定度理论,使用优化算法来对日电量进行估计的方法。首先,介绍了测量不确定度理论及其在电力系统中的应用,分析其用于电力系统的估计辨识的优势;其次,在给出的直流电量潮流计算模型和测量不确定度理论基础上确定了日电量的估计思路;最后,给出基于测量不确定度的日电量的评价函数,从而建立了基于测量不确定度的电量优化估计模型,给出了使用细菌群体趋药性(bacterial colony chemotaxis,BCC)优化算法对日电量进行估计的步骤。通过IEEE39和IEEE118节点系统仿真算例验证了所提方法的有效性和可行性。
With the development of smart grids the requirements of electric power enterprises to the accuracy of daily gateway electricity quantity and that of various statistical data based on daily gateway electricity quantity are ever-enhanced. To ensure the correctness of daily electricity quantity measured by electric energy metering system and rapidly locate the wrong data, based on the theory of measurement uncertainty a method to estimate daily electricity quantity by optimization algorithm is proposed. Firstly, the theory of measurement uncertainty and its application in power grids are presented, and the superiority of applying it to power grid estimation and identification is analyzed; secondly, based on the given calculation model for DC electricity quantity flow and the theory of measurement uncertainty the thinking to estimate the daily electricity quantity is decided; finally, the measurement uncertainty based evaluation function of daily electricity quantity is given, thus a measurement uncertainty based electricity quantity optimization model is built and the procedures of estimating daily electricity quantity by bacterial colony chemotaxis（BCC） optimization algorithm are given. The validity and feasibility of the proposed method are verified by simulation results of IEEE 39-bus system and IEEE 118-bus system.
出处
《电网技术》
EI
CSCD
北大核心
2013年第10期2788-2795,共8页
Power System Technology
基金
国家自然科学基金项目(61071201)~~
关键词
测量不确定度
直流潮流
电量估计
细菌群体趋药性
measurement uncertainty
DC power flow
power energy estimation
bacterial colony chemotaxis（BCC）
