Abstract
Parameters extraction of photovoltaic (PV) models is urgently desired for the simulation, control, and evaluation of PV systems. To accurately and reliably extract the parameters of different PV models, a triple-phase teaching-learning-based optimization (TPTLBO) is proposed in this paper. The novelty of TPTLBO lies in: i) teaching-learning-based optimization introduces a buffer phase and adopts a centroid strategy to update the position of intermediate learners, which further strengthens the exploration and exploitation; ii) the learners can select different phases and employ different learning strategies based on their knowledge level; iii) a dynamic control parameter replaces the original random parameter rand to enhance the search ability of algorithm. The parameters extraction performance of TPTLBO is verified through the single diode model, the double diode model, and three PV models. Experimental results demonstrate that TPTLBO achieves better performance in terms of accuracy and reliability compared to state-of-the-art algorithms.
Highlights
In recent years, to deal with the environment pollution, global warming, and increasing energy shortage, many countries have been looked for renewable energy [1]
To evaluate the performance of triplephase teaching-learning-based optimization (TPTLBO), the algorithm is used to extract the parameters of different PV models: the single diode model, double diode model, and PV module models
Based on the above comparisons, it illustrates that TPTLBO can obtain similar or better results compared with these approaches; it can be considered as an efficient alternative method for parameter extraction problems in different PV models
Summary
To deal with the environment pollution, global warming, and increasing energy shortage, many countries have been looked for renewable energy [1]. In [24], an improved TLBO algorithm (LETLBO) with learning experience of other learners was presented to extract the parameters of PV models, and promising results were obtained. This method needs to balance the diversity and the mean solution of the population. In ITLBO, authors adopt different strategies in teaching and learning phase to balance exploration and exploitation These TLBO variants encounter the dilemma of insufficient accuracy and low reliability. By comparing with other TLBO parameter estimation methods, TPTLBO demonstrates the accuracy and reliability in the experiments It can be considered as an effective alternative to parameter extraction of PV models.
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