Abstract
This study presents a metaheuristic method for the optimum design of multimachine Power System Stabilizers (PSSs). In the proposed method, referred to as Local Search-based Non-dominated Sorting Genetic Algorithm (LSNSGA), a local search mechanism is incorporated at the end of the second version of the non-dominated sorting genetic algorithm in order to improve its convergence rate and avoid the convergence to local optima. The parameters of PSSs are tuned using LSNSGA over a wide range of operating conditions, in order to provide the best damping of critical electromechanical oscillations. Eigenvalue-based objective functions are employed in the PSS design process. Simulation results based on eigenvalue analysis and nonlinear time-domain simulation proved that the proposed controller provided competitive results compared to other metaheuristic techniques.
Highlights
The complexity of electricity networks due to several economic, ecological, and technical exigencies has obliged electric companies to operate at full network capacity in order to achieve a balance between the increased consumption and the production, under severe conditions increasingly close to the stability limits
The simulation results based on eigenvalue analysis and nonlinear time domain simulation demonstrated that the proposed controller provided results competitive with the other metaheuristic techniques implemented recently for the resolution of the Power System Stabilizers (PSSs) design problem, such as NSGAII, Simulated Annealing (SA) [4] and Fuzzy Gravitational Search Algorithm (FGSA) [20]
In order to do this, this study presents an improved version of NSGAII, referred to as Local Searchbased Non-dominated Sorting Genetic Algorithm (LSNSGA), for robust PSS design over a wide range of operating conditions
Summary
The complexity of electricity networks due to several economic, ecological, and technical exigencies has obliged electric companies to operate at full network capacity in order to achieve a balance between the increased consumption and the production, under severe conditions increasingly close to the stability limits. Other metaheuristic techniques have been employed for the enhancement of power system stability, such as the Bacteria Foraging Optimization Algorithm (BFOA) [19] and Fuzzy Gravitational Search Algorithm (FGSA) [20] These randombased methods have been criticized for their low convergence rate and the fact that they can be trapped in local minima when complex multimodal problems are considered [21]. Within this context, a modified version of the Non-dominated Sorting Genetic Algorithm (NSGAII) for the optimal design of PSS regulators is presented in this study. The simulation results based on eigenvalue analysis and nonlinear time domain simulation demonstrated that the proposed controller provided results competitive with the other metaheuristic techniques implemented recently for the resolution of the PSS design problem, such as NSGAII, SA [4] and Fuzzy Gravitational Search Algorithm (FGSA) [20]
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