Robust design of multimachine power system stabilizers usingsimulated annealing

Robust design of multimachine power system stabilizers (PSSs) using simulated annealing (SA) optimization technique is presented in this paper. The proposed approach employs SA to search for optimal parameter settings of a widely used conventional fixed-structure lead-lag PSS (CPSS). The parameters of the proposed simulated annealing based power system stabilizer (SAPSS) are optimized in order to shift the system electromechanical modes at different loading conditions and system configurations simultaneously to the left in the s-plane. Incorporation of SA as a derivative-free optimization technique in PSS design significantly reduces the computational burden. One of the main advantages of the proposed approach is its robustness to the initial parameter settings. In addition, the quality of the optimal solution does not rely on the initial guess. The performance of the proposed SAPSS under different disturbances and loading conditions is investigated for two multimachine power systems. The eigenvalue analysis and the nonlinear simulation results show the effectiveness of the proposed SAPSS's to damp out the local as well as the interarea modes and enhance greatly the system stability over a wide range of loading conditions and system configurations     

Optimal design of power system stabilizers using evolutionary programming

The optimal design of power system stabilizers (PSSs) using evolutionary programming (EP) optimization technique is presented in this paper. The proposed approach employs EP to search for optimal settings of PSS parameters that shift the system eigenvalues associated with the electromechanical modes to the left in the s-plane. Incorporation of EP algorithm in the design of PSSs significantly reduces the computational burden. The performance of the proposed PSSs under different disturbances, loading conditions, and system configurations is investigated for a multimachine power system. The eigenvalue analysis and the nonlinear simulation results show the effectiveness and robustness of the proposed PSSs to damp out the local as well as the interarea modes of oscillations and work effectively over a wide range of loading conditions and system configurations.     

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     

Theory and method for selection of power system stabilizer location

An approach for the selection of best PSS (power system stabilizer) locations in multimachine power systems is proposed. Study shows that the right-eigenvector measures the activity of state variables and the left-eigenvector measures the control effect of control signals. Based on the right and left eigenvector, the concept of sensitivity of PSS effect (SPE) is presented and used to identify the best PSS locations. The method is used to identify the best PSS location is a 13-machine system to increase the damping of an interarea mode. The time-domain simulation results confirm that the prediction of the best PSS location by the SPE method is correct and accurate     

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     

Power system output feedback stabilizer design via optimal subeigenstructure assignment

The paper addresses the design of a power system stabilizer using an optimal reduced order model whose state variables are torque angles and speeds. System damping can be improved using eigenvalue assignment, and the co-ordination of stabilizers can be achieved through eigenvector assignment by maintaining system mode shape. The proportional-integral PSS is derived via the optimal reduced order model instead of via the whole system model. The effectiveness of this stabilizer is evaluated, and this study reveals that the result of eigenstructure assignment is more stable and much better than in the assignment method based on the whole system model. A one-machine infinite-bus system and a multimachine system are given as examples to illustrate the advantages and effectiveness of the proposed approach. Results based on the whole system model are included for comparison.     

The modal performance measure for parameter optimization of powersystem stabilizers

This paper develops an optimization approach using the modal performance measure for the selection of power system stabilizer (PSS) parameters in multimachine power systems. The goal of the optimization problem is to damp out the sustained low frequency oscillations in the outputs of a linearized power system. Thus, the modal performance measure optimization problem is to select a set of PSS parameters so that the area under the envelop of the oscillatory output response will be minimum. This paper also considers bounded and unbounded PSS parameters and compares the effects of bounds on the end results. Furthermore, this work also shows that the performance measure is not a convex function in the PSS parameters. That is, there exist many local minima and possibly a global minimum. Single-machine infinite-bus and two-machine three-bus power systems are used to show the effectiveness of the proposed work     

Multimachine System Transient Stability Improvement using Transient Power System Stabilizers (TPSS)

Power system stabilizers (PSS) have been designed and installed [1,2] to improve the dynamic (smnall signal) response of multimachine systems. This paper presents the theory and test results for improving the transient (large disturbance) behaviour of a multimachine system by supplementing the conventional PSS with a transient power system stabilizer (TPSS). Each generator unit is fitted with a TPSS which, after computation, injects supplementary control signals into both the automatic voltage regulator (AVR) and speed governor control loops.     

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     

Tuning of power system stabilizers using an artificial neuralnetwork

A new approach using an artificial neural network is proposed to adapt power system stabilizer (PSS) parameters in real time. A pair of online measurements i.e., generator real-power output and power factor which are representative of the generator's operating condition, are chosen as the input signals to the neural net. The outputs of the neural net are the desired PSS parameters. The neural net, once trained by a set of input-output patterns in the training set, can yield proper PSS parameters under any generator loading condition. Digital simulations of a synchronous machine subject to a major disturbance of a three-phase fault under different operating conditions are performed to demonstrate the effectiveness of the proposed neural network     

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     

An enhanced adaptive neural network control scheme for powersystems

An enhanced adaptive neural network control scheme, based on the adaptive linear element (Adaline), is proposed and tested by applying it to a multimachine power system. Simulation results have shown that it is effective for different types of disturbances and over a wide range of operating conditions     

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     

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     

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 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     

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     

Stabilizing torsional oscillations using a hunt reactor controller

Results of a study on the application of shunt reactors for the damping of torsional oscillations that occur in a power system containing series-capacitor compensation are presented. The IEEE Second Benchmark Model, system-1 is used to investigate the benefits of the utilization of modulated reactive power in suppressing unstable subsynchronous resonance (SSR) modal interactions. A set of shunt reactors is connected to the generator bus of the affected synchronous machine whose shaft is directly coupled to the turbine system of the benchmark model. In order to stabilize all the torsional modes, a unified approach based on modal control theory is proposed for the design of a shunt reactor controller, which is essentially a dynamic output compensator. To demonstrate the effectiveness of the damping enhanced by the proposed scheme, eigenvalue analysis for different loading conditions and sensitivity analysis for controller parameters are performed     

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