基于BP神经网络的最优励磁控制器

设计了一种新型的基于BP神经网络的最优励磁控制器(NNOEC)。在线性最优励磁控制的基础上,利用4层BP神经网络对发电机的运行方式和系统所遭受的干扰类型进行辨识,通过对网络的训练,使得网络能够实时根据发电机的状态量来调节最优控制的反馈矩阵,以适应当前的运行点和所遭受的干扰。仿真结果表明,所设计的NNOEC在系统运行方式较大的变化范围内都能提供很好的控制性能,在大小扰动下均表现出很好的阻尼特性和良好的电压性能。     

基于在线辨识的实时最优励磁控制器

设计了一种在线实时最优励磁控制器（RTOEC）,其辨识器能够由发电机运行状态的变化采用非线性最小二乘法辨识法准确辨识出单机无穷大模型中的系统参数,从而可根据辨识结果及其系统所遭受的扰动,通过线性最优控制理论实时计算出线性最优励磁控制的反馈增益矩阵,以适应系统当前的运行点和所遭受的干扰。仿真结果表明,所设计的RTOEC能够适应系统运行状态的大范围变化,在大小扰动下均表现出良好的控制性能,有效提高了电力系统的静态和暂态稳定性。     

神经网络电力系统稳定器之研究

基于动态神经网络构造了一种电力系统稳定器(NNPSS)通过单、多机数字仿真研究了神经网络控制器应用于电力系统实时控制所面临的两个问题：未知扰动下控制输出的合理性；(2)单机—无穷大母线系统下训练的控制器在多机系统中的控效果。文章详细介绍了NNPSS的设计思想，训练样本选取单、多机仿真结果分析。     

采用输出反馈方式的电力系统非线性励磁控制

现有的电力系统非线性励磁控制器多基于状态反馈设计,需要测量同步发电机的转子角和部分变量的微分,并且控制器以发电机相对转子角驱至参考值为控制目标,在系统的网络参数或运行方式变化时无法维持机端电压恒定。采用输出反馈的方式提出一种新的非线性励磁控制器设计方法。分别针对单机无穷大系统及多机系统,对系统的非线性数学模型提出了新的变换方法,将其转换为以机端电压偏差、角速度偏差和有功功率偏差为状态变量的不确定线性系统,进而应用一种基于线性矩阵不等式（linear matrix inequality,LMI）的鲁棒控制理论设计出控制器。该控制器不需要测量发电机转子角及任何变量的微分,不依赖于电力系统某个特定运行点,能适应系统网络参数与运行方式变化,较好地抑制扰动并实现机端电压调节功能。仿真结果验证了所得结论。     

一种基于离线策略表的实时变增益励磁控制器

设计了一种基于离线策略表的实时励磁控制器（RSLOEC）。利用发电机实际可能的运行样本依据最优控制原理离线形成反馈增益策略表。通过在线辨识出的样本组[Pe,Ut]经离线策略表插值拟合出最优反馈控制增益，以满足实时性的要求，从而弥补传统LOEC不能适应大干扰的明显缺陷，提高励磁系统的稳定性。给出了Matlab仿真软件包的实现及励磁系统采用RSLOEC提高稳定性的仿真结果。     

同步发电机的非线性鲁棒电压控制

提出了一种同步发电机非线性鲁棒电压控制器设计方法,解决了目前多数非线性励磁控制方法依赖于系统的特定运行点并且难以考虑电压调节精度的局限。针对发电机经变压器接入电力系统的数学模型,将其转换为以机端电压偏差、角速度偏差和有功功率偏差为状态变量的不确定线性系统,进而采用基于LMI的鲁棒控制理论设计出非线性电压控制器。与现有的非线性励磁控制器相比,所提出的电压控制器不依赖于系统的特定运行点,能适应网络参数与运行方式变化,实现机端电压调节功能。控制器对模型干扰有强鲁棒性,无须针对单机无穷大系统和多机电力系统分别设计。此外,控制器无须测量发电机转子角及任何变量的微分与积分。利用PSCAD/EMTDC对某单机无穷大系统及WSCC四机系统分别测试,仿真结果验证了控制器的良好性能。     

多机电力系统附加NNPSS的在线学习神经网络逆励磁控制器

为提高发电机机端电压和转速的综合控制性能,设计了附加神经网络电力系统稳定器(NNPSS)的在线学习神经网络逆(OLANNI)励磁控制器。针对多机系统同步发电机组模型,根据逆系统方法得到发电机励磁系统的逆系统的表达形式,并通过离线训练得到发电机励磁系统的神经网络逆系统。借鉴传统的AVR/PSS控制方法,并考虑到其对电力系统不确定性的自适应能力的不足,在离线训练的基础上分别设计了自适应的OLANNI、NNPSS以取代传统的AVR、PSS,给出了基于在线梯度算法的OLANNI和NNPSS的在线学习算法,并根据Lyapunov稳定性理论证明了OLANNI和NNPSS在线学习的收敛性。将设计的控制器应用于一个典型的2区域4机系统,仿真研究结果表明:在系统遭受扰动时,所设计的控制器较AVR/PSS和OLANNI控制器具有更好的综合控制性能。     

基于神经网络的电力系统控制器在线自学习研究

分析了现有的基于人工神经网络的电力系统实时控制器设计与训练中存在的问题,在此基础上提出了一种新的具体训练样本在线自动生成能力的电力系统实时控制器设计方案,从理论上论证了该方案的可行性,并依据这一方案实现了神经网络电力系统稳定器的设计,在线训练与控制的仿真结果均以分显示出该方案的优越性。     

考虑交互作用的多阻尼控制器独立设计的控制环选择

针对交互作用引起的多控制器协调设计问题,提出基于BAB方法的控制环选择法。该方法选择出的控制环对相应弱阻尼或负阻尼模式有较大可控可观性,同时交互作用足够小,因此各控制器参数独立设计即可达到多控制器协调的目的。在考虑时滞电力系统模型的基础上,建立了多控制器控制环选择模型。所提方法包括:适用于求解控制环选择的多目标组合优化问题的BAB方法和从BAB法得到的Pareto最优控制环集中选择最适控制环的方法。最后以2区域4机系统和新英格兰10机39节点系统为例进行测试,验证了该方法的有效性,而且在这些控制环基础上独立设计的控制器具有很好的多控制器协调效果。     

基于Adaline神经网络参数辨识的PMSM鲁棒电流预测控制

针对复杂工况下永磁同步电机存在模型参数失配导致控制系统性能下降的问题，提出一种基于参数在线辨识的鲁棒电流预测控制方法。首先，建立永磁同步电机预测控制模型，详细分析电磁参数失配对电机响应电流及输出转矩和转速的影响。然后，设计了基于Adaline神经网络的参数在线辨识器，并在传统的权值调整算法上，提出一种应用于电机参数辨识系统的新型动态混合最小均方算法。最后，利用在线辨识的参数来实时更新电流预测控制器中的参数，以避免参数失配对控制系统性能的影响。通过仿真和实验验证了所提方法和新型算法的可行性和有效性，其结果表明了该方法不仅能够实现精准在线跟踪电机参数的变化，而且有效抑制了参数失配导致的响应电流偏差。     

考虑控制量限幅的发电机非线性分散鲁棒汽门控制

提出了一种冗余控制量情况下考虑控制量限幅的非线性反馈控制方法,这种方法通过在动态过程的任一时间点上求解一个具有不等式约束的最优化问题得到该时刻各控制量值,使实际能够给出的虚拟控制量尽量达到保证系统渐近稳定所需要的虚拟控制量要求。将该方法与引入量测变量的建模方法相结合,设计了可同时考虑高压、中低压缸汽门控制的多机系统非线性分散鲁棒汽门控制器。对一个多机交直流系统的数字仿真表明,所设计的控制器是有效的。     

Robust coordinated design of multiple and multi-type damping controller using differential evolution algorithm

A robust coordination scheme to improve the stability of a power system by optimal design of multiple and multi-type damping controllers is presented in this paper. The controllers considered are power system stabilizer (PSS) and static synchronous series compensator (SSSC)-based controller. Local measurements are provided as input signals to all the controllers. The coordinated design problem is formulated as an optimization problem and differential evolution (DE) algorithm is employed to search for the optimal controller parameters. The performance of the proposed controllers is evaluated for both single-machine infinite-bus power system and multi-machine power system. Nonlinear simulation results are presented over a wide range of loading conditions and system configurations to show the effectiveness and robustness of the proposed coordinated design approach. It is observed that the proposed controllers provide efficient damping to power system oscillations under a wide range of operating conditions and under various disturbances. Further, simulation results show that, in a multi-machine power system, the modal oscillations are effectively damped by the proposed approach.     

汽轮发电机汽门开度的分散非线性预测控制

基于非线性预测控制理论,设计出具有闭型解析解控制律的汽轮发电机汽门开度分散非线性预测控制器。该控制器并不要求进行在线优化计算,从而避免了繁重的计算负担。控制器的输入为当地可测量信号且与网络参数无关,从而实现了分散控制。通过在6机22母线系统的仿真结果表明,该控制器能提高电力系统的暂态稳定性和动态性能。     

Adaptive Power System Stabilizer Design Using Optimal Support Vector Machines Based on Harmony Search Algorithm

Abstract—This article presents the application of support vector machines to adaptive power system stabilizer design in a multi-machine power system based on the harmony search algorithm. Data from a multi-machine power system are the input features of the support vector machines. Support vector machine parameters and power system features are simultaneously optimized by harmony search based on the k-fold cross-validation technique. The proposed algorithm is trained by the optimal support vector machine parameters and optimal power system features. Power system stabilizer parameters produced by the proposed algorithm can be adapted by various operating conditions when the power system operates either inside or outside the training ranges. Simulation studies in the IEEJ Western Japan ten-machine power system demonstrate that the proposed algorithm is far superior to conventional power system stabilizers with fixed parameters and those designed by a robust coupled vibration model under various operating conditions and severe disturbances.     

An adaptive neuro-control approach for multi-machine power systems

We investigate an adaptive neuro-control approach, namely goal representation heuristic dynamic programming (GrHDP), and study the nonlinear optimal control on the multi-machine power system. Compared with the conventional control approaches, the proposed controller conducts the adaptive learning control and assumes unknown of the power system mathematic model. Besides, the proposed design can provide an adaptive reward signal that guides the power system dynamic performance over time. In this paper, we integrate the novel neuro-controller into the multi-machine power system and provide adaptive supplementary control signals. For fair comparative studies, we include the control performance with the conventional heuristic dynamic programming (HDP) approach under the same conditions. The damping performances with and without the conventional power system stabilizer (PSS) are also presented for comparison. Simulation results verify that the investigated neuro-controller can achieve improved performance in terms of the transient stability and robustness under different fault conditions.     

Modified vector controlled DFIG windenergy system based on barrier functionadaptive sliding mode control

Increased penetration of wind energy systems has serious concerns on power system stability. In spite of several advantages, doubly fed induction generator (DFIG) based wind energy systems are very sensitive to grid disturbances. DFIG system with conventional vector control is not robust to disturbances as it is based on PI controllers. The objective of this paper is to design a new vector control that is robust to external disturbances. To achieve this, inner current loop of the conventional vector control is replaced with sliding mode control. In order to avoid chattering effect and achieve finite time convergence, the control gains are selected based on positive semi-definite barrier function. The proposed barrier function adaptive sliding mode (BFASMC) is evaluated by testing it on a benchmark multi-machine power system model under various operating conditions. The simulated results show that the proposed method is robust to various disturbances.     

Intelligent optimal sieving method for FACTS device control in multi-machine systems

A multi-target oriented optimal control strategy for FACTS devices installed in multi-machine power systems is presented in this paper, which is named the intelligent optimal sieving control (IOSC) method. This new method divides the FACTS device output region into several parts and selects one typical value from each part, which is called output candidate. Then, an intelligent optimal sieve is constructed, which predicts the impacts of each output candidate on a power system and sieves out an optimal output from all of the candidates. The artificial neural network technologies and fuzzy methods are applied to build the intelligent sieve. Finally, the real control signal of FACTS devices is calculated according to the selected optimal output through inverse system method. Simulation has been done on a three-machine power system and the results show that the proposed IOSC controller can effectively attenuate system oscillations and enhance the power system transient stability.     

基于多状态Markov模型和逆最优理论的HVDC附加控制器

高压直流输电(HVDC)的附加控制器可以显著改善交直流混联系统的稳定性,但电力系统在随机大扰动下呈现出连续时间动态和离散事件相互作用的混杂特性,使得基于系统固定工况的传统建模方法无法全面描述系统的运行状态。针对此问题,提出了一种基于多状态Markov模型和逆最优理论的HVDC附加控制器。首先建立了多区域交直流混联系统的多状态Markov模型,在此基础上,提出了交直流混联Markov系统的逆最优反推控制策略,该策略将非线性系统最优控制问题转化为渐近稳定控制律的求解问题,不仅能够确保随机扰动下系统的鲁棒稳定性,还解决了具有非线性特性的系统全局优化问题,同时也避免了求解HJI偏微分方程,使控制策略易于工程实现。在选择控制参数时,以设备停运率替代Markov系统的状态转移率,因而可在满足系统多状态特性的同时,避免由于实际系统运行方式的复杂性给控制策略的实现带来的困难。仿真结果表明,与传统的控制器相比,所提的HVDC附加控制器能显著提高交直流混联系统的暂态稳定性。