基于代数法和优化法的异步电动机动态参数辨识方法对比研究
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摘要
针对在线应用中的电动机动态参数辨识问题,描述了异步电动机数模混合建模框架,提出了一种基于数据驱动及显式解析的数模混合辨识方法。分别以电机负荷电压/幅值及有功/无功功率两类PMU量测数据作为输入输出,基于PSASP三阶异步机动态方程,利用代数分析建立了电机惯性系数、暂态电抗、稳态电抗、转子回路时间常数4种基本参数的解析表达式;介绍了一类基于状态空间模型的数模混合参数优化辨识方法,通过遗传算法求取了最优参数使负荷有功/无功功率模型模拟信号与PMU测量信号拟合误差最小;在改进的3机9节点系统中,对基于模拟生成的PMU时序数据进行了对比分析。研究结果表明:代数法在辨识准确性和计算效率上均优于优化辨识法。
Aiming at identification of key asynchronous motor load( AML) parameters for online application,the data-model hybrid modelling framework was firstly given and a data-model hybrid analytic method of AML dynamic parameter identification was developed. With the active/reactive power,terminal voltage and current measured by PMU as input,the fundamental four parameters,including the rotor inertia,rotor transient reactance,rotor steady reactance,rotor time constant were derived by algebra analysis based on the AML dynamic equations.Another class of data-driven and state-space model based optimization modelling method was introduced,in which a genetic algorithm( GA)was adopted to seek the minimum fitting error between the modelled active/reactive power and the PMU counterparts. Finally,a comparison study between these two methods was conducted on a modified three-machine nine-bus systemthe. The results indicate that the proposed algebra analytic method can outperform the optimization method in both identification accuracy and computation efficiency.
Aiming at identification of key asynchronous motor load( AML) parameters for online application,the data-model hybrid modelling framework was firstly given and a data-model hybrid analytic method of AML dynamic parameter identification was developed. With the active/reactive power,terminal voltage and current measured by PMU as input,the fundamental four parameters,including the rotor inertia,rotor transient reactance,rotor steady reactance,rotor time constant were derived by algebra analysis based on the AML dynamic equations.Another class of data-driven and state-space model based optimization modelling method was introduced,in which a genetic algorithm( GA)was adopted to seek the minimum fitting error between the modelled active/reactive power and the PMU counterparts. Finally,a comparison study between these two methods was conducted on a modified three-machine nine-bus systemthe. The results indicate that the proposed algebra analytic method can outperform the optimization method in both identification accuracy and computation efficiency.
引文
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[2]蔡蓉,岳程燕,谢海莲.传输大规模陆上风电的传统高压直流输电系统控制策略(英文)[J].南方电网技术,2015,9(5):32-39.
[3]赵歆.感应电动机离线参数辨识方法研究[D].重庆:重庆大学电气工程学院,2008.
[4] CIRRINCIONE M,PUCCI M,CIRRINCIONE G,et al. A new experimental application of Least-squares techniques of the estimation of the in-duction motor parameters[J]. IEEE Transactions on Industry Applica-tions,2003,39(5):1247-1256.
[5]张鹏飞,彭卫东,刘小刚.基于模型参考自适应系统的感应电机控制[J].现代电子技术,2011,34(1):197-199.
[6]黄开胜,童怀,郑太胜.遗传算法在感应电动机动态模型参数识别中的应用[J].中国电机工程学报,2000,20(8):37-41.
[7] MA J,HAN D,et al. Reducing identified parameters of measurement-based composite load model[J]. IEEE Transactions on Power System,2008,23(1):76-83.
[8]李欣然,金群,刘艳阳.遗传策略的综合改进及其在负荷建模中的应用[J].电网技术,2006,30(11):40-46.
[9]马平,王茂海,吴新振,等.基于PMU实测小扰动数据的负荷参数辨识方法[J].电力系统自动化,2016,40(2):43-48.
[10]陆超,张俊勃,韩英铎.电力系统广域动态稳定辨识与控制[M].北京:科学出版社,2015.
[11] BECEDAS J,MAMANI G,et al. Algebraic parameters identification of DC motors methodology and analysis[J].International Journal of Systems Science,2016,41(10):1241-1255.
[12] MAMADOU M. Parameters estimation for signals described by differential equations[J]. Applicable Analysis,2016,88(1):29-52.
[13]鞠平,马大强.电力系统负荷建模[M].北京:中国电力出版社,2008.
[14] MICHEL V,VINCENT V. Filtering and system identification[M]. Cambridge:Cambridge University Press,2007.
[15] HE R M,MA J,HILL J. Composite load modeling via measurement approach[J]. IEEE Transactions on Power System,2006,21(2):663-672.
[16] ZIMMERMAN R D,MURILLO-SANCHEZ C,MATPOWER Vser’s Manual.[M]. Version 3. 2. Ithaca:Pewer Systems Engineering Research Center,School of Electvical Engineering,Cernell University,2007.