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Showing 4 results for Particle Swarm Optimization (pso)

C. Lucas, F. Tootoonchian, Z. Nasiri-Gheidari,
Volume 6, Issue 3 (9-2010)

In this paper a brushless permanent magnet motor is designed considering minimum thrust ripple and maximum thrust density (the ratio of the thrust to permanent magnet volumes). Particle Swarm Optimization (PSO) is used as optimization method. Finite element analysis (FEA) is carried out base on the optimized and conventional geometric dimensions of the motor. The results of the FEA deal to the significant improvement of the all objective functions.
C. Lucas , Z. Nasiri-Gheidari , F. Tootoonchian,
Volume 6, Issue 4 (12-2010)

In this paper particle swarm optimization (PSO) is used for a design optimization of a linear permanent magnet synchronous motor (LPMSM) considering ultra low thrust force ripples, low magnet consumption, improved efficiency and thrust. The influence of PM material is discussed, too and the modular poles are proposed to achieve the best characteristic. PM dimensions and material, air gap and motor width are chosen as design variables. Finally 2-D finite element analyses validate the optimization results.
A. A. Khodadoost Arani, J. S. Moghani, A. Khoshsaadat, G. B. Gharehpetian,
Volume 12, Issue 2 (6-2016)

Multilevel voltage source inverters have several advantages compare to traditional voltage source inverter. These inverters reduce cost, get better voltage waveform and decrease Total Harmonic Distortion (THD) by increasing the levels of output voltage. In this paper Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) methods are used to find the switching angles for achieving to the minimum THD for output voltage waveform of the Cascaded H-bridge Multi-Level Inverters (MLI). These methods are used for a 27-level inverter for different modulation indices. Result of two methods is identical and in comparison to other methods have the smallest THD. To verify results of two mentioned methods, a simulation using MATLAB/Simulink software is presented.

Jayati Vaish, Anil Kumar Tiwari, Seethalekshmi K.,
Volume 19, Issue 4 (12-2023)

In recent years, Microgrids in integration with Distributed Energy Resources (DERs) are playing as one of the key models for resolving the current energy problem by offering sustainable and clean electricity. Selecting the best DER cost and corresponding energy storage size is essential for the reliable, cost-effective, and efficient operation of the electric power system. In this paper, the real-time load data of Bengaluru city (Karnataka, India) for different seasons is taken for optimization of a grid-connected DERs-based Microgrid system. This paper presents an optimal sizing of the battery, minimum operating cost and, reduction in battery charging cost to meet the overall load demand. The optimization and analysis are done using meta-heuristic, Artificial Intelligence (AI), and Ensemble Learning-based techniques such as Particle Swarm Optimization (PSO), Artificial Neural Network (ANN), and Random Forest (RF) model for different seasons i.e., winter, spring & autumn, summer and monsoon considering three different cases. The outcome shows that the ensemble learning-based Random Forest (RF) model gives maximum savings as compared to other optimization techniques.

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