Reaching phase free adaptive fuzzy synergetic power system stabilizer

In this paper, an adaptive fuzzy power system stabilizer is developed based on robust synergetic control theory and terminal attractor techniques. The main contribution consists in making the dynamic system insensitive to parameters variation. This aim is achieved using a new synergetic controller design such that power system states start, evolve and remain on a designer chosen attractor toward the equilibrium point therefore avoiding transient mode. Rendering the design more robust, fuzzy logic systems are used to approximate the unknown power system dynamic functions without calling upon usual model linearization and simplifications. Based on an indirect adaptive scheme and Lyapunov theory, adaptation laws are developed to make the controller handle parameters variations due to the different operating conditions occurring on the power system and to guarantee stability. The performance of the proposed stabilizer is evaluated for a single machine infinite bus system and for a multi machine power system under different type of disturbances. Simulation results show the effectiveness and robustness of the proposed stabilizer in damping power system oscillations under various disturbances and better overall performance than classical PSS and some other types of power stabilizers.     

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.     

Damping of multi-modal oscillations in power systems using adual-rate adaptive stabilizer

The ability of a dual-rate adaptive stabilizer to damp multimode oscillations in a power system is investigated. The controller adjusts its parameters to track the dominant frequency of oscillation and damps different modes one by one according to their dominance. The results show that adaptive and conventional stabilizers working on different units within a system can operate cooperatively and mutually support each other     

组合电力系统混合自适应稳定器的设计

为了抑制电力系统因负阻尼而产生的低频振荡,提高电力系统稳定性,设计了一种组合电力系统混合自适应稳定器(GPSS)。与传统设计不同,它包括几个自适应GPSS,且这些稳定器同时又并联工作。稳定控制信号由各个单一的自适应GPSS加权获得,同时又研究了加权系数的选择方法。理论分析和仿真结果都表明该方案对抑制电力系统低频振荡有显著效果,能提高电网运行质量,保障电网可靠、优质供电。     

An indirect adaptive fuzzy-logic power system stabiliser

An indirect adaptive fuzzy power system stabiliser (AFPSS) is developed using the concept of fuzzy basis functions. The power system is modelled using differential equations with nonlinear parameters which are functions of the state of the system. These nonlinear functions may not be known, however, some linguistic information is available about them. Utilising this information, fuzzy logic systems are designed to model the system behaviour. The control law is obtained using the uncertainty principle. Based on the Lyapunov's synthesis method, adaptation rules are developed to make the controller adaptive to changes in operating conditions of the power system. The simulation studies are carried out for an industrial cogenerator and utilise a one-machine infinite-bus model. Nonlinear simulations reveal that the performance of AFPSS is better than the performance of a conventional (linear) power system stabiliser for a wide range of operating conditions.     

Applying structured singular value to multi-machine power system stabilizer design

A power system stabilizer design method for multi-machine power systems is proposed in this paper. In this design method, the design problem is translated into an equivalent problem of decentralized controller design for Multi-Input Multi-Output (MIMO) control systems. Subject to a condition based on the structured singular values, each stabilizer can be designed independently. The robust stability condition for power systems with stabilizers on can be easily stated as to achieve a sufficient interaction margin defined in this paper, and a sufficient gain and phase margin defined in the classical feedback theory during each independent design. Within this general framework, the conventional stabilizer design methodology based on the concept of synchronous and damping torques is used to decide the design details of each stabilizer. The suggested design method is applied to a model of a practical 10 machine power system.     

Adaptive fuzzy logic load frequency control of multi-area power system

Based on indirect adaptive fuzzy control technique, a new load frequency control (LFC) scheme for multi-area power system is proposed. The power systems under study have the characterization of unknown parameters. Local load frequency controller is designed using the frequency and tie-line power deviations of each area. In the controller design, the approximation capabilities of fuzzy systems are employed to identify the unknown functions, formulate suitable adaptive control law and updating algorithms for the controller parameters. It is proved that the proposed controller ensures the boundedness of all variables of the closed-loop system and the tracking error. Moreover, in the proposed controller an auxiliary control signal is introduced to attenuate the effect of fuzzy approximation error and to mitigate the effect of external disturbance on the tracking performance. Simulation results of a three-area power system are presented to validate the effectiveness of the proposed LFC and show its superiority over a classical PID controller.     

Adaptive tuning of power system stabilizers using radial basis function networks

A novel approach for on-line adaptive tuning of power system stabilizer (PSS) parameters using radial basis function networks (RBFNs) is presented in this paper. The proposed RBFN is trained over a wide range of operating conditions and system parameter variations in order to re-tune PSS parameters on-line based on real-time measurements of machine loading conditions. The orthogonal least squares (OLS) learning algorithm is developed for designing an adequate and parsimonious RBFN model. The simulation results of the proposed radial basis function network based power system stabilizer (RBFN PSS) are compared to those of conventional stabilizers in case of a single machine infinite bus (SMIB) system as well as a multimachine power system (MMPS). The effect of system parameter variations on the proposed stabilizer performance is also examined. The results show the robustness of the proposed RBFN PSS and its ability to enhance system damping over a wide range of operating conditions and system parameter variations. The major features of the proposed RBFN PSS are that it is of decentralized nature and does not require on-line model identification for tuning process. These features make the proposed RBFN PSS easy to tune and install.     

On the eigenvalue control of electromechanical oscillations byadaptive power system stabilizer

The eigenvalue control strategy, which utilizes an adaptive power system stabilizer, is presented for the decentralized control of damping and frequency of electromechanical oscillations in power systems. The control procedure includes the complete identification of the decoupled subsystem model in real-time from local measurements only and the assignment of its estimated electromechanical eigenvalue by the change of stabilizer parameters. The robustness and efficiency of the proposed adaptive controller to enhance overall system stability are illustrated by several examples, including the three-machine power system model     

Location and design of stabilizers in multimachine power systems

A new procedure for the design of decentralized power system stabilizers (PSS) in multimachine power systems (MMPS) is presented in this paper. In the proposed approach, the generators most effective for stabilizer applications are first identified using participation factors and mode controllability matrix. The method determines the parameters of stabilizers by assigning mechanical modes at desired locations. The algorithm uses transfer function matrix between inputs and outputs, to assign the mechanical modes.     

Design of a Proportional-Integral Power System Stabilizer

Stabilization of power systems is investigated using a proportional-integral (PI) power system stabilizer. Digital (sampled-data) PI stabilizers as well as analog (continuous-time) PI stabilizers are examined in this paper. Two approaches, viz., the root-locus method and the suboptimal regulator method, are presented for determining the optimal stabilizer gains of the proposed PI stabilizer. The dynamic responses following a step disturbance by digital simulation are obtained by means of three types of stabilizers: the conventional power system stabilizer, the optimal stabilizer and the PI stabilizer. Simulation results show that the proposed PI stabilizer yields better system dynamic performance than the others in the sense of having greater damping in response to a step disturbance. A characteristic feature of the proposed PI stabilizer is that it is very simple for practical implementation, especially in the digital case, as commercial PI controllers have been widely employed by the industry for years.     

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.     

电力系统自组织模糊神经网络稳定器

本文提出一种自组织模糊神经网络稳定器，首先根据专家经验知识构成稳定器模糊模型，然后用自组织模糊神经网络来表示这个模型，最后通过组织误差修正学习来实现模型的自适应过程，仿真试验表明它比普遍模型稳定器性能优且有较好的鲁棒性。     

A quantitative design approach to PSS tuning

This paper presents the application of quantitative feedback design techniques for tuning stabilizers in multi-machine power systems. This approach facilitates easy handling of multiple plant models thereby yielding robust and reliable stabilizer parameters. Methods of incorporating closed-loop stability and damping performance requirements into the design are explained. In the proposed sequential tuning technique, bounds on the stabilizer frequency response are computed for stability and performance at each of the given set of operating conditions of the system. A manual controller shaping then yields the desired stabilizer parameters. Application to an illustrative textbook example of an 11-bus, four-generator system is also included.     

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.     

电力系统稳定器的配置方法

电力系统稳定器PSS是一闭环运行的控制器,传统的参与因子配置法只考虑了PSS输入信号的可控可观性,当系统负载较重时,得到的配置结论可能不合理。考虑PSS输入信号和输出信号馈入侧2方面的影响,提出了电力系统稳定器配置的综合参与因子法。最后以4机2区系统为例,分析了运行方式改变对PSS控制效果的影响,并利用综合参与因子法选择了PSS配置机,该方法适用于重载系统,比传统的参与因子配置法更有效。     

Adaptive PSS using a simple on-line identifier and linear pole-shift controller

Implementation of an adaptive power system stabilizer (APSS) and experimental studies are presented in this paper. The APSS consists of an adaptive linear element (ADALINE) based identifier that identifies the power system as a third-order discrete auto-regressive moving average (ARMA) model and a pole-shift controller. The ADALINE is modeled so that its weights have a one-to-one relationship with the ARMA model parameters. The weights are updated at each sampling interval to track the dynamic characteristics of the actual system. The on-line updated ARMA parameters are used in the PS control algorithm to calculate the new closed-loop poles of the system that are always inside the unit circle in the z-plane. The calculated control is such that it achieves regulation of the system to a constant setpoint in the shortest interval of time. Experimental studies on a physical model of power system verify that the proposed adaptive PSS effectively damps the oscillations and improves power system stability.     

基于模糊推理的模糊自适应励磁控制器

本文将模糊控制理论和线性最优控制理论结合起来，提出一种模糊自适应励磁控制器的设计方法。     

Design of a model reference adaptive stabilizer for the exciter andgovernor loops of power generators

The authors present a decentralized model reference adaptive control (DMRAC) scheme for the design of power system stabilizers (PSS) and a means for coordinating the generating unit excitation and governor control loops. In the excitation and governor control loops. In the proposed scheme, the state variables of the generating unit (GU) are to track those of an explicitly specified reference system which is designed to have desirable performance characteristics. The adaptive control law for coordinating the exciter-governor stabilizer signals is derived from a Lyapunov energy function, and thus assures system stability. Decentralized regulation and tracking tests on simulated one-machine infinite bus system show significant improvement in system performance     

迭代学习控制用于StatCom阻尼区域间振荡的研究

ＳｔａｔＣｏｍ一般安装在长距离输电线路上,用于提高电力系统的稳定性,阻尼振荡,提高电压控制的精度及系统的传输能力。