水轮发电机及其调速系统的参数辨识方法与控制策略研究
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摘要
精确的水轮发电机组数学模型不仅是水力发电系统仿真的基础,而且对于互联电力系统的计算分析、规划运行和控制保护设计具有重要意义。随着水电能源的大力开发,我国已建及在建的水电站单机容量大、引水管道长、水流惯性巨大、引水系统布置复杂,同时承担电力系统调频、调峰和事故备用等任务,对电力系统的安全稳定运行及模拟分析提出了严峻挑战,然而目前国内电力系统计算分析所用的水轮机调速器数学模型往往采用简化的模型,与实际投运的水轮机调节系统模型结构存在较大差异。因此,有必要研究水轮发电机组的先进辨识方法及控制策略,建立精确的水轮发电机组数学模型,提高水电机组仿真精度及机组的控制性能,确保水电机组及电力系统的安全高效运行。
基于辨识的控制系统建模是获取研究对象数学模型的一种重要手段,传统的水力发电系统辨识研究中,辨识模型多采用线性模型,辨识方法多基于线性系统辨识理论,缺乏对水力发电系统非线性因素及参数时变的考虑,难以满足电力系统的动态稳定分析、中长期电压稳定分析、自动电压控制及系统频率稳定分析的需求。为此,在深入研究水轮发电机及其调速系统数学模型的基础上,凝练出水轮发电机组辨识及控制所面临的科学问题,结合多新息理论、先进智能优化方法、动态系统设计方法及变结构控制策略,对水电机组的参数辨识方法、先进控制策略进行了系统深入的研究,提出了水轮发电机组辨识-控制一体化体系。论文的主要工作及创新性成果如下：
(1)针对水轮发电机组频率调节及控制的需求,研究并建立了水轮机调节系统频率调节、开度调节及功率调节数学模型,对其中的非线性环节进行了重点解析,建立了基于Simulink的水力发电系统多工况仿真平台,为系统辨识及控制策略的研究打下了基础。
(2)以建立精确的水轮发电机及其调速系统线性模型为研究目标,引入多新息辨识理论,在传递函数模型的基础上推导出同步发电机任意工况下一次性抛载试验及水轮机调节系统的线性回归模型,并通过实例验证了多新息辨识理论在水电机组控制系统参数辨识的可行性。
(3)以水轮机调节系统非线性模型参数辨识为目标,在引入量子粒子群优化算法的理论框架下,将水轮机调节系统参数辨识问题转化为单目标函数有约束优化问题；针对水轮机调节系统对象参数时变、非最小相位、复杂非线性的特点,提出了一种基于混沌变异的QPSO (Quantum Particle Swarm Optimization)算法,并通过不同工况仿真试验验证了IQPSO (Improved Quantum Particle Swarm Optimization)在水轮机调节系统参数辨识中的有效性。
(4)针对水轮发电机非线性状态空间模型的参数辨识问题,在粒子群优化算法的基础上引入量子计算,提出了粒子群-量子操作(Particle Swarm Optimization with Quantum Operation, PSO-QO)优化算法,并将该方法应用到同步发电机非线性模型参数辨识中,通过实例验证了该方法在同步发电机参数的离线辨识中的有效性；并在粒子群-量子操作算法中引入敏感粒子,实现了同步发电机参数的在线跟踪与辨识。
(5)在总结以往参数辨识研究思路的基础上,尝试从控制论的角度认识参数辨识问题,提出了基于动态系统设计的参数辨识方法及动态系统设计原则,并以Hopfield神经网络为例,研究了基于Hopfield (?)神经网络动态系统的同步发电机非线性状态空间模型参数辨识,并通过实例验证了基于动态系统设计的参数辨识研究思路的可行性及有效性。
(6)针对传统PID参数难以满足水轮机调节系统参数时变、多工况运行的特点,将菌群优化(Bacterial Foraging Optimization, BFO)算法引入到水轮发电机组调速器PID参数优化整定中,综合BFO算法良好的全局搜索能力及粒子群优化(Paticle Swarm Optimization, PSO)算法收敛速度快的特点,提出了菌群-粒子群优化算法(BFO-PSO);在传统ITAE(?)旨标的基础上,考虑菌体间相互吸引、相互排斥、相互学习的行为特点,提出了一种新型适应度函数,构造出基于智能优化算法的水轮机调速器PID参数整定框架,实现了水轮机调节系统任意工况下参数整定。
(7)针对传统PID控制策略随水轮机工况切换、参数时变适应性差的问题,将滑模变结构控制引入到水轮机调节系统中；考虑到传统水轮机调节系统状态空间模型忽略了机组转速给定项无法仿真机组空载扰动且线性最优控制下机组转速存在稳态误差的问题,推导出一种三控制输入的水轮机调节系统状态方程,揭示了状态方程稳态误差产生机理,提出了消除状态方程稳态误差的方法,在此基础上设计了水轮机调节系统的滑模变结构控制器。
Accurate mathematic model of hydro turbine unit not only provides basis for hydro turbine system simulation, but also plays an important role in interconnected power system calculation and analysis, operation and plan, protection device and controller design. With great developing of the hydro-electric power, the built and building hydro power stations are with large capacity generators, long penstock, big water inertia, and complicated water diversion system, meanwhile, they also undertake the task of primary frequency modulation, peak load regulation and emergency reserve for accident. This causes great challenge for power system modeling and control. However, models of the hydraulic turbine governing system used in power system stability calculation nowadays in China usually cited simplified models, which is different from the field hydraulic turbine governing system (HTGS) in operation. Hence, it is very necessary to study of the identification method and control law for accurate modeling of hydro turbine unit not only for improving the simulation accuracy and the controller performance, but also for the hydro turbine operation with high efficient and interconnected power system operation safely.
System modeling based on identification is an important way to provide accurate models of control systems. Conventionally, linear system model and linear identification theory was studied for parameters identification of hydro power unit. While, they neglected the nonlinear factor such as saturation, amplitude limitation in servomotor system and time-varying factor in turbine, which are hard to satisfied the need of power system dynamic analysis, mid-term and long-term voltage stability calculation, auto generation control and system frequency analysis. Focus on these problems and research on models of synchronous generator (SG) and its speed mover governor system (PMGS) of hydropower unit, the paper proposed the scientific problems of parameters identification and control law design of hydropower unit. Combing the multi-innovation identification theory, intelligent optimization method, dynamic system design method and sliding mode control law, the paper studied detailed parameters identification technology and control laws of hydropower unit, and built the frame of parameters identification and system control for hydropower unit. Main contribution and innovations of the paper is organized as follows:
(1) Focus on the need of frequency regulation and control of power system, the paper studied and built models of HTGS under frequency regulations, opening regulations and power regulation. Meanwhile, nonlinear sets of the system are detailed described and a simulation platform for hydropower system is built in Simulink, which provide a basis for parameters estimation and control law design of hydropower system.
(2) To get accurate models of SG and its PMGS, the paper introduced the multi-innovation least square (MILS) identification method. A linear regressive model of SG and its PMGS is derived from their transfer function model. Case study showed the effect of the multi-innovation least square method in parameters identification of hydropower unit.
(3) To estimate parameters of HTGS, the paper introduced the Quantum Particle Swarm Optimization (QPSO) algorithm and converted the problem of parameters identification of HTGS into a constrained optimization problem with single object function. Focus on the characteristic of time-varing, non-minimum phase and complex nonlinearity of HTGS, an improved QPSO (IQPSO) with chaos mutation operator is proposed and is used to solve the problem of parameters estimate of nonlinear HTGS. Simulations under different operations show the effectiveness of IQPSO.
(4) To identification parameters of nonlinear state space of SG, this paper presents an improved algorithm named Particle Swarm Optimization with Quantum Operation (PSO-QO) to solve both offline and online parameters estimation problem for SG. An illustrative example for parameters identification of SG is provided to confirm the validity. Meanwhile, PSO-QO is also improved with a sentry particle introduced into the swarm to detect and determine parameters variation. Simulation results confirm that the proposed algorithm is a viable alternative for online parameters detection of SG.
(5) With reviewing the identification methods in literatures, this paper tried to provide a novel idea to deal with parameters estimation as a problem of dynamic system design. Principles for dynamic system design are proposed in this paper, and a specific dynamic system based on Hopfield Neural Networks (HNN) is listed in the paper. With the dynamic system based on HNN, the paper estimated parameters of the nonlinear SG model formulated by third order differential equations and feasibility of the method is verified by simulation test.
(6) Focus on conventional PID controller can not satisfied the need of HTGS with multi-condition and time-varying parameters, this paper introduced Bacterial Foraging Optimization (BFO) into PID parameter optimization tuning. Considering the slow convergence of BFO algorithm and the good convergence of particle swarm optimization (PSO) algorithm, a novel method named BFO-PSO algorithm was proposed. A new performance index integrated with ITAE and the Jcc index which can reflect the effect of bacterial swarm's mutual attraction, mutual repellence and mutual learning was proposed. Thus, the paper constructed the frame of PID parameter optimization tuning based on intelligent optimization method and provides an alternative for PID parameter optimization tuning of HTGS in any conditions.
(7) Considering that the PID control law is not suitable for HTGS with multiple conditions and parameter time-varying, the sliding mode control (SMC) law was illustrated in this paper. With control input of unit reference speed neglected, the no-load disturbance can not be simulated in conventional state space model of HTGS and steady speed error exists in this state space model under linear optimal control theory. To overcome these problems, the paper proposed an improved state space model of HTGS with three control inputs. With the steady error of the improved state space model analyzed, the paper display reason of the steady error and supplied a technique to eliminate the steady error of the improved model of HTGS. Meanwhile, a sliding mode controller for HTGS is designed based on the improved state space model of HTGS with three control inputs.
基于辨识的控制系统建模是获取研究对象数学模型的一种重要手段,传统的水力发电系统辨识研究中,辨识模型多采用线性模型,辨识方法多基于线性系统辨识理论,缺乏对水力发电系统非线性因素及参数时变的考虑,难以满足电力系统的动态稳定分析、中长期电压稳定分析、自动电压控制及系统频率稳定分析的需求。为此,在深入研究水轮发电机及其调速系统数学模型的基础上,凝练出水轮发电机组辨识及控制所面临的科学问题,结合多新息理论、先进智能优化方法、动态系统设计方法及变结构控制策略,对水电机组的参数辨识方法、先进控制策略进行了系统深入的研究,提出了水轮发电机组辨识-控制一体化体系。论文的主要工作及创新性成果如下：
(1)针对水轮发电机组频率调节及控制的需求,研究并建立了水轮机调节系统频率调节、开度调节及功率调节数学模型,对其中的非线性环节进行了重点解析,建立了基于Simulink的水力发电系统多工况仿真平台,为系统辨识及控制策略的研究打下了基础。
(2)以建立精确的水轮发电机及其调速系统线性模型为研究目标,引入多新息辨识理论,在传递函数模型的基础上推导出同步发电机任意工况下一次性抛载试验及水轮机调节系统的线性回归模型,并通过实例验证了多新息辨识理论在水电机组控制系统参数辨识的可行性。
(3)以水轮机调节系统非线性模型参数辨识为目标,在引入量子粒子群优化算法的理论框架下,将水轮机调节系统参数辨识问题转化为单目标函数有约束优化问题；针对水轮机调节系统对象参数时变、非最小相位、复杂非线性的特点,提出了一种基于混沌变异的QPSO (Quantum Particle Swarm Optimization)算法,并通过不同工况仿真试验验证了IQPSO (Improved Quantum Particle Swarm Optimization)在水轮机调节系统参数辨识中的有效性。
(4)针对水轮发电机非线性状态空间模型的参数辨识问题,在粒子群优化算法的基础上引入量子计算,提出了粒子群-量子操作(Particle Swarm Optimization with Quantum Operation, PSO-QO)优化算法,并将该方法应用到同步发电机非线性模型参数辨识中,通过实例验证了该方法在同步发电机参数的离线辨识中的有效性；并在粒子群-量子操作算法中引入敏感粒子,实现了同步发电机参数的在线跟踪与辨识。
(5)在总结以往参数辨识研究思路的基础上,尝试从控制论的角度认识参数辨识问题,提出了基于动态系统设计的参数辨识方法及动态系统设计原则,并以Hopfield神经网络为例,研究了基于Hopfield (?)神经网络动态系统的同步发电机非线性状态空间模型参数辨识,并通过实例验证了基于动态系统设计的参数辨识研究思路的可行性及有效性。
(6)针对传统PID参数难以满足水轮机调节系统参数时变、多工况运行的特点,将菌群优化(Bacterial Foraging Optimization, BFO)算法引入到水轮发电机组调速器PID参数优化整定中,综合BFO算法良好的全局搜索能力及粒子群优化(Paticle Swarm Optimization, PSO)算法收敛速度快的特点,提出了菌群-粒子群优化算法(BFO-PSO);在传统ITAE(?)旨标的基础上,考虑菌体间相互吸引、相互排斥、相互学习的行为特点,提出了一种新型适应度函数,构造出基于智能优化算法的水轮机调速器PID参数整定框架,实现了水轮机调节系统任意工况下参数整定。
(7)针对传统PID控制策略随水轮机工况切换、参数时变适应性差的问题,将滑模变结构控制引入到水轮机调节系统中；考虑到传统水轮机调节系统状态空间模型忽略了机组转速给定项无法仿真机组空载扰动且线性最优控制下机组转速存在稳态误差的问题,推导出一种三控制输入的水轮机调节系统状态方程,揭示了状态方程稳态误差产生机理,提出了消除状态方程稳态误差的方法,在此基础上设计了水轮机调节系统的滑模变结构控制器。
Accurate mathematic model of hydro turbine unit not only provides basis for hydro turbine system simulation, but also plays an important role in interconnected power system calculation and analysis, operation and plan, protection device and controller design. With great developing of the hydro-electric power, the built and building hydro power stations are with large capacity generators, long penstock, big water inertia, and complicated water diversion system, meanwhile, they also undertake the task of primary frequency modulation, peak load regulation and emergency reserve for accident. This causes great challenge for power system modeling and control. However, models of the hydraulic turbine governing system used in power system stability calculation nowadays in China usually cited simplified models, which is different from the field hydraulic turbine governing system (HTGS) in operation. Hence, it is very necessary to study of the identification method and control law for accurate modeling of hydro turbine unit not only for improving the simulation accuracy and the controller performance, but also for the hydro turbine operation with high efficient and interconnected power system operation safely.
System modeling based on identification is an important way to provide accurate models of control systems. Conventionally, linear system model and linear identification theory was studied for parameters identification of hydro power unit. While, they neglected the nonlinear factor such as saturation, amplitude limitation in servomotor system and time-varying factor in turbine, which are hard to satisfied the need of power system dynamic analysis, mid-term and long-term voltage stability calculation, auto generation control and system frequency analysis. Focus on these problems and research on models of synchronous generator (SG) and its speed mover governor system (PMGS) of hydropower unit, the paper proposed the scientific problems of parameters identification and control law design of hydropower unit. Combing the multi-innovation identification theory, intelligent optimization method, dynamic system design method and sliding mode control law, the paper studied detailed parameters identification technology and control laws of hydropower unit, and built the frame of parameters identification and system control for hydropower unit. Main contribution and innovations of the paper is organized as follows:
(1) Focus on the need of frequency regulation and control of power system, the paper studied and built models of HTGS under frequency regulations, opening regulations and power regulation. Meanwhile, nonlinear sets of the system are detailed described and a simulation platform for hydropower system is built in Simulink, which provide a basis for parameters estimation and control law design of hydropower system.
(2) To get accurate models of SG and its PMGS, the paper introduced the multi-innovation least square (MILS) identification method. A linear regressive model of SG and its PMGS is derived from their transfer function model. Case study showed the effect of the multi-innovation least square method in parameters identification of hydropower unit.
(3) To estimate parameters of HTGS, the paper introduced the Quantum Particle Swarm Optimization (QPSO) algorithm and converted the problem of parameters identification of HTGS into a constrained optimization problem with single object function. Focus on the characteristic of time-varing, non-minimum phase and complex nonlinearity of HTGS, an improved QPSO (IQPSO) with chaos mutation operator is proposed and is used to solve the problem of parameters estimate of nonlinear HTGS. Simulations under different operations show the effectiveness of IQPSO.
(4) To identification parameters of nonlinear state space of SG, this paper presents an improved algorithm named Particle Swarm Optimization with Quantum Operation (PSO-QO) to solve both offline and online parameters estimation problem for SG. An illustrative example for parameters identification of SG is provided to confirm the validity. Meanwhile, PSO-QO is also improved with a sentry particle introduced into the swarm to detect and determine parameters variation. Simulation results confirm that the proposed algorithm is a viable alternative for online parameters detection of SG.
(5) With reviewing the identification methods in literatures, this paper tried to provide a novel idea to deal with parameters estimation as a problem of dynamic system design. Principles for dynamic system design are proposed in this paper, and a specific dynamic system based on Hopfield Neural Networks (HNN) is listed in the paper. With the dynamic system based on HNN, the paper estimated parameters of the nonlinear SG model formulated by third order differential equations and feasibility of the method is verified by simulation test.
(6) Focus on conventional PID controller can not satisfied the need of HTGS with multi-condition and time-varying parameters, this paper introduced Bacterial Foraging Optimization (BFO) into PID parameter optimization tuning. Considering the slow convergence of BFO algorithm and the good convergence of particle swarm optimization (PSO) algorithm, a novel method named BFO-PSO algorithm was proposed. A new performance index integrated with ITAE and the Jcc index which can reflect the effect of bacterial swarm's mutual attraction, mutual repellence and mutual learning was proposed. Thus, the paper constructed the frame of PID parameter optimization tuning based on intelligent optimization method and provides an alternative for PID parameter optimization tuning of HTGS in any conditions.
(7) Considering that the PID control law is not suitable for HTGS with multiple conditions and parameter time-varying, the sliding mode control (SMC) law was illustrated in this paper. With control input of unit reference speed neglected, the no-load disturbance can not be simulated in conventional state space model of HTGS and steady speed error exists in this state space model under linear optimal control theory. To overcome these problems, the paper proposed an improved state space model of HTGS with three control inputs. With the steady error of the improved state space model analyzed, the paper display reason of the steady error and supplied a technique to eliminate the steady error of the improved model of HTGS. Meanwhile, a sliding mode controller for HTGS is designed based on the improved state space model of HTGS with three control inputs.
引文
[1]杨中强.中国能源安全及其战略选择[J].国际论坛,2004,6(5)：55-59.
[2]国家能源局.国家能源科技“十二五”规划.2011,12.
[3]谈广鸣,舒彩文.清洁能源与优质电源论析[J].水电与新能源,2010,(1)：74-77.
[4]雷亚洲.与风电并网相关的研究课题[J].电力系统自动化,2003,27(8)：84-89.
[5]裴哲义,董存,辛耀中.我国风电并网运行最新进展[J].中国电力,2010,43(11)：78-83.
[6]沈辉,曾祖勤.太阳能光伏发电技术[M].北京：化学工业出版社,2005.
[7]李广敏.考虑光伏并网发电的短期负荷预测[D],华北电力大学：华北电力大学图书馆,2007,12.
[8]李碧君,方勇杰,杨卫东,等.光伏发电并网大电网面临的问题与对策[J].电网与清洁能源,2010,26(4)：52-58.
[9]卢勇,刘友宽,孙鹏,等.用于系统分析的机组调速系统及其原动机建模问题研究[J].电网技术,2007,31(增刊2)：123-126.
[10]李华,史可琴,范越,等.电力系统稳定计算用水轮机调速器模型结构分析[J].电网技术,2007,31(5)：25-33.
[11]印永华,郭剑波,赵建军,等.美加“8·14”大停电事故初步分析以及应吸取的教训[J].电网技术,2003,27(10)：8-16.
[12]鞠平.电力系统建模理论与方法[M].北京：科学出版社,2010.
[13]鞠平,张建勇.电力系统建模基本理论研究综述[J].电力科学与技术学报,2011,26(1)：4-12.
[14]丁锋.系统辨识(1)：辨识导论[J].南京信息工程大学学报：自然科学版,2011,3(1)：1-22.
[15]徐枋同,李永华.系统辨识理论与实践—在水电控制工程中的应用[M].北京：中国电力出版社,1999.
[16]王正风,高涛.智能电网调度运行面临的关键技术研究[J].安徽电气工程职业技术学院学报,2011,16(增刊)：64-69.
[17]Zadeh L A. From circuit therey to system thery [J]. Procedings of the IRE,1962, 50(5):856-865.
[18]Ljung L. Convergence analysis of parametric identification methods [J]. IEEE Transactions on Automatic Control,1978,23(5):770-783.
[19]侯彬媛.系统辨识及其MATLAB仿真[M].北京：科学出版社,2004.
[20]李鹏波,胡德文.系统辨识基础[M].北京：中国水利水电出版社,2006.
[21]李言俊,张科.系统辨识理论及应用[M].北京：国防工业出版社,2002.
[22]沈善德.电力系统辨识[M].北京：清华大学出版社,1999.
[23]丁锋.系统辨识(4)：辅助模型辨识思想与方法[J].南京信息工程大学学报：自然科学版,2011,3(4)：289-318.
[24]Liu Y, BaiEW. Iterative identification of Hammerstein systems [J]. Automatica, 2007,43(2):346-354.
[25]袁廷奇,刘文江.非线性Hammerstein系统辨识的动态分离方法[J].控制理论与应用,2002,19(4)：619-622.
[26]向微,陈宗海.基于Hammerstein模型描述的非线性系统辨识新方法[J].控制理论与应用,2007,24(1)：143-147.
[27]Ding F, Shi Y, Chen T W. Auxiliary model based least-squares identification methods for Hammerstein output-error systems[J]. Systems and Control Letters, 2007,56(5):373-380.
[28]Ding F, Chen T W. Combined parameter and output estimation of dual-rate systems using an auxiliary model[J]. Automatica,2004,40(10):1739-1748.
[29]Ding F, Chen T W. Identification of dual-rate systems based on finite impulse response models[J]. International Journal of Adaptive Control and Signal Processing,2004,18(7):589-598.
[30]黄正良,吴坚,万百五.辨识Wiener模型的一种新方法[J].控制理论与应用,1996,13(3)：326-332.
[31]胡德文,王正志.非线性系统Wiener模型辨识[J].自动化学报,1991,17(2)：151-159.
[32]张广莹,邓正隆.基于小波分析的非线性系统Wiener模型辨识[J].电机与控制学报,2001,5(02)：95-128.
[33]Chen S, Billings S. A., Grant P. Nonlinear system identification using neural network[J]. International Journal of Control,1990,51(6):1191-1214.
[34]Narendra K. S, Parthasarathy L. Identification and Control of Dynamical Systems Using Neural Net works[J]. IEEE Trans On Neural Networks,1990,1(1):4-27.
[35]李岩,王东风,焦嵩鸣,等.采用微分进化算法和径向基函数神经网络的热工过程模型辨识[J].中国电机工程学报,2010,30(08)：110-116.
[36]董泽,黄宇,韩璞.量子遗传算法优化RBF神经网络及其在热工辨识中的应用[J].中国电机工程学报,2008,28(17)：99-104.
[37]邹敢,李涛,肖仁鑫.粒子群算法优化神经网络的异步电机转速估计[J].太原理工大学学报,2012,43(02)：158-162.
[38]郭亮,陈维荣,贾俊波,等.基于粒子群算法的BP神经网络光伏电池建模[J].电工电能新技术,2011,30(2)：84-88.
[39]葛宏伟,李小琳,梁艳春,等.基于免疫粒子群优化的一种动态递归神经网络辨识与控制非线性系统[J].吉林大学学报(工学版),2008,38(4)：858-864.
[40]汪镭,周国兴,吴启迪.基于Hopfield神经网络的线性系统参数辨识方案及在鼠笼式电机传动系统参数辨识中的应用研究[J].中国电机工程学报,2001,21(1)：9-17.
[41]Atencia, Miguel, Joya, etc. Parametric identification of robotic systems with stable time-varying Hopfield networks[J]. Neural Computing and Applications,2004, 13(4):270-280.
[42]Hugo Alonso, Teresa Mendonca, Paula Rocha. Hopfield neural networks for on-line parameter estimation[J]. Neural Networks,2009,22(4):450-462.
[43]王立新.模糊系统与模糊控制教程[M].北京：清华大学出版社,2003.
[44]诸静.模糊控制理论与系统原理[M].北京：机械工业出版社,2005.
[45]D. J. Trudnowski, J. C. Agee. Identifying a hydraulic-turbine model from measured field data[J]. IEEE Transactions on Energy Conversion,1995,10(4): 768-773.
[46]徐枋同,陈建.水力机组动态模型在线辨识[J].水利学报,1988,(3)：28-36.
[47]The Institute of Electrical and Electronic Engineers, Inc. IEEE Std 1110-2002 IEEE guide for synchronous generator modeling practices and applications in power system stability ananlysis[S]. New York,2003.
[48]中国国家标准化管理委员会,GB/T1029-2005三相同步电机试验方法[S].北京：中国标准出版社,2006.
[49]IEEE Committee Report. Supplementary definitions and associated test methods for obtaing parameters for synchronous machine stability study simulations [J]. IEEE Transactions on Power Apparatus and Systems,1980, PAS-99(4): 1625-1633
[50]Dandeno P L, Poray A T. Development of detailed turbogenerator equivalent circuits from standardstill frequency response measurements [J]. IEEE Transactions on Power Apparatus and Systems,1981, PAS-100(4):1646-1655.
[51]Coultes M E, Watson W. Synchronous machine models by standstill frequency response tests[J]. IEEE Transactions on Power Apparatus and Systems,1981, PAS-100(4):1480-1489.
[52]IEEE PES WG 12 Resport. Experience with standstill frequency response (SSFR) testing and analysis of salient pole synchronous machines [J]. IEEE Transactions on Energy Conversion,1999,14(4):1209-1217.
[53]Bortoni E C, Jardini J A. Experience with standstill frequency response (SSFR) testing and analysis of salient pole synchronous machines[J]. IEEE Transactions on Energy Conversion,2004,19(4):687-691.
[54]Verbeeck J, Pintelon R, Lataire P. Identification of synchronous machine parameters using a multiple input multiple output approach[J]. IEEE Transactions on Energy Conversion,1999,14(4):909-917.
[55]黄淼.基于抛载试验和在线时域测试的同步发电机参数辨识新方法[D],重庆大学：重庆大学图书馆,2011,5.
[56]张承慧,刘玉庆.水轮机建模与参数识别[J].电力系统自动化,1997,21(5)：53-56.
[57]刘宪林,杜晓勇,田云峰.基于随机负荷波动的抽蓄电站水轮机模型辨识[J].中国电机工程学报,2007,27(6)：49-52.
[58]乔俊飞, 孙雅明,杜红卫.水轮发电机组的一种自适应调速方法[J].中国电机工程学报,2000,20(10)：1-9.
[59]张霄元,郑玉森,刘长胜,等.水轮机参数在线辨识及调节器参数自整定[J].电力系统及其自动化学报,2000,12(2)：16-18.
[60]C. C. Lee, Owen T. Tan. A weighted-least-squares parameter estimator for synchronous machines[J]. IEEE Transactions on Power Apparatus and Systems, 1977, PAS-96(1):97-101.
[61]Emile Mouni, Slim Tnani, Gerard Champenois. Synchronous generator modelling and parameters estimation using least squares method[J]. Simulation Modelling Practice and Theory,2008,16(6):678-689.
[62]胡虔生,金俭.同步电机瞬变参数的测试数据处理方法的研究[J].大电机技术,1999,(3)：21-25.
[63]MICHAEL BURTH, GEORGE C. VERGHESE, etc. Subset Selection for Improved Parameter Estimation in On-Line Identification of a Synchronous Generator[J]. IEEE Transactions on Power Systems,1999,14(1):218-225.
[64]郑逢时,赵争鸣,阮仕荣,等.用最小二乘辨识方法测取水轮发电机参数[J].电工技术学报,1994,(3)：1-4.
[65]Z.M. ZHAO, F.S. ZHENG, J. D. GAO, etc. A dynamic on-line parameter identification and full-scale system experimental verification for large synchronous machines [J]. IEEE Transaction on Energy Conversion 1995, EC-10(3):392-398.
[66]Karrari, M.; Malik, O. P. Synchronous generator third-order model parameter estimation using online experimental data[J]. Generation, Transmission & Distribution, IET,2008,2(5):708-719.
[67]罗建,冯树辉,蔡明等.基于可观测量的同步发电机参数的时域辨识[J].电力系统自动化,2011,35(7)：24-80.
[68]周双喜,张恒.一种新型自校正PID励磁调节器的研究[J].清华大学学报(自然科学版),1997,37：31-34.
[69]沈善德 言茂松.推广卡尔曼滤波法应用于同步机参数估计和电力系统动态等效[J].清华大学学报,1984,24(1)：33-44.
[70]Namba M. Identification of parameters for system stability analysis using Kalman filter[J]. IEEE Transactions on Power Apparatus and Systems,1981. PAS-100(7): 3304.
[71]M. Miskovic, M. Mirosevic. Making the model of synchronous generator using the date from monitoring system[C]. In:Proceedings of the 12th IEEE Mediter-ranean Electrotechnical Conference, Croatia,2004,1129-1132.
[72]C. E. Nino, M. Velez-Reyes, Dealing with ill conditioning in recursive parameter estimation for a synchronous generator[C], In:32nd Annual Conference on IEEE Industrial Electronics, Paris,2006,1089-1094.
[73]R. E. Fairbairn, R. G. Harley, On-line measurement of synchronous machine parameters [J]. IEEE Transactions on Industry Applications,1992,28 (3):639-645.
[74]Miao Huang, Wenyuan Li, Wei Yan. Estimating parameters of synchronous generators using square-root unscented Kalman filter[J]. Electric Power Systems Research,2010,80(9):1137-1144.
[75]A. Yazdizadeh. K. Khorasani. Identification of a turbo generator system using adaptive time delay neural networks[J]. In:Proceedings of the 1998 IEEE International Conference on Control Applications Trieste, Italy,1998,1-4.
[76]Chang, J., Liu, B., Cai, W.. Nonlinear simulation of hydro turbine governing system based on neural network[C]. In:Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, Beijing,1996, 784-787.
[77]Nand Kishor, S. P. Singh. Simulated response of NN based identification and predictive control of hydro plant[J]. Expert Systems with Applications,2007,32(): 233-244.
[78]Nand Kishor, R. P. Saini,S. P. Singh. Small hydro power plant identification using NNARX structure[J]. Neural Computing and Applications,2005,14(3): 212-222.
[79]Nand Kishor, S. P. Singh, A. S. Raghuvanshi. Adaptive intelligent hydro turbine speed identification with water and random load disturbances[J]. Engineering Applications of Artificial Intelligence,2007,20(6): 795-808
[80]王珊,周建中,杜思存等.基于RBF神经网络的水轮机调节系统辨识[J].水力发电,2006,3(3)：42-44.
[81]王淑青,李朝晖.基于自适应模糊神经网络的水轮机特性辨识研究[J].武汉大学学报(工学版),2006,39(2)：24-27.
[82]常江,谢云敏.混流式水轮机神经网络模型非线性仿真[J].中国农村水利水电,2004,(6)：75-77.
[83]常江,陈光大.轴流转桨式水轮机神经网络建模与非线性仿真[J].中国农村水利水电,2004,(7)：82-85.
[84]程远楚,叶鲁卿,蔡维由.水轮机特性的神经网络建模[J].华中科技大学学报(自然科学版),2003,31(6)：68-70.
[85]Srinivas Pillutla, Ali Keyhani. Neural Network Based Modeling of Round Rotor Synchronous Generator Rotor Body Parameters from Operating Data[J]. IEEE Transactions on Energy Conversion,1999,14(3):321-327.
[86]Jung-Wook Park, Ganesh Kumar Venayagamoorthy, Ronald G. Harley. MLP/RBF Neural-Networks-Based Online Global Model I dentification of Synchronous Generator[J] IEEE Transactions on Industrial Electronics,2005,52(6):1685-1695.
[87]Jung-Wook Park, R. G. Harley, G. K. Venayagamoorthy. Comparison of MLP and RBF neural networks using deviation signals for on-line identification of a synchronous generator[C]. In:Proceedings of Winter Meeting of the Power Engineering Society, New York,2002,274-279.
[88]Soheil Ganjefar, Mojtaba Alizadeh. On-Line Identification of Synchronous Generator Using Self Recurrent Wavelet Neural Networks Via Adaptive Learning Rates [C] In:The 5th International Power Engineering and Optimization Conference (PEOCO2011), Shah Alam,2011,243-248.
[89]O. Shariati, A. A. Mohd Zin, M. R. Aghamohammadi. Application of Neural Network Observer for On-line Estimation of Salient-Pole Synchronous Generators'Dynamic Parameters using the Operating Data[C].In:2011 4th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO), Kuala Lumpur,2011,19.
[90]M. Rahimpour, M. A. Talebi, H.A. Shayanfar et. al. On Line Synchronous Generator Parameters Estimation Based on Applying Small Disturbance on Excitation System Using ANN[C]. In:2009 IEEE/PES Power Systems Conference and Exposition (PSCE 2009), Piscataway,2009,1-4.
[91]李超顺,周建中,安学利等.基于T-S模糊模型的水轮机调节系统辨识[J].武汉大学学报(工学版),2010,43(1)：108-111.
[92]李超顺,周建中,向秀桥等.基于超平面原型聚类的水轮机调速系统模糊模型辨识[J].动力工程,2009,29(4)：363-388.
[93]Li C., Zhou J., Xiang X., et al. T-S fuzzy model identification based on a novel fuzzy c-regression model clustering algorithm[J]. Engineering Applications of Artificial Intelligence,2009,22(4-5):646-653.
[94]Li C., Zhou J., Li Q., et al. A new T-S fuzzy-modeling approach to identify a boiler-turbine system[J]. Expert Systems with Applications,2010,37(3): 2214-2221.
[95]Nand Kishor. Zero-order TS fuzzy model to predict hydro turbine speed in closed loop operation[J]. Applied Soft Computing,2008,8(2):1074-1084.
[96]Lin Gao, Yiping Dai, Junrong Xia. Parameter Identification of Hydro Generation System with Fluid Transients Based on Improved Genetic Algorithm[C]. In:2009 Fifth International Conference on Natural Computation, Tianjian,2009,398-402.
[97]Chaoshun Li, Jianzhong Zhou. Parameters identification of hydraulic turbine governing system using improved gravitational search algorithm[J]. Energy Conversion and Management,2011,52(1):374-381.
[98]杨小东,董宸,卢文华,等.基于遗传算法的水轮发电机组调速系统参数辨识[J].继电器,2006,34(1)：27-30.
[99]沈祖诒.水轮机调节[M].北京：中国水电出版社,1997.
[100]魏守平.水轮机控制工程[M].武汉：华中科技大学出版社,2005.
[101]方红庆,孙美凤,沈祖诒.水轮机调节系统控制策略综述[J].人民长江,2004,35(1)： 45-49.
[102]Orelind G., Adept Technol., San Jose, CA. Optimal PID gain schedule for hydrogenerators-design and application[J]. IEEE Transactions on Energy Conversion,1989,4(3):300-307.
[103]叶鲁卿.水力发电过程控制—理论、应用及其发展(第一版)[M].武汉：华中科技大学出版社,2002.
[104]魏守平.水轮机调节系统的适应式变参数调节[J].大电机技术,1985,5：48-54.
[105]魏守平,卢本捷.水轮机调速器的PID调节规律[J].水力发电学报,2003,4：112-118.
[106]蔡维由,陈光大,刘炳文.水轮机调速器的极点配置法设计及自适应控制[J].大电机技术,1995,6：47-56.
[107]孟佐宏,蔡维由,陈光大等.水轮机调节系统最优鲁棒极点配置调速器的设计[J].大电机技术,2000,4：32-35.
[108]Zhaohui Li. An orthogonal test approach based control parameter optimization and its application to a hydro-turbine governor [J]. IEEE Transactions on Energy Conversion,1997,12(4):388-393.
[109]Lansberry J. E. Optimal hydrogenerator governor tuning with a genetic algorithm[J]. IEEE Transactions on Energy Conversion,1992,7(4):623-630
[110]Lansberry, J. E. Adaptive hydrogenerator governor tuning with a genetic algorithm [J]. IEEE Transactions on Energy Conversion,1994,9(1):179-185.
[111]方红庆,沈祖诒.基于改进粒子群算法的水轮发电机组PID调速器参数优化[J].中国电机工程学报,2005,25(22)：120-124.
[112]Xiangyang Yu, Feng Yang, Yongxuan Huang, Haipeng Nan. Fuzzy immune sliding mode control based hydro turbine governor[C]. Third International Conference on Natural Computation, Haikou,2007,171-176.
[113]王柏林.水轮发电机组的模型参考自适应控制[J].自动化学报,1987,13(6)：408-415.
[114]王伯林.水轮发电机组转速自适应控制[J].水力发电学报,1989,(4).
[115]陈光大,刘炳文,蔡维由等.基于测试系统频率的自适应水轮机调速器[J].水力发电学报,1993,2：34-37
[116]Bonnett, S. C. Adaptive speed control of hydrogenerators by recursive least squares identification algorithm[J]. IEEE Transactions on Energy Conversion, 1995,10(1):162-168.
[117]Malik O. P., Zeng, Y. Design of a robust adaptive controller for a water turbine governing system[J]. IEEE Transactions on Energy Conversion,1990,10(2): 354-359.
[118]Puleva T., Garipov, E., Ruzhekov, G. Adaptive power control modeling and simulation of a hydraulic turbine[C] In:2011 16th International Conference on Intelligent System Application to Power Systems (ISAP), Piscataway,2011,1-6.
[119]O. P. Malik et. Al. An intelligent self-Improving control strategy with a variable structure and time-varing parameters for water turbine[J]. La Houille Blanche, 1989, (6):463-476.
[120]龚崇权,肖惠民,蔡维由.模糊控制在水轮机调节应用中运行模式的探讨[J].长沙电力学院学报(自然科学版),2001,16(3)：52-54.
[121]林富华,沈恩源,林建亚,等.模糊PID技术在水轮机调节系统中的应用[J].动力工程,1996,16(2)：45-48.
[122]刘建业,郑玉森,张炳达.水轮机模糊调速器研究[J].控制理论与应用,1996,13(1)：47-51.
[123]南海鹏,辛华,余向阳.基于遗传算法的水轮机模糊自适应PID调节系统控制规则[J].水电自动化与大坝监测,2005,29(2)：5-9.
[124]程远楚,水电机组智能控制策略与调速励磁协调控制的研究[D].武汉大学：武汉大学图书馆,2002.
[125]王淑青.水轮机调节系统控制策略及模型辨识方法研究[D].华中科技大学：华中科技大学图书馆,2006,11.
[126]Natarajan, K. Robust PID Controller Design for Hydroturbines[J]. IEEE Transactions on Energy Conversion,2005,20(3):661-667.
[127]孙郁松,孙元章,卢强,等.水轮机调节系统非线性H∞控制规律的研究[J].中国电机工程学报,2001,21(2)：56-59.
[128]方庆红,沈祖诒,吴凯.水轮机调节系统非线性扰动解耦控制[J].中国电机工程学报,2004,24(3)：151-155.
[129]卢强,桂小阳,梅生伟,等.大型发电机组调速器的非线性最优PSS[J].电力系统自动化,2005,29(9)：15-19.
[130]桂小阳,梅生伟,刘锋,等.水轮机调速系统的非线性自适应控制[J].中国电机工程学报,2006,26(8)：66-71.
[131]Lu Qiang, Sun Yusong, Sun Yuanzhang, et al. Nonlinear decentralized robust governor control for hydroturbine-generator sets in multi-machine power systems[J]. International Journal of Electrical Power & Energy Systems,2004, 26(5):333-339.
[132]常乃超,刘锋,梅生伟,等.水轮机导叶开度的自适应非线性输出反馈控制[J].中国电机工程学报,2008,28(17)：87-91.
[133]凌代俭,沈祖诒.考虑饱和非线性环节的水轮机调节系统的分叉分析[J].水力发电学报,2007,26(6)：126-131.
[134]Daijian Ling, Yang Tao. An analysis of the hopf bifurcation in a hydroturbine governing system with saturation[J]. IEEE Transactions on Energy Conversion, 2006,21(2):512-515.
[135]郜宪林.间隙特性对自校正水轮机调速系统的影响[J].水电能源科学,1989,7(2)：159-167.
[136]李超顺.基于模糊理论的水电机组系统辨识及故障诊断研究[D],华中科技大学：华中科技大学图书馆,2010,5.
[137]陈嘉谋.水轮机调节系统计算机仿真[M].北京：水利电力出版社,1993.
[138]卢强,梅生伟,孙元章.电力系统非线性控制[M].北京：清华大学出版社,2008.
[139]秦勇,李卫平,张春丽,等.水轮机及其调速系统在单机无穷大系统低频振荡中的作用[J].水力发电,2006,32(8)：56-58.
[140]倪以信,陈寿孙,张宝霖.动态电力系统的理论和分析[M].北京：清华大学出版社,2002.
[141]刘昌玉,魏守平.水轮机调速器的多调节模式及相互切换[J].水电能源科学,1994,12(4)：254-259.
[142]邵文权.基于水位调节模式运行的水轮机调节系统的研究[D],西安理工大学：西安理工大学图书馆,2004,3.
[143]陈燚涛.水轮机调速系统优化维护理论与实践[D],华中科技大学：华中科技大学图书馆,2005,1.
[144]M. Dehghani, S. K. Y. Nikravesh. Nonlinear state space model identification of synchronous generators[J]. Electric Power Systems Research,2008,78(5): 926-940.
[145]De Mello F P, Ribeiro J R. Derivation of synchronous machine parameters from tests[J]. IEEE Transactions Power Apparatus and Systems,1977, PAS-96(4): 1211-1218.
[146]Bortoni E d C, Jardini J A. Identification of synchronous machine parameters using load rejection test data[J]. IEEE Transactions on Energy Conversion,2002,17(2): 242-247.
[147]Wamkeue R, Kamwa I, Baetscher, et al. A new and efficient approach for analysis of a saturated synchronous generator under the load rejection test[J]. Electric Power Components and Systems,2006,34(5):539-563.
[148]Wamkeue R, Jolette C, Kamwa I. Alternative approaches for linear analysis and prediction of a synchronous generator under partial- and full-load rejection tests[J]. IET Electric Power Application,2007,1(4):581-590.
[149]Wamkeue R, Jolette C, Kairous D. Estimation of synchronous generator parameters from load rejection analytical responses [C]. In:2010 9th International Conference on Environment and Electrical Engineering, Prague,2010,392-395.
[150]Feng Ding, Peter X. Liu, Guangjun Liu. Multiinnovation Least-Squares Identification for System Modeling[J]. IEEE Transactions on systems, man and cybernetics—part B:cybernetics,2010,40(3):767-778.
[151]Wamkeue R, Baetscher F, Kamwa I. Hybrid-state-model-based time-domain identification of synchronous machine parameters from saturated load rejection test records[J]. IEEE Transactions on Energy Conversion,2008,23(1):68-77.
[152]岳陈熙,甄威,刘柏私,等.同步发电机参数的一次性任意抛载测试方法[J].高电压技术,2008,34(2)：319-323.
[153]Emile Mouni, Slim Tnani, Gerard Champenois. Synchronous generator modeling and parameters estimation using least squares method[J]. Simulation Modeling Practice and Theory,2008,16(6):678-689.
[154]刘取.电力系统稳定性及发电机励磁控制[M].北京：中国电力出版社,2007.
[155]J. T. Ma, Q. H. Wu. Generator parameter identification using evolutionary programming[J]. Electrical Power and energy systems,1995,17(6):417-423.
[156]T. Niewierowicz, R. Escarela-Perez, E. Campero-Littlewood. Hybrid genetic algorithm for the identification of high-order synchronous machine two-axis equivalent circuits[J]. Computers and Electrical Engineering,2003,29(4): 505-522.
[157]Wenxin Liu, LiLiu, Il-Yop Chung, David A. Cartes. Real-time particle swarm optimization based parameter identification applied to permanent magnet synchronous machine [J]. Applied Soft Computing,2011,11(2):2556-2564.
[158]Li Liu, Wenxin Liu, David A. Cartes. Particle swarm optimization-based parameter identification applied to permanent magnet synchronous motors[J]. Engineering Applications of Artificial Intelligence,2008,21(7): 1092-1100
[159]Bipul Luitel, Ganesh K. Venayagamoorthy. Particle swarm optimization with quantum infusion for system identification[J]. Engineering Applications of Artificial Intelligence,2010,23(5):635-649.
[160]S. N. Omkar, Rahul Khandelwal, T. V. S. Ananth, G. Narayana Naik, S. Gopalakrishnan. Quantum behaved Particle Swarm Optimization (QPSO) for multi-objective design optimization of composite structures[J]. Expert Systems with Applications,2009,36(8):11312-11322.
[161]H. Shayeghi, H. A. Shayanfar, S. Jalilzadeh, A. Safari. Tuning of damping controller for UPFC using quantum particle swarm optimizer[J]. Energy Conversion and Management,2010,51(11):2299-2306.
[162]J. Sun, B. Feng, W. Xu. Particle swarm optimization with particles having quantum behavior[C]. In:Proceedings of the IEEE congress on evolutionary computation, Perth,2004,325-331.
[163]Tzuu Bin Ng, G. J. Walker and J. E. Sargison. Modelling of Transient Behaviour in a Franci s Turbine Power Plant[C]. In:15th Australasian Fluid Mechanics Conference, Sydney,2004,13-17.
[164]S. C. Bonnen, L. Wozniak. Adaptive speed control of hydrogenerators by recursive least squares identification algorithm[J]. IEEE Transactions on Energy Conversion,1995,10(1):162-168.
[165]F. Xu, Y. Li, Q. Cheng. Study of the modeling of hydro turbine generating set[C]. In: Proceedings of the IEEE/PES Conference on Industrial Automation and Control:Emerging Technologies, Taipi,1995,644-647.
[166]Leandro dos Santos Coelho. A quantum particle swarm optimizer with chaotic mutation operator[J]. Chaos, Solitons and Fractals,2008,37(5):1409-1418.
[167]Omkar, S. N., Khandelwal, R., Ananth, T. V. S., etc. Quantum behaved particle swarm optimization (QPSO) for multi-objective design optimization of composite structures[J]. Expert Systems with Applications,2009, 36(8):11312-11322.
[168]Sabat, S. L., Coelho, L.S., Abraham, A.. ME SFET DC model parameter extraction using quantum particle swarm optimization[J]. Microelectronics Reliability,2009,49(6):660-666.
[169]Jun Sun, Wei Chen, Wei Fang, etc. Gene expression data analysis with the clustering method based on an improved quantum-behaved Particle Swarm Optimization[J]. Engineering Applications of Artificial Intelligence,2012,25(2): 376-391
[170]Fang Liu, Haibin Duan, Yimin Deng. A chaotic quantum-behaved particle swarm optimization based on lateral inhibition for image matching[J]. Optik-International Journal for Light and Electron Optics, article in press
[171]Leandro dos Santos Coelho. Gaussian quantum-behaved particle swarm optimization approaches for constrained engineering design problems[J]. Expert Systems with Applications,2010,37(2):1676-1683.
[172]Maolong Xi, Jun Sun, Wenbo Xu. An improved quantum-behaved particle swarm optimization algorithm with weighted mean best position[J]. Applied Mathematics and Computation,2008,205(2):751-759.
[173]J. Sun, W. Fang, X. J. Wu, etc. Quantum-behaved particle swarm optimization:analysis of the individual particle's behavior and parameter selection[J]. Evolutionary Computation, doi:10.1162/EVCO_a_00049.
[174]Rasool Dalirrooy Fard, Mehdi Karri. Synchronous Generator Model Identification for Control Application Using Volterra Series [J]. IEEE Transaction on Energy Conversion,2005,20(4):852-858.
[175]J. Jesus Rico Melgoza, Gerald Thomas Heydt, Ali Keyhani, etc. Synchronous Machine Parameter Estimation Using the Hartley Series[J] IEEE Transaction on Energy Conversion,2001,16(1):49-54.
[176]M. Dehghani, M. Karrari, W. Rosehart, O. P. Malik. Synchronous machine model parameters estimation by a time-domain identifcation method[J]. Electrical Power and Energy Systems,2010,32(5):524-529.
[177]L. L. Lai, J. T. Ma. Application of Evolutionary Programming to Transient and Subtransient Parameter Estimation[J]. IEEE Transactions on Energy Conversion,1996,11(3):523-530.
[178]Arjona M A, Escarela-Perez R, Espinosa-Perez G, et al. Validity testing of third-order nonlinear models for synchronous generators[J]. Electric Power Systems Research,2009,79(6):953-958
[179]Ye Luqing ect. Microprocessor-Based Adaptive governor with variable PID parameters for hydroelectric generating unit. Proceedings of mini and microcomputers and their applications[C]. Bari,1984,241-244.
[180]Ye Luqing ect. Microprocessor-based fault tolerant adaptive parmeter-optimized governor for water turbine. Proceedings of mini and microcomputers and their applications[C]. Saint Feliu Spain, June 1985:57-62.
[181]Louis Wozniak, Daniel J. Bitz. Load-lever Sensitive governor for speed control of hydrogenerators [J]. IEEE Transactions on Energy conversion,1988,3(1): 78-84.
[182]A. H. Glattfelder. Frequency control of low-head hydro units in isolated networks [J]. Water power and dam construction.1988,40(3):44-46.
[183]李华,张文华.水轮机调速器调节参数对机组一次调频的影响[J].西北电力技术,2005,33(3)：6-8.
[184]何里,孟佐宏.水电机组一次调频功能关键问题的分析与探讨[J].湖南电力,2011,31(2)：1-18.
[185]Passino K M. Biomimicry of bacterial foraging for distributed optimization and control[J]. IEEE Control Systems Magazine,2002,22(3):52-67.
[186]Hongqing Fang, Long Chen, Zuyi Shen. Application of an improved PSO algorithm to optimal tuning of PID gains for water turbine governor[J]. Energy Conversion and Management,2011,52(4):1763-1770.
[187]Donald Davendra, Ivan Zelinka, Roman Senkerika. Chaos driven evolutionary algorithms for the task of PID control[J]. Computers and Mathematics with Applications,2010,60(4):1088-1104.
[188]姚琼荟,黄继起,吴汗松.变结构控制系统[M].重庆：重庆大学出版社,1997.
[189]高为炳.变结构控制理论基础[M].北京：中国科学技术出版社,1990.
[190]陈帝伊,杨朋超,马孝义,等.水轮机调节系统的混沌现象分析及控制[J].中国电机工程学报,2011,31(14)：113-120.
[191]张江滨,解建仓,焦尚彬.水轮机最优控制系统研究[J].水力发电学报,2004,23(4)：112-121.
[192]方红庆.水力机组非线性控制策略及其工程应用研究[D],河海大学：河海大学图书馆,2005,12.
[193]廖忠.小波网络及其在水轮机调节系统中的应用研究[D],河海大学：河海大学图书馆,2005,12.
[2]国家能源局.国家能源科技“十二五”规划.2011,12.
[3]谈广鸣,舒彩文.清洁能源与优质电源论析[J].水电与新能源,2010,(1)：74-77.
[4]雷亚洲.与风电并网相关的研究课题[J].电力系统自动化,2003,27(8)：84-89.
[5]裴哲义,董存,辛耀中.我国风电并网运行最新进展[J].中国电力,2010,43(11)：78-83.
[6]沈辉,曾祖勤.太阳能光伏发电技术[M].北京：化学工业出版社,2005.
[7]李广敏.考虑光伏并网发电的短期负荷预测[D],华北电力大学：华北电力大学图书馆,2007,12.
[8]李碧君,方勇杰,杨卫东,等.光伏发电并网大电网面临的问题与对策[J].电网与清洁能源,2010,26(4)：52-58.
[9]卢勇,刘友宽,孙鹏,等.用于系统分析的机组调速系统及其原动机建模问题研究[J].电网技术,2007,31(增刊2)：123-126.
[10]李华,史可琴,范越,等.电力系统稳定计算用水轮机调速器模型结构分析[J].电网技术,2007,31(5)：25-33.
[11]印永华,郭剑波,赵建军,等.美加“8·14”大停电事故初步分析以及应吸取的教训[J].电网技术,2003,27(10)：8-16.
[12]鞠平.电力系统建模理论与方法[M].北京：科学出版社,2010.
[13]鞠平,张建勇.电力系统建模基本理论研究综述[J].电力科学与技术学报,2011,26(1)：4-12.
[14]丁锋.系统辨识(1)：辨识导论[J].南京信息工程大学学报：自然科学版,2011,3(1)：1-22.
[15]徐枋同,李永华.系统辨识理论与实践—在水电控制工程中的应用[M].北京：中国电力出版社,1999.
[16]王正风,高涛.智能电网调度运行面临的关键技术研究[J].安徽电气工程职业技术学院学报,2011,16(增刊)：64-69.
[17]Zadeh L A. From circuit therey to system thery [J]. Procedings of the IRE,1962, 50(5):856-865.
[18]Ljung L. Convergence analysis of parametric identification methods [J]. IEEE Transactions on Automatic Control,1978,23(5):770-783.
[19]侯彬媛.系统辨识及其MATLAB仿真[M].北京：科学出版社,2004.
[20]李鹏波,胡德文.系统辨识基础[M].北京：中国水利水电出版社,2006.
[21]李言俊,张科.系统辨识理论及应用[M].北京：国防工业出版社,2002.
[22]沈善德.电力系统辨识[M].北京：清华大学出版社,1999.
[23]丁锋.系统辨识(4)：辅助模型辨识思想与方法[J].南京信息工程大学学报：自然科学版,2011,3(4)：289-318.
[24]Liu Y, BaiEW. Iterative identification of Hammerstein systems [J]. Automatica, 2007,43(2):346-354.
[25]袁廷奇,刘文江.非线性Hammerstein系统辨识的动态分离方法[J].控制理论与应用,2002,19(4)：619-622.
[26]向微,陈宗海.基于Hammerstein模型描述的非线性系统辨识新方法[J].控制理论与应用,2007,24(1)：143-147.
[27]Ding F, Shi Y, Chen T W. Auxiliary model based least-squares identification methods for Hammerstein output-error systems[J]. Systems and Control Letters, 2007,56(5):373-380.
[28]Ding F, Chen T W. Combined parameter and output estimation of dual-rate systems using an auxiliary model[J]. Automatica,2004,40(10):1739-1748.
[29]Ding F, Chen T W. Identification of dual-rate systems based on finite impulse response models[J]. International Journal of Adaptive Control and Signal Processing,2004,18(7):589-598.
[30]黄正良,吴坚,万百五.辨识Wiener模型的一种新方法[J].控制理论与应用,1996,13(3)：326-332.
[31]胡德文,王正志.非线性系统Wiener模型辨识[J].自动化学报,1991,17(2)：151-159.
[32]张广莹,邓正隆.基于小波分析的非线性系统Wiener模型辨识[J].电机与控制学报,2001,5(02)：95-128.
[33]Chen S, Billings S. A., Grant P. Nonlinear system identification using neural network[J]. International Journal of Control,1990,51(6):1191-1214.
[34]Narendra K. S, Parthasarathy L. Identification and Control of Dynamical Systems Using Neural Net works[J]. IEEE Trans On Neural Networks,1990,1(1):4-27.
[35]李岩,王东风,焦嵩鸣,等.采用微分进化算法和径向基函数神经网络的热工过程模型辨识[J].中国电机工程学报,2010,30(08)：110-116.
[36]董泽,黄宇,韩璞.量子遗传算法优化RBF神经网络及其在热工辨识中的应用[J].中国电机工程学报,2008,28(17)：99-104.
[37]邹敢,李涛,肖仁鑫.粒子群算法优化神经网络的异步电机转速估计[J].太原理工大学学报,2012,43(02)：158-162.
[38]郭亮,陈维荣,贾俊波,等.基于粒子群算法的BP神经网络光伏电池建模[J].电工电能新技术,2011,30(2)：84-88.
[39]葛宏伟,李小琳,梁艳春,等.基于免疫粒子群优化的一种动态递归神经网络辨识与控制非线性系统[J].吉林大学学报(工学版),2008,38(4)：858-864.
[40]汪镭,周国兴,吴启迪.基于Hopfield神经网络的线性系统参数辨识方案及在鼠笼式电机传动系统参数辨识中的应用研究[J].中国电机工程学报,2001,21(1)：9-17.
[41]Atencia, Miguel, Joya, etc. Parametric identification of robotic systems with stable time-varying Hopfield networks[J]. Neural Computing and Applications,2004, 13(4):270-280.
[42]Hugo Alonso, Teresa Mendonca, Paula Rocha. Hopfield neural networks for on-line parameter estimation[J]. Neural Networks,2009,22(4):450-462.
[43]王立新.模糊系统与模糊控制教程[M].北京：清华大学出版社,2003.
[44]诸静.模糊控制理论与系统原理[M].北京：机械工业出版社,2005.
[45]D. J. Trudnowski, J. C. Agee. Identifying a hydraulic-turbine model from measured field data[J]. IEEE Transactions on Energy Conversion,1995,10(4): 768-773.
[46]徐枋同,陈建.水力机组动态模型在线辨识[J].水利学报,1988,(3)：28-36.
[47]The Institute of Electrical and Electronic Engineers, Inc. IEEE Std 1110-2002 IEEE guide for synchronous generator modeling practices and applications in power system stability ananlysis[S]. New York,2003.
[48]中国国家标准化管理委员会,GB/T1029-2005三相同步电机试验方法[S].北京：中国标准出版社,2006.
[49]IEEE Committee Report. Supplementary definitions and associated test methods for obtaing parameters for synchronous machine stability study simulations [J]. IEEE Transactions on Power Apparatus and Systems,1980, PAS-99(4): 1625-1633
[50]Dandeno P L, Poray A T. Development of detailed turbogenerator equivalent circuits from standardstill frequency response measurements [J]. IEEE Transactions on Power Apparatus and Systems,1981, PAS-100(4):1646-1655.
[51]Coultes M E, Watson W. Synchronous machine models by standstill frequency response tests[J]. IEEE Transactions on Power Apparatus and Systems,1981, PAS-100(4):1480-1489.
[52]IEEE PES WG 12 Resport. Experience with standstill frequency response (SSFR) testing and analysis of salient pole synchronous machines [J]. IEEE Transactions on Energy Conversion,1999,14(4):1209-1217.
[53]Bortoni E C, Jardini J A. Experience with standstill frequency response (SSFR) testing and analysis of salient pole synchronous machines[J]. IEEE Transactions on Energy Conversion,2004,19(4):687-691.
[54]Verbeeck J, Pintelon R, Lataire P. Identification of synchronous machine parameters using a multiple input multiple output approach[J]. IEEE Transactions on Energy Conversion,1999,14(4):909-917.
[55]黄淼.基于抛载试验和在线时域测试的同步发电机参数辨识新方法[D],重庆大学：重庆大学图书馆,2011,5.
[56]张承慧,刘玉庆.水轮机建模与参数识别[J].电力系统自动化,1997,21(5)：53-56.
[57]刘宪林,杜晓勇,田云峰.基于随机负荷波动的抽蓄电站水轮机模型辨识[J].中国电机工程学报,2007,27(6)：49-52.
[58]乔俊飞, 孙雅明,杜红卫.水轮发电机组的一种自适应调速方法[J].中国电机工程学报,2000,20(10)：1-9.
[59]张霄元,郑玉森,刘长胜,等.水轮机参数在线辨识及调节器参数自整定[J].电力系统及其自动化学报,2000,12(2)：16-18.
[60]C. C. Lee, Owen T. Tan. A weighted-least-squares parameter estimator for synchronous machines[J]. IEEE Transactions on Power Apparatus and Systems, 1977, PAS-96(1):97-101.
[61]Emile Mouni, Slim Tnani, Gerard Champenois. Synchronous generator modelling and parameters estimation using least squares method[J]. Simulation Modelling Practice and Theory,2008,16(6):678-689.
[62]胡虔生,金俭.同步电机瞬变参数的测试数据处理方法的研究[J].大电机技术,1999,(3)：21-25.
[63]MICHAEL BURTH, GEORGE C. VERGHESE, etc. Subset Selection for Improved Parameter Estimation in On-Line Identification of a Synchronous Generator[J]. IEEE Transactions on Power Systems,1999,14(1):218-225.
[64]郑逢时,赵争鸣,阮仕荣,等.用最小二乘辨识方法测取水轮发电机参数[J].电工技术学报,1994,(3)：1-4.
[65]Z.M. ZHAO, F.S. ZHENG, J. D. GAO, etc. A dynamic on-line parameter identification and full-scale system experimental verification for large synchronous machines [J]. IEEE Transaction on Energy Conversion 1995, EC-10(3):392-398.
[66]Karrari, M.; Malik, O. P. Synchronous generator third-order model parameter estimation using online experimental data[J]. Generation, Transmission & Distribution, IET,2008,2(5):708-719.
[67]罗建,冯树辉,蔡明等.基于可观测量的同步发电机参数的时域辨识[J].电力系统自动化,2011,35(7)：24-80.
[68]周双喜,张恒.一种新型自校正PID励磁调节器的研究[J].清华大学学报(自然科学版),1997,37：31-34.
[69]沈善德 言茂松.推广卡尔曼滤波法应用于同步机参数估计和电力系统动态等效[J].清华大学学报,1984,24(1)：33-44.
[70]Namba M. Identification of parameters for system stability analysis using Kalman filter[J]. IEEE Transactions on Power Apparatus and Systems,1981. PAS-100(7): 3304.
[71]M. Miskovic, M. Mirosevic. Making the model of synchronous generator using the date from monitoring system[C]. In:Proceedings of the 12th IEEE Mediter-ranean Electrotechnical Conference, Croatia,2004,1129-1132.
[72]C. E. Nino, M. Velez-Reyes, Dealing with ill conditioning in recursive parameter estimation for a synchronous generator[C], In:32nd Annual Conference on IEEE Industrial Electronics, Paris,2006,1089-1094.
[73]R. E. Fairbairn, R. G. Harley, On-line measurement of synchronous machine parameters [J]. IEEE Transactions on Industry Applications,1992,28 (3):639-645.
[74]Miao Huang, Wenyuan Li, Wei Yan. Estimating parameters of synchronous generators using square-root unscented Kalman filter[J]. Electric Power Systems Research,2010,80(9):1137-1144.
[75]A. Yazdizadeh. K. Khorasani. Identification of a turbo generator system using adaptive time delay neural networks[J]. In:Proceedings of the 1998 IEEE International Conference on Control Applications Trieste, Italy,1998,1-4.
[76]Chang, J., Liu, B., Cai, W.. Nonlinear simulation of hydro turbine governing system based on neural network[C]. In:Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, Beijing,1996, 784-787.
[77]Nand Kishor, S. P. Singh. Simulated response of NN based identification and predictive control of hydro plant[J]. Expert Systems with Applications,2007,32(): 233-244.
[78]Nand Kishor, R. P. Saini,S. P. Singh. Small hydro power plant identification using NNARX structure[J]. Neural Computing and Applications,2005,14(3): 212-222.
[79]Nand Kishor, S. P. Singh, A. S. Raghuvanshi. Adaptive intelligent hydro turbine speed identification with water and random load disturbances[J]. Engineering Applications of Artificial Intelligence,2007,20(6): 795-808
[80]王珊,周建中,杜思存等.基于RBF神经网络的水轮机调节系统辨识[J].水力发电,2006,3(3)：42-44.
[81]王淑青,李朝晖.基于自适应模糊神经网络的水轮机特性辨识研究[J].武汉大学学报(工学版),2006,39(2)：24-27.
[82]常江,谢云敏.混流式水轮机神经网络模型非线性仿真[J].中国农村水利水电,2004,(6)：75-77.
[83]常江,陈光大.轴流转桨式水轮机神经网络建模与非线性仿真[J].中国农村水利水电,2004,(7)：82-85.
[84]程远楚,叶鲁卿,蔡维由.水轮机特性的神经网络建模[J].华中科技大学学报(自然科学版),2003,31(6)：68-70.
[85]Srinivas Pillutla, Ali Keyhani. Neural Network Based Modeling of Round Rotor Synchronous Generator Rotor Body Parameters from Operating Data[J]. IEEE Transactions on Energy Conversion,1999,14(3):321-327.
[86]Jung-Wook Park, Ganesh Kumar Venayagamoorthy, Ronald G. Harley. MLP/RBF Neural-Networks-Based Online Global Model I dentification of Synchronous Generator[J] IEEE Transactions on Industrial Electronics,2005,52(6):1685-1695.
[87]Jung-Wook Park, R. G. Harley, G. K. Venayagamoorthy. Comparison of MLP and RBF neural networks using deviation signals for on-line identification of a synchronous generator[C]. In:Proceedings of Winter Meeting of the Power Engineering Society, New York,2002,274-279.
[88]Soheil Ganjefar, Mojtaba Alizadeh. On-Line Identification of Synchronous Generator Using Self Recurrent Wavelet Neural Networks Via Adaptive Learning Rates [C] In:The 5th International Power Engineering and Optimization Conference (PEOCO2011), Shah Alam,2011,243-248.
[89]O. Shariati, A. A. Mohd Zin, M. R. Aghamohammadi. Application of Neural Network Observer for On-line Estimation of Salient-Pole Synchronous Generators'Dynamic Parameters using the Operating Data[C].In:2011 4th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO), Kuala Lumpur,2011,19.
[90]M. Rahimpour, M. A. Talebi, H.A. Shayanfar et. al. On Line Synchronous Generator Parameters Estimation Based on Applying Small Disturbance on Excitation System Using ANN[C]. In:2009 IEEE/PES Power Systems Conference and Exposition (PSCE 2009), Piscataway,2009,1-4.
[91]李超顺,周建中,安学利等.基于T-S模糊模型的水轮机调节系统辨识[J].武汉大学学报(工学版),2010,43(1)：108-111.
[92]李超顺,周建中,向秀桥等.基于超平面原型聚类的水轮机调速系统模糊模型辨识[J].动力工程,2009,29(4)：363-388.
[93]Li C., Zhou J., Xiang X., et al. T-S fuzzy model identification based on a novel fuzzy c-regression model clustering algorithm[J]. Engineering Applications of Artificial Intelligence,2009,22(4-5):646-653.
[94]Li C., Zhou J., Li Q., et al. A new T-S fuzzy-modeling approach to identify a boiler-turbine system[J]. Expert Systems with Applications,2010,37(3): 2214-2221.
[95]Nand Kishor. Zero-order TS fuzzy model to predict hydro turbine speed in closed loop operation[J]. Applied Soft Computing,2008,8(2):1074-1084.
[96]Lin Gao, Yiping Dai, Junrong Xia. Parameter Identification of Hydro Generation System with Fluid Transients Based on Improved Genetic Algorithm[C]. In:2009 Fifth International Conference on Natural Computation, Tianjian,2009,398-402.
[97]Chaoshun Li, Jianzhong Zhou. Parameters identification of hydraulic turbine governing system using improved gravitational search algorithm[J]. Energy Conversion and Management,2011,52(1):374-381.
[98]杨小东,董宸,卢文华,等.基于遗传算法的水轮发电机组调速系统参数辨识[J].继电器,2006,34(1)：27-30.
[99]沈祖诒.水轮机调节[M].北京：中国水电出版社,1997.
[100]魏守平.水轮机控制工程[M].武汉：华中科技大学出版社,2005.
[101]方红庆,孙美凤,沈祖诒.水轮机调节系统控制策略综述[J].人民长江,2004,35(1)： 45-49.
[102]Orelind G., Adept Technol., San Jose, CA. Optimal PID gain schedule for hydrogenerators-design and application[J]. IEEE Transactions on Energy Conversion,1989,4(3):300-307.
[103]叶鲁卿.水力发电过程控制—理论、应用及其发展(第一版)[M].武汉：华中科技大学出版社,2002.
[104]魏守平.水轮机调节系统的适应式变参数调节[J].大电机技术,1985,5：48-54.
[105]魏守平,卢本捷.水轮机调速器的PID调节规律[J].水力发电学报,2003,4：112-118.
[106]蔡维由,陈光大,刘炳文.水轮机调速器的极点配置法设计及自适应控制[J].大电机技术,1995,6：47-56.
[107]孟佐宏,蔡维由,陈光大等.水轮机调节系统最优鲁棒极点配置调速器的设计[J].大电机技术,2000,4：32-35.
[108]Zhaohui Li. An orthogonal test approach based control parameter optimization and its application to a hydro-turbine governor [J]. IEEE Transactions on Energy Conversion,1997,12(4):388-393.
[109]Lansberry J. E. Optimal hydrogenerator governor tuning with a genetic algorithm[J]. IEEE Transactions on Energy Conversion,1992,7(4):623-630
[110]Lansberry, J. E. Adaptive hydrogenerator governor tuning with a genetic algorithm [J]. IEEE Transactions on Energy Conversion,1994,9(1):179-185.
[111]方红庆,沈祖诒.基于改进粒子群算法的水轮发电机组PID调速器参数优化[J].中国电机工程学报,2005,25(22)：120-124.
[112]Xiangyang Yu, Feng Yang, Yongxuan Huang, Haipeng Nan. Fuzzy immune sliding mode control based hydro turbine governor[C]. Third International Conference on Natural Computation, Haikou,2007,171-176.
[113]王柏林.水轮发电机组的模型参考自适应控制[J].自动化学报,1987,13(6)：408-415.
[114]王伯林.水轮发电机组转速自适应控制[J].水力发电学报,1989,(4).
[115]陈光大,刘炳文,蔡维由等.基于测试系统频率的自适应水轮机调速器[J].水力发电学报,1993,2：34-37
[116]Bonnett, S. C. Adaptive speed control of hydrogenerators by recursive least squares identification algorithm[J]. IEEE Transactions on Energy Conversion, 1995,10(1):162-168.
[117]Malik O. P., Zeng, Y. Design of a robust adaptive controller for a water turbine governing system[J]. IEEE Transactions on Energy Conversion,1990,10(2): 354-359.
[118]Puleva T., Garipov, E., Ruzhekov, G. Adaptive power control modeling and simulation of a hydraulic turbine[C] In:2011 16th International Conference on Intelligent System Application to Power Systems (ISAP), Piscataway,2011,1-6.
[119]O. P. Malik et. Al. An intelligent self-Improving control strategy with a variable structure and time-varing parameters for water turbine[J]. La Houille Blanche, 1989, (6):463-476.
[120]龚崇权,肖惠民,蔡维由.模糊控制在水轮机调节应用中运行模式的探讨[J].长沙电力学院学报(自然科学版),2001,16(3)：52-54.
[121]林富华,沈恩源,林建亚,等.模糊PID技术在水轮机调节系统中的应用[J].动力工程,1996,16(2)：45-48.
[122]刘建业,郑玉森,张炳达.水轮机模糊调速器研究[J].控制理论与应用,1996,13(1)：47-51.
[123]南海鹏,辛华,余向阳.基于遗传算法的水轮机模糊自适应PID调节系统控制规则[J].水电自动化与大坝监测,2005,29(2)：5-9.
[124]程远楚,水电机组智能控制策略与调速励磁协调控制的研究[D].武汉大学：武汉大学图书馆,2002.
[125]王淑青.水轮机调节系统控制策略及模型辨识方法研究[D].华中科技大学：华中科技大学图书馆,2006,11.
[126]Natarajan, K. Robust PID Controller Design for Hydroturbines[J]. IEEE Transactions on Energy Conversion,2005,20(3):661-667.
[127]孙郁松,孙元章,卢强,等.水轮机调节系统非线性H∞控制规律的研究[J].中国电机工程学报,2001,21(2)：56-59.
[128]方庆红,沈祖诒,吴凯.水轮机调节系统非线性扰动解耦控制[J].中国电机工程学报,2004,24(3)：151-155.
[129]卢强,桂小阳,梅生伟,等.大型发电机组调速器的非线性最优PSS[J].电力系统自动化,2005,29(9)：15-19.
[130]桂小阳,梅生伟,刘锋,等.水轮机调速系统的非线性自适应控制[J].中国电机工程学报,2006,26(8)：66-71.
[131]Lu Qiang, Sun Yusong, Sun Yuanzhang, et al. Nonlinear decentralized robust governor control for hydroturbine-generator sets in multi-machine power systems[J]. International Journal of Electrical Power & Energy Systems,2004, 26(5):333-339.
[132]常乃超,刘锋,梅生伟,等.水轮机导叶开度的自适应非线性输出反馈控制[J].中国电机工程学报,2008,28(17)：87-91.
[133]凌代俭,沈祖诒.考虑饱和非线性环节的水轮机调节系统的分叉分析[J].水力发电学报,2007,26(6)：126-131.
[134]Daijian Ling, Yang Tao. An analysis of the hopf bifurcation in a hydroturbine governing system with saturation[J]. IEEE Transactions on Energy Conversion, 2006,21(2):512-515.
[135]郜宪林.间隙特性对自校正水轮机调速系统的影响[J].水电能源科学,1989,7(2)：159-167.
[136]李超顺.基于模糊理论的水电机组系统辨识及故障诊断研究[D],华中科技大学：华中科技大学图书馆,2010,5.
[137]陈嘉谋.水轮机调节系统计算机仿真[M].北京：水利电力出版社,1993.
[138]卢强,梅生伟,孙元章.电力系统非线性控制[M].北京：清华大学出版社,2008.
[139]秦勇,李卫平,张春丽,等.水轮机及其调速系统在单机无穷大系统低频振荡中的作用[J].水力发电,2006,32(8)：56-58.
[140]倪以信,陈寿孙,张宝霖.动态电力系统的理论和分析[M].北京：清华大学出版社,2002.
[141]刘昌玉,魏守平.水轮机调速器的多调节模式及相互切换[J].水电能源科学,1994,12(4)：254-259.
[142]邵文权.基于水位调节模式运行的水轮机调节系统的研究[D],西安理工大学：西安理工大学图书馆,2004,3.
[143]陈燚涛.水轮机调速系统优化维护理论与实践[D],华中科技大学：华中科技大学图书馆,2005,1.
[144]M. Dehghani, S. K. Y. Nikravesh. Nonlinear state space model identification of synchronous generators[J]. Electric Power Systems Research,2008,78(5): 926-940.
[145]De Mello F P, Ribeiro J R. Derivation of synchronous machine parameters from tests[J]. IEEE Transactions Power Apparatus and Systems,1977, PAS-96(4): 1211-1218.
[146]Bortoni E d C, Jardini J A. Identification of synchronous machine parameters using load rejection test data[J]. IEEE Transactions on Energy Conversion,2002,17(2): 242-247.
[147]Wamkeue R, Kamwa I, Baetscher, et al. A new and efficient approach for analysis of a saturated synchronous generator under the load rejection test[J]. Electric Power Components and Systems,2006,34(5):539-563.
[148]Wamkeue R, Jolette C, Kamwa I. Alternative approaches for linear analysis and prediction of a synchronous generator under partial- and full-load rejection tests[J]. IET Electric Power Application,2007,1(4):581-590.
[149]Wamkeue R, Jolette C, Kairous D. Estimation of synchronous generator parameters from load rejection analytical responses [C]. In:2010 9th International Conference on Environment and Electrical Engineering, Prague,2010,392-395.
[150]Feng Ding, Peter X. Liu, Guangjun Liu. Multiinnovation Least-Squares Identification for System Modeling[J]. IEEE Transactions on systems, man and cybernetics—part B:cybernetics,2010,40(3):767-778.
[151]Wamkeue R, Baetscher F, Kamwa I. Hybrid-state-model-based time-domain identification of synchronous machine parameters from saturated load rejection test records[J]. IEEE Transactions on Energy Conversion,2008,23(1):68-77.
[152]岳陈熙,甄威,刘柏私,等.同步发电机参数的一次性任意抛载测试方法[J].高电压技术,2008,34(2)：319-323.
[153]Emile Mouni, Slim Tnani, Gerard Champenois. Synchronous generator modeling and parameters estimation using least squares method[J]. Simulation Modeling Practice and Theory,2008,16(6):678-689.
[154]刘取.电力系统稳定性及发电机励磁控制[M].北京：中国电力出版社,2007.
[155]J. T. Ma, Q. H. Wu. Generator parameter identification using evolutionary programming[J]. Electrical Power and energy systems,1995,17(6):417-423.
[156]T. Niewierowicz, R. Escarela-Perez, E. Campero-Littlewood. Hybrid genetic algorithm for the identification of high-order synchronous machine two-axis equivalent circuits[J]. Computers and Electrical Engineering,2003,29(4): 505-522.
[157]Wenxin Liu, LiLiu, Il-Yop Chung, David A. Cartes. Real-time particle swarm optimization based parameter identification applied to permanent magnet synchronous machine [J]. Applied Soft Computing,2011,11(2):2556-2564.
[158]Li Liu, Wenxin Liu, David A. Cartes. Particle swarm optimization-based parameter identification applied to permanent magnet synchronous motors[J]. Engineering Applications of Artificial Intelligence,2008,21(7): 1092-1100
[159]Bipul Luitel, Ganesh K. Venayagamoorthy. Particle swarm optimization with quantum infusion for system identification[J]. Engineering Applications of Artificial Intelligence,2010,23(5):635-649.
[160]S. N. Omkar, Rahul Khandelwal, T. V. S. Ananth, G. Narayana Naik, S. Gopalakrishnan. Quantum behaved Particle Swarm Optimization (QPSO) for multi-objective design optimization of composite structures[J]. Expert Systems with Applications,2009,36(8):11312-11322.
[161]H. Shayeghi, H. A. Shayanfar, S. Jalilzadeh, A. Safari. Tuning of damping controller for UPFC using quantum particle swarm optimizer[J]. Energy Conversion and Management,2010,51(11):2299-2306.
[162]J. Sun, B. Feng, W. Xu. Particle swarm optimization with particles having quantum behavior[C]. In:Proceedings of the IEEE congress on evolutionary computation, Perth,2004,325-331.
[163]Tzuu Bin Ng, G. J. Walker and J. E. Sargison. Modelling of Transient Behaviour in a Franci s Turbine Power Plant[C]. In:15th Australasian Fluid Mechanics Conference, Sydney,2004,13-17.
[164]S. C. Bonnen, L. Wozniak. Adaptive speed control of hydrogenerators by recursive least squares identification algorithm[J]. IEEE Transactions on Energy Conversion,1995,10(1):162-168.
[165]F. Xu, Y. Li, Q. Cheng. Study of the modeling of hydro turbine generating set[C]. In: Proceedings of the IEEE/PES Conference on Industrial Automation and Control:Emerging Technologies, Taipi,1995,644-647.
[166]Leandro dos Santos Coelho. A quantum particle swarm optimizer with chaotic mutation operator[J]. Chaos, Solitons and Fractals,2008,37(5):1409-1418.
[167]Omkar, S. N., Khandelwal, R., Ananth, T. V. S., etc. Quantum behaved particle swarm optimization (QPSO) for multi-objective design optimization of composite structures[J]. Expert Systems with Applications,2009, 36(8):11312-11322.
[168]Sabat, S. L., Coelho, L.S., Abraham, A.. ME SFET DC model parameter extraction using quantum particle swarm optimization[J]. Microelectronics Reliability,2009,49(6):660-666.
[169]Jun Sun, Wei Chen, Wei Fang, etc. Gene expression data analysis with the clustering method based on an improved quantum-behaved Particle Swarm Optimization[J]. Engineering Applications of Artificial Intelligence,2012,25(2): 376-391
[170]Fang Liu, Haibin Duan, Yimin Deng. A chaotic quantum-behaved particle swarm optimization based on lateral inhibition for image matching[J]. Optik-International Journal for Light and Electron Optics, article in press
[171]Leandro dos Santos Coelho. Gaussian quantum-behaved particle swarm optimization approaches for constrained engineering design problems[J]. Expert Systems with Applications,2010,37(2):1676-1683.
[172]Maolong Xi, Jun Sun, Wenbo Xu. An improved quantum-behaved particle swarm optimization algorithm with weighted mean best position[J]. Applied Mathematics and Computation,2008,205(2):751-759.
[173]J. Sun, W. Fang, X. J. Wu, etc. Quantum-behaved particle swarm optimization:analysis of the individual particle's behavior and parameter selection[J]. Evolutionary Computation, doi:10.1162/EVCO_a_00049.
[174]Rasool Dalirrooy Fard, Mehdi Karri. Synchronous Generator Model Identification for Control Application Using Volterra Series [J]. IEEE Transaction on Energy Conversion,2005,20(4):852-858.
[175]J. Jesus Rico Melgoza, Gerald Thomas Heydt, Ali Keyhani, etc. Synchronous Machine Parameter Estimation Using the Hartley Series[J] IEEE Transaction on Energy Conversion,2001,16(1):49-54.
[176]M. Dehghani, M. Karrari, W. Rosehart, O. P. Malik. Synchronous machine model parameters estimation by a time-domain identifcation method[J]. Electrical Power and Energy Systems,2010,32(5):524-529.
[177]L. L. Lai, J. T. Ma. Application of Evolutionary Programming to Transient and Subtransient Parameter Estimation[J]. IEEE Transactions on Energy Conversion,1996,11(3):523-530.
[178]Arjona M A, Escarela-Perez R, Espinosa-Perez G, et al. Validity testing of third-order nonlinear models for synchronous generators[J]. Electric Power Systems Research,2009,79(6):953-958
[179]Ye Luqing ect. Microprocessor-Based Adaptive governor with variable PID parameters for hydroelectric generating unit. Proceedings of mini and microcomputers and their applications[C]. Bari,1984,241-244.
[180]Ye Luqing ect. Microprocessor-based fault tolerant adaptive parmeter-optimized governor for water turbine. Proceedings of mini and microcomputers and their applications[C]. Saint Feliu Spain, June 1985:57-62.
[181]Louis Wozniak, Daniel J. Bitz. Load-lever Sensitive governor for speed control of hydrogenerators [J]. IEEE Transactions on Energy conversion,1988,3(1): 78-84.
[182]A. H. Glattfelder. Frequency control of low-head hydro units in isolated networks [J]. Water power and dam construction.1988,40(3):44-46.
[183]李华,张文华.水轮机调速器调节参数对机组一次调频的影响[J].西北电力技术,2005,33(3)：6-8.
[184]何里,孟佐宏.水电机组一次调频功能关键问题的分析与探讨[J].湖南电力,2011,31(2)：1-18.
[185]Passino K M. Biomimicry of bacterial foraging for distributed optimization and control[J]. IEEE Control Systems Magazine,2002,22(3):52-67.
[186]Hongqing Fang, Long Chen, Zuyi Shen. Application of an improved PSO algorithm to optimal tuning of PID gains for water turbine governor[J]. Energy Conversion and Management,2011,52(4):1763-1770.
[187]Donald Davendra, Ivan Zelinka, Roman Senkerika. Chaos driven evolutionary algorithms for the task of PID control[J]. Computers and Mathematics with Applications,2010,60(4):1088-1104.
[188]姚琼荟,黄继起,吴汗松.变结构控制系统[M].重庆：重庆大学出版社,1997.
[189]高为炳.变结构控制理论基础[M].北京：中国科学技术出版社,1990.
[190]陈帝伊,杨朋超,马孝义,等.水轮机调节系统的混沌现象分析及控制[J].中国电机工程学报,2011,31(14)：113-120.
[191]张江滨,解建仓,焦尚彬.水轮机最优控制系统研究[J].水力发电学报,2004,23(4)：112-121.
[192]方红庆.水力机组非线性控制策略及其工程应用研究[D],河海大学：河海大学图书馆,2005,12.
[193]廖忠.小波网络及其在水轮机调节系统中的应用研究[D],河海大学：河海大学图书馆,2005,12.
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