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Energy Enhancement of Permanent Magnet Synchronous Generators Using Particle Swarm Optimization

S. Marisargunam1,*, L. Kalaivani2, R. V. Maheswari2

1 Department of EEE, Grace College of Engineering, Thoothukudi, Tamilnadu, India
2 Department of EEE, National Engineering College, Kovilpatti, Tamilnadu, India

* Corresponding Author: S. Marisargunam. Email: email

Intelligent Automation & Soft Computing 2022, 33(3), 1711-1727.


Wind Energy Conversion Systems (WECS) are extensively used for connecting directly to grid sources. Permanent magnet synchronous generator (PMSG) based WECS is coupled to both grid and machine through converters. PMSG usually associated rectifiers with converters and voltage source converters at machine side. In this work, PMSG associated rectifiers with converters are considered for analysis of grid stability. The proposed work used Particle Swarm Optimization (PSO) based optimization methods for extraction of maximum power within boundary condition in WECS operation using PMSG. This high-tech optimization has MPPT controller for pitch angle controller (PAC) combines with PSO optimized controllers for converter and grid power converters. PSO produces noteworthy optimum coefficients ensuring competent MPPT operation in PAC. Extensive simulation work has been carried out with power converter for current controller of rotor and grid side current controller in MATLAB Simulink environment to assess the proposed system performance in terms output voltage, output current, maximum power extraction. Energy and efficiency of the system when the PMSG based WECS energy injected into the electric grid. To prove the efficiency of the proposed method, the performances have been compared with and Bacteria foraging Algorithm (BFOA). Subsequently, it is evident that proposed PSO based method subsidizes maximum efficiency of MPPT controller by extracting maximum power compared to other conventional techniques.


Cite This Article

S. Marisargunam, L. Kalaivani and R. V. Maheswari, "Energy enhancement of permanent magnet synchronous generators using particle swarm optimization," Intelligent Automation & Soft Computing, vol. 33, no.3, pp. 1711–1727, 2022.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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