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.     

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.     

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 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.     

Chaotic ant swarm optimization for fuzzy-based tuning of power system stabilizer

In this paper, chaotic ant swarm optimization (CASO) is utilized to tune the parameters of both single-input and dual-input power system stabilizers (PSSs). This algorithm explores the chaotic and self-organization behavior of ants in the foraging process. A novel concept, like craziness, is introduced in the CASO to achieve improved performance of the algorithm. While comparing CASO with either particle swarm optimization or genetic algorithm, it is revealed that CASO is more effective than the others in finding the optimal transient performance of a PSS and automatic voltage regulator equipped single-machine-infinite-bus system. Conventional PSS (CPSS) and the three dual-input IEEE PSSs (PSS2B, PSS3B, and PSS4B) are optimally tuned to obtain the optimal transient performances. It is revealed that the transient performance of dual-input PSS is better than single-input PSS. It is, further, explored that among dual-input PSSs, PSS3B offers superior transient performance. Takagi 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 variables.     

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.     

加速功率型电力系统稳定器参数整定方法研究

电力系统稳定器（PSS）是抑制系统低频振荡的有效手段,通常认为以发电机转速偏差和电磁功率偏差为输入信号的加速功率型PSS可避免发电机无功反调问题。结合实例和时域仿真分析,指出在参数整定不合理情况下此类PSS仍可能出现无功反调现象,分析了典型PSS参数对无功反调的影响,在此基础上提出一种基于数字仿真的PSS参数整定方法。对实际电网的时域仿真结果验证了该方法的有效性。     

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.     

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.     

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.     

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.     

Robust decentralized PID-based power system stabilizer design using an ILMI approach

Thanks to its essential functionality and structure simplicity, proportional-integral-derivative (PID) controllers are commonly used by industrial utilities. A robust PID-based power system stabilizer (PSS) is proposed to properly function over a wide range of operating conditions. Uncertainties in plant parameters, due to variation in generation and load patterns, are expressed in the form of a polytopic model. The PID control problem is firstly reduced to a generalized static output feedback (SOF) synthesis. The derivative action is designed and implemented as a high-pass filter based on a low-pass block to reduce its sensitivity to sensor noise. The proposed design algorithm adopts a quadratic Lyapunov approach to guarantee α-decay rate for the entire polytope. A constrained structure of Lyapunov function and SOF gain matrix is considered to enforce a decentralized scheme. Setting of controller parameters is carried out via an iterative linear matrix inequality (ILMI). Simulation results, based on a benchmark model of a two-area four-machine test system, are presented to compare the proposed design to a well-tuned conventional PSS and to the standard IEEE-PSS4B stabilizer.     

基于SVG的双馈风电场电压稳定控制策略研究

双馈异步发电机(DFIG)在大规模风电并网环境下提供的无功功率无法满足并网需求。虽然引入固定电容器能够提供无功补偿,但系统受功率耦合的影响无法有效实时维持电压稳定。提出了一种静止无功发生器(SVG)与DFIG协调补偿无功的控制策略,同时引入电力系统稳定器(PSS)抑制系统的低频振荡,充分利用DFIG风电机组自身发出无功的能力,减少了SVG的配置容量。在MATLAB/Simulink软件仿真平台建立DFIG风电机组并网模型,仿真结果证实了此控制策略能够完成连续无功补偿,有效维持并网点电压稳定,增强系统输电能力。     

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.     

抑制频率振荡的电力系统稳定器参数优化

超低频频率振荡是有功频率控制过程的小扰动稳定问题。由于负荷电压调节效应使得无功电压控制和有功频率控制产生耦合,传统用于抑制低频振荡的电力系统稳定器(PSS)可用于抑制频率振荡。提出了在多机系统中选择抑制频率振荡的PSS的方法,该方法综合了PSS对低频振荡和频率振荡的影响大小。构建了抑制频率振荡的PSS参数优化模型,该模型仍然以低频振荡模式阻尼比作为优化目标,但加入频率振荡对应频段发电机励磁系统相位要求作为约束,保证机组励磁系统为频率振荡提供足够的正阻尼。采用粒子群优化算法对模型进行求解得到PSS最优参数。仿真结果验证了所提方法的有效性。     

提升运行工况适应性的PSS相位和增益参数协同整定方法

传统的电力系统稳定器（power system stabilizer,PSS）参数整定方法未计及系统运行方式变化的影响,参数对运行工况适应性较难保证。而且只强调相位补偿要求,将相位参数和增益参数分开进行整定,忽略了相频、幅频特性之间的协调对PSS阻尼特性的重要影响,致使PSS整体作用难以得到充分发挥。为此,本文提出一种提升运行工况适应性的PSS相位和增益参数协同优化的整定方法。在优化模型中,兼顾相频、幅频特性之间的协调,采用PSS阻尼力矩分量最大化目标,并新增对多种工况下的相位补偿约束,从而提升参数对运行工况的适应性和整体阻尼效果。算例验证表明：所提方法在满足多种工况相位补偿要求的同时,可显著增强PSS阻尼效果,具有一定的工程应用价值。     

多频段PSS结构设计和参数协调

为了在提高PSS对振荡模式选择性的同时,改善传统多频段电力系统稳定器（PSS）结构复杂、频段数固定的问题,提出了一种新型的功率输入型多频段PSS,并对PSS的结构进行了频率特性分析。在系统多运行方式下,使用概率灵敏度分析法进行分析,确定PSS的频段个数;进而根据该PSS参数设计方法,利用非线性规划技术对参数进行协调优化。在一8机系统内进行了验证,表明该PSS可以根据具体需要确定补偿频段数,既可作为单频段PSS仅提高某一个模式的阻尼比,也可作为多频段PSS同时兼顾多个振荡模式。     

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.     

提高集中电源外送系统输电能力的发电机励磁系统协调优化

能源基地电源通过交流线路远距离送至负荷中心,电力系统发生N－1故障后系统阻尼不足,造成输电能力降低。为提高动态稳定水平,改善机组开机出力受限的现状,对发电机励磁控制系统开展协调优化策略的研究。首先对发电机进行了励磁系统AVR主环、附加调差及PSS参数优化,优化后发电机及励磁系统滞后时间减小,无补偿相频特性滞后角度减小了约10°。然后对PSS参数进行相位补偿研究,使有补偿特性更接近-90°,具有更优的相位补偿特性,同时PSS提供的增益显著提高。结果表明,发电机励磁系统、PSS对电网的动态特性有重要影响,在原始参数配置下,外送通道的送出能力不能满足送端发电机组满出力的要求。通过试验验证了机组在优化参数配置下负载试验结果满足机组和电网安全稳定运行需求,优化参数具有可行性,N－1故障后的送出能力得到提高。此优化策略的研究和实施,可以安全、高效、经济地提高集中电源的外送能力。     

电力系统负荷建模和算法的研究及进展

为了给研究人员提供负荷建模研究及在电力系统分析中具有的重要意义较全面的认识,分析了现有的负荷建模方法,系统地阐述了负荷模型及算法上的进展,它包括在静态负荷方面通过采用连续动态方法,根据负荷电压的变化实时修正负荷模型参数,在临界状态下通过分段多项式模型描述加以改进;在负荷建模方面分析了负荷动特性描述、负荷时变性、模型参数的分散性、负荷模型无功分析和负荷的非线性、变结构特性方面问题解决的进展。结果表明,通过采用上述解决方法,使得负荷模型的精确性和适应性得到了较大程度的提高,从而满足复杂大电力系统的动态行为和安全稳定分析的要求。