Flexible AC Transmission Systems (FACTS) controllers with its ability to directly control the power flow can offer great opportunities in modern power system, allowing better and safer operation of transmission network. In this paper, in order to find type, size and location of FACTS devices in a power system a Dedicated Improved Particle Swarm Optimization (DIPSO) algorithm is developed for de...

In order to enhance inter-particle cooperation and information sharing capabilities, an improved particle swarm algorithm optimization model is proposed by introducing the centroid of particle swarm in the standard PSO model to improve global optimum efficiency and accuracy of algorithm, then parameter selection guidelines are derived in the convergence of new algorithm. The results of Benchmar...

The high search speed and efficiency, and simple algorithm of particle swarm optimization algorithm make it suitable for actual-value processing. Starting from the angle of weight, this paper studies several improved particle swarm optimization algorithms and divides the improvement into three types as linear decreasing weight strategy, self-adaptive weight strategy and random weight strategy. ...

This Paper presents a comparative study of Genetic Algorithm method (GA) and Particle swarm optimization (PSO) method to determine the optimal proportional-integral-derivative (PID) controller parameters, for load frequency control in a single area power system. Comparing with conventional Proportional–Integral (PI) method and the proposed PSO the performance of the controller is improved for t...

This paper builds the power transformer diagnosis model based on the improved particle swarm optimization— support vector machine (MPSO-SVM). Speed update and particle self-adaptation self-variation are introduced to optimize the standard particle swarm algorithm, thus overcoming the defect of the standard particle swarm optimization algorithm and increasing the power transformer fault diagnosi...

Particle filter algorithm is a filtering method which uses Monte Carlo idea within the framework of Bayesian estimation theory. It approximates the probability distribution by using particles and discrete random measure which is consisted of their weights, it updates new discrete random measure recursively according to the algorithm. When the sample is large enough, the discrete random measure ...

Optimization algorithms are proposed to tackle different complex problems in different areas. In this paper, we firstly put forward a new memetic evolutionary algorithm, named Monkey King Evolutionary (MKE) Algorithm, for global optimization. Then we make a deep analysis of three update schemes for the proposed algorithm. Finally we give an application of this algorithm to solve least gasoline ...

During the past decade, the particle swarm optimization (PSO) with various versions showed competitiveness on the constrained optimization problems. In this paper, an improved Gaussian particle swarm optimization algorithm (GPSO) is proposed to improve the diversity and local search ability of the population. A mutation operator based on differential evolution (DE) is designed and employed to u...

The biologically inspired world comprising of social insect metaphor for solving out wide range of dilemma has become potentially promising area in most recent duration focusing on indirect or direct coordination’s among diverse artificial agents. Swarm [8] apparently is a disorganized collection / population of moving individual that tends to cluster together while each individual seems to be ...