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.     

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.     

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.     

基于PMU的可控制动电阻协调控制器研究

同步相量测量单元(PMU)的广泛应用使得利用广域同步信息设计多机系统的协调控制器,进一步提高电力系统的暂态稳定性成为可能。建立了多机系统可控制动电阻(TCBR)控制器的数学模型,并设计了基于广域同步信息的协调控制器。控制器分为协调层和本地层,协调层利用PMU提供的全局实时信息处理各发电机之间的非线性关联量,产生全局优化向量;本地层则结合本地信息和全局优化向量,对发电机进行闭环反馈控制。所提方法避免了分散控制器之间的不协调动作,并且所采用的三级共态预估算法计算量小,适合于在线应用。对一个4机系统的仿真表明所提方法可以有效地提高系统的暂态稳定性。     

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.     

多机系统调速侧电力系统稳定器GPSS的设计

低频振荡是一种不利于电力系统安全和稳定运行的现象,而电力系统稳定器(PSS)可以有效抑制低频振荡。由于励磁系统和电力系统运行方式及工况之间的密切关系,致使电力系统中PSS的协调设计和安装地点的选择成了PSS能否用于电力系统的关键。作者设计了一种调速侧电力系统稳定器,其设计原理和传统的PSS一样简单,且具有较好的鲁棒性,同时还具有多机解耦特性,给本机带来阻尼的同时不会给其它机组带来负阻尼,避免了参数协调和安装地点的选择。最后利用算例仿真验证了调速侧电力系统稳定器不仅可以抑制低频振荡,还可以提高电力系统暂态稳定性。     

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.     

多机电力系统神经网络最优励磁控制器

针对多机电力系统，提出了一种基于辨识的神经网络实时最优控制器(NNOEC)，在所设计的控制器中，神经网络被用来根据系统状态量的变化实时调整最优控制的反馈增益矩阵，使控制器能够适应不同的运行点和干扰种类。并始终提供最优控制输出。针对多机系统中神经网络训练样本不易获得的问题，提出了一种等效的设计方法，并采用非线性最小二乘辨识法对系统参数进行辨识，在辨识的基础上通过线性最优控制理论计算出用于神经网络训练的样本。三机系统中的数字仿真结果表明，所训练出的NNOEC能够适应系统运行方式的大范围变化，在大小扰动下均表现出良好的控制性能。     

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.     

基于FASSA-SVM的充电桩故障预测算法研究

为了电动汽车直流充电桩的安全稳定运行,本文提出一种基于改进支持向量机的充电桩故障预测算法。该算法首先针对充电桩的运行参数进行缺失值填充、归一化等预处理;然后将预处理后的数据输入支持向量机模型训练,之后引入萤火虫算法改进麻雀算法对支持向量机模型进行参数寻优,得到最优模型;最后利用得到的最优模型预测诊断充电桩运行状态,来判断充电桩是否发生故障。实验结果表明,本文的预测算法预测精度可达94.68%,远高于传统的支持向量机模型的72.34%,能较准确地预测充电桩运行状态,为其预知维修、保障安全运行提供有力保障。     

基于BGSA算法的SVM分类模型设计研究

为了设计性能更优的支持向量机（SVM）分类模型,对影响其分类性能的参数和样本特征子集进行优化选择,对支持向量机理论和万有引力搜索算法（GSA）进行了研究,提出了一种基于二进制万有引力搜索算法（BGSA）的支持向量机分类模型构建方法,能够对影响支持向量机分类性能的相关参数及有效样本特征子集同时进行优化选择,获得最优组合解,并通过实验对其有效性进行了对比分析和验证。实验结果表明,所提出的BGSA-SVM分类模型能够有效提高支持向量机的分类性能,可进一步推广到工程实际中应用。     

基于随机聚焦粒子群算法的多机系统PSS参数优化

随机聚焦粒子群算法（SFPSO）是一种应用于连续空间的、具有较好的全局搜索能力和寻优速度的群体智能优化算法。通过采用SFPSO算法,对多机系统的PSS参数进行优化。该方法是以最优控制原理为基础,综合考虑PSS与励磁系统的性能,将PSS 参数优化协调转化为带有不等式约束的优化问题,控制目标为系统输出按照最小误差跟踪给定值的能力。通过仿真测试以及不同算法优化结果的对比,表明基于SFPSO算法优化的PSS在不同的干扰下都具有良好的性能,能够抑制低频振荡,并保持系统稳定,同时证明了SFPSO算法的有效性和优越性。     

Optimal tunning of type-2 fuzzy logic power system stabilizer based on differential evolution algorithm

In this paper, a type-2 fuzzy logic power system stabilizer with differential evolution algorithm is proposed. As an extension of type-1 fuzzy logic theory, type-2 fuzzy logic theory can effectively improve the control performance by uncertainty of membership function especially when we have to confront with less expert knowledge or unpredicted external disturbances. The corresponding parameters and rule base of type-2 fuzzy logic power system stabilizer are optimally tuned by using differential evolution algorithm for multi-machine power system. Through simulation under different operational conditions, the results demonstrate the effectiveness of the proposed approach for damping the power system electromechanical oscillations.     

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 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.     

基于SVM与合分闸线圈电流参数的高压断路器机械故障诊断

高压断路器故障的早期诊断是有效提高电力系统运行可靠性的重要手段,笔者提出应用高性能的支持向量机(SVM)进行高压断路器的机械故障诊断.支持向量机核函数参数的选择直接影响分类结果的好坏,该诊断方法采用群智能算法PSO确定支持向量机中核函数的最优参数以提升分类器性能,将从高压断路器的机械参数信号中提取的动作特性特征量作为支...     

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.     

基于随机聚焦粒子群算法的多机系统PSS参数优化

随机聚焦粒子群算法(SFPSO)是一种应用于连续空间的、具有较好的全局搜索能力和寻优速度的群体智能优化算法.通过采用SFPSO算法,对多机系统的PSS参数进行优化.该方法是以最优控制原理为基础,综合考虑PSS与励磁系统的性能,将PSS 参数优化协调转化为带有不等式约束的优化问题,控制目标为系统输出按照最小误差跟踪给定值的能力.通过仿真测试以及不同算法优化结果的对比,表明基于SFPSO算法优化的PSS在不同的干扰下都具有良好的性能,能够抑制低频振荡,并保持系统稳定,同时证明了SFPSO算法的有效性和优越性.     

An Adaptive Synchronous Machine Stabilizer

An adaptive synchronous machine stabilizer utilizing the least-squares identification with varying forgetting factor and a self-searching control strategy is proposed in this paper. The use of varying forgetting factor in the identification algorithm improves parameter tracking under both transient and dynamic conditions, and the use of a self-searching pole shifting control technique increases the flexibility when applied to varying operating conditions encountered in power systems. These features make this stabilizer very robust. Studies given in the paper show that the proposed self-tuning stabilizer provides excellent damping under varying operating conditions and with different types of disturbances.