动态负荷模型参数辨识的微分进化算法

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摘要参数辨识是基于量测负荷建模的一项关键技术。针对微分进化算法进行静态负荷模型参数辨识收敛速度慢、种群规模大的问题, 提出一种适合动态负荷模型参数辨识的微分进化算法。所提算法鲁棒性好, 收敛速度快, 全局寻优能力强, 种群规模缩小, 计算量降低。对某大城市两个220kV变电站实测扰动数据进行动态负荷模型参数辨识表明, 计算精度明显优于遗传算法和Levenberg-Marquardt(L-M)算法相结合混合学习算法的计算精度, 验证了所提算法的实用性。

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	黄玉龙
	陈迅
	刘明波
	陈晓科
	杨汾艳
	曾艳
	林舜江

关键词 ：微分进化, 动态负荷模型, 参数辨识

Abstract：Parameter identification is the key technology in measurement-based load modeling. A differential evolution algorithm is proposed in this paper to identify dynamic load model’s parameter, which overcomes the slowly convergent speed and oversize population problem existing in the differential evolution algorithm for static load model parameter identification. The proposed differential evolution algorithm shows good robustness, fast convergence, strong global optimization searching capability, and reduces population size and computation burden. Dynamic load model parameter identification for field measurement data from two substations in a metropolitan shows that the accuracy of the proposed algorithm is obviously better than the genetic algorithm and the Levenberg-Marquardt algorithm combined hybrid learning algorithm, and the practicability of the proposed algorithm is validated accordingly.

Key words： Differential evolution dynamic load model parameter identification

收稿日期: 2013-02-16 出版日期: 2014-03-26

PACS:

TM714

基金资助:国家自然科学基金(50777021) 和暨南大学人才引进计划资助项目

作者简介: 黄玉龙男, 1976年生, 博士, 讲师, 主要研究方向为负荷建模, 电力系统优化、运行与控制。陈迅男, 1958年生, 教授级高工, 主要研究方向为电力系统分析。

引用本文:

黄玉龙, 陈迅, 刘明波, 陈晓科, 杨汾艳, 曾艳, 林舜江. 动态负荷模型参数辨识的微分进化算法[J]. 电工技术学报, 2013, 28(11): 270-277. Huang Yulong, Chen Xun, Liu Mingbo, Chen Xiaoke, Yang Fenyan, Zeng Yan, Lin Shunjiang. Differential Evolution Algorithm for Dynamic Load Model Parameter Identification. Transactions of China Electrotechnical Society, 2013, 28(11): 270-277.

链接本文:

http://dgjsxb.ces-transaction.com/CN/Y2013/V28/I11/270

[1] 鞠平, 李德丰. 电力系统综合负荷特性的辨识方法研究[J]. 电力系统自动化, 1997, 21(8): 11-14.
Ju Ping, Li Defeng. A study on the identification method of the composite electric load models[J]. Automation of Electric Power Systems, 1997, 21(8): 11-14.
[2] 李彩玲, 王进, 冯烨, 等. 基于PSO-动态修正算法的静态负荷模型辨识[J]. 电力科学与技术学报, 2009, 24(3): 54-57.
Li Cailing, Wang Jin, Feng Ye, et al. PSO-dynamic modification algorithm based static load models identification[J]. Journal of Electric Power Science and Technology, 2009, 24(3): 54-57.
[3] 唐巍, 郭镇明, 李殿璞. 混沌优化用于电力系统静态负荷模型参数辨识[J]. 电力系统自动化, 2000, 21(8): 27-30.
Tang Wei, Guo Zhengming, Li Dianpu. Chaotic optimization method for parameter estimation of static load model[J]. Automation of Electric Power Systems, 2000, 21(8): 27-30.
[4] 程颖, 鞠平, 吴峰. 负荷模型参数辨识的粒子群优化法及其与基因算法比较[J]. 电力系统自动化, 2003, 27(11): 25-29.
Cheng Ying, Ju Ping, Wu Feng. PS algorithm in load parameter identification and its comparison with genetic algorithm[J]. Automation of Electric Power Systems, 2003, 27(11): 25-29.
[5] 王进, 苏盛, 李欣然. 电力系统综合负荷模型的辨识算法研究[J]. 华北电力技术, 2002(11): 8-12.
Wang Jin, Su Sheng, Li Xinran. Study on aggregate power load parameter estimation algorithm[J]. North China Electric Power, 2002(11): 8-12.
[6] 苟北, 陈陈, 提兆旭. Prony算法在电力系统负荷动态模型辨识中的应用研究[J]. 电力系统自动化, 1997, 21(1): 21-24.
Gou Bei, Chen Chen, Ti Zhaoxu. An application of prony method in identification of power system dynamic load model[J]. Automation of Electric Power Systems, 1997, 21(1): 21-24.
[7] 李培强, 李欣然, 刘艳阳. 基于Lemke优化算法的负荷静态模型参数辨识[J]. 电力自动化设备, 2006, 26(8): 12-15.
Li Peiqiang, Li Xinran, Liu Yanyang. Parameter identification of static load model based on Lemke optimal algorithm[J]. Electric Power Automation Equipment, 2006, 26(8): 12-15.
[8] 艾芊, 陈陈, 沈善德, 等. 简化前馈网络用于负荷模型参数辨识的研究[J]. 中国电机工程学报, 2005, 25(5): 21-27.
Ai Qian, Chen Chen, Shen Shande, et al. Research on parameter identification of load models by using linear BP networks[J]. Proceedings of the CSEE, 2005, 25(5): 21-27.
[9] 王爽心, 姜妍, 韩芳. 一种综合负荷模型参数辨识的混沌优化策略[J]. 中国电机工程学报, 2006, 26(12): 111-116.
Wang Shuangxin, Jiang Yan, Han Fang. A chaotic optimization strategy used in parameter identification of composite load model[J]. Proceedings of the CSEE, 2006, 26(12): 111-116.
[10] 朱守真, 沈善德, 郑宇辉, 等. 负荷建模和参数辨识的遗传进化算法[J]. 清华大学学报(自然科学版), 1999, 39(3): 37-40.
Zhu Shouzhen, Shen Shande, Zheng Yuhui, et al. Evolutionary programming algorithm for load modeling and parameter identification[J]. Journal of Tsinghua University (Science and Technology), 1999, 39(3): 37-40.
[11] 吴骅, 吴耀武, 娄素华, 等. 基于改进微分进化算法的负荷模型参数辨识[J]. 高电压技术, 2008, 34(9): 1977-1981.
Wu hua, Wu Yaowu, Lou Suhua, et al. Identification of parameters of static load model by improved differential evolution algorithm[J]. High Voltage Engineering, 2008, 34(9): 1977-1981.
[12] 仲卫进, 艾芊. 扩展卡尔曼滤波在动态负荷参数辨识中应用[J]. 电力自动化设备, 2007, 12(2): 47-50.
Zhong Weijin, Ai Qian. Application of extended Kalman filter in parameter identification of dynamic load model[J]. Electric Power Automation Equipment, 2007, 12(2): 47-50.
[13] Xu Weihua, Chen Chen, Ai Qian, et al. Load modeling during asymmetric disturbance in a metropolitan power grid[J]. IEEE Transactions on Power Systems, 2009, 24(3): 1191-1198.
[14] Hua Bai, Pei Zhang, Venkataramana Ajjarapu. A novel parameter identification approach via hybrid learning for aggregate load modeling[J]. IEEE Transactions on Power Systems, 2009, 24(3): 1145-1154.
[15] Storn R, Price K. Differential evolution-a simple and efficient adaptive scheme for global optimization over continuous spaces[R]. Int. Comput. Sci. Inst. , Berkeley, CA, Tech. Rep. , 1995.
[16] Cai H R, Chung C Y, Wong K P. Application of differential evolution algorithm for transient stability constrained optimal power flow[J]. IEEE Transactions on Power Systems, 2008, 23(2): 719-728.
[17] Huang Yulong, Liu Mingbo. Transient stability constrained optimal power flow based on trajectory sensitivity, one-machine infinite bus equivalence and differential evolution[C]. International Conference on Power Systems Technology, Hangzhou, China, 2010: 1-6.
[18] 黄玉龙, 刘明波. 结合轨迹灵敏度和微分进化技术的暂态稳定约束最优潮流计算[J]. 电力系统保护与控制, 2011, 39(10): 18-26.
Huang Yulong, Liu Mingbo. Transient stability constrained optimal power flow computing by combination of trajectory sensitivity and differential evolution technique[J]. Power System Protection and Control, 2011, 39(10): 18-26.
[19] 黄玉龙, 刘明波. 求解暂态稳定约束最优潮流的混合算法[J]. 电工技术学报, 2012, 27(5): 229-237.
Huang Yulong, Liu Mingbo. Hybrid algorithm for solution of transient stability constrained optimal power flow[J]. Transactions of China Electrotechnical Society, 2012, 27(5): 229-237.
[20] 黄玉龙. 电力系统暂态稳定最优控制算法研究[D]. 广州:华南理工大学, 2012.
[21] 尹建华, 黄东启, 艾芊. 基于电能质量装置数据和改进克隆选择算法的电力动态负荷建模[J]. 南方电网技术, 2011, 5(6): 42-47.
Yin Jianhua, Huang Dongqi, Ai Qian. Electrical dynamic load modeling based on the power quality equipments data and the improved clone selection algorithm[J]. Southern Power System Technology, 2011, 5(6): 42-47.
[22] IEEE Task Force on Load Representation for Dynamic Performance. Standard load models for power flow and dynamic performance simulation[J]. IEEE Transactions on Power Systems, 1995, 10(3): 1302-1313.
[23] H Renmu, M Jin, D J Hill. Composite load modeling via measurement approach[J]. IEEE Transactions on Power Systems, 2006, 21(2): 663-672.