Open AccessSensorless fuzzy‐logic‐based maximum power point tracking control for a small‐scale wind power generation systems with a switched‐mode rectifier
Author(s) -
Lee Joonmin,
Kim YoungSeok
Publication year - 2016
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
ISSN - 1752-1424
DOI - 10.1049/iet-rpg.2015.0250
Subject(s) - maximum power point tracking , control theory (sociology) , maximum power principle , rectifier (neural networks) , turbine , wind power , permanent magnet synchronous generator , computer science , fuzzy logic , power (physics) , engineering , photovoltaic system , inverter , magnet , control (management) , voltage , physics , electrical engineering , stochastic neural network , quantum mechanics , artificial intelligence , recurrent neural network , artificial neural network , mechanical engineering , machine learning
This study proposes a sensorless maximum power point tracking (MPPT) control using an improved variable‐speed small‐scale wind power generation system (WPGS) with a permanent magnet synchronous generator. The passive elements of a small‐scale WPGS that use a conventional boost converter suffer from disadvantages that result in power losses, increased costs, and increased system size. Therefore, to eliminate the passive elements, the proposed system performs MPPT control to vary the duty ratio of a switched‐mode rectifier. To evaluate the MPPT algorithms, a comparison was made between a fuzzy‐logic‐based perturb‐and‐observe (P&O) control with variable step size and a conventional P&O control with fixed‐step size. A wind turbine model is investigated using a squirrel‐cage induction motor, and the variable torque is controlled by varying the generator speed. The presented system and algorithm are verified by the simulations and experiments.