**1. Introduction**

Solar energy is emerged as a potential renewable source of energy. For the eighth year in a row, solar power has received the greatest proportion of groundbreaking investment opportunities in renewable energy sources. Because of the high investment cost of PVgenerating installations, it is necessary to estimate the behavior of the PV-system from the designing phase to assure efficient utilization of solar energy in electricity generation [1,2]. Solar energy is also reflected as an extremely capable renewable resource owing to its usage and non-polluting nature [1–3]. Moreover, its modularity and scalability have added to its extensive acceptance in power systems through different photovoltaic (PV) configurations [4]. For simulating, controlling, and evaluating the photovoltaic systems, modeling of the solar-cell installation must be done. Whenever photovoltaics start operating, the solar-cell parameters could be utilized for accounting for the detectability and analysis [3]. However, the practical aspect is that photovoltaic devices are majorly bare compared to several outer atmospheric belongings, and its photovoltaic arrays do not last always efficiently which will harm the production of sun-based devices [4]. Accordingly, this is a critical estimation of the practical performance of photovoltaic arrays in the process to achieve, enhance, and simulate these types of systems/devices. With this aim, we frequently use a reliable prototype to measure current and voltage files [5].

**Citation:** Sharma, A.; Dasgotra, A.; Tiwari, S.K.; Sharma, A.; Jately, V.; Azzopardi, B. Parameter Extraction of Photovoltaic Module Using Tunicate Swarm Algorithm. *Electronics* **2021**, *10*, 878. https://doi.org/10.3390/ electronics10080878

Academic Editor: Edris Pouresmaeil

Received: 3 March 2021 Accepted: 2 April 2021 Published: 7 April 2021

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The importance of photovoltaics is estimated to be a major stimulating topic by scientists/researchers and firms to progress energy adaption and reduce costs [6–8]. To boost the systematic performance of photovoltaics, modeling the photovoltaic cells and their segments is a crucial part. The non-linear dimensions and sporadic nature of meteorologic static make it difficult to identify cell constraints [9]. Furthermore, the production firms require assurance of the performance of photovoltaic units for approx. twenty-five years; photovoltaic arrangements are dependent on location and unavoidably undergo degradation, along with possible occurrences of electrical faults. So, we can considerably work on a systematic model that predicts the practical behavior of the photovoltaic cell in possible working conditions [10].

Generally, PV systems are vulnerable to outside atmospheric aspects such as temperature and irradiance, which affect the effectiveness of solar energy [11]. Thus, it is essential to generate current–voltage modeling setups for enhancing and controlling PV arrangements [12]. Generally, single, double, and triple diode models are majorly used for photovoltaic cells [13–15], and are extensively used to specify the current–voltage connections. Parameters of the photovoltaics help to determine the accurateness and dependability of the models. However, due to unbalanced operational cases, such as faults and aging, the models' parameters are not accessible. Therefore, the development of an active methodology to accurately extract these parameters turn out to be critical. The single diode model (SDM) is majorly used in the approximation of these constraints because of ease and acceptance. The double diode model (DDM) is expected to be as accurate as SDM, especially in lower solar irradiance; nevertheless, it desires to exist for a long consuming time [16–20]. To get more accurate and precise parameters from nonlinear implicit equations with high accuracy, evolutionary algorithms [21–31] were proposed. The bio-related algorithms are more accurate and powerful optimization algorithms for simplifying nonlinear transcendental equations, as they do not include complex mathematics. In the proposed work, TSA is implemented for the parameter extraction of the solar cell/module, and the results clearly show the superiority of the TSA over particle swarm optimization (PSO). The reason for this is that PSO has the problem of getting stuck in the local optima solution due to poor exploration capabilities for searching for the optimal solution in the search space, while the searching mechanism of TSA provides a good trade-off between exploration and exploitation capabilities [18]. Hence, TSA provides a more optimal solution as compared with PSO and other existing algorithms.

In this manuscript, we have discussed, initially, the problem formulation followed by a mathematical model for solar PV cell/module, as presented in Section 2. In Section 3, a brief introduction of the TSA algorithm is discussed and is implemented to estimate the optimized value of the unknown parameters of a PV module model. In Section 4, the simulation results of the TSA algorithm are discussed and compared with those of pre-existing metaheuristic algorithms. Section 5 entails the discussion and finally, the manuscript is concluded in Section 6.
