The main aim of this paper si the improvement of the output voltage quality of the AC/AC choppers to minimize the harmonics continents and to increase the power factor of the voltage source. Several works were done in this field using classical algorithms to fulfill the objective mentioned. In the present paper the Particle Swarm Optimization (PSO) algorithm is used to achieve the minimization of the harmonic continents of the delivered voltage, to improve the power factor of the voltage source and finally to increase the control range of the outer voltage.

Particle Swarm Optimization applied for the improvement of the PWM AC/AC choppers voltage

This paper deals with the improvement of the voltage quality of the AC/AC choppers delivered to the loads in a way to minimize the harmonics continents and to increase the power factor of the voltage source. Indeed a lot of works were done in this field using several algorithms to fulfill the objective mentioned. In the present work the Swarm Particle Optimization (PSO) algorithm is used to achieve the minimization of the harmonic continents of the deliver voltage, to improve the power factor of the voltage source and finally to increase the control range of the outer voltage .

Voltage quality enhancement of PWM AC Voltage controller using Particle Swarm Optimization

Kennedy has proposed the bare bones particle swarm (BBPS) by the elimination of the velocity formula and its replacement by the Gaussian sampling strategy without parameter tuning However, a delicate balance between exploitation and exploration is the key to the success of an optimizer This paper firstly analyzes the sampling distribution in BBPS, based on which we propose an adaptive BBPS inspired by the cloud model (ACM-BBPS) The cloud model adaptively produces a different standard deviation of the Gaussian sampling for each particle according to the evolutionary state in the swarm, which provides an adaptive balance between exploitation and exploration on different objective functions Meanwhile, the diversity of the swarms is further enhanced by the randomness of the cloud model itself Experimental results show that the proposed ACM-BBPS achieves faster convergence speed and more accurate solutions than five other contenders on twenty-five unimodal, basic multimodal, extended multimodal and hybrid composition benchmark functions The diversity enhancement by the randomness in the cloud model itself is also illustrated

Adaptive Bare Bones Particle Swarm Inspired by Cloud Model

This paper deals with the improvement of the voltage quality of the AC/AC choppers delivered to the loads in a way to minimize the harmonics continents and to increase the power factor of the voltage source. Indeed a lot of works were done in this field using several algorithms to fulfill the objective mentioned. In the present work the Swarm Particle Optimization (PSO) algorithm is used to achieve the minimization of the harmonic continents of the deliver voltage, to improve the power factor of the voltage source and finally to increase the control range of the outer voltage.

The use of the Particle Swarm Optimization for the improvement of the AC/AC choppers output voltage

The main objective of this present paper is the study of the Shunt Active Power Filter (APF) compensations capability for different perturbations in AC power system such as current unbalance, phase shift current and undesired harmonics generated by nonlinear load and/or by the power system voltage. This capability is determined by the maximum rate of the apparent power that can be delivered. This study is based on the definition of the effective apparent power as defined in IEEE 1459-2000 which was proved to be the suitable amount to be concerned in the design process of different devices.

Evaluation of the Shunt Active Power Filter apparent power ratio using particle swarm optimization

This paper presents a novel parameter automation strategy for the particle swarm optimization algorithm; the normal cloud model is used to improve the performance of the particle swarm optimization algorithm. First, the normal cloud model is used to initialize the population; particles are no longer uniformly distributed throughout the search space. Second, one and the same normal cloud is used to nonlinearly, dynamically adjust inertia weight and update two random numbers in velocity update equation. Therefore, three components in the velocity update equation do interact in the PSO search process, which maintains the diversity of the population, provides balance between the global and local search abilities and makes the convergence faster. Experimental results are provided to show that the improved particle swarm optimization algorithm can successfully locate all optima on a small set of benchmark functions. A comparison of the improve algorithm with the standard particle swarm optimization algorithm is also made.

Particle swarm algorithm based on normal cloud

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Particle swarm algorithm based on normal cloud