Abstract
In this paper, an efficient and feasible algorithm to extract the maximum power point (MPP) in wind energy conversion systems (WECS) by implementing machine learning (ML) into perturb and observe (P&O) algorithm is presented. The proposed algorithm is simulated on a separately-excited DC generator. This model uses instantaneous measurements of wind speed, humidity, temperature, pressure and generator speed to estimate a MPP by using ML at the end of each iteration. From this estimated power point, the controller follows quick perturbation to calculate the accurate MPP and is used as training data for further predictions in the next iteration. The controller learns from this training set and estimates the MPP closer to the maximum achievable power (MAP) which is corrected again through perturbation and is recorded. With the progress of time, the approximation of the maximum power point becomes more accurate whilst the time in further perturbation required for modification decreases. This model adapts to the versatile climatic conditions and yields an efficiency of 99.95% in predicting the MAP at the end of 1000 iterations corresponding to 2 hours 30 minutes.
Highlights
INTRODUCTIONThis Majority of the energy requirements in today's world are met with fossil fuels which are costly, non-renewable and pollute the environment
The system comprises of a buck converter is coupled with a DC generator This acts as a DC to DC converter that controls the proportion of input to output voltage by a pulse width modulation (PWM) signal via charger controller. where, ρ is the air density, A is the area covered under the rotor blade, v is the wind speed (m/s)
If there is an increment in the power on increasing the duty cycle, the power point close to the maximum achievable power(MAP)
Summary
This Majority of the energy requirements in today's world are met with fossil fuels which are costly, non-renewable and pollute the environment. An effective mechanism is required to efficiently harness maximum wind energy power. A load impedance is required for the generator to change the values of current and track the maximum power at different wind speeds. Various methods like perturb and observe (P&O) [3], method of incremental conductance [4], method of fractional voltage[5], neural network [6] and fuzzy logic control [7], etc., are used in a charger controller to generate efficient power outputs. Results have been compared with the existing methods on the basis of efficiency and performance
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