Growth Optimizer for Parameter Identification of Solar Photovoltaic Cells and Modules

Abstract

One of the most significant barriers to broadening the use of solar energy is low conversion efficiency, which necessitates the development of novel techniques to enhance solar energy conversion equipment design. The correct modeling and estimation of solar cell parameters are critical for the control, design, and simulation of PV panels to achieve optimal performance. Conventional optimization approaches have several limitations when solving this complicated issue, including a proclivity to become caught in some local optima. In this study, a Growth Optimization (GO) algorithm is developed and simulated from humans’ learning and reflection capacities in social growing activities. It is based on mimicking two stages. First, learning is a procedure through which people mature by absorbing information from others. Second, reflection is examining one’s weaknesses and altering one’s learning techniques to aid in one’s improvement. It is developed for estimating PV parameters for two different solar PV modules, RTC France and Kyocera KC200GT PV modules, based on manufacturing technology and solar cell modeling. Three present-day techniques are contrasted to GO’s performance which is the energy valley optimizer (EVO), Five Phases Algorithm (FPA), and Hazelnut tree search (HTS) algorithm. The simulation results enhance the electrical properties of PV systems due to the implemented GO technique. Additionally, the developed GO technique can determine unexplained PV parameters by considering diverse operating settings of varying temperatures and irradiances. For the RTC France PV module, GO achieves improvements of 19.51%, 1.6%, and 0.74% compared to the EVO, FPA, and HTS considering the PVSD and 51.92%, 4.06%, and 8.33% considering the PVDD, respectively. For the Kyocera KC200GT PV module, the proposed GO achieves improvements of 94.71%, 12.36%, and 58.02% considering the PVSD and 96.97%, 5.66%, and 61.20% considering the PVDD, respectively.

Dwarf Mongoose Optimizer for Optimal Modeling of Solar PV Systems and Parameter Extraction

“…The first assessment is centered upon R.T.C France, which operates at a sun radiation and a temperature of 1000 w/m 2 and 33 • C, respectively. The second case studied involves the Kyocera KC200GT module, which consists of 54 multi-crystalline cells linked in series and that operates at 25 • C [60,61]. Additionally, in comparison to current optimizing methodologies recently disclosed in the literature, the suggested MDMO technique has been evaluated and applied to parameter extraction problems across multiple PVSDM, PVDDM, and PVTDM arrangements.…”

An efficient, fast, and robust algorithm for single diode model parameters estimation of photovoltaic solar cells

“…A PV cell is composed of two semiconductor layers (P and N) and a metal grid for wiring and connections. In the dark, it acts as a simple standard P-N junction diode that obeys the Shockley equation formulated in (1). In the presence of irradiation, solar energy frees up charge carriers and generates a photovoltaic current, which is denoted by I PV .…”

“…1000, 800, 600, 400) at a constant value of temperature equal to 25 • C. The SDM parameters SRs are given in Table 2. [1,100] respectively. The five unknown parameters of the SDM for the mono-crystalline PV module are estimated using different optimization algorithms and the proposed AO algorithm.…”

“…The SDM parameters search regions are given in Table 2. [1,75] modules at different temperatures are illustrated in Figure 15. It is worth mentioning that the best RMSE is achieved after a maximum of five iterations, regardless of the temperature value.…”

“…On the other hand, solar energy is clean, sustainable, and widespread worldwide. In conjunction with technological developments, solar photovoltaic (PV) technology has gained efficiency as one of the most popular competitive electric power generators, along with oil, coal, and gas [1,2].…”