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.     

Optimal robust adaptive fuzzy synergetic power system stabilizer design

A new particle swarm optimized robust indirect adaptive power system stabilizer is developed based on recently developed synergetic control methodology. Fuzzy systems are used in an adaptive scheme to approximate the system using a nonlinear model while synergetic control guarantees robustness and the use of a chatter free continuous control law which makes the controller easy to implement. In addition the controller parameters are optimized using PSO approach. Simulation of severe operating conditions of a power system is conducted to validate the effectiveness of the proposed approach while stability is guaranteed via Lyapunov synthesis.     

Robust fuzzy logic power system stabilizer based on evolution and learning

A robust fuzzy logic power system stabilizer (FLPSS) based on evolution and learning is proposed in this paper. A hybrid algorithm that combines learning and evolution is developed whereby each one complements other’s strength. Parameters of FLPSS are encoded in chromosome (individual) of genetic algorithm (GA) population. Population of FLPSS in GA learns to stabilize electromechanical oscillations in power system at an operating point, as the best fitness becomes large steady value during successive generations. Operating region of FLPSS is enlarged by learning more operating points over the operating domain. Best FLPSS drawn from last generation is saved as designed FLPSS. Effectiveness of the proposed method is validated on a single machine infinite bus (SMIB) power system. Promising optimal stabilizing performance with designed FLPSS for considered power system is obtained at wide range of operating points.     

Design of a robust power system stabilizer using fuzzy logic for a multi-machine power system

This paper presents a design procedure for a fuzzy logic based power system stabilizer (FPSS) and investigates the robustness of the FPSS for a multi-machine power system. Speed deviation of synchronous generator and its derivative are chosen as the input signals to the FPSS. A two area, five generator power system is used to illustrate the robustness of the FPSS. A normalized sum-squared deviation (NSSD) index is used for designing the FPSS and demonstrating its robustness. Nonlinear simulation tests for different disturbances and their results are discussed.     

Damping inter-area modes of oscillation using an adaptive fuzzy power system stabilizer

This paper introduces an indirect adaptive fuzzy controller as a power system stabilizer used to damp inter-area modes of oscillation following disturbances in power systems. Compared to the IEEE standard multi-band power system stabilizer (MB-PSS), indirect adaptive fuzzy-based stabilizers are more efficient because they can cope with oscillations at different operating points. A nominal model of the power system is identified on-line using a variable structure identifier. A feedback linearization-based control law is implemented using the identified model. The gains of the controller are tuned via a particle swarm optimization routine to ensure system stability and minimum sum of the squares of the speed deviations. A bench-mark problem of a 4-machine 2-area power system is used to demonstrate the performance of the proposed controller and to show its superiority over other conventional stabilizers used in the literature.     

An online adaptive neuro-fuzzy power system stabilizer for multimachine systems

A fuzzy-logic-based adaptive power system stabilizer (PSS) is proposed in this paper. The parameters of the fuzzy-logic-based PSS are tuned by neural networks online. The system is divided into two subsystems, a recursive least square identifier with a variable forgetting factor for the generator and a fuzzy-logic-based adaptive controller to damp oscillations. The effectiveness of the proposed PSS in increasing the damping of local and inter-area modes of oscillation is demonstrated in a one-machine-infinite-bus system; a two-area, 4-machine, 13-bus system, and a 16-machine, 68-bus system.     

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.     

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.     

模糊电力系统稳定器的设计与实现

针对模糊控制系统具有多参数和非线性的特点，给出了一种基于Matlab的模糊电力系统稳定器快速设计开发方法，以克服传统的设计方法所具有的实现复杂且开发周期长等不足，简要介绍了如何由模糊逻辑工具箱和集成仿真环境Simulink来建立整个系统的仿真模型，由Real-Time Workshop将已取得满意仿真效果的模型转换为应用程序，然后将其下载到目标环境中运行，在线调整参数以达到最佳控制效果，从而实现快速原型开发，即可应用于实际系统。仿真结果表明所设计的模糊电力系统稳定器比传统的电力系统稳定器能更有效地抑制系统低频振荡。     

基于最小二乘法的最优自适应电力系统稳定器

针对电力系统的强非线性特征及其运行过程中易受扰动的特点,采用基于系统辨识的自适应控制方法,通过选取合适的线性模型对典型的电力系统进行线性化处理,研究了对一般最小二乘法的改进,并结合最优自适应控制方法,设计出一种新型的最优自适应电力系统稳定器.在单机-无穷大系统中将其与传统的电力系统稳定器分别进行仿真.结果表明,在系统受到多种扰动时,这种最优自适应电力系统稳定器都能够有效地抑制低频振荡,使系统迅速恢复稳定运行,从而显示出了该最优自适应电力系统稳定器的设计具有理论研究和实际应用价值.     

基于Prony算法的模糊电力系统稳定器设计

在简介Prony算法步骤的基础上,阐述了电力系统低频振荡分析的Prony方法。提出了可将Prony算法应用于模糊电力系统稳定器FPSS(Fuzzy Power System Stabilizer)修正因子、比例因子的调节、整定。采用Prony算法为一个单机无穷大系统设计的FPSS作仿真试验,仿真结果显示了其可行性和有效性,为FPSS的设计提供了一种新方法。     

Speed deviation driven adaptive neural network based power system stabilizer

The paper presents an online adaptive artificial neural network (ANN) based power system stabilizer (PSS). The proposed controller is first trained offline using a pole placement based state feedback gain technique at different operating points. The trained ANN parameters (weights and biases) are updated and tuned online using the speed deviation as the reinforcement signal. The proposed PSS is tested at different operating conditions and a variety of regulator gains. The digital results validate the effectiveness and reliability of the new PSS in terms of fast system response under different loading conditions compared with the conventional PI controller and the modern control theory approach of pole placement.     

水轮发电机组调速器侧模糊电力系统稳定器研究

在同步发电机组调速器侧装设电力系统稳定器是抑制电力系统低频振荡的有效措施.采用水力系统弹性水击模型和同步发电机Philips-Heffron模型,设计了水轮发电机组调速器侧模糊电力系统稳定器(FGPSS),并应用Matlab/Simulink对其效果进行了仿真研究.结果表明,FGPSS对于改善机组动态性能具有良好的作用,而且具有较强的鲁棒性.     

An adaptive stabilizer for thyristor controlled static VARcompensators for power systems

The authors present the analytical development of a controller scheme for a static VAr system and present detailed simulation results to highlight its effectiveness in comparison with a discrete PI (proportional-integral) stabilizer under a variety of operating conditions for power transmission systems. The adaptive stabilizer is suitable for microprocessor digital implementation as the numerical steps involved are minimal. The adaptive stabilizer uses a stochastic variant simplified parameter estimator routine, thus providing a faster sampling rate and identification of a larger number of parameters in real time. The implementation of this stabilizer is simple as it involves additions, multiplications without matrix inversion, etc     

调速器侧模糊电力系统稳定器改善水电系统稳定性的仿真研究

由于水力系统的水锤效应和强非线性特性，水轮发电机组采用常规控制难以取得令人满意的效果。介绍了水轮发电机组调速器侧模糊电力系统稳定器FGPSS(Fuzzy Governor Power System Stabilizer)的设计原理。应用Simulink仿真软件构建了以水力系统弹性水击模型和同步发电机非线性模型为基础的仿真模型，并对采用PID，PID 常规GPSS和PID FGPSS三种控制方式的控制效果进行了仿真对比研究。结果表明，FGPSS对于改善电力系统的稳定性具有良好的作用，而且具有较强的鲁棒性。     

Three-dimensional power system stabilizer

Power system stabilizers (PSSs) are used to enhance damping of power system oscillations through excitation control of synchronous generator. The objective of the PSS is to generate a stabilizing signal, which produces a damping torque component on the generator shaft. Conventional PSSs are designed with the phase compensation technique in the frequency domain and include the lead-lag blocks whose parameters are determined according to a linearized power system model. The performance of conventional PSSs (CPSSs) depends upon the generator operating point and the system parameters, but a reasonable level of robustness can be achieved depending on the tuning method. This paper presents a new three-dimensional PSS (3D PSS), which uses rotor speed deviation, rotor acceleration and load angle deviation as input signals. The 3D PSS attempts to return the generator to the state-space origin, based on the generator’s trajectory in state-space and the achievement of torque equilibrium. The 3D PSS is robust to system parameters changes. The proposed algorithm was implemented in a digital control system, tested in a laboratory environment on a synchronous generator connected to the power system, and then compared with CPSS. Experimental results show that the proposed PSS achieves better performance than the CPSS in damping oscillations.     

Adaptive fuzzy logic load frequency control of multi-area power system

Based on indirect adaptive fuzzy control technique, a new load frequency control (LFC) scheme for multi-area power system is proposed. The power systems under study have the characterization of unknown parameters. Local load frequency controller is designed using the frequency and tie-line power deviations of each area. In the controller design, the approximation capabilities of fuzzy systems are employed to identify the unknown functions, formulate suitable adaptive control law and updating algorithms for the controller parameters. It is proved that the proposed controller ensures the boundedness of all variables of the closed-loop system and the tracking error. Moreover, in the proposed controller an auxiliary control signal is introduced to attenuate the effect of fuzzy approximation error and to mitigate the effect of external disturbance on the tracking performance. Simulation results of a three-area power system are presented to validate the effectiveness of the proposed LFC and show its superiority over a classical PID controller.     

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.     

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.