Machine learning based maximum power point tracking in solar energy conversion systems

Author(s): Mounil Memayaa, C. Balakrishna Moorthyb, Sahitya Tahilianic, Siddarth Sreenid
aDept. of Computer Science and Information Systems, BITS Pilani, K K Birla Goa Campus, Zuarinagar 403726, Goa, India
bDept. of Electrical and Electronics Engineering, Maria College of Engineering, Thiruvattaru, Tamil Nadu 629177, India
cDept. of Electronics and Instrumentation Engineering, BITS Pilani, K K Birla Goa Campus, Zuarinagar 403726, Goa, India
dDept. of Electrical and Electronics Engineering, BITS Pilani, K K Birla Goa Campus, Zuarinagar 403726, Goa, India
International Journal of Smart Grid and Clean Energy, vol. 8, no. 6, November 2019: pp. 662-669
ISSN: 2315-4462 (Print)
ISSN: 2373-3594 (Online)
Digital Object Identifier: 10.12720/sgce.8.6.662-669

Abstract: Extraction of solar energy from photovoltaic cells has different efficiencies corresponding to different algorithms. In the paper, a power efficient algorithm is suggested for tracking of maximum power point (MPP) in solar energy conversion systems by implementing machine learning (ML) in the pre-existing perturb and observe (P&O) methodology. P&O works on the principle of varying duty cycles step by step in the direction of the MPP and is the most feasible and accurate algorithm. However, the speed of convergence to the MPP is usually less in this method and it varies in different climatic conditions. This paper describes the application of ML in decreasing the perturbation time significantly leading to the significant increase in the efficiency to predict the MPP. The suggested algorithm predicts an MPP based on instantaneous values of solar irradiation, solar cell temperature and humidity as input features to the localized multivariate regression ML model and is used to fetch maximum available power (MAP). It is a self-learning algorithm and as the time progresses, the estimation becomes much closer to the theoretically available power. The simulation was done in python and yielded an average efficiency of 99.8% in estimating the MPP after 83 hours of training.

Keywords: Photovoltaic systems, solar energy conversion systems, maximum power point tracking, perturb and observe, machine learning
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