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Optimal multiobjective design of robust multimachine power system stabilizers (PSSs) using genetic algorithms is presented in this paper. A conventional speed-based lead-lag PSS is used in this work. The multimachine power system operating at various loading conditions and system configurations is treated as a finite set of plants. The stabilizers are tuned to simultaneously shift the lightly damped and undamped electromechanical modes of all plants to a prescribed zone in the s-plane. A multiobjective problem is formulated to optimize a composite set of objective functions comprising the damping factor, and the damping ratio of the lightly damped electromechanical modes. The problem of robustly selecting the parameters of the power system stabilizers is converted to an optimization problem which is solved by a genetic algorithm with the eigenvalue-based multiobjective function. The effectiveness of the suggested technique in damping local and interarea modes of oscillations in multimachine power systems, over a wide range of loading conditions and system configurations, is confirmed through eigenvalue analysis and nonlinear simulation results. Index Terms--Dynamic stability, genetic algorithms, multiple objective optimization, robustness, simultaneous stabilization.

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. Index Terms--Dynamic stability, evolutionary programming, PSS design.

Applying tabu search (TS) optimization technique to multimachine power system stabilizer (PSS) design is presented in this paper. The proposed approach employs TS to search for optimal or near optimal settings of PSS parameters that shift the system eigenvalues associated with the electromechanical modes to the left of a vertical line in the s-plane. Incorporation of TS algorithm in PSS design significantly reduces the computational burden. One of the main advantages of the proposed approach is its robustness to the initial guess. The performance of the proposed PSS 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 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. Keywords: Power system stabilizer; Tabu search algorithm; Dynamic stability

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 demonstrated. Keywords - Rule-based systems, Genetic algorithms, Power system stabilizer, Hybrid systems.

A Fuzzy Basis Function Network (FBFN) based Power System Stabilizer (PSS) is presented in this paper to improve power system dynamic stability. The proposed FBFN based PSS provides a natural framework for combining numerical and linguistic information in a uniform fashion. The proposed FBFN is trained over a wide range of operating conditions in order to re-tune the PSS parameters in real-time based on machine loading conditions. The orthogonal least squares (OLS) learning algorithm is developed for designing an adequate and parsimonious FBFN model. Time domain simulations of a single machine infinite bus system and a multimachine power system subject to major disturbances are investigated. The performance of the proposed FBFN PSS is compared with that of conventional (CPSS). The results show the capability of the proposed FBFN PSS to enhance the system damping of local modes of oscillations over a wide range of operating conditions. The decentralized nature of the proposed FBFN PSS makes it easy to install and tune. Keywords: power system stabilizer, neural networks, fuzzy logic, fuzzy basis function network.

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. Keywords: Dynamic stability, Genetic algorithms, Power system stability, Simultaneous stabilization.

Power system stability enhancement via excitation and FACTS-based stabilizers is thoroughly investigated in this paper. This study presents a singular value decomposition-based approach to assess and measure the controllability of the poorly damped electromechanical modes by different control inputs. The design problem of a power system stabilizer and different FACTS-based stabilizers is formulated as an optimization problem. An eigenvalue-based objective function to increase the system damping and improve the system response is developed. Then, a real-coded genetic algorithm is employed to search for optimal controller parameters. In addition, the damping characteristics of the proposed schemes are also evaluated in terms of the damping torque coefficient with different loading conditions for better understanding of the coordination problem requirements. The proposed stabilizers are tested on a weakly connected power system with different loading conditions. The damping torque coefficient analysis, nonlinear simulation results, and eigenvalue analysis show the effectiveness and robustness of the proposed control schemes over a wide range of loading conditions. [ABSTRACT FROM AUTHOR]