Evaluation of advanced fuzzy logic PSS on analog network simulatorand actual installation on hydro generators

This paper presents the experimental evaluation of an advanced fuzzy logic PSS on the analog network simulator at the Research Laboratory of Kyushu Electric Power Co., Inc. The proposed power system stabilizer (PSS) is set up by using a personal computer with A/D and D/A conversion interfaces. The personal computer based PSS was set on the analog network simulator to evaluate the effectiveness of the advanced fuzzy logic control scheme, and to provide sufficient data for the actual installation of the proposed PSS on hydro-units in the Kyushu Electric Power System. This paper also describes the actual installation of the proposed PSS on hydro-units in the Kyushu Electric Power System for the long term evaluation of the proposed PSS using its prototypes     

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 excitation system for stability enhancement of powersystems with multi-mode oscillations

A fuzzy logic excitation system has been proposed to enhance the overall stability of power systems. The proposed excitation system has two control loops. One is the voltage control loop which achieves the automatic voltage regulator (AVR) function, and the other is the damping control loop which gives the PSS function. Simple fuzzy logic control rules are applied to both loops. The input signal to the voltage control loop is the terminal voltage, and the input signal to the damping control loop is the real power output. Simulation studies show the advantages of the fuzzy logic excitation system     

Design and analysis of an adaptive fuzzy power system stabilizer

Power system stabilizers (PSS) must be capable of providing appropriate stabilization signals over a broad range of operating conditions and disturbances. Traditional PSS rely on robust linear design methods. In an attempt to cover a wider range of operating conditions, expert or rule-based controllers have also been proposed. Fuzzy logic as a novel robust control design method has shown promising results. The emphasis in fuzzy control design centers around uncertainties in system parameters and operating conditions. Such an emphasis is of particular relevance as the difficulty of accurately modelling the connected generation is expected to increase under power industry deregulation. Fuzzy logic controllers are based on empirical control rules. In this paper, a systematic approach to fuzzy logic control design is proposed. Implementation for a specific machine requires specification of performance criteria. This performance criteria translates into three controller parameters which can be calculated off-line or computed in real-time in response to system changes. The robustness of the controller is emphasized. Small signal and transient analysis methods are discussed. This work is directed at developing robust stabilizer design and analysis methods appropriate when fuzzy logic is applied     

An artificial neural network based adaptive power system stabilizer

An artificial neural network (ANN)-based power system stabilizer (PSS) and its application to power systems are presented. The ANN-based PSS combines the advantages of self-optimizing pole shifting adaptive control strategy and the quick response of ANN to introduce a new generation PSS. A popular type of ANN, the multilayer perceptron with error backpropagation training method, is used in this PSS. The ANN was trained by the training data group generated by the adaptive power system stabilizer (APSS). During the training, the ANN was required to memorize and simulate the control strategy of APSS until the differences were within the specified criteria. Results show that the proposed ANN-based PSS can provide good damping of the power system over a wide operating range and significantly improve the dynamic performance of the system     

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.     

An adaptive power system stabilizer based on recurrent neuralnetworks

Application of recurrent, neural networks in the design of an adaptive power system stabilizer (PSS) is presented in this paper. The architecture of the proposed adaptive PSS has two recurrent neural networks. One functions as a tracker to learn the dynamic characteristics of the power plant and the second one functions as a controller to damp the oscillations caused by the disturbances. In the proposed approach, the weights of the neural networks are updated on-line. Therefore, any new information available during actual control of the plant is considered. Simulation studies show that the artificial neural network (ANN) based PSS can provide very good damping over a wide range of operating conditions     

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.     

Design of augmented fuzzy logic power system stabilizers to enhancepower systems stability

This paper presents an augmented fuzzy logic power system stabilizer (PSS) for stability enhancement of multimachine power systems. In order to accomplish a satisfactory damping characteristic over a wide range of operating points, speed deviation (Δω) and acceleration (Δω) of a synchronous generator were taken as the input signals to the fuzzy controller. It is well known that these variables have significant effects on damping the generators' shaft mechanical oscillations. A modification of the terminal voltage feedback signal to the excitation system as a function of the accelerating power on the unit, is also used to enhance the stability of the system. The stabilizing signals are computed using the standard fuzzy membership function depending on these variables. The performance of the proposed augmented fuzzy controller is compared to an optimal controller and its effectiveness is demonstrated by a detailed digital computer simulation of a single machine infinite bus and a multimachine power systems     

Comparative study of various artificial intelligence approaches applied to direct torque control of induction motor drives

In this paper, three intelligent approaches were proposed, applied to direct torque control (DTC) of induction motor drive to replace conventional hysteresis comparators and selection table, namely fuzzy logic, artificial neural network and adaptive neuro-fuzzy inference system (ANFIS). The simulated results obtained demonstrate the feasibility of the adaptive network-based fuzzy inference system based direct torque control (ANFIS-DTC). Compared with the classical direct torque control, fuzzy logic based direct torque control (FL-DTC), and neural networks based direct torque control (NN-DTC), the proposed ANFIS-based scheme optimizes the electromagnetic torque and stator flux ripples, and incurs much shorter execution times and hence the errors caused by control time delays are minimized. The validity of the proposed methods is confirmed by simulation results.     

Application of an inverse input/output mapped ANN as a power systemstabilizer

An artificial neural network (ANN), trained as an inverse of the controlled plant, to function as a power system stabilizer (PSS) is presented in this paper. In order to make the proposed ANN PSS work properly, it was trained over the full working range of the generating unit with a large variety of disturbances. Data used to train the ANN PSS consisted of the control input and the synchronous machine response with an adaptive PSS (APSS) controlling the generator. During training, the ANN was required to memorize the reverse input/output mapping of the synchronous machine. After the training, the output of the synchronous machine was applied as the input of the ANN PSS and the output of the ANN PSS was used as the control signal. Simulation results show that the proposed ANN PSS can provide good damping of the power system over a wide operating range and significantly improve the system performance     

Integrated fuzzy logic generator controller for stabilityenhancement

An integrated fuzzy logic controller is proposed in this paper for the generator excitation and speed governing control. The proposed controller has three control loops: the first one is the voltage control loop which has the function of automatic voltage regulator (AVR), the second one is the damping control loop which has the function of power system stabilizer (PSS), and the last one is the speed governing control loop which has the function of governor (GOV). A simple fuzzy logic control scheme is applied to all these three loops. The control scheme is simple enough so as not to require heavy computation for the controller, therefore, its real time application is feasible. The effectiveness is demonstrated through nonlinear simulations using a one machine infinite bus system. Comparison studies are also performed to show the advantages of the proposed controller with conventional excitation and speed governing control systems     

An adaptive power system stabilizer using on-line trained neuralnetworks

This paper presents an approach to the design of an adaptive power system stabilizer (PSS) based on on-line trained neural networks. Only the inputs and outputs of the generator are measured and there is no need to determine the states of the generator. The proposed neural adaptive PSS (NAPSS) consists of an adaptive neuro-identifier (ANI), which tracks the dynamic characteristics of the plant, and an adaptive neuro-controller (ANC) to damp the low frequency oscillations. These two subnetworks are trained in an on-line mode utilizing the backpropagation method. The use of a single-element error vector along with a small network simplifies the learning algorithm in terms of computation time. The improvement of the dynamic performance of the system is demonstrated by simulation studies for a variety of operating conditions and disturbances     

A comparison of rotor damping from adaptive and conventional PSS ina multi-machine power system

A comparison between damping available from a conventional power system stabilizer (PSS) and an adaptive controller is made using a transient stability program simulation of a nine-machine power system. A step change to terminal voltage and variable duration bus faults is used for network disturbances to illustrate the added damping available from the adaptive PSS. Simulation results showing the relative damping contributions of the two stabilizers are presented     

Observer-based type-2 fuzzy approach for robust control and energy management strategy of hybrid energy storage systems

In this paper, an observer-based type-2 fuzzy method is proposed for control and energy management strategy (EMS) of the hybrid energy storage system (HESS) which can be composed of the fuel cell (FC), battery (BA), and supercapacitor (SC). The objective and main contribution of the suggested strategy is to provide: 1) Appropriate tracking performance of power sources by an observer-based control method in the presence of noise and signal ripples. 2) An observer-based composite adaptive type-2 fuzzy (OCAT2F) to approximate the voltage of power sources. 3) A dynamical model of DC-bus to guarantee the stability of closed-loop system. 4) An intelligent EMS. To have a high-power supply, the proposed EMS includes two parts; a type-2 fuzzy logic control rule table (T2FLCRT), and an observer-based robust adaptive fuzzy type-2 fuzzy (ORAT2F). Furthermore, stability analyses of the closed-loop system are provided by the input-output linearization (I-OL) approach and based on the Lyapunov theorem. The simulation results of the proposed control scheme under MATLAB/Simulink indicate that the suggested strategy can provide a suitable control performance, and stability of the whole system is achieved.     

Fuzzy logic application for intelligent control of a variable speeddrive

The slip power recovery configuration is an attractive scheme of variable speed drive, with high efficiency and low power converter rating; however, high performance control has been difficult. In this paper, novel applications of fuzzy logic for the intelligent control of a slip power recovery system are presented. A direct fuzzy logic controller and an adaptive fuzzy controller, based on model reference adaptive control, are developed and simulated for the doubly-excited induction machine and power converter system. Compared with field orientation control, the intelligent control of a complex slip power recovery system reduces costs and enhances robust and desired performance     

Artificial neural network power system stabilizers in multi-machinepower system environment

The effectiveness of an artificial neural network (ANN), functioning as a power system stabilizer (PSS), in damping multi-mode oscillations in a five-machine power system environment is investigated in this paper. Accelerating power of the generating unit is used as the input to the ANN PSS. The proposed ANN PSS using a multilayer neural network with error-backpropagation training method was trained over the full working range of the generating unit with a large variety of disturbances. The ANN was trained to memorize the reverse input/output mapping of the synchronous machine. Results show that the proposed ANN PSS can provide good damping for both local and inter-area modes of oscillations     

Adaptive fuzzy gain scheduling PID controller for maximum power point tracking of photovoltaic system

This paper proposes a methodology of designing a Maximum Power Point Tracking (MPPT) controller for photovoltaic systems (PV) using a Fuzzy Gain Scheduling of Proportional-Integral-Derivative (PID) type controller (FGS-PID) with adaptation of scaling factors (SF) for the input signals of FGS. The proposed adaptive FGS-PID method is based on a two-level control system architecture, which combines the advantages of fuzzy logic and conventional PID control. The initial values of the PID's gains are determined by the Ziegler–Nichols tuning method. During transient and steady states, the PID's gains are adapted by the FGS-PID to damp out the transient oscillations, to reduce settling time and to guarantee system stability and accuracy. Also, the conditioned input signals of the FGS-PID are tuned dynamically by gain factors which are based on fuzzy logic system (FLS). The FLS is characterized by a set of fuzzy rules which are fuzzy conditional statements expressing the relationship between inputs (error and change of error) and outputs. This approach creates an adaptive MPPT controller and achieves better overall system performance. The simulation results demonstrate the effectiveness of the proposed adaptive FGS-PID and show that this approach can achieve a good maximum power operation under any conditions such as different levels of solar radiation and PV cell temperature for varying PV sources. Compared to conventional methods (PID, perturb and observe method P&O), this method shows a considerable high tracking performance.     

Variable structure battery-based fuel cell hybrid power system and its incremental fuzzy logic energy management strategy

A hybrid power system consists of a fuel cell and an energy storage device like a battery and/or a supercapacitor possessing high energy and power density that beneficially drives electric vehicle motor. The structures of the fuel cell-based power system are complicated and costly, and in energy management strategies (EMSs), the fuel cell's characteristics are usually neglected. In this study, a variable structure battery (VSB) scheme is proposed to enhance the hybrid power system, and an incremental fuzzy logic method is developed by considering the efficiency and power change rate of fuel cell to balance the power system load. The principle of VSB is firstly introduced and validated by discharge and charge experiments. Subsequently, parameters matching of the fuel cell hybrid power system according to the proposed VSB are designed and modeled. To protect the fuel cell as well as ensure the efficiency, a fuzzy logic EMS is formulated via setting the fuel cell operating in a high efficiency and generating an incremental power output within the affordable power slope. The comparison between a traditional deterministic rules-based EMS and the designed fuzzy logic was implemented by numerical simulation in three different operation conditions: NEDC, UDDS, and user-defined driving cycle. The results indicated that the incremental fuzzy logic EMS smoothed the fuel cell power and kept the high efficiency. The proposed VSB and incremental fuzzy logic EMS may have a potential application in fuel cell vehicles.     

A neural network based power system stabilizer suitable for on-linetraining-a practical case study for EGAT system

This paper presents the development of a neural network based power system stabilizer (PSS) designed to enhance the damping characteristics of a practical power system network representing a part of Electricity Generating Authority of Thailand (EGAT) system. The proposed PSS consists of a neuro-identifier and a neuro-controller which have been developed based on functional link network (FLN) model. A recursive on-line training algorithm has been utilized to train the two neural networks. Simulation results have been obtained under various operating conditions and severe disturbance cases which show that the proposed neuro-PSS can provide a better damping to the local as well as interarea modes of oscillations as compared to a conventional PSS