Open AccessModelling, control and sensorless speed estimation of micro‐wind turbines for deployment in Antarctica
Author(s) -
Llano Danilo Xavier,
McMahon Richard Anthony
Publication year - 2018
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
eISSN - 1752-1424
pISSN - 1752-1416
DOI - 10.1049/iet-rpg.2016.0528
Subject(s) - turbine , wind power , wind speed , estimator , control theory (sociology) , kalman filter , small wind turbine , rectifier (neural networks) , computer science , permanent magnet synchronous generator , computation , converters , power optimizer , engineering , control engineering , maximum power point tracking , magnet , aerospace engineering , meteorology , electrical engineering , control (management) , algorithm , mathematics , physics , inverter , artificial intelligence , voltage , stochastic neural network , recurrent neural network , machine learning , artificial neural network , statistics
This study presents the modelling, control and sensorless speed estimation of two micro‐wind turbines deployed by the British Antarctic Survey (BAS) in Antarctica. Mathematical models for the generators attached to an Ampair 100 and Rutland 913 wind turbines and their experimental validation are given. A model for the wind turbines, particularly taking into account the power coefficient C p versus tip speed ratio λ relationship, was proposed and successfully evaluated on a wind turbine emulator test rig. This study describes an analogue speed estimator board and a Kalman filter for estimating the shaft speed. These estimators use only DC‐side measurements to match the characteristics of the current version of the turbine control board. The wind turbine control and speed estimators were tested on the emulator test rig using real wind data from BAS research bases in Antarctica. Using only DC‐side measurements leads to low computation requirements to execute the algorithms in comparison with commonly used schemes that rely on AC measurements. The estimation algorithms are based on the model of a permanent magnet generator connected to a diode rectifier, as they can be used in a wider range of applications including DC–DC converters with maximum power point tracking algorithms.