Optimal robust adaptive fuzzy synergetic power system stabilizer design

A new particle swarm optimized robust indirect adaptive power system stabilizer is developed based on recently developed synergetic control methodology. Fuzzy systems are used in an adaptive scheme to approximate the system using a nonlinear model while synergetic control guarantees robustness and the use of a chatter free continuous control law which makes the controller easy to implement. In addition the controller parameters are optimized using PSO approach. Simulation of severe operating conditions of a power system is conducted to validate the effectiveness of the proposed approach while stability is guaranteed via Lyapunov synthesis.     

Robust nonlinear adaptive backstepping excitation controller design for rejecting external disturbances in multimachine power systems

This paper proposes a new approach to design a robust adaptive backstepping excitation controller for multimachine power systems in order to reject external disturbances. The parameters which significantly affect the stability of power systems (also called stability sensitive parameters) are considered as unknown and the external disturbances are incorporated into the power system model. The proposed excitation controller is designed in such a way that it is adaptive to the unknown parameters and robust to external disturbances. The stability sensitive parameters are estimated through the adaptation laws and the convergences of these adaptation laws are obtained through the negative semi-definiteness of control Lyapunov functions (CLFs). The proposed controller not only provides robustness property against external disturbances but also overcomes the over-parameterization problem of stability sensitive parameters which usually appears in some conventional adaptive methods. Finally, the performance of the proposed controller is tested on a two-area four machine 11-bus power system by considering external disturbances under different scenarios and is compared to that of an existing nonlinear adaptive backstepping controller. Simulation results illustrate the robustness of the proposed controller over an existing one in terms of rejecting external disturbances.     

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.     

基于滑模控制的模糊电力系统稳定器

为改善模糊电力系统稳定器(FPSS)的动态性能,提出了基于滑模控制的FPSS的控制策略.在该控制策略中,电力系统稳定器(PSS)由主控制器、自组织控制器和滑模控制器三部分构成,其中,主控制器用于产生PSS的基本输出信号;自组织控制器可根据滑模控制器输出的优化条件对主控制器进行优化设计.还提出了采用滑模控制器代替传统的线性控制器来输出优化条件.理论分析和仿真结果表明,文中FPSS的动态性能优于常规的模糊稳定器.     

模糊式电力系统稳定器的研究

本文成功地将模糊控制理论应用于电力系统稳定器设计，设计了一种基于规则和表格的智能型电力系统稳定器。数字仿真实验和动模实验表明了其有效性。由于采用了基于规则的人工智能技术，避免了因建立数学模型以及控制中测量信号不真实带来的麻烦。以８０８６为主机的ＳＴＤ工业控制机和一台ＰＣ机为基础，研制出一台用微机实现的模糊式电力系统稳定器的样机。     

交直流互联系统鲁棒自适应直流功率调制

设计了应用于交直流互联电力系统的直流功率调制的非线性鲁棒自适应控制器。该控制器基于驱动各互联区域电网的惯量中心至统一平衡点的设计思想，采用广域测量系统的全局信号，用以阻尼交直流互联系统的区域间功率振荡。采用反步法设计的自适应鲁棒控制规律使控制器对未知参数具有自适应性，对模型误差、扰动和平衡点变化具有较强的鲁棒性。仿真结果表明，与传统的线性直流功率调制控制器相比，该控制器对联络线的功率振荡具有优良的阻尼性能，可显著提高输电极限，而且能很好地适应运行点的变化。     

Optimal tunning of type-2 fuzzy logic power system stabilizer based on differential evolution algorithm

In this paper, a type-2 fuzzy logic power system stabilizer with differential evolution algorithm is proposed. As an extension of type-1 fuzzy logic theory, type-2 fuzzy logic theory can effectively improve the control performance by uncertainty of membership function especially when we have to confront with less expert knowledge or unpredicted external disturbances. The corresponding parameters and rule base of type-2 fuzzy logic power system stabilizer are optimally tuned by using differential evolution algorithm for multi-machine power system. Through simulation under different operational conditions, the results demonstrate the effectiveness of the proposed approach for damping the power system electromechanical oscillations.     

采用协同控制理论的同步发电机非线性励磁控制

提出了一种基于协同控制理论的非线性励磁控制器。首先依据同步发电机励磁控制的基本要求和特点,选择机端电压、有功功率和转子角速度三个变量的偏差的线性组合构成流形,以保证有效控制机端电压和抑制系统功率振荡。然后以同步发电机非线性模型为对象,推导出了非线性协同励磁控制器（Synergetic excitation controller,SEC）的控制律,并根据电力系统的运行特性,探讨了控制器参数的选取原则。最后,单机无穷大系统仿真结果表明,无论在大扰动还是在小扰动下,所提非线性协同励磁控制器比常规的AVR+PSS方式下的励磁控制器都能更快更精确地调节机端电压,还能够有效地抑制系统的功率振荡。     

Damping inter-area modes of oscillation using an adaptive fuzzy power system stabilizer

This paper introduces an indirect adaptive fuzzy controller as a power system stabilizer used to damp inter-area modes of oscillation following disturbances in power systems. Compared to the IEEE standard multi-band power system stabilizer (MB-PSS), indirect adaptive fuzzy-based stabilizers are more efficient because they can cope with oscillations at different operating points. A nominal model of the power system is identified on-line using a variable structure identifier. A feedback linearization-based control law is implemented using the identified model. The gains of the controller are tuned via a particle swarm optimization routine to ensure system stability and minimum sum of the squares of the speed deviations. A bench-mark problem of a 4-machine 2-area power system is used to demonstrate the performance of the proposed controller and to show its superiority over other conventional stabilizers used in the literature.     

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.     

基于遗传算法的模糊式电力系统稳定器自寻优设计法

通过对模糊控制器工作原理的深入分析,提出了模糊控制本质上是一种分段定常控制的观点,并以此为基础,提出了模糊式电力系统稳定器的自寻优设计新方法,它将模糊控制器的整个设计过程转化成对一组参数进行寻优的过程,解决了在传统设计过程中存在的规则难于获取和参数值难于确定的问题。为了提高多参数寻优的效率,文中提出了一种群体差异度遗传算法,它可以大幅度地缩小搜索空间,从而极大地减少了参数优化过程中的计算工作量。仿真结果显示,用该方法设计的模糊式电力系统稳定器的性能得到了较大提高。     

基于强跟踪滤波器的自适应励磁控制器

该文引入强跟踪滤波器用于发电机励磁控制,建立了一种新的自适应励磁控制器.该控制器根据发电机运行状态由强跟踪滤波器实时估计系统的扰动信息,准确地获得单机无穷大模型中的系统参数,以调整控制器,使之得以适应系统运行点的变化以及系统遭受的干扰.仿真研究表明,文中所建立的自适应励磁控制器可以有效地提高电力系统的稳定性,在系统遭受不同干扰下均表现出良好的动、静态性能.     

A variable structure adaptive neural network static VAR controller

A variable structure adaptive neural network power system static VAR stabilizer is developed. The static VAR compensator (SVC) controlled by the above proposed controller is used for voltage regulation and enhancing power system stability. The artificial neural network (ANN) is trained off-line using the variable structure control system Benchmark data at different operating conditions and external disturbances. Moreover, the trained ANN parameters (weights and biases) are tuned and updated on-line using the synchronous machine speed deviation state as the ANN output error to increasingly improve the power system performance. A sample digital simulation result of the power system speed deviation state responses when reference voltage, speed deviation state and input power disturbances take place are obtained. The digital simulation results prove the effectiveness and robustness of the present adaptive neural network in terms of a high performance power system.     

基于模糊神经网络的模糊控制器综合优化设计Ⅱ．模糊电力系统稳定器

针对单机—无穷大电力系统，利用所提出的模糊控制器综合优化设计方法设计了基于模糊神经网络的模糊电力系统稳定器（ＦＮＮ－ＰＳＳ），并由数字仿真证明了所提出方法的有效性和实用性。     

PSS与HVDC调制器的在线协调控制策略

提出了模糊神经网络电力系统稳定器与高压直流调制器的自适应在线协调控制策略,改善了系统在大扰动后的动态品质。该协调控制策略采用辨识器预测各个发电机在下一个采样点的角速度变化,并以其为变量构造目标函数对模糊控制器进行在线训练,使功角摇摆过程中各发电机间角速度差值的平方和最小。算法用于三区域双馈入直流系统的在线协调控制。仿真结果表明,自适应在线协调策略使系统在不同的运行状态和不同的故障下均具有良好的暂态稳定性,抑制了故障后系统的振荡。     

Design of a nonlinear power system stabilizer using synergetic control theory

Electromechanical oscillations of small magnitude and low frequency exist in the interconnected power system and often persist for long periods of time. Power system stabilizers (PSSs) are traditionally used to provide damping torque for the synchronous generators to suppress the oscillations by generating supplementary control signals for the generator excitation system. Numerous techniques have previously been proposed to design PSSs but many of them are synthesized based on a linearized model. This paper presents a nonlinear power system stabilizer based on synergetic control theory. Synergetic synthesis of the PSS is based fully on a simplified nonlinear model of the power system. The dynamic characteristics of the proposed PSS are studied in a typical single-machine infinite-bus power system and compared with the cases with a conventional PSS and without a PSS. Simulation results show the proposed PSS is robust for such nonlinear dynamic system and achieves better performance than the conventional PSS in damping oscillations.     

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.     

电力系统非线性鲁棒自适应分散励磁控制设计

利用反步法设计了多机电力系统中的非线性鲁棒自适应励磁控制方案,控制目标是调节发电机功角和频率至稳态运行点的极小领域,并使闭环系统对发电机阻尼系数和电抗参数的不确定性具有自适应能力,且对模型误差和外部有界干扰具备鲁棒性,同时保证各控制器是分散化和本地化的。采用4机系统进行的数字仿真结果表明,实施此方案能有效地提高发电机的功角稳定性。     

A QFT-based robust SVC controller for improving the dynamic stability of power systems

A new design technique for an SVC-based power system damping controller has been proposed. The controller attempts to place all plant poles within a specified region on the s-plane to guarantee the desired closed loop performance. The use of Horowitz's quantitative feedback theory (QFT) permits the design of a `fixed gain controller' that maintains its performance in spite of large variations in the plant parameters during its normal course of operation. The required controller parameters are arrived at by solving an optimization problem that incorporates the control specifications. The performance of this robust controller has been evaluated on a single machine infinite bus system equipped with a mid point SVC, and the results are shown to be consistent with the expected performance of the stabilizer.     

A fuzzy basis function network based power system stabilizer for generator excitation control

A fuzzy basis function network (FBFN) based power system stabilizer (PSS) is presented in this paper. The proposed FBFN-based PSS provides a natural framework for combining numerical and linguistic information in a uniform fashion. The proposed FBFN is trained over a wide range of operating conditions in order to retune the PSS parameters in real-time, based on machine loading conditions. The orthogonal least squares (OLS) learning algorithm is developed for designing an adequate and parsimonious FBFN model. Time domain simulations of a synchronous machine equipped with the proposed stabilizer subject to major disturbances are investigated. The performance of the proposed FBFN PSS is compared with that of a conventional power system stabilizer (CPSS) to demonstrate the superiority of the proposed stabilizer. The effect of parameter changes on the proposed stabilizer performance is also examined. The results show the robustness of the proposed FBFN PSS and its capability to enhance system damping over a wide range of operating conditions.