An interval type-2 fuzzy logic controller for TCSC to improve the damping of power system oscillations

In this paper an interval type-2 fuzzy logic controller (IT2FLC) was proposed for thyristor controlled series capacitor (TCSC) to improve power system damping. For controller design, memberships of system variables were represented using interval type-2 fuzzy sets. The three-dimensional membership function of type-2 fuzzy sets provided additional degree of freedom that made it possible to directly model and handle uncertainties. Simulations conducted on a single machine infinite bus (SMIB) power system showed that the proposed controller was more effective than particle swarm optimization (PSO) tuned and type-1 fuzzy logic (T1FL) based damping controllers. Robust performance of the proposed controller was also validated at different operating conditions, various disturbances and parameter variation of the transmission line parameters.     

Neurofuzzy Power System Stabilizer

An adaptive fuzzy logic power system stabilizer (AFPSS) consisting of a generalized neuron (GN)-based predictor and a fuzzy logic controller (FLC) is described. The inference mechanism of the FLC is represented by a rule-base and a database. Two parameters, decided on the basis of the GN-predictor output and the current system conditions, are used to tune the AFPSS. This mechanism of tuning makes the fuzzy logic-based power system stabilizer adaptive to changes in the operating conditions. Therefore, variation in the system response, under a wide range of operating conditions, is less compared to the system response with a fixed-parameter conventional PSS. The performance of the AFPSS has been tested by simulation and experimental studies.      

A fuzzy logic based power system stabilizer with learning ability

A fuzzy logic-based power system stabilizer (PSS) with learning ability is proposed in this paper. The proposed PSS employs a multilayer adaptive network. The network is trained directly from the input and the output of the generating unit. The algorithm combines the advantages of artificial neural networks (ANNs) and fuzzy logic control (FLC) schemes. Studies show that the proposed adaptive network-based fuzzy logic PSS (ANF PSS) can provide good damping of power systems over a wide range of operating conditions and improve the dynamic performance of the power system     

A robust H∞ power system stabilizer with noadverse effect on shaft torsional modes

The H^∞ optimal control theory has been used to design a robust power system stabilizer (PSS) to improve transient and dynamic stabilities of a turbogenerator connected to an infinite busbar. It is demonstrated that the effects of disturbances in the machine output can be minimized and sufficient closed-loop stability margins (robustness) can be obtained to tolerate variations in the loop transfer functions, such as those which might arise from unmodeled low-damped high-frequency modes of oscillations. The resulting controller would effectively enhance the synchronizing and damping torques of the machine without the risk of exciting the shaft torsional modes. This is in marked contrast with the unstable performance of linear quadratic (LQ) optimal controllers under similar conditions. The H^∞ design methodology also ensures a satisfactory performance of the PSS under a wide range of system operating conditions     

Neuro-fuzzy control for autonomous wind–diesel systems using biomass

This paper deals with the development of a neuro-fuzzy controller for a wind–diesel system composed of a stall regulated wind turbine with an induction generator connected to an ac bus-bar in parallel with a diesel generator set having a synchronous generator. A gasifier is capable of converting tons of wood chips per day into a gaseous fuel that is fed into a diesel engine. The controller inputs are the engine speed error and its derivative for the governor part of the controller, and the voltage error and its derivative for the automatic voltage regulator. These are readily measurable quantities leading to a simple controller which can be easily implemented. It is shown that by tuning the fuzzy logic controllers, optimal time domain performance of the autonomous wind–diesel system can be achieved in a wide range of operating conditions compared to fixed-parameter fuzzy logic controllers and PID controllers.     

Application of multi-model control with fuzzy switching to a micro hydro-electrical power plant

Modelling hydraulic turbine generating systems is not an easy task because they are non-linear and uncertain where the operating points are time varying. One way to overcome this problem is to use Takagi–Sugeno (TS) models, which offer the possibility to apply some tools from linear control theory, whereas those models are composed of linear models connected by a fuzzy activation function. This paper presents an approach to model and control a micro hydro power plant considered as a non-linear system using TS fuzzy systems. A TS fuzzy system with local models is used to obtain a global model of the studied plant. Then, to combine efficiency and simplicity of design, PI controllers are synthesised for each considered operating point to be used as conclusion of an electrical load TS Fuzzy controller. The latter ensures the global stability and desired performance despite the change of operating point. The proposed approach (model and controller) is tested on a laboratory prototype, where the obtained results show their efficiency and their capability to ensure good performance despite the non-linear nature of the plant.     

Maximum power point tracking using adaptive fuzzy logic control for grid-connected photovoltaic system

This paper proposes a method of maximum power point tracking using adaptive fuzzy logic control for grid-connected photovoltaic systems. The system is composed of a boost converter and a single-phase inverter connected to a utility grid. The maximum power point tracking control is based on adaptive fuzzy logic to control a switch of a boost converter. Adaptive fuzzy logic controllers provide attractive features such as fast response, good performance. In addition, adaptive fuzzy logic controllers can also change the fuzzy parameter for improving the control system. The single phase inverter uses predictive current control which provides current with sinusoidal waveform. Therefore, the system is able to deliver energy with low harmonics and high power factor. Both conventional fuzzy logic controller and adaptive fuzzy logic controller are simulated and implemented to evaluate performance. Simulation and experimental results are provided for both controllers under the same atmospheric condition. From the simulation and experimental results, the adaptive fuzzy logic controller can deliver more power than the conventional fuzzy logic controller.     

Multivariable robust control of a power plant deaerator

The design of a robust controller for the deaerator of the Experimental Breeder Reactor-II (EBR-II) that uses the linear quadratic Gaussian with loop transfer recovery (LQG/LTR) procedure is described. At present, classical proportional-integral (PI) controllers are used to control the deaerator. When the operating condition changes, the system is disturbed, or a fault occurs, and the PI controllers may fail to maintain the desired performance. A robust controller that can accommodate system faults and obtain a reasonable behavior for a wide range of model uncertainty was designed. The controller provides the desired performance despite a considerable change in the operating condition, accommodates some of the failures that can occur, and provides the choice of penalizing one variable over another. The design is tested for robustness by varying the system operating conditions and simulating a steam valve failure. The set of nonlinear simulations using the modular modeling system and the advanced continuous simulation language is included     

A boiler-turbine system control using a fuzzy auto-regressive moving average (FARMA) model

This paper presents an application of an online self-organizing fuzzy logic controller to a boiler-turbine system of a fossil power plant. The control rules and the membership functions of the proposed fuzzy logic controller are generated automatically without using a plant model. A boiler-turbine system is described as a multi-input multioutput (MIMO) nonlinear system in this paper. Then, three single-loop fuzzy logic controllers are designed independently. Simulation shows robust results for various kinds of electric load demand changes and parameter variations of boiler-turbine system.     

Bilateral based robust load frequency control

Load frequency control (LFC) has been one of the major subjects in electric power system design/operation and is becoming much more significant today in accordance with increasing size and the changing structure and complexity of interconnected power systems. In practice, power systems use simple proportional-integral (PI) controllers for frequency regulation and load tracking. However, since the PI controller parameters are usually tuned based on classical or trial and error approaches, they are incapable of obtaining good dynamical performance for a wide range of operating conditions and various load changes scenarios in a restructured power system.This paper addresses a new decentralized robust LFC design in a deregulated power system under a bilateral based policy scheme. In each control area, the effect of bilateral contracts is taken into account as a set of new input signals in a modified traditional dynamical model. The LFC problem is formulated as a multi-objective control problem via a mixed H₂/H_∞ control technique. In order to design a robust PI controller, the control problem is reduced to a static output feedback control synthesis, and then, it is solved using a developed iterative linear matrix inequalities algorithm to get a robust performance index close to a specified optimal one. The proposed method is applied to a 3 control area power system with possible contract scenarios and a wide range of load changes. The results of the proposed multi-objective PI controllers are compared with H₂/H_∞ dynamic controllers.     

A systematic approach to performance weights selection in design ofrobust H∞ PSS using genetic algorithms

Selection of suitable performance weights is the main problem in design of a robust H^∞ power system stabilizer (PSS). In this paper, a systematic and automated approach based on genetic algorithms (GAs) is proposed. It gives rise to selection of optimal performance weights without any trial and error attempt. The resulting H ^∞ PSS performs quite satisfactorily under a wide range of turbogenerator operating conditions and is robust against unmodelled low-damped torsional modes. It also provides sufficient robustness against significant changes in transmission line configuration and parameters     

Neuro-fuzzy controller of low head hydropower plants usingadaptive-network based fuzzy inference system

This paper presents an attempt of nonlinear, multivariable control of low-head hydropower plants, by using adaptive-network based fuzzy inference system (ANFIS). The new design technique enhances fuzzy controllers with self-learning capability for achieving prescribed control objectives in a near optimal manner. The controller has flexibility for accepting more sensory information, with the main goal to improve the generator unit transients, by adjusting the exciter input, the wicket gate and runner blade positions. The developed ANFIS controller whose control signals are adjusted by using incomplete on-line measurements, can offer better damping effects to generator oscillations over a wide range of operating conditions, than conventional controllers. Digital simulations of hydropower plant equipped with low-head Kaplan turbine are performed and the comparisons of conventional excitation-governor control, state-feedback optimal control and ANFIS based output feedback control are presented. To demonstrate the effectiveness of the proposed control scheme and the robustness of the acquired neuro-fuzzy controller, the controller has been implemented on a complex high-order nonlinear hydrogenerator model     

模糊自学习控制在水电机组控制中应用研究

采用具有自学习能力的自适应模糊控制器来控制水电机组运行。自适应模糊控制器将模糊控制和神经网络结合,根据运行情况在线调整模糊推理规则和隶属函数,使控制系统具有自适应学习的特性。学习中学习速率和平滑因子可根据误差情况在线修改,克服了网络学习速度慢和局部最优的缺点。仿真实验表明,设计的自适应模糊控制器具有良好的鲁棒性,可有效地改善水轮发电机组系统的动、静态性能。     

A new control strategy for a stand-alone self-excited induction generator driven by a variable speed wind turbine

This paper presents a new control strategy of a stand-alone self-excited induction generator (SEIG) driven by a variable speed wind turbine. The proposed system consists of a three phase squirrel-cage induction machine connected to a wind turbine through a step-up gear box. A current controlled voltage source inverter (CC–VSI) with an electronic load controller (ELC) is connected in parallel with the main consumer load to the AC terminals of the induction machine. The proposed control strategy is based on fuzzy logic control principles which enhance the dynamic performance of the proposed system. Three fuzzy logic PI controllers and one hysteresis current controller (HCC) are used to extract the maximum available energy from the wind turbine as well as to regulate the generator terminal voltage simultaneously against wind speed and main load variations. However, in order to extract the maximum available energy from the turbine over a wide range of wind speeds, the captured energy is limited due to electrical constraints. Therefore the control strategy proposed three modes of control operation. The steady state characteristics of the proposed system are obtained and examined in order to design the required control parameters. The proposed system is modeled and simulated using Matlab/Simulink software program to examine the dynamic characteristics of the system with proposed control strategy. Dynamic simulation results demonstrate the effectiveness of the proposed control strategy.     

Hybrid fuzzy controller for speed control of switched reluctance motor drives

A hybrid fuzzy controller for speed control of switched reluctance motor (SRM) drives is presented in this paper. The developed hybrid fuzzy control law consists of a proportional integral (PI) controller at steady state, a PI type fuzzy logic controller (FLC) at transient state and a simple logic controller between the steady and transient states to achieve the desired performance at various operating conditions under soft chopping operation. The importance of the hybrid fuzzy controller is highlighted by comparing the performance of various control approaches, including PI control, PI type fuzzy logic control and PD type fuzzy control for speed control of SRM motor drives. The complete control algorithm is demonstrated by intensive experimental results. It is shown that the presented hybrid controller for SRM drive has fast tracking capability, less steady state error and is robust to load disturbance. The complete speed control scheme of the SRM drive incorporating the hybrid control is experimentally implemented and validated using a high speed digital signal processor board TMS320F2812 for a prototype 1.2 kW SRM.     

Design of H∞-PSS using numerator-denominatoruncertainty representation

This paper presents a new method of designing a robust H_∞-PSS to deal with some limitations of the existing H _∞-PSSs (standard H_∞-PSSs). These limitations include: (i) the inability to treat the system uncertainty when a stable nominal plant becomes an unstable perturbed plant; and (ii) the cancellation of the plant's poorly damped poles by the controller's zeros. The proposed multiple inputs single output controller for the excitation system is based on the “numerator-denominator” uncertainty representation which is not restricted in the modeling of uncertainty as compared to the standard additive or multiplicative uncertainty representation. Furthermore, the bilinear transformation has been used in the design to prevent the pole-zero cancellation of the poorly damped poles and to improve the control system performance. Simulation results have shown satisfactory performance of the proposed PSS for a wide range of operating conditions and good stability margin as compared to both the conventional PSS and the standard H_∞-PSS     

Fuzzy self-adaptive PID control technique for driving HHO dry cell systems

When the standard operating temperatures are exceeded in hydroxy generators, production performance decreases and power consumption increases. Self-adaptive fuzzy proportional integral derivative systems have been used to prevent this situation and to maintain optimum production conditions. The Fuzzy Logic is provided to tune the optimum PID parameters by using a gain scheduling method. The tuning scheme is demonstrated by a fuzzy decision process, which consists of fuzzification, knowledge and rule base, inference and defuzzification. Results of developed fuzzy proportional integral derivative (FPID), on-off, conventional PID and fuzzy control approach are discussed and compared. The presented result shows that self-adaptive fuzzy PID controllers achieve better control performance than conventional control methods mentioned. The last but not the least, developed approach minimizes the expertise needed to apply pulse width modulation technique.     

Assessment of a fuzzy logic based MRAS observer used in a photovoltaic array supplied AC drive

In this paper a fuzzy logic (FL) based model reference adaptive system (MRAS) speed observer for high performance AC drives is proposed. The error vector computation is made based on the rotor-flux derived from the reference and the adaptive model of the induction motor. The error signal is processed in the proposed fuzzy logic controller (FLC) for speed adaptation. The drive employs an indirect vector control scheme for achieving a good closed loop speed control. For powering the drive system, a standalone photovoltaic (PV) energy source is used. To extract the maximum power from the PV source, a constant voltage controller (CVC) is also proposed. The complete drive system is modeled in MATLAB/Simulink and the performance is analyzed for different operating conditions.     

Doubly-fed induction generator drive based WECS using fuzzy logic controller

The purpose of this paper is to improve the control performance of the variable speed, constant frequency doubly-fed induction generator in the wind turbine generation system by using fuzzy logic controllers. The control of the rotor-side converter is realized by stator flux oriented control, whereas the control of the grid-side converter is performed by a control strategy based on grid voltage orientation to maintain the DC-link voltage stability. An intelligent fuzzy inference system is proposed as an alternative of the conventional proportional and integral (PI) controller to overcome any disturbance, such as fast wind speed variation, short grid voltage fault, parameter variations and so on. Five fuzzy logic controllers are used in the rotor side converter (RSC) for maximum power point tracking (MPPT) algorithm, active and reactive power control loops, and another two fuzzy logic controllers for direct and quadratic rotor currents components control loops. The performances have been tested on 1.5 MW doubly-fed induction generator (DFIG) in a Matlab/Simulink software environment.     

Implementation and laboratory test results for a fuzzy logic basedself-tuned power system stabilizer

Implementation of a fuzzy logic-based self-tuned controller as a power system stabilizer (PSS) is described. The stabilizing signal generated by the controller is computed using a standard fuzzy membership function and a self-tuned parameter. The calculation is based on the representation of the alternator state in the phase plane. Real-time test results are presented for various operating conditions and disturbances. The results obtained demonstrate the effectiveness and improved response of the implemented PSS compared with the conventional stabilizer. The proposed controller does not require any parameter identification in real time and has a much simpler algorithm than that for a self-tuning controller. The performance of the proposed stabilizer is demonstrated by practical implementation using a digital signal processor mounted on a PC-AT. Results of the experimental tests on a physical model of a power system are presented