Analysis of power system stability enhancement via excitation and FACTS-based stabilizers

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

Robust Coordinated Design of Excitation and TCSC-Based Stabilizers Using genetic algorithms

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 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 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

Robust Coordinated Design of Excitation and TCSC-Based Stabilizers Using genetic algorithms

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 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 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

Analysis of power system stability enhancement via excitation and FACTS-based stabilizers

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

Application of PSO to design UPFC-based stabilizers

Today, power demand grows rapidly and expansion in transmission and generation is restricted with the limited availability of resources and the strict environmental constraints. Consequently, power systems are today much more loaded than before. In addition, interconnection between remotely located power systems turned out to be a common practice. These give rise to low frequency oscillations in the range of 0.1-3.0 Hz. If not well damped, these oscillations may keep growing in magnitude until loss of synchronism results. Power system stabilizers (PSSs) have been used in the last few decades to serve the purpose of enhancing power system damping to low frequency oscillations. PSSs have proved to be efficient in performing their assigned tasks. The objective of this chapter is to investigate the potential of particle swarm optimization as a tool in designing UPFC-based stabilizers to improve power system transient stability. To estimate the controllability of each of the UPFC control signals on the electromechanical modes, singular value decomposition is employed. The problem of designing all the UPFCbased stabilizers individually is formulated as an optimization problem. Particle swarm optimizer is utilized to search for the optimum stabilizer parameter settings that optimize a given objective function. Coordinated design of the different stabilizers is also carried out by finding the best parameter settings for more than one stabilizer at a given operating condition in a coordinated manner

A New Genetic-Based Tabu Search Algorithm For Unit Commitment Problem

This paper presents a new algorithm based on integrating the use of genetic algorithms and tabu search methods to solve the unit commitment problem. The proposed algorithm, which is mainly based on genetic algorithms incorporates tabu search method to generate new population members in the reproduction phase of the genetic algorithm. In the proposed algorithm, genetic algorithm solution is coded as a mix between binary and decimal representation. A fitness function is constructed from the total operating cost of the generating units without penalty terms. In the tabu search part of the algorithm, a simple short term memory procedure is used to counterthe danger of entrapment at a local optimum by preventing cycling of solutions, and the premature convergence of the genetic algorithm. A significant improvement of the proposed algorithm results, over those obtained by either genetic algorithm or tabu search, has been achieved. Numerical examples also showed the superiority of the proposed algorithm compared with two classical methods in the literature

A New Genetic-Based Tabu Search Algorithm For Unit Commitment Problem

This paper presents a new algorithm based on integrating the use of genetic algorithms and tabu search methods to solve the unit commitment problem. The proposed algorithm, which is mainly based on genetic algorithms incorporates tabu search method to generate new population members in the reproduction phase of the genetic algorithm. In the proposed algorithm, genetic algorithm solution is coded as a mix between binary and decimal representation. A fitness function is constructed from the total operating cost of the generating units without penalty terms. In the tabu search part of the algorithm, a simple short term memory procedure is used to counterthe danger of entrapment at a local optimum by preventing cycling of solutions, and the premature convergence of the genetic algorithm. A significant improvement of the proposed algorithm results, over those obtained by either genetic algorithm or tabu search, has been achieved. Numerical examples also showed the superiority of the proposed algorithm compared with two classical methods in the literature

Simultaneous Stabilization Of Power System Using UPFC-Based Controllers

This article studies the use of robust UPFC-based stabilizers to damp low frequency oscillations. The potential of the UPFC-based stabilizers to enhance the dynamic stability is evaluated by singular value decomposition. Particle swarm optimization technique is used to optimize the parameters of each stabilizer, first individually, then concurrently. To ensure the robustness of the proposed stabilizers, the design process considers a wide range of operating conditions. The effectiveness of the proposed controllers is verified through several linear and nonlinear analysis techniques. These techniques prove that the coordinated design of UPFC-based stabilizers is superior over any of the individual designs