Parameter optimization of multimachine power system stabilizers using genetic local search

A genetic local search (GLS) algorithm for optimal design of multimachine power system stabilizers (PSSs) is presented in this paper. The proposed approach hybridizes the genetic algorithm (GA) with a heuristic local search in order to combine their strengths and overcome their shortcomings. The potential of the proposed approach for optimal parameter settings of the widely used conventional lead–lag PSSs has been investigated. Unlike the conventional optimization techniques, the proposed approach is robust to the initial guess. The performance of the proposed GLS-based PSS (GLSPSS) under different disturbances, loading conditions, and system configurations is investigated for different multimachine power systems. Eigenvalue analysis and simulation results show the effectiveness and robustness of the proposed GLSPSS to damp out local as well as interarea modes of oscillations and work effectively over a wide range of loading conditions and system configurations.     

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.     

Coordinated design of PSSs and SVC via bacteria foraging optimization algorithm in a multimachine power system

This paper develops a novel algorithm for simultaneous coordinated designing of power system stabilizers (PSSs) and static var compensator (SVC) in a multimachine power system. The coordinated design problem of PSS and SVC over a wide range of loading conditions is formulated as an optimization problem. The Bacterial Foraging Optimization Algorithm (BFOA) is employed to search for optimal controllers parameters. By minimizing the proposed objective function, in which the speed deviations between generators are involved; stability performance of the system is improved. To compare the capability of PSS and SVC, both are designed independently, and then in a coordinated manner. Simultaneous tuning of the bacterial foraging based coordinated controller gives robust damping performance over wide range of operating conditions and large disturbance in compare to optimized PSS controller based on BFOA (BFPSS) and optimized SVC controller based on BFOA (BFSVC). Moreover, a statistical T test is performed to ensure the effectiveness of coordinated controller versus uncoordinated one.     

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。     

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.     

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 hybrid Particle Swarm Optimization and Bacterial Foraging for optimal Power System Stabilizers design

A novel hybrid approach involving Particle Swarm Optimization (PSO) and Bacterial Foraging Optimization Algorithm (BFOA) called Bacterial Swarm Optimization (BSO) is illustrated for optimal Power System Stabilizers (PSSs) design in a multimachine power system. In BSO, the search directions of tumble behavior for each bacterium are oriented by the individual’s best location and the global best location of PSO. The proposed hybrid algorithm has been extensively compared with the original BFOA algorithm and the PSO algorithm. Simulation results have shown the validity of the proposed BSO in tuning PSSs compared with BFOA and PSO. Moreover, the results are presented to demonstrate the effectiveness of the proposed controller to improve the power system stability over a wide range of loading conditions and various disturbances.     

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.     

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.     

Proposed mechanism for performance of power system stabilizers in the condition of strong resonance

This paper suggests a mechanism for the dynamic performance of damping controllers in the condition of strong resonance. The mechanism explains theoretically how the variation of a control parameter can move the resonance point in such a way to stabilize or destabilize the coupled modes. As an application, this mechanism is applied to justify the performance of power system stabilizers (PSSs) in a 2-area 4-machine test system, in which an exciter mode and an inter-area mode interact near a strong resonance. It makes the performance of the PSSs on the stability of the inter-area mode become severely dependent on the place of the PSS and the position of operating point with respect to the resonance point. In this circumstance, the PSSs of one of the system areas destabilize the inter-area mode. Considering the proposed mechanism, the appropriate location of the PSS and its proper gain value are identified to obtain the maximum damping of inter-area mode at each of the operating points. In addition, it is shown that due to the strong resonance, conventional methods make incorrect placement of the PSS; however, by using the real part of speed participation factors, suitable machines are chosen to place the PSS. This index provides important information regarding the impacts of strong resonance on the performance of PSSs.     

Comparative seeker and bio-inspired fuzzy logic controllers for power system stabilizers

Seeker optimization algorithm (SOA) is a new heuristic population-based search algorithm. In this paper, SOA is utilized to tune the parameters of both single-input and dual-input power system stabilizers (PSSs). In SOA, the act of human searching capability and understandings are exploited for the purpose of optimization. In SOA-based optimization, the search direction is based on empirical gradient by evaluating the response to the position changes and the step length is based on uncertainty reasoning by using a simple fuzzy rule. Conventional PSS (CPSS) and the three dual-input IEEE PSSs (namely PSS2B, PSS3B and PSS4B) are optimally tuned to obtain the optimal transient performances. From simulation study it is revealed that the transient performance of the dual-input PSS is better than the single-input PSS. It is further explored that among the dual-input PSSs, PSS3B offers the best optimal transient performance. While comparing the SOA with recently reported optimization algorithms like bacteria foraging optimization (BFO) and genetic algorithm (GA), it is revealed that the SOA is more effective than either BFO or GA in finding the optimal transient performance. Sugeno fuzzy logic (SFL)-based approach is adopted for on-line, off-nominal operating conditions. On real time measurements of system operating conditions, SFL adaptively and very fast yields on-line, off-nominal optimal stabilizer parameters.     

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 using Strength Pareto multi-objective optimization approach

Power system stabilizers (PSSs) are the most well-known and effective tools to damp power system oscillation caused by disturbances. To gain a good transient response, the design methodology of the PSS is quite important. The present paper, discusses a new method for PSS design using the multi-objective optimization approach named Strength Pareto approach. Maximizations of the damping factor and the damping ratio of power system modes are taken as the goals or two objective functions, when designing the PSS parameters. The program generates a set of optimal parameters called Pareto set corresponding to each Pareto front, which is a set of optimal results for the objective functions. This provides an excellent negotiation opportunity for the system manager, manufacturer of the PSS and customers to pick out the desired PSS from a set of optimally designed PSSs. The proposed approach is implemented and examined in the system comprising a single machine connected to an infinite bus via a transmission line. This is also done for two familiar multi-machine systems named two-area four-machine system of Kundur and ten-machine 39-bus New England system. Parameters of the Conventional Power System Stabilizer (CPSS) are optimally designed by the proposed approach. Finally, a comparison with famous GAs is given.     

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.     

Optimal power flow with FACTS devices by hybrid TS/SA approach

In this paper, a hybrid tabu search and simulated annealing (TS/SA) approach is proposed to minimize the generator fuel cost in optimal power flow (OPF) control with flexible AC transmission systems (FACTS) devices. The problem is decomposed into the optimal setting of FACTS parameters subproblem that is searched by the hybrid TS/SA approach and the OPF with fixed FACTS parameters subproblem that is solved by the quadratic programming (QP). Two types of FACTS devices are used: thyristor-controlled series capacitor (TCSC) and thyristor-controlled phase shifting (TCPS). Test results on the modified IEEE 30 bus system indicates that the proposed hybrid TS/SA approach can obtain better solutions and require less CPU times than genetic algorithm (GA), SA, or TS alone.     

Effects of various power system stabilizers on improving power system dynamic performance

To ensure the small-signal stability of a power system, power system stabilizers (PSSs) are extensively applied for damping low frequency power oscillations through modulating the excitation supplied to synchronous machines, and increasing interest has been focused on developing different PSS schemes to tackle the threat of damping oscillations to power system stability. This paper examines four different PSS models and investigates their performances on damping power system dynamics using both small-signal eigenvalue analysis and large-signal dynamic simulations. The four kinds of PSSs examined include the Conventional PSS (CPSS), Single Neuron based PSS (SNPSS), Adaptive PSS (APSS) and Multi-band PSS (MBPSS). A steep descent parameter optimization algorithm is employed to seek the optimal PSS design parameters. To evaluate the effects of these PSSs on improving power system dynamic behaviors, case studies are carried out on an 8-unit 24-bus power system through both small-signal eigenvalue analysis and large-signal time-domain simulations.     

Parallel simulated annealing for power system decomposition

This paper proposes a parallel optimization approach to power system decomposition for voltage control. In this paper, a parallel simulated annealing (PSA) technique is developed to decompose power systems so that subsystems are equally separated in terms of the number of nodes and control variables. System decomposition is one of difficult discrete number combinatorial problems. The PSA technique provides better solutions than the conventional SA because of searching a solution near a global minimum over a wide range. The proposed method is tested in IEEE 30, 57, and 118 node systems     

PSS4B型电力系统稳定器参数整定

PSS4B是一种新型的多频段电力系统稳定器,因能同时抑制多个振荡模式且设置更加灵活而具有广泛的应用前景,但PSS4B参数整定相对复杂,目前相关的研究还比较少。按实际应用要求,在系统多运行方式下,用相位补偿法整定超前滞后环节的时间常数,用根轨迹法确定增益;在概率特征根分析的基础上,部分计及PSS相互间的影响,使用概率灵敏度分析法进行分析。由此进行的PSS4B参数整定,将使PSS4B在多运行方式下充分发挥优势。在一8机24节点系统上进行分析,结果表明安装的6台PSS4B均能提供对关注的振荡模式的有效阻尼,具有较强的适应性和通用性。     

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。