S.A. Al-Baiyat

Economic load dispatch multiobjective optimization procedures using linear programming techniques

This paper outlines the optimization problem of real and reactive power, and presents the new algorithm for studying the load shedding and generation reallocation problem in emergencies where a portion of the transmission system is disabled and an AC power solution cannot be found for the overloaded system. The paper describes a novel and efficient method and algorithm to obtain the optimal shift in power dispatch related to contingency states or overload situations in power system operation and planning phases under various objectives such as economy, reliability and environmental conditions. The optimization procedures basically utilize linear programming with bounded variables and it incorporates the techniques of the Section Reduction Method and the Third Simplex Method. The validity and effectiveness of the algorithm is verified by means of two examples: a 10-bus system and the IEEE 30-Bus, six generators system. >

Simultaneous stabilization of multimachine power systems via genetic algorithms

This paper demonstrates the use of genetic algorithms for the simultaneous stabilization of multimachine power systems over a wide range of operating conditions via single-setting power system stabilizers. The power system operating at various conditions is treated as a finite set of plants. The problem of selecting the parameters of power system stabilizers which simultaneously stabilize this set of plants is converted to a simple optimization problem which is solved by a genetic algorithm with an eigenvalue-based objective function. Two objective functions are presented, allowing the selection of the stabilizer parameters to shift some of the closed-loop eigenvalues to the left-hand side of a vertical line in the complex s-plane, or to a wedge-shape sector in the complex s-plane. The effectiveness of the suggested technique in damping local and inter-area modes of oscillations in multimachine power systems is verified through eigenvalue analysis and simulation results.

New model reduction scheme for bilinear systems

A new method for the approximation of bilinear systems is proposed. The reduction scheme applies to both stable and unstable bilinear systems. The technique uses generalized input normal representations to retain the dominant part of the original system. The algorithm is evaluated on a synchronous induction generator and is shown to lead to acceptable reduced approximations of the original system. A frequency weighting is also introduced in the reduction scheme to further improve the approximation.

A new model reduction scheme for k-power bilinear systems

A model reduction scheme of k-power bilinear systems is proposed in this work. The canonical state space structure of k-power systems is used to simplify a balancing like model reduction scheme for bilinear systems. The derived model reduction algorithm reduces to computational steps similar in complexity to the balanced approximation of linear systems. Controllability and observability gramians turn out to have simple block diagonal structures and their properties are easily derived. The simulation of an 11th order system shows good performances of the reduced order models.<<ETX>>

Variable structure load frequency controller using particle swarm optimization technique

In this paper, selection of the variable structure controller feedback gains by Particle Swarm Optimization (PSO) technique is presented contrary to the trial and error selection of the variable structure feedback gains reported in literature. The proposed design has been applied to the load frequency problem of a single area power system. The system performance against a step load variations has been simulated and compared to some previous methods. Simulation results show that not only dynamic system performance has been improved, but also the control effort is reduced. The results show the reliability of the proposed technique.

Transient approximation of a bilinear two-area interconnected power system

Abstract A balancing model reduction scheme for high order bilinear systems, similar to the linear balanced reduction algorithm, is applied to power system modelling. An original 17th-order two-area interconnected bilinear power system is reduced, using both linear and bilinear balancing algorithms, to a 10th-order reduced model. Overall superiority of the bilinear reduction scheme over linear balancing is observed in the performed simulation study. The reduction scheme also leads to a very acceptable approximate response compared with that of the original system.

On the Design of Variable Structure Load Frequency Controllers by Tabu Search Algorithm: Application to Nonlinear Interconnected Models

A variable structure controller (VSC) with chattering reduction feature applied to interconnected load frequency control (LFC) problem is presented. Formulating the design of VSC as an optimization problem and utilizing tabu search (TS) algorithm provides a simple and systematic way of arriving at the optimal feedback gains and switching vector values of the controller. In addition, this will cut down the need for nonlinear or coordinate transformation as reported before. The tested interconnected LFC model incorporates nonlinearities in terms of generation rate constraint (GRC) and a limiter on the integral control value. In order to guarantee the enhancement of the system, dynamical performance and chattering reduction of the VSC, different objective functions were investigated in the optimization process. In addition, the complexity of the controller is reduced by using only the accessible states in designing the VSC. Comparison with previous LFC methods reported in literature validates the significance of the proposed VSC design.

Control design with transfer functions associated to higher order Volterra kernels

The Volterra series provides a convolution-oriented method for representing the input/output behavior of a nonlinear system. For the case of constant system parameters, such a representation is naturally suited to control design with transfer functions: zeroth order, first order, second order, and so forth. In 1979, Peczkowski, Sain, and Leake[1] introduced a Total Synthesis Problem (TSP) approach to linear feed-back synthesis; and in 1981, Peczkowski and Sain[2] demonstrated how to schedule TSP into a nonlinear controller. For plants with one input and one output, Al-Baiyat and Sain [3] extended TSP to higher order transfer functions for the class of linear analytic systems. In this paper, we complete the extension by treating multiple inputs and multiple outputs. The method is illustrated by designing a control system for a DC to AC converter.

DYNAMIC BRAKING RESISTOR-REACTOR SWITCHING STRATEGIES THROUGH A NOVEL LINEAR TRANSFORMATION TECHNIQUE

ABSTRACT Optimum switching strategies for dynamic braking resistor and shunt reactor is proposed for transient stability of a single machine infinite bus power system. The strategy is derived through a novel method of transforming the nonlinear dynamic model of the system to linear one. The simple optimum strategies derived from the linear model was observed to be very effective in stabilization.

Dynamic brake switching strategies for stabilization of power systems using artificial neural networks

Abstract When a large disturbance appears on a power system, it may render the system unstable. One way to stabilize the post-disturbance system is to connect resistors or brakes at the generator terminals, and switch them dynamically. In this study, artificial neural networks have been trained to predict the switching times of these dynamic braking resistors for stability improvement. Training data for the nets were generated from a minimum time stabilizing strategy. Comparison of the back-propagation and radial-basis-function networks demonstrate that while both are suitable in estimating the switch times, the radial-basis-function networks are superior in terms of convergence characteristics as well as accuracy of prediction. The nets were also trained with different input features from the various generators.