Robust power system stabiliser design under multi-operating conditions using differential evolution

Abstract

A power system stabilizer (PSS) design method, which aims at enhancing the damping of multiple electromechanical modes in a multi-machine system over a large and pre-specified set of operating conditions, is introduced in this paper. With the assumption of normal distribution, the statistical nature of the eigenvalues corresponding to different operating conditions is described by their expectations and variances. A probabilistic eigenvalue-based optimization problem used for determining PSS parameters is then formulated. Differential evolution (DE) is applied for solving this highly nonlinear optimization problem. Different strategies for control parameter settings of DE have been studied to verify the robustness of DE in PSS optimization problems. The performance of the proposed PSS, with a conventional lead/lag structure, has been demonstrated based on two test systems by probabilistic eigenvalue analysis and nonlinear simulation.

A review on applications of heuristic optimization algorithms for optimal power flow in modern power systems

“…The main body of the algorithm takes four or five lines of code in any programming language. Despite its simplicity, the gross performance of DE in terms of accuracy, convergence rate and robustness makes it attractive for applications to various real-world optimization problems [10][11][12], where finding an approximate solution in a reasonable amount of computational time is of considerable importance. The spatial complexity of DE is lower than that of some highly competitive real parameter optimizers.…”

An overview of grid computing application for distributed heterogeneous power systems: history, technologies, and prospect

“…Recently, many heuristic search optimization techniques have been proposed to solve some of these problems. Evolutionary Algorithms (EAs), for instance, are robust and flexible in handling nonlinearities and discrete variables, and have been successfully applied in different power system optimization problems such as load flow calculation [31], optimal reactive power flow (ORPF) [19,[32][33][34][35], transient stability constrained OPF [36], damping control design [37], TNEP [38], unit commitment [39], unit maintenance scheduling [40], and power market analysis [41][42][43][44]. These algorithms can be implemented on GC platform and provide efficient tools for power system operation and planning.…”

Application of Swarm Mean-Variance Mapping Optimization on location and tuning damping controllers

“…Earlier reported approaches based on modified versions of genetic algorithm (GA) [5], particle swarm optimization (PSO) [6], and differential evolution (DE) [7] highlight the potential of metaheuristic optimization algorithms for solving the OPCDC. Due to the stochastic nature of the underlying evolutionary mechanism, further research is needed to ascertain the robustness of these algorithms, which also motivates the application and extension of emerging metaheuristic optimization algorithms.…”