Diminution of Real Power Loss by Hybridization of Particle Swarm Optimization with Extremal Optimization

This paper presents an algorithm for solving the multi-objective reactive power dispatch problem in a power system. Modal analysis of the system is used for static voltage stability assessment. Loss minimization and maximization of voltage stability margin are taken as the objectives. Generator terminal voltages, reactive power generation of the capacitor banks and tap changing transformer setting are taken as the optimization variables. Particle swarm optimization (PSO) has received increasing interest from the optimization community due to its simplicity in implementation and its inexpensive computational overhead. However, PSO has premature convergence, especially in complex multimodal functions. Extremal Optimization (EO) is a recently developed local-search heuristic method and has been successfully applied to a wide variety of hard optimization problems. To overcome the limitation of PSO, this paper proposes a novel hybrid algorithm, called hybrid PSO-EO algorithm, through introducing EO to PSO. The hybrid approach elegantly combines the exploration ability of PSO with the exploitation ability of EO. The proposed approach is shown to have superior performance and great capability of preventing pre- mature convergence across it comparing favourably with the other algorithms. We demonstrated that our proposed HPSOEO (hybrid particle swarm optimization – Extremal optimization) presents a better performance when compared to the other algorithms. In order to evaluate the proposed algorithm, it has been tested on IEEE 30 bus system and compared to other algorithms reported those before in literature. Results show that HPSOEO is more efficient than others for solution of single-objective Optimal Reactive Power Dispatch problem. Keywords: Modal analysis, optimal reactive power, Transmission loss, particle swarm, Particle swarm optimization, Extremal optimization, Numerical optimization, Metaheuristic

A new approach to particle swarm optimization algorithm

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Stochastic Systems: Modeling, Optimization, and Applications

The special issue of Mathematical Problems in Engineering deals with the issues of modeling, optimization, and applications associated with stochastic systems. This special issue provides a forum for researchers and practitioners to publish quality research work on modeling, optimization approaches, and their applications in the context of theory analysis and engineering developments. The accepted papers in this special issue include stochastic stability, stabilization and control optimization, stochastic optimization, particle swarm optimization, modeling and identification methods, signal processing, and robust filtering. The issue includes thirty-nine papers out of which six consider the stability and stabilization problems of stochastic systems. Twelve papers cover the problems of the controller design and relevant optimization algorithms

Fuzzy Clustering Image Segmentation Based on Particle Swarm Optimization

Image segmentation refers to the technology to segment the image into different regions with different characteristics and to extract useful objectives, and it is a key step from image processing to image analysis. Based on the comprehensive study of image segmentation technology, this paper analyzes the advantages and disadvantages of the existing fuzzy clustering algorithms; integrates the particle swarm optimization (PSO) with the characteristics of global optimization and rapid convergence and fuzzy clustering (FC) algorithm with fuzzy clustering effects starting from the perspective of particle swarm and fuzzy membership restrictions and gets a PSO-FC image segmentation algorithm so as to effectively avoid being trapped into the local optimum and improve the stability and reliability of clustering algorithm. The experimental results show that this new PSO-FC algorithm has excellent image segmentation effects

Particle swarm stability : a theoretical extension using the non-stagnate distribution assumption

This paper presents an extension of the state of the art theoretical model utilized for understanding the stability criteria of the particles in particle swarm optimization algorithms. Conditions for order-1 and order-2 stability are derived by modeling, in the simplest case, the expected value and variance of a particle’s personal and neighborhood best positions as convergent sequences of random variables. Furthermore, the condition that the expected value and variance of a particle’s personal and neighborhood best positions are convergent sequences is shown to be a necessary condition for order-1 and order-2 stability. The theoretical analysis presented is applicable to a large class of particle swarm optimization variants.http://link.springer.com/journal/117212019-03-01hj2017Computer Scienc

Robust Coordinated Design of PSS and TCSC using PSO Technique for Power System Stability Enhancement

Power system stability improvement by coordinated design of a Power System Stabilizer (PSS) and a Thyristor Controlled Series Compensator (TCSC) controller is addressed in this paper. Particle Swarm Optimization (PSO) technique is employed for optimization of the parameterconstrained nonlinear optimization problem implemented in a simulation environment. The proposed controllers are tested on a weakly connected power system. The non-linear simulation results are presented for wide range of loading conditions with various fault disturbances and fault clearing sequences as well as for various small disturbances. The eigenvalue analysis and simulation results show the effectiveness and robustness of proposed controllers to improve the stability performance of power system by efficient damping of low frequency oscillations under various disturbances

KENDALI OPTIMAL PEMBANGKIT LISTRIK TENAGA MIKRO HIDRO (PLTMH) DENGAN PID OPTIMAL MENGGUNAKAN BACTERIAL FORAGING PARTICLE SWARM OPTIMIZATION (BF-PSO)

This research aims to optimally improve the efficiency and performance of the power system. This research method aims to find the type of method that is suitable for the Micro Hydro Power Plant (MHP) system by increasing the excitation voltage on the generator and adding the PID algorithm control system to reduce undershoot and overshoot in a system and also the Bacterial Foraging Particle Swarm Optimization (BF-PSO) algorithm to optimize. The power system model used to determine more stable parameters and perform stability analysis. The results of the PID method with the Bacterial Foraging Particle Swarm Optimization (BF-PSO) algorithm are better or more optimal which can be seen from the lowest overshoot value of 1.14%, and has the fastest time to reach a stable state of 5 seconds. In addition, the BF-PSO method also has the smallest percentage error of 0.017% compared to other methods