Robust Solar Irradiation Forecasting Mechanism for Maximum Power Point Trackers: A Comparative Review

N. B. SUSHMI, D. Subbulekshmi

Abstract


Solar power generation has gained worldwide attention due to high potentiality and effortless energy conversion process. However, the uncertain nature of the Photovoltaic (PV) source makes the conventional Maximum Power Point Tracking (MPPT) controllers difficult in tracking the optimal operating point under all dynamic environmental conditions, causing impacts on PV system performance. Therefore, a robust forecasting technique is suggested to predict the irradiation level using various environmental parameters. Such prediction helps the controller in quickly exploiting the optimal decisions without any false trapping and exploitation process under any rapidly fluctuating profile. For that, the probabilistic and deterministic irradiation forecasting methods are generally discussed to ensure the method’s suitability. As a result, the paper mainly concentrates on Machine Learning (ML) based Artificial Neural Network (ANN) and Support Vector Regression (SVR) deterministic approaches as the most suggested prediction techniques from the literature survey for competent irradiation forecasting in PV systems in the last decades. Therefore, a comprehensive, systematic, and comparative review of ANN and SVR-based irradiation forecasting articles from 2014 to 2022 are considered, especially for PV system applications are analyzed and discussed with their benefits, demerits, and requirements in the irradiation forecasting field. It reveals that among those approaches, the performance and handling of ANN on non-linear, time series, massive as well as small datasets created wide attention than another approach confesses the suitable criteria in solving the MPPT problem stated. Also, the article conveys the formulation and functionalities of the 13 most commonly used performance indices for analyzing the responses.

Keywords


Photovoltaic systems; Irradiation forecasting; Maximum Peak Point Tracking (MPPT); Artificial Neural Network (ANN); Support Vector Regression (SVR).

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References


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