基于改进算法的模糊神经网络电力系统稳定器

基于模糊神经网络的电力系统稳定器具有适应电力系统非线性,且不依赖电力系统数学模型的特点,针对模糊神经网络隶属度函数的中心参数选取问题,提出了一种基于极大熵原理优化模糊神经网络的设计方法.该方法利用一个最优化的目标函数导出中心向量和宽度的学习算法,改善了网络的回归能力和泛化能力.针对电力系统发生的低频振荡问题,提出了一种基于熵优化模糊神经网络电力系统稳定器的设计方案.该方案避免了控制器对系统精确数学模型的依赖,利用神经网络的学习能力,在线自动生成训练样本,实现了电力系统的实时控制.仿真结果表明,提出的电力系统稳定器控制方案可以显著地提高被控机组的稳定性及电力系统的动态性能.     

针对单机无穷大系统模糊电力系统稳定器的设计

针对一种典型的单机无穷大电力系统,进行了基于MATLAB／SIMULINK及模糊逻辑工具箱的传统电力系统稳定器（CPSS）和模糊电力系统稳定器（FPSS）的设计研究。提出了一种利用模糊控制参数及控制规则进行调整和修正模糊控制器的设计方法,并应用于电力系统稳定器的设计,仿真结果表明：相比传统电力系统稳定器．模糊电力系统稳定器能在较大的运行范围内更有效地抑制系统低频振荡,系统鲁棒性好,且调试方便。     

基于模糊控制的广域阻尼控制器设计

针对互联电力系统低频振荡问题,提出了一种用广域反馈信号作为附加稳定信号的励磁控制器的设计方法。广域信号传输存在的通信时滞用模糊控制方法进行补偿。描述了确定模糊推理规则的方法,首先分析模糊逻辑控制器的输入量和理想输出量,根据输入量和输出量之间的关系确定模糊论域、隶属度函数和模糊推理规则,从而建立模糊逻辑控制器。用Matlab/Simulink软件对4机2区域系统进行仿真,测试模糊广域阻尼控制器性能。仿真结果表明,所设计的控制器能补偿通信过程中存在的较大时滞,增加互联电力系统阻尼。仿真结果验证了模糊控制具有较好的鲁棒性和适应性。     

基于滑模控制的模糊电力系统稳定器

为改善模糊电力系统稳定器(FPSS)的动态性能,提出了基于滑模控制的FPSS的控制策略.在该控制策略中,电力系统稳定器(PSS)由主控制器、自组织控制器和滑模控制器三部分构成,其中,主控制器用于产生PSS的基本输出信号;自组织控制器可根据滑模控制器输出的优化条件对主控制器进行优化设计.还提出了采用滑模控制器代替传统的线性控制器来输出优化条件.理论分析和仿真结果表明,文中FPSS的动态性能优于常规的模糊稳定器.     

一种新的可提高电力系统暂态稳定性的SVC模糊逻辑控制器

暂态稳定性问题是目前电力系统遇到的几种主要的稳定稳定性问题之一。由于SVC等电力电子型FACTS设备的快速响应特性,它们不仅能够完成稳态潮流控制,还能够提高电力系统动态和暂态稳定性。传统的SVC模糊逻辑控制器均以被控量的偏差和偏差变化率为输入,以SVC的电纳B_(svc)直接作为控制输出来进行设计。提出一种新的能够在暂态过程中动态调整传统SVC一阶动态控制器增益的模糊逻辑控制器的设计方法。该模糊逻辑控制器以传统SVC一阶动态控制增益为输出。间接完成SVC的控制。通过安德森3机9节点算例系统验证了该模糊逻辑控制器的有效性。     

基于改进遗传算法的发电机调速器侧模糊PSS优化设计

基于模糊控制理论设计了发电机组调速器侧模糊电力系统稳定器,采用遗传算法优化其参数以克服模糊控制器参数设计的主观性.为了提高多参数控制器参数优化的速度和精度,对传统单点交叉遗传算法进行改进,提出了一种首尾轮换交叉遗传算法.该方法交替采用尾部和头部单点交叉,保证了各参数参与优化的概率基本均等.采用该算法对某水轮发电机组调速器侧模糊电力系统稳定器的参数进行优化计算,表明其优化速度和精度较试探法和传统单点交叉遗传算法有显著提高.     

同步发电机励磁系统模糊逻辑控制

本文基于发电机每个瞬间的速度偏差及加速度偏差，按照相平面上的标准模糊隶属函数提出了高步发电机励磁模糊逻辑控制方法，其中控制器增益可自适应调整，通过仿真表明，模糊逻辑控制器对系统各种干扰能有效提高其稳定性并具有非常强的鲁棒性。     

采用GA提高模糊控制器适应性的方法及应用研究

对于工业控制中广泛存在的多容量、大惯性、大延迟的被控对象,模糊控制是一种较有效的方法,模糊控制中隶属函数的正确选择是模糊控制器设计的关键,针对传统的获取隶属度函数方法的不足,本文设计了一种基于遗传算法的模糊控制器,系统采用遗传算法优化模糊控制器的隶属函数及其量化因子和比例因子的初值.仿真结果表明,采用该算法设计的模糊控制器较传统的PID控制器与模糊控制器具有鲁棒性强、超调量小的特点.这种改进型的模糊控制器具有良好的控制性能,体现了遗传算法在参数寻优方面的优越性.     

基于模糊神经网络的温度控制器研制

本文提出一种基于模糊神经网络的智能温度控制器，给出了模糊神经网络模糊帮一种快速的学习算法，并通过自学习砂断修正模糊控制器的隶属函数和权值，实现了模糊逻辑规则的自动更新。     

基于阶梯隶属度函数的离散T-S模糊系统H_∞控制

针对离散T-S模糊控制系统,提出了一种基于阶梯隶属度函数的H_∞控制器设计方法。为了增加控制器设计的自由度,采用模糊Lyapunov函数作为候选Lyapunov函数,并利用非并行补偿控制器进行闭环控制。进一步利用阶梯隶属度函数逼近T-S模糊模型的隶属度函数,根据阶梯隶属度函数取值的离散性和有限性,描述闭环系统稳定性和性能的模糊矩阵不等式可以被转换为若干线性矩阵不等式。由于隶属度函数的信息被引入到控制器设计之中,降低了控制器设计的保守程度。相比于传统的H_∞控制方法,所提出的方法能够设计出具有更小H_∞性能指标的模糊控制器。给出了一个数值仿真示例验证了方法的有效性。     

STATCOM模糊控制的综合优化设计

针对严重非线性静止同步补偿器模糊逻辑控制隶属函数的参数和模糊控制规则调整困难的问题,根据微遗传算法,并利用模糊系统的逼近能力,提出了一种模糊控制综合优化设计方法.选择模糊控制器的输入/输出观测信号,应用MATLAB中的模糊控制工具箱快速地获得初始的模糊控制规则,再采用微遗传算法优化其隶属函数参数和模糊控制规则,并给出了模糊控制规则优化后的结果.对带有一个静止同步补偿器的四机电力系统,在三种不同的负荷和三种不同的控制方式下进行仿真实验,结果表明:在宽负荷变化范围内,该控制器的追踪控制能力和鲁棒性等比常规的超前滞后控制、未优化模糊控制都优越.     

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.     

Comparison of an adaptive stabilizer and a fuzzy logic stabilizer for superconducting generator governor control

This paper describes the development and application of two different control schemes for stability enhancement of a superconducting generator (SCG). The findings of study of the system performance with an adaptive scheme and a fuzzy logic control scheme are presented and compared. In the first scheme, the stabilizing signal is based on the minimization of a modified version of a quadratic performance index, which includes an additional weighted derivative term. In the second scheme, the stabilizing signal is based on the instantaneous speed deviation and acceleration of the SCG using two fuzzy membership functions and a few simple control rules. A new tuning parameter is introduced to increase the efficiency of the fuzzy logic stabilizer. A genetic algorifimi is used to search for optimal settings of each stabilizer parameters. Simulation results show that both stabilizers are suitable for and effective in damping oscillations and enhancing system stability over a range of operating conditions.     

Design of PSO‐based fuzzy logic controller for single axis magnetic levitation system

In this paper, a particle swarm optimization (PSO) method is proposed to design an optimal robust fuzzy logic controller (FLC). The objective of this paper is to design a nonlinear optimal robust controller for the single axis magnetic levitation system with high accuracy. PSO algorithm is applied to search globally optimal parameters of FLCs. Three different FLCs are designed. First, proportional derivative (PD)‐like FLC. Second, the FLC is based on the PSO algorithm to find the optimal range of the eight linguistic membership functions (FLC1 with PSO algorithm). Finally, the FLC is based on the PSO algorithm to find the optimal range and shape of the four linguistic membership functions (FLC2 with PSO algorithm). The performances of three different FLCs are compared. Simulation results show that PSO‐based optimal FLCs find the optimal range and shape of the four linguistic membership functions and achieved better performance than the other proposed controllers, minimizing 48 fuzzy rules. © 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

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.     

An adaptive PID stabilizer for power systems using fuzzy logic

A new gain scheduling PID stabilizer is designed for excitation control of power systems using fuzzy logic. The parameters of the proposed stabilizer are tuned on-line using a fuzzy rule base and a fuzzy inferencing mechanism for manipulating the speed error and its derivative. Although the new gain scheduled stabilizer does not have an apparent structure of PID controllers, fuzzy logic based controllers may be considered as nonlinear PID controllers, whose parameters can be determined on-line based on the error signal and their time derivative or difference. The new power system stabilizer is applied to single and multimachine power systems subject to various transient disturbances including faults. The superior performance of this stabilizer in comparison to the conventional fixed gain stabilizer proves the efficacy of this new approach.     

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.     

Hybrid micro-GA based FLCs for TCSC and UPFC in a multi-machine environment

In this paper, a systematic fuzzy logic based damping control system design for TCSC and UPFC is proposed to enhance the power system stability spectrum. Firstly, the fuzzy membership functions and control rules are extracted based on the recorded or monitored signals. Then, the novel “Grand-Parenting” technique is employed to incorporate the non-optimal fuzzy logic controllers in micro-GA (μ-GA) to search for optimal fuzzy membership functions and rules simultaneously.Simulation studies have been carried out to show the effectiveness and robustness of the proposed damping control strategy in a multi-machine power system setup.     

Improved fuzzy logic controller for SVC in power system damping using global signals

Static Var Compensator (SVC) is a shunt-type FACTS device, which is used in power systems primarily for the purpose of voltage and reactive power control. In this paper, an improved fuzzy logic-based supplementary controller for SVC is developed for damping the rotor angle oscillations and to improve the stability of the power system. The generator speed and the electrical power are chosen as global input signals for the proposed fuzzy logic controller (FLC). The effectiveness and feasibility of the proposed control is demonstrated with single-machine infinite bus (SMIB) system, three-machine nine-bus WSCC system and New England 10-machine system, which shows the improvement over the use of a fixed parameter controller and existing FLC.