Open AccessReview on parameter estimation techniques of solar photovoltaic systems
Author(s) -
Venkateswari Radhakrishnan,
Rajasekar Natarajan
Publication year - 2021
Publication title -
international transactions on electrical energy systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.428
H-Index - 42
ISSN - 2050-7038
DOI - 10.1002/2050-7038.13113
Subject(s) - photovoltaic system , computer science , estimation theory , heuristic , set (abstract data type) , mathematical optimization , noise (video) , function (biology) , reliability engineering , algorithm , engineering , artificial intelligence , mathematics , electrical engineering , evolutionary biology , image (mathematics) , biology , programming language
Summary Beyond meeting power demand, switching to solar energy especially solar photovoltaic (PV) offers many advantages like modularity, minimal maintenance, pollution free, and zero noise. Yet, its cell modeling is critical in design, simulation analysis, evaluation, and control of solar PV system; most importantly to tap its maximum potential. However, precise PV cell modeling is complicated by PV nonlinearity, presence of large unknown model parameter, and absence of a unique method. Since number of model parameters involved is directly related to model accuracy, and efficiency; determination of its values assume high priority. Besides, application of meta‐heuristic algorithms via numerical extraction is popular as it suits for any PV cell/module types and operating conditions. However, existence of many algorithms have drawn attention toward assessment of each method based on its merits, demerits, suitability/ability to parameter estimation problem, and complexity involved. Hence, few authors reviewed the subject of PV model parameter estimation. But existing reviews focused on comparative analysis of analytical and meta‐heuristic approaches, analysis of models, and application of meta‐heuristic methods for model parameter extraction. Thus, lack a comprehensive analysis on methods based on different objective function, assessment on environmental conditions, and cumulative analysis on selective set of algorithm based on efficiency. Therefore, this work reviews optimization algorithms presented for parameter estimation focusing on (a) objective function used, (b) modeling type, (c) algorithm employed for parameter extraction, and (d) PV technology. Further, provides a comprehensive assessment on various modules types used for validation, comparisons made with methods, advantages and disadvantages associated with each method with respect to parameter estimation platform, critical analysis on each method at STC, and varying irradiance conditions. In addition, a critical evaluation on specific set of algorithm based on objective function values is also carried out. Thus explores and display the characteristics of various techniques related to PV cell modeling and serve to be a single reference for researchers working in the field of PV parameter estimation.