Wyniki 1-3 spośród 3 dla zapytania: authorDesc:"Wanchai KHAMSEN"

Improvement of Input Power Factor in PWM AC Chopper by Selecting the Optimal Parameters

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A technique for selecting the element values of the PWM AC chopper circuits to improve the input power factor is presented. This technique analyzes the phase angles of input current, output current and voltage for selecting the optimal value of the filter capacitance. This produces the phase angle of input current in phase with that of input voltage. Therefore, the PWM AC chopper can operate at unity input power factor. The simulation by PSpice program and experimental results are used to verify the proposed technique. Streszczenie. Zaprezentowano metodę selekcji elementów choppera PWM AC w celu poprawy wejściowego współczynnika mocy. Metoda polega na analizie kąta fazowego prądu wejściowego, prądu wyjściowego i napięcia w celu optymalizacji pojemności filtru. Dzięki temu chopper pracuje przy współczynniku mocy równym 1. (Optymalny dobór elementów choppera PWM AC w celu poprawy wejściowego współczynnika mocy) Keywords: Buck, boost, buck-boost AC choppers, capacitor filter, pulse width modulated (PWM) AC chopper, power factor Słowa kluczowe: chopper PWM AC, optymalizacja, współczynnik mocy Introduction To variable AC voltage from fixed AC source, there are three basic techniques that have been widely used in AC power applications such as lighting control, industrial heating, soft start induction motor and speed controller for fans and pumps [1]. The first one is the auto transformer. Its winding ratio is controlled by servo motor or by manual regulation. Although, it offers some advantages such as durability and reliability, it has low voltage regulation speed and large size [2]-[4]. The second technique is the phase angle control. The output voltage average can be controlled by firing angle of thyristor [5]. It has some advantages such as simplicity of the control circuit and capability of controlling a large amount of economical power. However, the delay of firing angle causes discontinuation of power flow to appear at both input a[...]

Hybrid of Lamda and Bee Colony Optimization for Solving Economic Dispatch DOI:10.15199/48.2016.09.54

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This paper proposes the method to solve the economic dispatch problem with hybrid of lamda and bee colony optimization (HLBCO). The fundamental constraints of economic dispatch problem are the load demand and power loss into consideration. The generation cost function considering smooth cost function characteristic. To verify the performance of the proposed HLBCO algorithm, it is operated by the simulation on the MATLAB program and tested the two case studies. The simulation results indicate that the HLBCO can provide a better solution than the others in terms of quality solution, computational and convergence efficiently. Streszczenie. W artykule zapropponowano metode optymalizacji rozsyłu energii prze wykorzystanie hybrydy dwóch metod: lamda i algorytmów rojowych HLBCO. Symulacja przeprowadzona nakilku przykładach dowodzi że zaproponowany algorytm lepiej rozwiązuje prtoblemy ekonomicznego rozsyłu biorąc pod uwagę jakość I skuteczność. Optymalizacja ekonomii rozsyłu enegii z wykorzystaniem metod rojowych I metody lamda.. Keywords: lamda, bee colony, optimization, economic dispatch. Słowa kluczowe: metody rojowe, metoda lamda, optymalizacja rozsyłu enrgii. Introduction The electricity is an important for economic and social development. Planning, security and reliability of electrical power are necessary for electrical power generation. Economic dispatch is the method of determinative the most efficient, low cost and reliable operation of a power. The objective function of economic dispatch is to minimize the total fuel cost of electrical power generation which the demand, power loss and constraints are satisfied. There are many methods to solving the economic dispatch problem. The conventional methods for solving economic dispatch problem are lamda iteration method, gradient method, newton’s method, piecewise linear cost functions and dynamic programming [1] that owing to tedious calculations and its incapability to solve mu[...]

An improved local search involving bee colony optimization using lambda iteration combined with a golden section search method to solve an economic dispatch problem DOI:10.15199/48.2019.01.49

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The operating cost of a power plant mainly depends on the fuel cost of generators which is minimized via economic dispatch. The Economic Dispatch (ED) problem is one of the fundamental issues in power system operation. The main objective is to reduce the cost of energy production taking into account transmission losses while satisfying equality and inequality constraints. The rational distribution of economic load between running units can lead to significant cost savings making it important to research the economic dispatch problem. Several classical methods, such as the lambda iteration method [1], quadratic programming [2], the gradient method [3], dynamic programming [4], linear programming [5], and nonlinear programming [6] have been applied to solve ED problems. However, these methods are not feasible in practical power systems owing to the non-linear characteristics of the generators. Solutions can be limited to achieving a local optimum which leads to less desirable performance. In addition, these methods often use approximations to limit complexity. Recently, a number of researchers have used meta-heuristic optimization techniques, which are unlike conventional mathematical techniques, to solve ED problems in power systems. Different meta-heuristic approaches have proved to be effective with promising performance, such as a Genetic Algorithm (GA) [7]-[9]. Such methods have been inspired by the Darwinian law of optimal survival of a species, Particle Swarm Optimization (PSO) [10]-[12] inspired by the social behavior of bird raising or fish production, Ant Colony Optimization (ACO) [13]-[14] inspired by food habits in an ant colony, and by Tabu Search (TS) [15] as a way to build a better foundation from prior knowledge. This latter method records previous answers and forbids the new solution to converge at the same point for different input data. Other methods to be used include the Cuckoo Search Algorithm (CSA) [1[...]

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