电力系统参数辨识研究
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摘要
机组及负荷的模型和参数,是进行电力系统分析、控制、监视及规划的基础。其参数及模型的准确性直接影响着电力系统运行的安全性、稳定性及经济性。若因机组参数及电力负荷模型及参数的不准确而产生悲观的分析结果,则将会因不必要的加强系统结构所采取的额外措施而投入过多的资金,而造成巨大浪费;而若产生乐观冒进的分析结果,则可能使系统运行在非常危险的状态下,甚至具有很大的可能性使得系统崩溃。故如何获得准确机组参数及负荷模型及参数多年来一直是一备受关注的课题。
本文以发电机组参数的在线辨识及负荷建模及辨识为研究课题,进行了研究,并取得了相应的研究成果。
本文首先提出了基于故障录波器(Digital Fault Recorder,DFR)的机组参数在线辨识新方法。该方法采用DFR所记录发电机、励磁系统、调速系统的相关量测值,进行数学建模,而获得各子系统输入、输出及待测量之间的函数关系;然后采用粒子群优化技术,对所有待测量进行迭代调整和优化,使得模型输出逼近实际输出,而获得了各个子系统的所有待测量。该方法的特点是无需现场做试验,只是基于DFR记录数据而实现了机组各子系统所有参数的辨识。算例表明该方法具有辨识精度高,计算速度快,且实际应用方便的优点。
本文进而提出了一种新的负荷建模与辨识方法。通过对linearized-GNLD模型进行改进,获得了负荷曲线具有更高拟合精度的新的负荷模型。在此基础上,提出了先聚类再辨识思想的基于随机模糊聚类(random fuz ziness clustering algorithm)的负荷模型辨识新方法。该辨识方法以节点电压、有功功率、无功功率作为聚类的特征向量,对所收集到的曲线进行聚类分析,而得到多组负荷曲线组,每一组对应于一个负荷模型,即实现了依据负荷曲线对负荷特性的合理分类;对于每一组负荷曲线,辨识出相应的负荷模型参数,从而在一定意义上实现了每一类负荷模型的综合。所建立的模型相比于其它方法,更具推广性和更高的精度。
本文的机组参数及负荷模型辨识方法具有很高的实用性,故基于本文方法所研发的软件系统拥有较强的工程实用潜力。
The param eters of generator system a nd load m odel are the basis of power system analysis, control, monitoring and planning. Its Un-accuracy directly affects the security, stability and economy of power system operation, dispatch, and planning. On one hand, it m ight cause huge waste to inve st too m uch capital to take unnecessary measure to strengthen system architecture for the pessimistic analysis results. On the other hand, the system might take large risk to operate at dangerous situation, even at great possibility to collapse for the aggressively optimism analysis results. Therefore, how to obtain accurate parameters of generator system and load model has been a hot topic for years.
To focus on the def icits and dif ficulties of parameters of generator system and load model, a thoroughly exploration and st udy have been done in this thesis and many satisfactory achievem ents are obtaine d. The m ain achievements and research contents are listed as follows.
Firstly, a new on-line m ethod of unit para meters identification based on Digital Fault Recorder for generator system is pr esented. All the mathematical model for all sub-systems, including generator, exciter and governor-turbine, are first derived out in the method; then the function relationship of input, output and the variables whose parameters needing to be tested is acquire d; the optim al values for the param eters concerned are achieved by iteratively adjusting and optimizing their values around the initial values provided by m anufactories through PSO(Particle Swarm Optimization) algorithm, in order to let the m odel outputs of each sub-sy stem optimally match the real outputs recorded by DF R; after the iteration is conver gence, the optim al parameters are gained. The advantage of the method is that it is only based on the data recorded by DFR to realize all the parameters identification without doing experiment on the field. The results of the example s hows that the method has high identification accuracy, fast calculation speed, and is convenient for the practical application.
Secondly, a new m ethod for load m odel and new method for param eter identification are further proposed. By i mproving the linearized-GNLD model, a new load model with higher accuracy of curve fitting is achieved. Then, a new m ethod of parameter identif ication f or load mode l based on random fuzziness clustering algorithm is presented, the idea of which is to cluster the sample load curves first, and then to identify one set of param eters for each cluster of sample load curves. By th e method, node voltage, activ e power and reactiv e power are set as feature vecto r to classify all the collected sam ple curves in to multiple g roups curves , each grou p corresponding to a load model and a set of parameters identified for it, which could be also called to class ify the load character istic by the load curves, and realize the synthesis of each g roup curves in a certain extent. Thus, th e model obtained by the method proposed has higher accu racy and strong promotion capability than those b y other methods.
The methods of modeling and parameter identification for generator system and load model proposed in the thesis has high practicability; so the software developed based on it has a strong potential to be applied for practical power system
本文以发电机组参数的在线辨识及负荷建模及辨识为研究课题,进行了研究,并取得了相应的研究成果。
本文首先提出了基于故障录波器(Digital Fault Recorder,DFR)的机组参数在线辨识新方法。该方法采用DFR所记录发电机、励磁系统、调速系统的相关量测值,进行数学建模,而获得各子系统输入、输出及待测量之间的函数关系;然后采用粒子群优化技术,对所有待测量进行迭代调整和优化,使得模型输出逼近实际输出,而获得了各个子系统的所有待测量。该方法的特点是无需现场做试验,只是基于DFR记录数据而实现了机组各子系统所有参数的辨识。算例表明该方法具有辨识精度高,计算速度快,且实际应用方便的优点。
本文进而提出了一种新的负荷建模与辨识方法。通过对linearized-GNLD模型进行改进,获得了负荷曲线具有更高拟合精度的新的负荷模型。在此基础上,提出了先聚类再辨识思想的基于随机模糊聚类(random fuz ziness clustering algorithm)的负荷模型辨识新方法。该辨识方法以节点电压、有功功率、无功功率作为聚类的特征向量,对所收集到的曲线进行聚类分析,而得到多组负荷曲线组,每一组对应于一个负荷模型,即实现了依据负荷曲线对负荷特性的合理分类;对于每一组负荷曲线,辨识出相应的负荷模型参数,从而在一定意义上实现了每一类负荷模型的综合。所建立的模型相比于其它方法,更具推广性和更高的精度。
本文的机组参数及负荷模型辨识方法具有很高的实用性,故基于本文方法所研发的软件系统拥有较强的工程实用潜力。
The param eters of generator system a nd load m odel are the basis of power system analysis, control, monitoring and planning. Its Un-accuracy directly affects the security, stability and economy of power system operation, dispatch, and planning. On one hand, it m ight cause huge waste to inve st too m uch capital to take unnecessary measure to strengthen system architecture for the pessimistic analysis results. On the other hand, the system might take large risk to operate at dangerous situation, even at great possibility to collapse for the aggressively optimism analysis results. Therefore, how to obtain accurate parameters of generator system and load model has been a hot topic for years.
To focus on the def icits and dif ficulties of parameters of generator system and load model, a thoroughly exploration and st udy have been done in this thesis and many satisfactory achievem ents are obtaine d. The m ain achievements and research contents are listed as follows.
Firstly, a new on-line m ethod of unit para meters identification based on Digital Fault Recorder for generator system is pr esented. All the mathematical model for all sub-systems, including generator, exciter and governor-turbine, are first derived out in the method; then the function relationship of input, output and the variables whose parameters needing to be tested is acquire d; the optim al values for the param eters concerned are achieved by iteratively adjusting and optimizing their values around the initial values provided by m anufactories through PSO(Particle Swarm Optimization) algorithm, in order to let the m odel outputs of each sub-sy stem optimally match the real outputs recorded by DF R; after the iteration is conver gence, the optim al parameters are gained. The advantage of the method is that it is only based on the data recorded by DFR to realize all the parameters identification without doing experiment on the field. The results of the example s hows that the method has high identification accuracy, fast calculation speed, and is convenient for the practical application.
Secondly, a new m ethod for load m odel and new method for param eter identification are further proposed. By i mproving the linearized-GNLD model, a new load model with higher accuracy of curve fitting is achieved. Then, a new m ethod of parameter identif ication f or load mode l based on random fuzziness clustering algorithm is presented, the idea of which is to cluster the sample load curves first, and then to identify one set of param eters for each cluster of sample load curves. By th e method, node voltage, activ e power and reactiv e power are set as feature vecto r to classify all the collected sam ple curves in to multiple g roups curves , each grou p corresponding to a load model and a set of parameters identified for it, which could be also called to class ify the load character istic by the load curves, and realize the synthesis of each g roup curves in a certain extent. Thus, th e model obtained by the method proposed has higher accu racy and strong promotion capability than those b y other methods.
The methods of modeling and parameter identification for generator system and load model proposed in the thesis has high practicability; so the software developed based on it has a strong potential to be applied for practical power system
引文
[1]方崇智,萧德云,过程辨识,北京:清华大学出版社,1988
[2] P. Pourbeik . Autom ated Model V alidation f or Power Plants Using On-Line Disturbance Monitoring[R]. Palo Alto, CA:EPRI, 2009.
[3] M. J. Gibbard, Q. H. Kaan. Identificati on of excitation system parameters. IEEE Trans. PAS, 1975,94(4) :1201~1207.
[4] Bollinger K.E., V oltage regulator models using autom ated frequency response equipment. IEEE Trans. PAS, 1982,101(8):2899~2905.
[5]沈善德,电力系统辨识,北京:清华大学出版社,1993.
[6]荀吉辉,熊尚峰,稳定计算用发电机励磁系统模型参数测试及校核分析,电力系统保护与控制,2009,37(17):20~25.
[7]刘永奇,苏为民,李丹,等,华北电网发电机励磁系统参数测试及建模关键技术研究报告,北京:华北电力科学研究院,2004.
[8] LI Chuanshen, HSU Y uanyih, JE NG Linhe r, et al. Id entification of exciter constants using a coherence functi on based weighted least squares approach[J].IEEE Trans on Energy Conversion,1993, 8(3):460~467.
[9] GUO Tzongyih, LIU Chuanshe n, CHEN Yungtien, et al. Identification of model parameters of excitation system and power system stabilizer of Mingtan #6 via finalization field test[J].IEEE Trans on Power Systems, 1995, 10(2):795~802.
[10]蒋平,戴列峰,黄霆,等,频域法在励磁系统参数辨识中的应用,电力系统自动化, 2001, 25( 16):30~33.
[11]蒋蕾,曲祖义,FFT/LSE方法在励磁系统参数辨识中的应用,东北电力技术,2003(3):11~13.
[12]舒辉,文劲宇,罗春风,等,含有非线性环节的发电机励磁系统参数辨识,电力系统自动化, 2005, 29( 6):66~70.
[13]舒辉.电机励磁系统参数辨识方法研究,[博士学位论文],武汉:华中科技大学,2005.
[14]李敏强,寇纪凇,林丹,等,遗传算法的基本理论与应用,北京:科学出版社,2002.
[15]王兴贵,王言徐,智勇.遗传算法在发电机励磁系统参数辨识中的应用,电力系统及其自动化学报,2010,22(1):76~79.
[16]黄其新,孙黎霞,甄威,等,同步发电机参数辨识的蚁群算法及扰动分析,电力自动化设备, 2009, 29(11): 50~53.
[17]孙黎霞,鞠平,高运华,等,基于Park模型的同步发电机参数辨识,中国电机工程学报, 2009, 29(19):50~56.
[18]鞠平,倪腊琴,韩敬东,等,同步发电机参数辨识的模拟进化方法,电工技术学报,1999,14(5): 1~5.
[19]金宇清,岳陈熙,甄威,等,基于功角测量的同步发电机参数辨识频域法,电力系统自动化,2007, 31 (4) : 7~11.
[20] Bi T ianshu, On-line pa rameter ide ntification f or exc itation system based on PMU data [C] //2009 Fourth Intern ational Confer ence on Critical Infrastructures (CRIS). Piscataway, NJ, USA:IEEE,4 pp, 2009.
[21]鞠萍,马大强,电力系统非线性辨识,南京:河海大学出版社,1999
[22] Kundur, Power System Stability and Control. New York: McGraw-Hill, 1993.
[23] General Electric Company , Load Modeling for Power Flow and T ransient Stability Computer Studies, EPRI, Rep. EL-5003,1987
[24] W. W. Price, K. A. W irgau, A. Mu rdoch, J. V. Mitsche, E. Vaahedi, and M. A. El-kady,“Load modeling for load flowand transient stability computer studies,”IEEE Trans. Power Syst., 1988,3(1):180–187.
[25] He Renmu, Ma Jin, David J. Hill. Co mposite Load Modeling via Measurem ent Approach. IEEE TRANSACTIONS ON POWER SYSTEMS, 2006 ,21(2): 663-672
[26] M. Daqiang and J. Ping,“A novel appr oach to dynam ic load m odeling,”IEEE Trans. Power Syst., 1989,4(2):396–402,
[27] C. J. Lin, Y. T. Chen, H. D. Jiang, and J. C.Wang,“Dynamic load modelsin power systems using the measurement approach,”IEEE Trans. Power Syst.,: 1993,8(1): 309– 315
[28] P. Ju, F. Wu, Z.-Y. Shao. et,al. Compos ite load models based on field m easure- ments and their applications in dynam ic analysis. IET Generation, Transmission and Distribution, 2007,1(5):724-730
[29] Ju P, Handschin E. Identif iability of load m odels. IEE Proc Gener , Transm & Distrib, 1997, 144(1): 45–49
[30] W.-S. Kao, C.-J. Lin, C.-T. Huang, Y.-T. Chen, and C.-Y. Chiou,“Comparison of simulated power system dyna mics applyi ng various load m odels with actual recorded data,”IEEE Trans. Power Syst., 1994,9(1):248–254.
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[51]贺仁睦,周文,电力系统负荷模型的分类与综合,电力系统自动化,1999,23(19):12-16.
[2] P. Pourbeik . Autom ated Model V alidation f or Power Plants Using On-Line Disturbance Monitoring[R]. Palo Alto, CA:EPRI, 2009.
[3] M. J. Gibbard, Q. H. Kaan. Identificati on of excitation system parameters. IEEE Trans. PAS, 1975,94(4) :1201~1207.
[4] Bollinger K.E., V oltage regulator models using autom ated frequency response equipment. IEEE Trans. PAS, 1982,101(8):2899~2905.
[5]沈善德,电力系统辨识,北京:清华大学出版社,1993.
[6]荀吉辉,熊尚峰,稳定计算用发电机励磁系统模型参数测试及校核分析,电力系统保护与控制,2009,37(17):20~25.
[7]刘永奇,苏为民,李丹,等,华北电网发电机励磁系统参数测试及建模关键技术研究报告,北京:华北电力科学研究院,2004.
[8] LI Chuanshen, HSU Y uanyih, JE NG Linhe r, et al. Id entification of exciter constants using a coherence functi on based weighted least squares approach[J].IEEE Trans on Energy Conversion,1993, 8(3):460~467.
[9] GUO Tzongyih, LIU Chuanshe n, CHEN Yungtien, et al. Identification of model parameters of excitation system and power system stabilizer of Mingtan #6 via finalization field test[J].IEEE Trans on Power Systems, 1995, 10(2):795~802.
[10]蒋平,戴列峰,黄霆,等,频域法在励磁系统参数辨识中的应用,电力系统自动化, 2001, 25( 16):30~33.
[11]蒋蕾,曲祖义,FFT/LSE方法在励磁系统参数辨识中的应用,东北电力技术,2003(3):11~13.
[12]舒辉,文劲宇,罗春风,等,含有非线性环节的发电机励磁系统参数辨识,电力系统自动化, 2005, 29( 6):66~70.
[13]舒辉.电机励磁系统参数辨识方法研究,[博士学位论文],武汉:华中科技大学,2005.
[14]李敏强,寇纪凇,林丹,等,遗传算法的基本理论与应用,北京:科学出版社,2002.
[15]王兴贵,王言徐,智勇.遗传算法在发电机励磁系统参数辨识中的应用,电力系统及其自动化学报,2010,22(1):76~79.
[16]黄其新,孙黎霞,甄威,等,同步发电机参数辨识的蚁群算法及扰动分析,电力自动化设备, 2009, 29(11): 50~53.
[17]孙黎霞,鞠平,高运华,等,基于Park模型的同步发电机参数辨识,中国电机工程学报, 2009, 29(19):50~56.
[18]鞠平,倪腊琴,韩敬东,等,同步发电机参数辨识的模拟进化方法,电工技术学报,1999,14(5): 1~5.
[19]金宇清,岳陈熙,甄威,等,基于功角测量的同步发电机参数辨识频域法,电力系统自动化,2007, 31 (4) : 7~11.
[20] Bi T ianshu, On-line pa rameter ide ntification f or exc itation system based on PMU data [C] //2009 Fourth Intern ational Confer ence on Critical Infrastructures (CRIS). Piscataway, NJ, USA:IEEE,4 pp, 2009.
[21]鞠萍,马大强,电力系统非线性辨识,南京:河海大学出版社,1999
[22] Kundur, Power System Stability and Control. New York: McGraw-Hill, 1993.
[23] General Electric Company , Load Modeling for Power Flow and T ransient Stability Computer Studies, EPRI, Rep. EL-5003,1987
[24] W. W. Price, K. A. W irgau, A. Mu rdoch, J. V. Mitsche, E. Vaahedi, and M. A. El-kady,“Load modeling for load flowand transient stability computer studies,”IEEE Trans. Power Syst., 1988,3(1):180–187.
[25] He Renmu, Ma Jin, David J. Hill. Co mposite Load Modeling via Measurem ent Approach. IEEE TRANSACTIONS ON POWER SYSTEMS, 2006 ,21(2): 663-672
[26] M. Daqiang and J. Ping,“A novel appr oach to dynam ic load m odeling,”IEEE Trans. Power Syst., 1989,4(2):396–402,
[27] C. J. Lin, Y. T. Chen, H. D. Jiang, and J. C.Wang,“Dynamic load modelsin power systems using the measurement approach,”IEEE Trans. Power Syst.,: 1993,8(1): 309– 315
[28] P. Ju, F. Wu, Z.-Y. Shao. et,al. Compos ite load models based on field m easure- ments and their applications in dynam ic analysis. IET Generation, Transmission and Distribution, 2007,1(5):724-730
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