Upravljanje asimetričnim inverterom ujednačenog koraka s 13 razina korištenjem optimizacije roja čestica

Harmonic Elimination Strategy (HES) has been a widely researched alternative to traditional PWM techniques. This paper presents the harmonic elimination strategy of a Uniform Step Asymmetrical Multilevel Inverter (USAMI) using Particle Swarm Optimization (PSO) which eliminates specified higher order harmonics while maintaining the required fundamental voltage. This method can be applied to USAMI with any number of levels. As an example, in this paper a 13-level USAMI is considered and the optimum switching angles are calculated to eliminate the 5th, 7th, 11th, 13th and 17th harmonics. The HES-PSO approach is compared to the well-known Sinusoidal Pulse-Width Modulation (SPWM) strategy. Simulation results demonstrate the better performances and technical advantages of the HES-PSO controller in feeding an asynchronous machine. Indeed, the harmonic distortions are efficiently cancelled providing thus an optimized control signal for the asynchronous machine. Moreover, the technique presented here substantially reduces the torque undulations.Strategija eliminacije harmonika je dobro istražena alternativa tradicionalnoj pulso-širinskoj modulaciji. U ovom radu opisana je strategija eliminacije harmonika asimetričnog višerazinskog invertera ujednačenog koraka uz korištenje optimizacije roja čestica čime se eliminiraju harmonici višeg reda uz zadržavanje fundamentalnog napona. Takva metoda može se primijeniti neovisno o broju razina invertera. Kao primjer korišten je inverter s 13 razina kod kojeg se eliminiraju peti, sedmi, jedanaesti, trinaesti i sedamnaesti harmonik. Predloženo rješenje uspoređeno je s dobro poznatom sinusnom pulsno-širinskom modulacijom. Simulacijski rezultati pokazuju prednosti predloženog rješenja. Harmonička distorzija je uspješno poništena te je upravljački signal za asinkroni stroj optimalan. Štoviše, predložena tehnika znatno smanjuje promjene momenta

IJTPE Journal A MULTI-OBJECTIVE PSO BASED ALGORITHM FOR A VEHICLE ROUTING

Abstract-In this paper a novel method is presented for robot motion planning with respect to two objectives, the shortest and smoothest path criteria. A Particle Swarm Optimization (PSO) algorithm is employed for global path planning, while the Probabilistic Roadmap method (PRM) is used for obstacle avoidance (local planning). The two objective functions are incorporated in the PSO equations in which the path smoothness is measured by the difference of the angles of the hypothetical lines connecting the robot's two successive positions to its goal. The PSO and PRM are combined by adding good PSO particles as auxiliary nodes to the random nodes generated by the PRM. The proposed algorithm is compared in path length and runtime with the mere PRM method searched by Dijkstra's algorithm, and the results showed that the generated paths are shorter and smoother and are calculated in less time

Multi-Objective PSO- and NPSO-based Algorithms for Robot Path Planning

In this paper two novel Particle Swarm Optimization (PSO)-based algorithms are presented for robot path planning with respect to two objectives, the shortest and smoothest path criteria. The first algorithm is a hybrid of the PSO and the Probabilistic Roadmap (PRM) methods, in which the PSO serves as the global planner whereas the PRM performs the local planning task. The second algorithm is a combination of the New or Negative PSO (NPSO) and the PRM methods. Contrary to the basic PSO in which the best position of all particles up to the current iteration is used as a guide, the NPSO determines the most promising direction based on the negative of the worst particle position. The two objective functions are incorporated in the PSO equations, and the PSO and PRM are combined by adding good PSO particles as auxiliary nodes to the random nodes generated by the PRM. Both the PSO+PRM and NPSO+PRM algorithms are compared with the pure PRM method in path length and runtime. The results showed that the NPSO has a slight advantage over the PSO, and the generated paths are shorter and smoother than those of the PRM and are calculated in less time

Particle swarm optimization with crossover: a review and empirical analysis

Since its inception in 1995, many improvements to the original particle swarm optimization (PSO) algorithm have been developed. This paper reviews one class of such PSO variations, i.e. PSO algorithms that make use of crossover operators. The review is supplemented with a more extensive sensitivity analysis of the crossover PSO algorithms than provided in the original publications. Two adaptations of a parent-centric crossover PSO algorithm are provided, resulting in improvements with respect to solution accuracy compared to the original parent-centric PSO algorithms. The paper then provides an extensive empirical analysis on a large benchmark of minimization problems, with the objective to identify those crossover PSO algorithms that perform best with respect to accuracy, success rate, and efficiency.http://link.springer.com/journal/104622017-02-20hb201