Abstract
Large-scale wind turbines with a large blade radius rotates under fluctuating conditions depending on the blade position. The wind speed is maximum in the highest point when the blade in the upward position and minimum in the lowest point when the blade in the downward position. The spatial distribution of wind speed, which is known as the wind shear, leads to periodic fluctuations in the turbine rotor, which causes fluctuations in the generator output voltage and power. In addition, the turbine torque is affected by other factors such as tower shadow and turbine inertia. The space between the blade and tower, the tower diameter, and the blade diameter are very critical design factors that should be considered to reduce the output power fluctuations of a wind turbine generator. To model realistic characteristics while considering the critical factors of a wind turbine system, a wind turbine model is implemented using a squirrel-cage induction motor. Since the wind speed is the most important factor in modeling the aerodynamics of wind turbine, an accurate measurement or estimation is essential to have a valid model. This paper estimates the average wind speed, instead of measuring, from the generator power and rotating speed and models the turbine’s aerodynamics, including tower shadow and wind shear components, without having to measure the wind speed at any height. The proposed algorithm overcomes the errors of measuring wind speed in single or multiple locations by estimating the wind speed with estimation error less than 2%.
Highlights
IntroductionElectrical power is the main energy source in homes, industries, and workplaces
Electrical power is the main energy source in homes, industries, and workplaces.Industrial development and population growth have led to increased power consumption in the last three decades
The tower shadow shape was calculated forat different radial from 6 shows wind shear and tower shadow effects the tip of eachcirculating blade at anpoints, average wind m to m in diameter, measured from the hub center, with an incremental distance of m, as shown speed of 8 m/s
Summary
Electrical power is the main energy source in homes, industries, and workplaces. Artificial intelligence and machine-learning methods, such as adaptive neuro-fuzzy inference systems [13,14], multilayer perceptron neural networks [15], and support vector regressions (SVR) [16,17], have been widely implemented to estimate effective wind speed These methods help map the relation between electrical measured quantities, such as turbine power and rotational speed, and wind speed using numerous samples, and use this online map to estimate the wind speed and the optimum wind speed, which produce the maximum turbine power point. In this framework, a wind turbine modeling based on the estimated wind speed is presented by simulating both the static and dynamic characteristics using a SCIM.
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