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Abstract: Under rapidly fluctuating wind speed, high inertia cup anemometers have a tendency to overspeed. The main objective of this article is to develop an inverse time domain model that could be used in real-time during cup anemometer operation to minimize the so-called “u-error”. A model proposed by Kristensen and an artificial neural network (ANN) direct model were first investigated to simulate the dynamic behavior of a heated cup anemometer with relatively high rotor inertia. Once the anemometer behavior was known, several virtual inputs were generated and the direct model was used to predict the instrument behavior. These models were built to emphasis the non-linear relationship between the free stream fluctuating wind and the wind speed measured by the anemometer. A semi-empirical inverse model derived from Kristensen's model was then studied and an ANN inverse model was suggested in order to minimize the so-called u-error. A methodology is proposed to gather the appropriate data to create both the direct and inverse model using an artificial neural network. The output of each model was compared with experimental data for validation and good agreement was found between the ANN models and the experimental data used for validation. [Copyright &y& Elsevier]

Abstract: The wind industry has grown over the years along with the collective desire to produce clean energy. High wind potential sites are often located in northern regions where harsh climatic conditions arise. The performance of anemometers, which provide essential measurements to wind turbine operation and wind resource assessment, is then jeopardized by ice build-up and snow accumulation. These conditions result in the need for heated instruments able to remain ice-free regardless of weather conditions while minimizing energy consumption. The main objective of this work was to develop an intelligent heating control for a new generation of ice-free anemometers. The controller is based on vision recognition of ice build-up on the cups of the anemometer using a neural network. Heating power is applied according to the icing state and the icing history of the instrument. Primary results have shown that, for a completely ice-free performance, the developed controller uses an average of less than two thirds of the energy consumed by common control systems similar to those used by other ice-free anemometers available on the market. [Copyright &y& Elsevier]