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.     

A hybrid neuro-fuzzy power system stabilizer for multimachine powersystems

A fuzzy basis function network (FBFN) based power system stabilizer (PSS) is presented in this paper to improve power system dynamic stability. 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 re-tune 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 single machine infinite bus system and a multimachine power system subject to major disturbances are investigated. The performance of the proposed FBFN PSS is compared with that of conventional (CPSS). The results show the capability of the proposed FBFN PSS to enhance the system damping of local modes of oscillations over a wide range of operating conditions. The decentralized nature of the proposed FBFN PSS makes it easy to install and tune     

A fuzzy logic-based self tuning power system stabilizer optimized with a genetic algorithm

This paper presents an approach for designing power system stabilizers (PSS) with a fuzzy logic based parameter tuner. In the initial design step, Prony analysis is used to identify linear models for the synchronous generator at a large number of operating points, consisting of various power outputs and machine terminal voltages. Next, optimal parameter settings for a conventional PSS are generated using the linearized models. From the operating point settings, a selection of fuzzy rules is used to tune the stabilizer parameters online according to real-time measurements. The membership functions of the fuzzy parameter tuner are optimized using a genetic algorithm (GA). Simulation studies show that the proposed stabilizer performs well over a wide range of operating conditions and provides better dynamic performance than a fixed parameter PSS.     

Discrete-time adaptive sliding mode power system stabilizer with only input/output measurements

In this paper a discrete-time adaptive sliding mode control method is newly developed and applied to the power system stabilization problem. A controllable canonical form of state space realization is constructed using the parameters identified by the on-line recursive least squares method and the system state is estimated from the input/output measurements and the simple state transformations. The identified parameters and the estimated state are then used by the discrete-time sliding mode control, which is suitable for the digital equipment. The most important advantage of the proposed power system stabilizer (PSS) is that it is able to maintain its regulating performance with a slower sampling period than that of the conventional sliding mode PSS because it is developed in a pure discrete-time domain. Another advantage of the proposed PSS is that it needs neither a mathematical model of the power system nor the full-state measurements because they are identified through on-line identifications. Several computer simulations for the linear power system are performed to verify the performance of the proposed PSS. In the computer simulations for various circumstances which are probable in a power system are considered, such as transitions of the active and reactive powers, change of parameters of the synchronous machine, line-to-ground faults and measurement noise. As a result, a new power system stabilizer which can operate in a wide range of operating conditions and can overcome various disturbances and measurement noises is proposed.     

Systematic approach to consider system contingencies in PSS design

This paper proposes a method that can incorporate system contingencies into power system stabilizer (PSS) design. Selection of critical contingencies is first performed by a ranking of contingencies under a wide range of operating conditions according to a small signal stability index. A multi-objective optimization model is formulated to pursue satisfactory system damping performance under both pre-contingency and post-contingency situations. A recursive Genetic Algorithm is then presented to tune the PSS parameters so that the dynamic security criteria subject to contingencies are met under a wide range of operating conditions. Finally, an eight-machine system is utilized to demonstrate the effectiveness of the proposed approach and a comparison of the proposed method with a pre-contingency tuning scheme is reported.     

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.     

A new SPARLS algorithm for tuning power system stabilizer

Generators have to meet the change in real and reactive power demand of practical power system. The real power variations in the system are met out by the rescheduling process of the generators. But there is a huge trust to meet out the reactive power load demand. The excitation loop of the corresponding generator is adjusted with its electric limits to activate the reactive power of the network. To expedite the reactive power delivery, power system stabilizer (PSS) is connected in the exciter loop of the generator for various system conditions. In this paper, a new Sparse Recursive Least Square (SPARLS) algorithm is demonstrated to tune the power system stabilizer parameters to meet the vulnerable conditions. The proposed SPARLS algorithm makes use of expectation maximization (EM) updation to tune the PSS. A comparative study between the proposed SPARLS and RLS algorithm has been performed on single machine infinite bus system (SMIB). The simulation results obtained will validate the effectiveness of the proposed algorithm and the impact of stability studies of the power system operation under disturbances. The SPARLS algorithm is also used to tune the parameters of PSS to achieve quicker settling time for the system parameter such as load angle, field voltage and speed deviation. It is found that the SPARLS is a better algorithm for the determination of optimum stabilizer parameter.     

新型PSS-NB模型参数整定与工程化应用

新型电力系统稳定器模型PSS-NB的工程投运需要有配套的参数整定试验方法和性能验证方法。分析论证PSS-NB在中、低频段功率振荡抑制能力优势的基础上,提出该型PSS配套的参数整定试验内容与参数整定计算方法。以某电厂330 MW机组PSS-NB投运试验为实际案例,给出了工程投运过程中的参数整定计算方法应用过程和低频振荡抑制效果的验证过程。通过扫频扰动和负载电压阶跃试验对比验证了PSS-NB相对于PSS2B在全频段的低频功率振荡抑制能力上具有明显提升,证明了经参数整定的新型PSS-NB既能满足全频段相位补偿要求,又能在中低频段提供较强抑制能力。     

A GENETIC-BASED POWER SYSTEM STABILIZER

ABSTRACT

A Genetic-based Power System Stabilizer (GPSS) is presented in this paper to improve power system dynamic stability. The proposed GPSS parameters are optimized using Genetic Algorithms (GA). The main advantage of the proposed GPSS is that far less information than other design techniques is required without the need for linearization process. Time domain simulations of a synchronous machine subject to major disturbances are investigated. The performance of the proposed GPSS is compared with that of conventional lead-lag power system stabilizer (CPSS) to demonstrate the superiority of the proposed GPSS. The effect of parameter changes on the proposed stabilizer performance is also examined. The results show the robustness of the proposed GPSS and its capability to enhance the system damping over a wide range of operating conditions and system parameter variations.     

On the Rotor Angle and Voltage Dynamics Decoupling in Synchronous Generators with Nonlinear AVR

Abstract

In this paper, rotor angle and voltage dynamics decoupling in a synchronous generator connected to an infinite bus using a partial feedback linearization-based nonlinear voltage regulator (FBLAVR) is described. It is well known that the fast voltage regulation using static excitation system introduces negative damping in the system. If this coupling can be eliminated then one can simultaneously achieve fast voltage regulation and desired damping performance. This paper identifies such a decoupling behavior using an FBLAVR and shows that the damping with the FBLAVR only depends on the inherent damping of the machine. The necessity of a power system stabilizer (PSS) is identified even with the FBLAVR, if the natural damping is not sufficient. Thus motivated, an FBLAVR?+?PSS control scheme is proposed and compared with a linear automatic voltage regulator (AVR)?+?PSS control scheme. It is shown that the generator-exciter-power system [GEP(s)] characteristics with FBLAVR does not change with the system operating conditions. The superiority of the proposed control scheme is validated over a wide range of operating conditions on a single machine infinite bus (SMIB) power system.     

A Hybrid Power System Stabilizer Using Neuro-identifier and Predictive Controller

This paper presents the development of a hybrid power system stabilizer (PSS) model designed to enhance the damping characteristics of a practical power system network representing a part of the Electricity Generating Authority of Thailand (EGAT) system. The PSS consists of a predictive controller and a neuro-identifier which has been developed based on the functional link network (FLN) model. A recursive on-line training algorithm has been proposed to train the neural network. Simulation results have been obtained under various operating conditions and severe disturbance cases that show that the proposed PSS can provide a better damping to the local as well as interarea modes of oscillations, as compared with a conventional PSS.     

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.     

Tuning of power system stabilizers using genetic algorithms

Several techniques exist for developing optimal controllers. This paper investigates the tuning of power system stabilizers (PSS) using genetic algorithms (GA). A digital simulation of a linearized model of a single-machine infinite bus power system at some operating point is used in conjunction with the genetic algorithm optimization process. The integral of the square of the error and the time-multiplied absolute value of the error performance indices are considered in the search for the optimal PSS parameters. In order to have good damping characteristics over a wide range of operating conditions, the PSS parameters are optimized off-line for a selected set of grid points in the real power (P)-reactive power (Q) domain. The optimal settings thus obtained can then be stored and retrieved on-line to update the PSS parameters based on measurements of the generator real and reactive power. Time domain simulations of the system with GA-tuned PSS show the improved dynamic performance under widely varying load conditions.     

Pole placement technique for PSS and TCSC-based stabilizer design using simulated annealing

A pole placement technique for power system stabilizer (PSS) and thyristor controlled series capacitor (TCSC) based stabilizer using simulated annealing (SA) algorithm is presented in this paper. The proposed approach employs SA optimization technique to PSS (SAPSS) and TCSC-based stabilizer (SACSC) design. The design problem is formulated as an optimization problem where SA is applied to search for the optimal setting of the proposed SAPSS and SACSC parameters. A pole placement-based objective function to shift the dominant eigenvalues to the left in the s-plane is considered. The proposed SAPSS and SACSC have been examined on a weakly connected power system with different disturbances, loading conditions, and system parameter variations. Eigenvalue analysis and nonlinear simulation results show the effectiveness and the robustness of the proposed stabilizers and their ability to provide efficient damping of low frequency oscillations. In addition, the performance of the proposed stabilizers outperforms that of the conventional power system stabilizer (CPSS). It is also observed that the proposed SACSC improves greatly the voltage profile of the system under severe disturbances.     

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.     

基于双层规划的非线性鲁棒电力系统稳定器参数整定方法

使用先进的励磁控制技术是改善电力系统稳定性和动态品质的有效手段。采用反馈线性化和鲁棒控制理论设计的非线性鲁棒电力系统稳定器(NR-PSS)是一种先进的励磁控制装置。在深入研究电力系统稳定器(PSS)参数整定原理和NR-PSS现有参数整定方法的基础上,提出了一种基于双层规划的NR-PSS线性部分参数整定方法,以满足阻尼频带宽和控制代价低两方面要求。通过等价非线性规划可求出最优参数。仿真结果表明,应用所提出方法设定的参数可以改善NRPSS的控制效果。     

Strict-Tabu Based Eigenvalue Optimization of Power System Stabilizer

A Strict-Tabu (S-Tabu) optimization of power system stabilizer (PSS) parameters has been developed and presented in this paper. To achieve good damping characteristics over a wide range of operating conditions, S-Tabu is used to search for the best eigenvalue of the PSS and later installed into the phase lead-lag controller. The implementation studies are carried out on both singlemachine, infinite-bus, and multimachine power systems, respectively. Simulation studies for a variety of disturbances on the power system with optimized PSS demonstrate its effectiveness in improving system performance. Comparison studies are also performed to show the advantages of the proposed controller over the nonoptimized Conventional PSS (CPSS).     

Small signal stability enhancement of a large scale power system using a bio-inspired whale optimization algorithm

A whale optimization algorithm (WOA)-based power system stabilizer (PSS) design methodology on modified single machine infinite bus (MSMIB) and multi-machine systems to enhance the small-signal stability (SSS) of the power system is presented. The PSS design methodology is implemented using an eigenvalue (EV)-based objective function. The performance of the WOA is tested with several CEC14 and CEC17 test functions to investigate its potential in optimizing the complex mathematical equations. The New England 10-generator 39-bus system and the MSMIB system operating at various loading conditions are considered as the test systems to examine the proposed method. Extensive simulation results are obtained which validate the effectiveness of the proposed WOA method when compared with other algorithms.     

适应复杂电力系统多种运行方式的PSS优化配置

低频振荡的抑制是电力系统规划阶段和运行过程中必须考虑的一个重要问题。抑制低频振荡一般的方法是在励磁调节器上装设电力系统稳定器(PSS)。多机系统中，PSS的配置包括PSS安装位置的选择和参数整定。现有的研究工作主要考虑一种运行方式来进行PSS的优化配置，本文提出了一种同时适应系统的多种运行方式来进行PSS优化配置的方法。通过对西北电网三种典型运行方式的计算结果表明：为了获得更好的抑制低频振荡的效果，PSS的放大倍数不应限定为正值；如按一种运行方式对PSS的参数进行优化则对其它运行方式效果较差，而同时考虑多种运行方式，统一地进行PSS的优化配置则对各种运行方式都能良好地抑制低频振荡。     

Power-system stabilizer (PSS) design by probabilistic sensitivity indexes (PSIs)

A probabilistic power system stabilizer (PSS) design considering multioperating conditions is proposed in this paper. Under the assumption of normal distribution, the conventional eigenvalue sensitivity analysis is extended to a probabilistic environment by describing the statistical nature of eigenvalues as expectations and variances. Two probabilistic indexes derived from the sensitivities of eigenvalue expectations and variances are introduced to the PSS site and parameter selection. The robustness of the system can be guaranteed because wide range of the load variation has been taken into account in probabilistic representation. The effectiveness of the proposed PSS is demonstrated on a 3-machine system by probabilistic eigenvalue analysis and transient response simulation.