基于改进鸡群算法的静止无功补偿器模型参数辨识方法
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摘要
为获取准确的静止无功补偿器模型参数以满足电力系统日益精细化的仿真要求,提出一种基于改进鸡群算法的静止无功补偿器模型参数辨识方法。首先建立考虑各环节特性的静止无功补偿器数学模型;然后对鸡群算法进行改进,并应用于静止无功补偿器模型参数辨识;最后,针对多参数同时辨识引起的辨识结果不准确问题,提出一种基于参数敏感度的静止无功补偿器模型参数逐步辨识方法,为准确辨识静止无功补偿器模型参数提供了新的辨识策略。算例结果证明了所提方法的有效性和准确性。
In order to obtain accurate parameters of static var compensator(SVC) model to meet increasingly sophisticated simulation requirements of power system, a method of SVC model parameter identification based on chicken swarm optimization algorithm(CSO) is proposed in this paper. Firstly, an SVC mathematic model considering the characteristics of each link is established. Secondly, the CSO is improved and applied to identify the SVC model parameters. Finally, according to the problem of inaccurate identification result caused by simultaneous identification of multiple parameters, a stepwise identification method of SVC model parameters based on parameter sensitivity is proposed, providing a new identification strategy for accurate identification of SVC model parameters. Simulation results show that the proposed method is effective and accurate in parameter identification of the SVC model.
In order to obtain accurate parameters of static var compensator(SVC) model to meet increasingly sophisticated simulation requirements of power system, a method of SVC model parameter identification based on chicken swarm optimization algorithm(CSO) is proposed in this paper. Firstly, an SVC mathematic model considering the characteristics of each link is established. Secondly, the CSO is improved and applied to identify the SVC model parameters. Finally, according to the problem of inaccurate identification result caused by simultaneous identification of multiple parameters, a stepwise identification method of SVC model parameters based on parameter sensitivity is proposed, providing a new identification strategy for accurate identification of SVC model parameters. Simulation results show that the proposed method is effective and accurate in parameter identification of the SVC model.
引文
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[15]杨家然,王兴成,隋林涛,等.基于风险的风电并网可容许性评估[J].电力自动化设备,2017,37(8):60-65.Yang Jiaran,Wang Xingcheng,Sui Lintao,et al.Risk-based admissibility assessment of wind power integration[J].Electric Power Automation Equipment,2017,37(8):60-65(in Chinese).
[16]李建平,宫耀华,卢爱平,等.改进的粒子群算法及在数值函数优化中应用[J].重庆大学学报,2017,40(5):95-103.Li Jianping,Gong Yaohua,Lu Aiping,et al.Application of improved particle swarm optimization to numerical function optimization[J].Journal of Chongqing University,2017,40(5):95-103(in Chinese).
[17]谢会玲,鞠平,罗建裕,等.基于灵敏度计算的电力系统参数可辨识性分析[J].电力系统自动化,2009,33(7):17-21.Xie Huiling,Ju Ping,Luo Jianyu,et al.Identifiability analysis of load parameters based on sensitivity calculation[J].Automation of Electric Power Systems,2009,33(7):17-21(in Chinese).
[18]韩睿,郑竞宏,朱守真,等.基于灵敏度分析的同步发电机参数分步辨识策略[J].电力自动化设备,2012,32(5):74-80.Han Rui,Zheng Jinghong,Zhu Shouzhen,et al.Step identification of synchronous generator parameters based on sensitivity analysis[J].Electric Power Automation Equipment,2012,32(5):74-80(in Chinese).
[19]张红斌,贺仁睦,刘应梅.感应电动机负荷模型参数解析灵敏度分析及参数辨识策略研究[J].电网技术,2004,28(6):10-14.Zhang Hongbin,He Renmu,Liu Yingmei.Analysis on parameter analytic sensitivity of induction motor load model and parameter identification strategy[J].Power System Technology,2004,28(6):10-14(in Chinese).
[2]高磊,褚晓杰,汤涌,等.电力系统多FACTS交互作用与协调控制综述[J].电网技术,2016,40(12):3749-3755.Gao Lei,Chu Xiaojie,Tang Yong,et al.Reviews on interactions and coordinated control among multiple FACTS[J].Power System Technology,2016,40(12):3749-3755(in Chinese).
[3]L Barthold.Modeling of Static Var Systems(SVS)for System Analysis[J].CIGRE Working Group,1977(51):205-209.
[4]Taylor C W,Scott G,Hammad A.Static var compensator models for power flow and dynamic performance simulation[J].IEEETransactions on Power Systems,1994,9(1):229-240.
[5]周孝信,李汉香,吴中习.静止无功补偿器的数学模型及其在电力系统暂态稳定计算中的应用[J].电网技术,1980(z1):12-19.Zhou Xiaoxin,Li Hanxiang,Wu Xizhong.Mathematical model of static var compensator and its application in transient stability calculation of power system[J].Power System Technology,1980(z1):12-19(in Chinese).
[6]孙元章,王志芳,卢强.静止无功补偿器对电压稳定性的影响[J].中国电机工程学报,1997,17(6):373-376.Sun Yuanzhang,Wang Zhifang,Lu Qiang.The effect of SVC on the voltage stability[J].Proceedings of the CSEE,1997,17(6):373-376(in Chinese).
[7]李向荣,牟道槐.采用静止无功补偿器和异步发电机改善高压电力系统无功调节特性[J].电网技术,2002,26(10):58-61.Li Xiangrong,Mou Daohuai.Improvement of reactive power regulation performance of high voltage power system by use of asynchronous generator coupled with SVC[J].Power System Technology,2002,26(10):58-61(in Chinese).
[8]雷邦军,费树岷,翟军勇,等.静止无功补偿器(SVC)的一种新型非线性鲁棒自适应控制设计方法[J].中国电机工程学报,2013,33(30):65-70.Lei Bangjun,Fei Shumin,Zhai Junyong,et al.A novel improved nonlinear robust adaptive control design method for SVC[J].Proceedings of the CSEE,2013,33(30):65-70(in Chinese).
[9]张东辉,金小明,周保荣,等.PSS/E的自定义建模及其仿真研究[J].电力系统保护与控制,2016,44(5):82-87.Zhang Donghui,Jin Xiaoming,Zhou Baorong,et al.User-defined modeling in PSS/E and its applicability in simulations[J].Power System Protection and Control,2016,44(5):82-87(in Chinese).
[10]张少康,李兴源,王渝红.Prony算法在电力系统参数辨识中的应用[J].电网技术,2010,34(7):20-24.Zhang Shaokang,Li Xingyuan,Wang Yuhong.Application of Prony algorithm in power system parameter identification[J].Power System Technology,2010,34(7):20-24(in Chinese).
[11]金宇清,赵泽,鞠平,等.双馈感应风力发电机的参数辨识分析[J].高电压技术,2011,37(7):1700-1705.Jin Yuqing,Zhao Ze,Ju Ping,et al.Analysis on the identification of doubly fed induction generator[J].High Voltage Engineering,2011,37(7):1700-1705(in Chinese).
[12]潘学萍,高远,金宇清,等.风电机组驱动系统参数辨识[J].电网技术,2013,37(7):1990-1994.Pan Xueping,Gao Yuan,Jin Yuqing,et al.Parameter identification of drive system for fixed-speed wind power generation units[J].Power System Technology,2013,37(7):1990-1994(in Chinese).
[13]Meng X,Liu Y,Gao X,et al.A new bio-inspired algorithm:chicken swarm optimization[C]//5th International Conference in Swarm Intelligence.Hefei,China:Springer International Publishing,2014:86-94.
[14]王玲玲,王昕,郑益慧,等.计及多个风电机组出力相关性的配电网无功优化[J].电网技术,2017,41(11):3463-3469.Wang Lingling,Wang Xin,Zheng Yihui,et al.Reactive power optimization of distribution network considering output correlation of multiple wind turbines[J].Power System Technology,2017,41(11):3463-3469(in Chinese).
[15]杨家然,王兴成,隋林涛,等.基于风险的风电并网可容许性评估[J].电力自动化设备,2017,37(8):60-65.Yang Jiaran,Wang Xingcheng,Sui Lintao,et al.Risk-based admissibility assessment of wind power integration[J].Electric Power Automation Equipment,2017,37(8):60-65(in Chinese).
[16]李建平,宫耀华,卢爱平,等.改进的粒子群算法及在数值函数优化中应用[J].重庆大学学报,2017,40(5):95-103.Li Jianping,Gong Yaohua,Lu Aiping,et al.Application of improved particle swarm optimization to numerical function optimization[J].Journal of Chongqing University,2017,40(5):95-103(in Chinese).
[17]谢会玲,鞠平,罗建裕,等.基于灵敏度计算的电力系统参数可辨识性分析[J].电力系统自动化,2009,33(7):17-21.Xie Huiling,Ju Ping,Luo Jianyu,et al.Identifiability analysis of load parameters based on sensitivity calculation[J].Automation of Electric Power Systems,2009,33(7):17-21(in Chinese).
[18]韩睿,郑竞宏,朱守真,等.基于灵敏度分析的同步发电机参数分步辨识策略[J].电力自动化设备,2012,32(5):74-80.Han Rui,Zheng Jinghong,Zhu Shouzhen,et al.Step identification of synchronous generator parameters based on sensitivity analysis[J].Electric Power Automation Equipment,2012,32(5):74-80(in Chinese).
[19]张红斌,贺仁睦,刘应梅.感应电动机负荷模型参数解析灵敏度分析及参数辨识策略研究[J].电网技术,2004,28(6):10-14.Zhang Hongbin,He Renmu,Liu Yingmei.Analysis on parameter analytic sensitivity of induction motor load model and parameter identification strategy[J].Power System Technology,2004,28(6):10-14(in Chinese).