基于Kalman滤波算法的状态估计及风电机组可靠性建模与优化研究
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摘要
论文的研究由两部分组成。一方面从状态估计的重要性出发研究了基于Kalman滤波(KF)算法的几种状态估计方法。从最基本的Kalman滤波算法出发,研究了扩展Kalman滤波(EKF)算法,基于Unscented变换的Kalman滤波算法(UKF),及其带约束条件的UKF算法,并提出了一种改进型扩展卡尔曼滤波一抗衰减因子扩展Kalman滤波算法。并将这些算法进行了应用研究。
另一方面以风力发电为背景,研究了复杂系统的可靠性建模与优化的问题。风力发电是目前最有发展前景的新能源之一,论文研究了风力发电装置运行可靠性的计算方法,建立了基于马尔可夫过程的风力发电系统整机的可靠性模型,并在此基础提出了一种新的风电装置的维修策略的优化方法。论文的主要研究工作及研究成果如下：
1.研究了几种基于KF算法的状态估计算法,主要包括KF算法、EKF算法、UKF算法和约束UKF算法。通过三相异步电机控制系统中重要变量的状态估计,进行了EKF算法和UKF算法性能的对比研究。
2.将Kalman滤波器用于电机控制系统中。基于实验室风力发电半物理实验装置,根据现场采集的数据对发电机进行建模和控制。研究表明,带有Kalman滤波器的内模PID (IMC-PID)控制方法取得较好的控制效果。
3.针对系统噪声统计特性不准确时会导致滤波器滤波发散的问题,提出了一种新的抗衰减因子扩展卡尔曼滤波算法,通过仿真验证了新算法的有效性。
4.鉴于UKF算法优越的性能,以及发酵过程复杂、高度非线性和难于在线测量重要反应物变量等特点,本文提出一应用创新—将UKF算法用在酿酒酵母发酵过程中进行重要状态变量的状态估计,仿真结果表明了这一应用的有效性。
5.以目前世界各国使用较多的水平轴双馈型风力发电机组为例进行可靠性建模与维修策略的优化研究。首先建立了基于马尔可夫转移过程理论的可靠性数学模型,然后构造了包含机组老化、故障和维修等环节的马尔可夫过程模型,以维修间隔为优化变量,对机组可靠性实施优化。在此基础上,确定一个优化后的维修间隔,应用马尔可夫决策方法对风电机组进行维修策略的优化。
The thesis consists of two parts. On the one hand, due to the importance of the state estimation several state estimation methods based on Kalman filter (KF) algorithm are studied. According to the principle of the basic Kalman filter algorithm, extended Kalman filter (EKF) algorithm, Kalman filter algorithm based on Unscented Transform (UKF), and UKF algorithm with state constraints are studied, and an improved extended Kalman filter-Anti-fading extended Kalman filter algorithm is proposed. Furthermore, the applications of the algorithms above are studied.
On the other hand, taking wind power generation as the background, reliability modeling and optimization method of complex system is studied. Wind power generation is one of the most promising new energy, firstly, the calculation method of wind power plant operation reliability is studied, then a reliability model based on Markov processes of wind power generation system is established, and at last a new maintenance strategy optimization method of wind power plant is proposed. The main contribution and the results of the thesis are as follows:
1. Several state estimation algorithms based on the KF are studied, including KF algorithm, EKF algorithm, UKF algorithm and constrained UKF algorithm. In order to compare the performance of EKF and UKF algorithm, state estimation of important variables in three-phase induction motor control system is implemented.
2. Kalman filter is applied in the motor control system. Based on semi-physical simulation system of wind turbine in the laboratory, modeling and controlling of the generator are performed according to the field collected data. The results show that IMC-PID control method with Kalman filter obtains better control effect.
3. According to the problem that the wrong system noise attributes will lead to filter divergence, the thesis proposes a new Anti-fading extended Kalman filter algorithm and shows the effectiveness of the new algorithm through the simulation results.
4. In view of excellent performance of UKF algorithm, and features on complex reaction process, highly nonlinear and difficulty to measure the important reactants variables online of the fermentation process, the thesis presents an application innovation--the UKF algorithm is applied in state estimation of saccharomyces cerevisiae fermentation reaction process for the important state variables, simulation results show the effectiveness of this application.
5. In the thesis, a horizontal axis double-fed wind generator which is used in many countries around the world nowadays is studied. A reliability model is established and a maintenance strategy optimization method is proposed for the whole wind turbine system. Firstly, a reliability model is established based on Markov processes theory. Then, a Markov processes model of a wind turbine's deterioration, failures and maintenance is built. Taking maintenance interval as the optimization variable, developed model is applied for reliability optimization of the maintenance strategy. And further, Markov decision method is applied to optimize the maintenance strategy based on a determined maintenance interval time of the wind turbine.
另一方面以风力发电为背景,研究了复杂系统的可靠性建模与优化的问题。风力发电是目前最有发展前景的新能源之一,论文研究了风力发电装置运行可靠性的计算方法,建立了基于马尔可夫过程的风力发电系统整机的可靠性模型,并在此基础提出了一种新的风电装置的维修策略的优化方法。论文的主要研究工作及研究成果如下：
1.研究了几种基于KF算法的状态估计算法,主要包括KF算法、EKF算法、UKF算法和约束UKF算法。通过三相异步电机控制系统中重要变量的状态估计,进行了EKF算法和UKF算法性能的对比研究。
2.将Kalman滤波器用于电机控制系统中。基于实验室风力发电半物理实验装置,根据现场采集的数据对发电机进行建模和控制。研究表明,带有Kalman滤波器的内模PID (IMC-PID)控制方法取得较好的控制效果。
3.针对系统噪声统计特性不准确时会导致滤波器滤波发散的问题,提出了一种新的抗衰减因子扩展卡尔曼滤波算法,通过仿真验证了新算法的有效性。
4.鉴于UKF算法优越的性能,以及发酵过程复杂、高度非线性和难于在线测量重要反应物变量等特点,本文提出一应用创新—将UKF算法用在酿酒酵母发酵过程中进行重要状态变量的状态估计,仿真结果表明了这一应用的有效性。
5.以目前世界各国使用较多的水平轴双馈型风力发电机组为例进行可靠性建模与维修策略的优化研究。首先建立了基于马尔可夫转移过程理论的可靠性数学模型,然后构造了包含机组老化、故障和维修等环节的马尔可夫过程模型,以维修间隔为优化变量,对机组可靠性实施优化。在此基础上,确定一个优化后的维修间隔,应用马尔可夫决策方法对风电机组进行维修策略的优化。
The thesis consists of two parts. On the one hand, due to the importance of the state estimation several state estimation methods based on Kalman filter (KF) algorithm are studied. According to the principle of the basic Kalman filter algorithm, extended Kalman filter (EKF) algorithm, Kalman filter algorithm based on Unscented Transform (UKF), and UKF algorithm with state constraints are studied, and an improved extended Kalman filter-Anti-fading extended Kalman filter algorithm is proposed. Furthermore, the applications of the algorithms above are studied.
On the other hand, taking wind power generation as the background, reliability modeling and optimization method of complex system is studied. Wind power generation is one of the most promising new energy, firstly, the calculation method of wind power plant operation reliability is studied, then a reliability model based on Markov processes of wind power generation system is established, and at last a new maintenance strategy optimization method of wind power plant is proposed. The main contribution and the results of the thesis are as follows:
1. Several state estimation algorithms based on the KF are studied, including KF algorithm, EKF algorithm, UKF algorithm and constrained UKF algorithm. In order to compare the performance of EKF and UKF algorithm, state estimation of important variables in three-phase induction motor control system is implemented.
2. Kalman filter is applied in the motor control system. Based on semi-physical simulation system of wind turbine in the laboratory, modeling and controlling of the generator are performed according to the field collected data. The results show that IMC-PID control method with Kalman filter obtains better control effect.
3. According to the problem that the wrong system noise attributes will lead to filter divergence, the thesis proposes a new Anti-fading extended Kalman filter algorithm and shows the effectiveness of the new algorithm through the simulation results.
4. In view of excellent performance of UKF algorithm, and features on complex reaction process, highly nonlinear and difficulty to measure the important reactants variables online of the fermentation process, the thesis presents an application innovation--the UKF algorithm is applied in state estimation of saccharomyces cerevisiae fermentation reaction process for the important state variables, simulation results show the effectiveness of this application.
5. In the thesis, a horizontal axis double-fed wind generator which is used in many countries around the world nowadays is studied. A reliability model is established and a maintenance strategy optimization method is proposed for the whole wind turbine system. Firstly, a reliability model is established based on Markov processes theory. Then, a Markov processes model of a wind turbine's deterioration, failures and maintenance is built. Taking maintenance interval as the optimization variable, developed model is applied for reliability optimization of the maintenance strategy. And further, Markov decision method is applied to optimize the maintenance strategy based on a determined maintenance interval time of the wind turbine.
引文
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[4]凌禹,张同庄.变速风力发电系统控制技术综述[J].电力自动化设备,2008,28(3)：122-125
[5]Boukhezzar B, Lupu L, Siguerdidjane H, et al. Multivariable control strategy for variable speed, variable pitch wind turbines[J]. Renewable Energy,2007(32):1273-1287
[6]赵仁德,王永军,张加胜.直驱式永磁同步风力发电系统最大功率追踪控制[J].中国电机工程学报,2009,29(27)：106-112
[7]Luenberger D. An introduction to observers[J]. IEEE Transactions on automatic control, 1971 (6):596-602
[8]高媛.最优和自校正多传感器观测融合滤波方法和算法研究[D].哈尔滨：黑龙江大学,2010
[9]张爱军.水下潜器组合导航定位及数据融合技术研究[D].南京：南京理工大学,2009
[10]Kalman R. A new approach to linear filtering and prediction problems [J]. Journal of basic Engineering,1960 (1):35-45
[11]潘泉,杨峰,叶亮,梁彦,程咏梅.一类非线性滤波器——UKF综述[J].控制与决策,2005(05)：481-489,494
[12]Julier S., Uhlmann J. Unscented filtering and nonlinear estimation[J]. Proceedings of the IEEE,2004 (3):401-422
[13]程水英.无味变换与无味卡尔曼滤波[J].计算机工程与应用,2008(24)：25-35
[14]Julier S., Uhlmann J. The scaled unscented transformation. Proceedings of the American Control Conference,2002:4555-4559
[15]Julier S., Uhlmann J., Durrant-Whyte H. A new approach for filtering nonlinear systems. American Automatic Control Council, Evanston, IL,1995:1628-1632
[16]耿延睿,李大字,郭文荣.衰减因子自适应估计卡尔曼滤波比较研究[J].控制工程,2006(S2)：70-72
[17]Simon D., Chia T. Kalman filtering with state equality constraints[J]. IEEE transactions on Aerospace and Electronic Systems,2002 (1):128-136
[18]Saha N., Roy D. Extended Kalman filters using explicit and derivative-free local linearizations[J]. Applied Mathematical Modelling,2009 (6):2545-2563
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