Pole placement approach for robust optimum design of PSS and TCSC-based stabilizers using reinforcement learning automata

Power system stability enhancement via robust optimum design of power system stabilizers (PSSs) and thyristor controlled series capacitor (TCSC)-based stabilizers is thoroughly investigated in this paper. The design problem of PSS and TCSC-based stabilizers is formulated as an optimization problem where a reinforcement learning automata-based optimization algorithm is applied to search for the optimal setting of the proposed PSS and CSC parameters. A pole placement based objective function is considered to shift the dominant system eigenvalues to the left in the s-plane. For evaluation of the effectiveness and robustness of the proposed stabilizers, their performances have been examined on a weakly connected power system subjected to different disturbances, loading conditions, and system parameter variations. The nonlinear simulation results and eigenvalues analysis demonstrate the high performance of the proposed stabilizers and their ability to provide efficient damping of low frequency oscillations. In addition, it is observed that the proposed CSC has greatly improved the voltage profile of system under severe disturbances.     

Coordinated Design of Power System Stabilizers and Static Phase Shifters Using Genetic Algorithm

Coordinated design of a power system stabilizer (PSS) and a static phase shifter (SPS) using genetic algorithm (GA) is investigated in this paper. The design problem of PSS and SPS controller is formulated as an optimization problem. An eigenvalue-based objective function to increase the system damping is proposed. Then, GA is employed to search for optimal controller parameters. Different control schemes have been proposed and tested on a weakly connected power system with different disturbances, loading conditions, and parameter variations. It was observed that although the PSS enhances the power system stability, the SPS controller provides most of the damping and improves the voltage profile of the system. The nonlinear simulation results show the effectiveness and robustness of the proposed control schemes over a wide range of loading conditions and system parameter variations.     

基于新型模拟退火算法的PSS参数优化方法

提出一种基于单纯形-模拟退火算法的电力系统稳定器(PSS)参数优化方法.以弱阻尼机电模式阻尼比构建目标函数,将单纯形法搜寻机理嵌入到模拟退火算法的基本步骤中,综合模拟退火算法全局搜索能力强、单纯形算法局部收敛速度快的优点.在新英格兰典型系统上的特征值分析表明,该方法是一种有效的PSS优化方法,所得的参数对系统运行方式的变化具有良好的鲁棒性.     

An efficient heuristic optimization technique for robust power system stabilizer design

Design of a power system stabilizer (PSS) using simulated annealing (SA) heuristic optimization technique is presented in this paper. Two different PSSs are proposed, namely, simulated annealing based PSS (SPSS) and robust SPSS (RSPSS). The proposed approach employs SA to search for optimal or near optimal settings of (RSPSS). The proposed approach employs SA to search for optimal or near optimal settings of PSS parameters. An objective function that shifts the system eigenvalues associated with the electromechanical modes to the left in the s-plane is proposed. The robustness of the proposed SPSS and RSPSS over a wide range of loading conditions and system parameter uncertainities is investigated. The nonlinear simulation results show the effectiveness of the proposed PSSs to damp out the low frequency oscillations and work effectively over a wide range of loading conditions and system parameter uncertainities.     

Analysis and assessment of STATCOM-based damping stabilizers for power system stability enhancement

Power system stability enhancement via STATCOM-based stabilizers is thoroughly investigated in this paper. This study presents a singular value decomposition (SVD)-based approach to assess and measure the controllability of the poorly damped electromechanical modes by STATCOM different control channels. The coordination among the proposed damping stabilizers and the STATCOM internal ac and dc voltage controllers has been taken into consideration. The design problem of STATCOM-based stabilizers is formulated as an optimization problem. For coordination purposes, a time domain-based multiobjective junction to improve the system stability as well as ac and dc voltage regulation is proposed. Then, a real-coded genetic algorithm (RCGA) is employed to search for optimal stabilizer parameters. This aims to enhance both rotor angle stability and voltage regulation of the power system. The proposed stabilizers are tested on a weakly connected power system with different disturbances and loading conditions. The nonlinear simulation results show the effectiveness and robustness of the proposed control schemes over a wide range of loading conditions. It is also observed that the proposed STATCOM-based damping stabilizers extend the critical clearing time (CCT) and enhance greatly the power system transient stability.     

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.     

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.     

Multi-machine power system stabilizer design by rule based bacteria foraging

Several power system stabilizers (PSS) connected in number of machines in a multi-machine power systems, pose the problem of appropriate tuning of their parameters so that overall system dynamic stability can be improved in a robust way. Based on the foraging behavior of Escherichia coli bacteria in human intestine, this paper attempts to optimize simultaneously three constants each of several PSS present in a multi-machine power system. The tuning is done taking an objective function that incorporates a multi-operative condition, consisting of nominal and various changed conditions, into it. The convergence with the proposed rule based bacteria foraging (RBBF) optimization technique is superior to the conventional and genetic algorithm (GA) techniques. Robustness of tuning with the proposed method was verified, with transient stability analysis of the system by time domain simulations subjecting the power system to different types of disturbances.     

Interconnected multi-machine powersystem stabilizer design using whaleoptimization algorithm

The role of Power System Stabilizer (PSS) in the power system is to provide necessary damping torque to the system in order to suppress the oscillations caused by a variety of disturbances that occur frequently and maintain the stability of the system. In this paper, a PSS design technique is proposed using Whale Optimization Algorithm (WOA) by considering eigenvalue objective function. Two bench mark multi machine test systems: three- generator nine- bus system, two- area four- generator inter connected system working on various operating conditions are considered as case studies and tested with the proposed technique. Extensive simulation results are obtained and effectiveness of proposed WOA-PSS are compared with well - known PSO and DE based stabilizers under several disturbances.     

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.     

基于混合粒子群优化算法的PSS参数优化

将一种新的进化算法—粒子群优化算法(PSO)应用到电力系统稳定器(PSS)参数优化当中,文中使用引入交叉操作的混合粒子群优化算法(HPSO),可以获得更好的全局搜索能力和收敛速度。先以低频振荡范围内(0.1~2Hz)PSS产生的附加阻尼转矩ΔTe与Δω尽可能同相位为目标优化PSS超前-滞后环节参数;再以小扰动时发电机功率和角速度振荡最小为目标整定PSS放大倍数。优化结果表明,HPSO算法可以有效地解决PSS参数优化问题。     

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.     

Power system stabilizer design based on the pole assignment technique for SIMO systems

A new power system stabilizer (PSS) design method for single-machine infinite-bus systems is developed. This design method not only assigns the poles corresponding to the electro-mechanical oscillation modes in the system, but also locates other system poles in suitable places in the s-plane. The design procedure is used first to translate a PSS design problem to an equivalent constant output feedback controller design problem for a single-input multi-output (SIMO) system. Then, a new algorithm developed in this paper is applied to design a constant output feedback controller to achieve the desired closed-loop system pole locations. Finally, the controller gains are transferred back to the parameters in the PSS. The extension of the method to multi-machine power systems is also illustrated.     

Robust decentralized multi-machine power system stabilizer design using quantitative feedback theory

A new robust power system stabilizer (PSS) design using Quantitative Feedback Theory (QFT) for damping electromechanical modes of oscillations and enhancing power system stability is proposed in this paper. The design procedure is carried out on a multi-input–multi-output (MIMO), non-minimum phase and unstable plant. A multi-machine electric power system with system parametric uncertainties is considered as a case study. To show the effectiveness of the QFT technique, the proposed method is compared with a conventional PSS (CPSS) whose parameters are tuned using the classical lead-lag compensation and genetic algorithms. Several nonlinear time-domain simulation tests indicate that the suggested control scheme is robust to the changes in the system parameters and also to successfully reject the disturbances. The results also show that the performance of the QFT method given in this paper is more desirable than CPSS and genetic algorithm (GA).     

Time delay approach for PSS and SSSC based coordinated controller design using hybrid PSO–GSA algorithm

In this work we present a novel approach in order to improve the power system stability, by designing a coordinated structure composed of a power system stabilizer and static synchronous series compensator (SSSC)-based damping controller. In the design approach various time delays and signal transmission delays owing to sensors are included. This is a coordinated design problem which is treated as an optimization problem. A new hybrid particle swarm optimization and gravitational search algorithm (hPSO–GSA) algorithm is used in order to find the controller parameters. The performance of single-machine infinite-bus power system as well as the multi-machine power systems are evaluated by applying the proposed hPSO–GSA based controllers (PSS and damping controller). Various results are shown here with different loading condition and system configuration over a wide range which will prove the robustness and effectiveness of the above design approach. From the results it can be observed that, the proposed hPSO–GSA based controller provides superior damping to the power system oscillation on a wide range of disturbances. Again from the simulation based results it can be concluded that, for a multi-machine power system, the modal oscillation which is very dangerous can be easily damped out with the above proposed approach.     

基于进化策略的多机系统PSS参数优化

该文提出了一种基于进化策略的多机系统PSS参数优化的新方法。在这种方法中，目标函数设定为度量所有机电振荡模态性能的函数，将PSS的参数优化表示为带不等式约束的非光滑优化问题。进化策略用于该优化问题的求解，从而找出PSS的优化参数。进化策略属现代全局优化方法的一种，它对优化问题本身几乎无任何限制，冈其具有全局寻优能力，故可得到比常规优化方法更好的结果。且进化策略直接采用实型编码，因而可提高优化计算的效率。Anderson 3机系统和New England 10机系统的仿真结果表明，该方法是一种有效的参数优化方法，得到的优化参数对系统运行方式的变化具有良好的鲁棒性。     

Coordinated Synthesis of Multimachine Power System Stabilizer Using an Efficient Decentralized Modal Control (DMC) Algorithm

This paper presents a new method for simultaneously selecting the power system stabilizer (PSS) parameters in multimachine power systems. Design of local output feedback power system stabilizer using local signal of the generator (e.g. speed or power) is formulated as a decentralized modal control (DMC) problem. Exact model reduction based on modal control theory is proposed to make the determination of PSS parameters highly efficient. Results obtained from the coordinated tuning of the parameters of three power system stabilizers equipped on a multimachine power system show that exact assignment of the eigenvalues associated with the poorly damped electromechanical modes can be achieved in a very efficient manner.     

Design of stabilizers by pole assignment with output feedback

Large interconnected power systems are provided with power system stabilizers to damp out the low frequency rotor oscillations introduced by fast and high gain excitation systems. Stabilizing signals from rotor speed, frequency and accelaration are commonly used. Classical control techniques have been widely used in the design of power system stabilizers although modern control theory has been applied recently.Three methods for stabilizer design using pole assignment technique with output feedback are presented. The first method uses modal control techniques with a least-square minimization method. The second and third involve static optimization. An algorithm for pole assignment by Sirisena and Choi is used in the second method, while a new algorithm for placing the closed loop poles inside a sector is used in the third method. Numerical examples are given to illustrate the methods.     

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.