10.35940/ijeat.D7025.049420
https://zenodo.org/records/5555754
oai:zenodo.org:5555754
S.Venkatesh Kumar
S.Venkatesh Kumar
Assistant Professor(Sr.Gr), Department of EEE, Department of EEE Coimbatore, Tamilnadu, India
P.Sebastian Vindro Jude
P.Sebastian Vindro Jude
Assistant Professor(Sr.Gr), Department of EEE, Department of EEE Coimbatore, Tamilnadu, India
K.Balamurugan
K.Balamurugan
Assistant Professor(Sr.Gr), Department of EEE, Department of EEE Coimbatore, Tamilnadu, India
A Maximum Power Point Tracking in Wind Energy Conversion Systems using Machine Learning
Zenodo
2020
Wind energy conversion systems, Maximum power point tracking, Perturb and Observe, Machine Learning, Artificial Intelligence
Blue Eyes Intelligence Engineering & Sciences Publication(BEIESP)
Blue Eyes Intelligence Engineering & Sciences Publication(BEIESP)
Publisher
2020-04-30
eng
2249-8958
Creative Commons Attribution 4.0 International
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.