81 research outputs found

    Real power loss reduction by Rock Dove optimization and Fuligo Septica optimization algorithms

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    This paper aims to use the Rock Dove (RD) optimization algorithm and the Fuligo Septica optimization (FSO) algorithm for power loss reduction. Rock Dove towards a particular place is based on the familiar (sight) objects on the traveling directions. In the formulation of the RD algorithm, atlas and range operator, and familiar sight operators have been defined and modeled. Every generation number of Rock Dove is reduced to half in the familiar sight operator and Rock Dove segment, which hold the low fitness value that occupying the lower half of the generation will be discarded. Because it is implicit that individual’s Rock Dove is unknown with familiar sights and very far from the destination place, a few Rock Doves will be at the center of the iteration. Each Rock Dove can fly towards the final target place. Then in this work, the FSO algorithm is designed for real power loss reduction. The natural vacillation mode of Fuligo Septica has been imitated to develop the algorithm. Fuligo Septica connects the food through swinging action and possesses exploration and exploitation capabilities. Fuligo Septica naturally lives in chilly and moist conditions. Mainly the organic matter in the Fuligo Septica will search for the food and enzymes formed will digest the food. In the movement of Fuligo Septica it will spread like a venous network, and cytoplasm will flow inside the Fuligo Septica in all ends. THE proposed RD optimization algorithm and FSO algorithm have been tested in IEEE 14, 30, 57, 118 and 300 bus test systems and simulation results show the projected RD and FSO algorithm reduced the real power loss

    A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

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    Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

    Real power loss reduction by cultivation of soil optimization algorithm

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    In this paper, the optimal reactive power problem has been solved by the cultivation of soil optimization (CSO) algorithm. The reduction of real power loss is a key objective of this work. The projected CSO algorithm has been modeled based on the quality of soil which has been used in the cultivation of various crops season to season. With respect to the quality of the soil in the cultivation land, there will be a change in the poor-quality soil since there will up the gradation of the poor soil is done through by adding the nutrient contents. Depend upon the needs and about the type of cultivation farmers will improve the quality of the soil by adding valuable and various types of fertilizers (natural and artificial) such that it will enhance the fertile and growth (green) of the crops. Time to time farmers will choose appropriate nutrient contents that will be mixed with the soil in order to enhance the fertility of the soil. In standard IEEE 14, 30, 57 bus test systems Cultivation of Soil Optimization (CSO) algorithm has been tested. The CSO algorithm reduced the real power loss and control variables are within the limits

    Algoritmo de Optimización de Mapeo de Media Varianza Aplicado al Despacho Óptimo de Potencia Reactiva

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    Introduction: The optimal reactive power dispatch (ORPD) problem consists on finding the optimal settings of several reactive power resources in order to minimize system power losses. The ORPD is a complex combinatorial optimization problem that involves discrete and continuous variables as well as a nonlinear objective function and nonlinear constraints. Objective: This article seeks to compare the performance of the mean-variance mapping optimization (MVMO) algorithm with other techniques reported in the specialized literature applied to the ORPD solution. Methodology: Two different constraint handling approaches are implemented within the MVMO algorithm: a conventional penalization of deviations from feasible solutions and a penalization  by means of  a product of subfunctions that serves to identify both when a solution is optimal and feasible. Several tests are carried out in IEEE benchmark power systems of 30 and 57 buses. Conclusions: The MVMO algorithm is effective in solving the ORPD problem. Results evidence that the MVMO algorithm outperforms or matches the quality of solutions reported by several solution techniques reported in the technical literature. The alternative handling constraint proposed for the MVMO reduces the computation time and guarantees both feasibility and optimality of the solutions found.  Introducción: El problema del despacho óptimo de potencia reactiva (DOPR) consiste en encontrar la configuración óptima de diferentes recursos de potencia reactiva para minimizar las pérdidas de potencia del sistema. El DOPR es un problema complejo de optimización combinatorial que involucra variables discretas y continuas, así como una función objetivo no lineal y restricciones no lineales.   Objetivo: En este artículo se busca comparar el desempeño del algoritmo de optimización de mapeo de media varianza (MVMO, por sus siglas en inglés) con otras técnicas reportadas en la literatura especializada aplicadas a la solución del DOPR. Metodología: En el algoritmo MVMO se aplican dos enfoques diferentes de manejo de restricciones: penalización convencional de las desviaciones de las soluciones factibles y penalización por medio del producto de subfunciones que sirve para identificar cuándo una solución es óptima y factible. Se realizan simulaciones en sistemas de prueba IEEE de 30 y  57 barras. Conclusiones: El algoritmo MVMO es efectivo para solucionar el DOPR. Los resultados evidencian que el algoritmo MVMO supera o iguala a varias técnicas reportadas en la literatura técnica en la calidad de soluciones. El manejo alternativo de restricciones propuesto para el  MVMO  reduce el tiempo de cálculo y garantiza tanto factibilidad como optimalidad de las soluciones encontradas.   &nbsp

    Optimization-Based Evolutionary Data Mining Techniques for Structural Health Monitoring

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    In recent years, data mining technology has been employed to solve various Structural Health Monitoring (SHM) problems as a comprehensive strategy because of its computational capability. Optimization is one the most important functions in Data mining. In an engineering optimization problem, it is not easy to find an exact solution. In this regard, evolutionary techniques have been applied as a part of procedure of achieving the exact solution. Therefore, various metaheuristic algorithms have been developed to solve a variety of engineering optimization problems in SHM. This study presents the most applicable as well as effective evolutionary techniques used in structural damage identification. To this end, a brief overview of metaheuristic techniques is discussed in this paper. Then the most applicable optimization-based algorithms in structural damage identification are presented, i.e. Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Imperialist Competitive Algorithm (ICA) and Ant Colony Optimization (ACO). Some related examples are also detailed in order to indicate the efficiency of these algorithms

    Contribution à l’optimisation de l’écoulement de puissance par les méthodes d’intelligence artificielle améliorées

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    La répartition optimale de la puissance réactive (ORPD) est une tâche importante pour atteindre un meilleur état d’économie, de sécurité et de stabilité du système de l’énergie électrique. Il s'agit d'un problème d'optimisation complexe qui vise à identifier les variables de contrôle optimales des différents équipements de régulation du réseau afin de minimiser une fonction objective sous contraintes. De nombreuses techniques méta-heuristiques ont été proposées pour surmonter les diverses complexités dans la résolution du problème ORPD, qui sont caractérisées par l'exploration et l'exploitation du mécanisme de recherche. L'équilibre entre ces deux caractéristiques est un défi à surmonter pour aboutir à une meilleure qualité de solution. L'algorithme de la colonie Artificiel des Abeilles (Artificial Bee Colony - ABC) est une méthode méta-heuristique réputée, s'est avéré efficace en matière d'exploration et faible en matière d'exploitation, ce qui rend nécessaire l'amélioration de la version de base de l'algorithme ABC. L'algorithme Salp Swarm (SSA) est une méta-heuristique nouvellement développée, basée sur un essaim, qui possède la meilleure capacité de recherche locale en utilisant la meilleure solution globale à chaque itération pour découvrir des solutions III prometteuses. Dans ce sujet de recherche, une nouvelle approche hybride basée sur les algorithmes ABC et SSA (ABC-SSA) est développée et appliquée pour résoudre le problème ORPD. L'approche proposée tente d'améliorer la capacité d'exploitation de l'algorithme ABC en utilisant SSA. L'efficacité de l'ABC-SSA est examinée en utilisant quatre réseaux électriques d'essai standard : IEEE 30 bus, IEEE 57 bus, IEEE 118 bus et IEEE 300 bus à grande échelle, en tenant compte des célèbres fonctions objectives du problème ORPD, notamment les pertes totales de puissance active de transmission (Ploss), l'écart total de tension (TVD) par rapport à l’amplitude de tension nominale et l'indice de stabilité de la tension (VSI) des jeux de barres de charge. Les résultats de simulation obtenus ont prouvé que l'ABC-SSA proposé est plus efficace que l'ABC, le SSA et d'autres techniques d'optimisation méta-heuristiques récemment développées dans la littérature du domaine d’application

    Review on the cost optimization of microgrids via particle swarm optimization

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    Economic analysis is an important tool in evaluating the performances of microgrid (MG) operations and sizing. Optimization techniques are required for operating and sizing an MG as economically as possible. Various optimization approaches are applied to MGs, which include classic and artificial intelligence techniques. Particle swarm optimization (PSO) is one of the most frequently used methods for cost optimization due to its high performance and flexibility. PSO has various versions and can be combined with other intelligent methods to realize improved performance optimization. This paper reviews the cost minimization performances of various economic models that are based on PSO with regard to MG operations and sizing. First, PSO is described, and its performance is analyzed. Second, various objective functions, constraints and cost functions that are used in MG optimizations are presented. Then, various applications of PSO for MG sizing and operations are reviewed. Additionally, optimal operation costs that are related to the energy management strategy, unit commitment, economic dispatch and optimal power flow are investigated. © 2019, The Author(s)

    An analysis-forecast system for uncertainty modeling of wind speed: A case study of large-scale wind farms

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    © 2017 Elsevier Ltd The uncertainty analysis and modeling of wind speed, which has an essential influence on wind power systems, is consistently considered a challenging task. However, most investigations thus far were focused mainly on point forecasts, which in reality cannot facilitate quantitative characterization of the endogenous uncertainty involved. An analysis-forecast system that includes an analysis module and a forecast module and can provide appropriate scenarios for the dispatching and scheduling of a power system is devised in this study; this system superior to those presented in previous studies. In order to qualitatively and quantitatively investigate the uncertainty of wind speed, recurrence analysis techniques are effectively developed for application in the analysis module. Furthermore, in order to quantify the uncertainty accurately, a novel architecture aimed at uncertainty mining is devised for the forecast module, where a non-parametric model optimized by an improved multi-objective water cycle algorithm is considered a predictor for producing intervals for each mode component after feature selection. The results of extensive in-depth experiments show that the devised system is not only superior to the considered benchmark models, but also has good potential practical applications in wind power systems
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