Smart Power Distribution Network State Estimation Based on Extra-short Term Load Forecast
JIA Dongli, MENG Xiaoli, SONG Xiaohui
Electric Power Construction ›› 2013, Vol. 34 ›› Issue (1) : 31-35.
Smart Power Distribution Network State Estimation Based on Extra-short Term Load Forecast
As the core module of “data export” and “situation awareness tools” in smart power distribution network self-healing control, one state estimation calculation should be carried out in a data gathering period, however, the conventional state estimation calculation can’t meet the above demands. Therefore, an efficient state estimation algorithm of smart power distribution network should be studied. Aimed to these problems, this paper presents a state estimation method for smart distribution network based on extra-short term load forecast, which could provide data to the self-healing control evaluation module and power flow calculation module. The extra-short term load forecast with fast prediction speed and high prediction accuracy is introduced to the state estimation of smart power distribution network in the algorithm, to realize the real-time prediction and track the nodal load in power distribution system. The index function is adopted to inhibit the impact of bad data, which could improve the accuracy of state estimation. In order to improve the convergence of the algorithm, forward-backward sweep method is used in the distribution network power flow calculation to calculate the initial amplitude and phase angle of state variables. The distributed power sources are permitted to access power distribution network in this algorithm, which shows the transparent and open characteristics of smart grid. Finally, the calculation and analysis based on IEEE 36 standard test example have verified the validity of the algorithm.
state estimation / extra-short term load forecast / smart power distribution network / self-healing control
［1］陆一鸣,刘东,柳劲松,等. 智能配电网信息集成需求及模型分析［J］. 电力系统自动化，2010,34(8): 1-4,96.
［2］贾东梨，孟晓丽，宋晓辉. 智能配电网自愈控制技术体系框架研究［J］.电网与清洁能源，2011,27（2）：14-18.
［3］Sebastian M, Devaux O, Huet O. Description and benefits of a situation awareness tool based on a distribution state estimator and adapted to smart grids［C］//Proceedings of 2008 CIRED Seminar: Smart Grids for Distribution, 2008:23-24.
［4］许琼,白云, 安静宇. 配电网状态估计算法综述［J］. 宁波工程学院学报,2008, 20(2):38-40.
［5］于尔铿. 电力系统状态估计［M］. 北京: 水利水电出版社, 1985.
［6］宋三女, 钱珊珠, 李汉成, 等. 基于负荷电流的配电网状态估计研究［J］.电力设备, 2008, 9(4): 70-73.
［7］谭晓明. 含分布式电源的配电系统状态估计［D］. 天津: 天津大学, 2008.
［8］吴建中,余贻鑫. 具有分布式电源的配电网络抗差状态估计方法［C］// 2006中国国际供电会议,北京：2006:1-5.
［9］贾东梨，孟晓丽. 实时数据库在用电信息、采集系统中的应用研究［J］.电力建设，2012，33（1）：17-21.
［10］Alfuhaid A S，El-Sayed M A，Mahmoud M S.Cascaded artificial neural networks for short-term load forecasting［J］.IEEE Trans on Power Systems，1997，12(4)：1 524-1 529.
［11］汪峰，谢开，于尔铿，等.一种简单使用的超短期负荷预报方法［J］.电网技术，1995，20(3)：41-43，48.
［12］谢开，汪峰，于尔铿，等.应用Kalman滤波方法的超短期负荷预报［J］.中国电机工程学报，1996，16(4)：245-249.
［13］杨争林，宋燕敏，曹荣章，等.超短期负荷预测在发电市场中的应用［J］.电力系统自动化，2000，24(11)：14-17.
［14］杨争林，唐国庆，宋燕敏，等.改进的基于聚类分析的超短期负荷预测方法［J］.电力系统自动化，2005，29(24)：83-87.
［15］谢铁明，卫志农，袁安建. 基于超短期负荷预测的电力系统动态状态估计［J］.江苏电机工程,2009,28(1):8-10.
［16］卫志农，谢铁明，孙国强.基于超短期负荷预测和混合量测的线性动态状态估计［J］.中国电机工程学报, 2010, 30(1): 47-51.
［17］于尔铿，刘广一，周京阳，等.能量管理系统(EMS)［M］.北京：科学出版社，2001.
［18］颜伟，刘方，王官洁，等.辐射型网络潮流的分层前推回代算法［J］. 中国电机工程学报，2003,23（28）：76-80.
［19］卫志农，李阳林，郑玉平.基于混合量测的电力系统线性动态状态估计算法［J］.电力系统自动化，2007，31(6)：39-43.
［20］史光荣.电力系统动态状态估计与负载预测之研究［D］.台南：国立成功大学，2002.
［21］王志群，朱守真，周双喜，等.分布式发电对配电网络电压分布的影响［J］.电力系统自动化，2004,28（16）：56-60.
［22］黄丽娟. 分布式发电系统的三相潮流计算方法［D］. 天津: 天津大学, 2007.
［23］吴为麟，侯勇，方鸽飞.基于支路电流的配电网状态估计［J］.电力系统及其自动化学报，2001,13（6）:13-19.
/
| 〈 |
|
〉 |
