AGC tuning of interconnected reheat thermal systems with particle swarm optimization

This paper demonstrates the use of particle swarm optimization for optimizing the parameters of automatic generation control systems (AGC). An integral controller and a proportional-plus-integral controller are considered. A two-area reheat thermal system is considered to exemplify the optimum parameter search. The optimal AGC parameters search is formulated as an optimization problem with a standard infinite time quadratic objective function. A time domain simulation of the system is then used in conjunction with the particle swarm optimizer to determine the controller gains. The integral square of the error and the integral of time-multiplied absolute value of the error performances indices are considered. The results reported in this paper demonstrate the effectiveness of the particle swarm optimizer in the tuning of the AGC parameters. The enhancement in the dynamic response of the power system is verified through simulation results

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

Radial basis function network based power system stabilizers formultimachine power systems

A radial basis function network (RBFN) based power system stabilizer (PSS) is presented in this paper to improve the dynamic stability of multimachine power systems. The proposed RBFN is trained over a wide range of operating conditions in order to re-tune the parameters of the PSS in real-time. Time domain simulations of a multimachine power system with different operating conditions subject to a three phase fault are studied and investigated. The performance of the proposed RBFN PSS is compared to that of conventional power system stabilizer (CPSS). The results show the good damping characteristics of the proposed RBFN PSS over a wide range of operating condition

Genetic algorithms applications in load frequency control

This paper deals with the application of genetic algorithms for optimizing the parameters of conventional automatic generation control (AGC) systems. A two-area nonreheat thermal system is considered to exemplify the optimum parameter search. A digital simulation is used in conjunction with the genetic algorithm optimization process. The integral of the square of the error and the integral of time-multiplied absolute value of the error performance indices are considered in the search for the optimal AGC parameters. The results reported in this paper demonstrate the effectiveness of the genetic algorithms in the tuning of the AGC parameter

Power system stability enhancement via coordinated design of a PSS and an SVC-based controller

Power system stability enhancement via robust coordinated design of a power system stabilizer (PSS) and a static VAR compensator (SVC)-based stabilizer is thoroughly investigated in this paper. The coordinated design problem of robust excitation and SVC-based controllers over a wide range of loading conditions and system configurations is formulated as an optimization problem with an eigenvalue-based objective function. The real-coded genetic algorithm (RCGA) is employed to search for optimal controller parameters. This study also presents a singular value decomposition (SVD) based approach to assess and measure the controllability of the poorly damped electromechanical modes by different control inputs. The damping characteristics of the proposed schemes are also evaluated in terms of the damping torque coefficient over a wide range of loading conditions. The proposed stabilizers are tested on a weakly-connected power system. The nonlinear simulation results and eigenvalue analysis show the effectiveness and robustness of the proposed approach

Genetic algorithms applications in load frequency control

This paper deals with the application of genetic algorithms for optimizing the parameters of conventional automatic generation control (AGC) systems. A two-area nonreheat thermal system is considered to exemplify the optimum parameter search. A digital simulation is used in conjunction with the genetic algorithm optimization process. The integral of the square of the error and the integral of time-multiplied absolute value of the error performance indices are considered in the search for the optimal AGC parameters. The results reported in this paper demonstrate the effectiveness of the genetic algorithms in the tuning of the AGC parameter

Hybridizing rule-based power system stabilizers with geneticalgorithms

A hybrid genetic rule-based power system stabilizer (GRBPSS) is presented in this paper. The proposed approach uses genetic algorithms (GA) to search for optimal settings of rule-based power system stabilizer (RBPSS) parameters. Incorporation of GA in RBPSSs design will add an intelligent dimension to these stabilizers and significantly reduce the time consumed in the design process. It is shown in this paper that the performance of RBPSS can be improved significantly by incorporating a genetic-based learning mechanism. The performance of the proposed GRBPSS under different disturbances and loading conditions is investigated for a single machine infinite bus system and two multimachine power systems. The results show the superiority of the proposed GRBPSS as compared to both conventional lead-lag PSS (CPSS) and classical RBPSS. The capability of the proposed GRBPSS to damp out the local as well as the interarea modes of oscillations is also demonstrate

Hybridizing rule-based power system stabilizers with geneticalgorithms

A hybrid genetic rule-based power system stabilizer (GRBPSS) is presented in this paper. The proposed approach uses genetic algorithms (GA) to search for optimal settings of rule-based power system stabilizer (RBPSS) parameters. Incorporation of GA in RBPSSs design will add an intelligent dimension to these stabilizers and significantly reduce the time consumed in the design process. It is shown in this paper that the performance of RBPSS can be improved significantly by incorporating a genetic-based learning mechanism. The performance of the proposed GRBPSS under different disturbances and loading conditions is investigated for a single machine infinite bus system and two multimachine power systems. The results show the superiority of the proposed GRBPSS as compared to both conventional lead-lag PSS (CPSS) and classical RBPSS. The capability of the proposed GRBPSS to damp out the local as well as the interarea modes of oscillations is also demonstrate

Robust design of multimachine power system stabilisers using tabusearch algorithm

Robust design of multimachine power system stabilisers (PSSs) using the tabu search (TS) optimisation technique is presented. The proposed approach employs TS for optimal parameter settings of a widely used conventional fixed-structure lead-lag PSS (CPSS). The parameters of the proposed stabilisers are selected using TS in order to shift the system poorly damped electromechanical modes at several loading conditions and system configurations simultaneously to a prescribed zone in the left hand side of the s-plane. Incorporation of TS as a derivative-free optimisation technique in PSS design significantly reduces the computational burden. In addition, the quality of the optimal solution does not rely on the initial guess. The performance of the proposed PSSs under different disturbances and loading conditions is investigated for multimachine power systems. The eigenvalue analysis and the nonlinear simulation results show the effectiveness of the proposed PSSs in damping out the local, as well as the interarea, modes and enhance greatly the system stability over a wide range of loading conditions and system configuration

Coordinated design of robust excitation and TCSC-based damping controllers

Power system stability enhancement via robust coordinated design of a power system stabilizer (PSS) and a thyristor-controlled series capacitor (TCSC)-based stabilizer is thoroughly investigated in this paper. The coordinated design problem of robust excitation and TCSC-based controllers over a wide range of loading conditions and system configuration is formulated as an optimization problem with an eigenvalue-based objective function. The real-coded genetic algorithm (RCGA) is employed to search for optimal controller parameters. This study also presents a singular value decomposition (SVD) based approach to assess and measure the controllability of the poorly damped electromechanical modes by different control inputs. The damping characteristics of the proposed control schemes are also evaluated in terms of the damping torque coefficient over a wide range of loading conditions. The proposed stabilizers were tested on a weakly connected power system. The damping torque coefficient analysis, nonlinear simulation results, and eigenvalue analysis show the effectiveness and robustness of the proposed approach over a wide range of loading conditions