Parameter Estimation of the Photovoltaic System Using Bald Eagle Search (BES) Algorithm

Abstract

The global demand for renewable energy is growing, and one of the proposed solutions to this energy crisis is the use of photovoltaic systems. So far, they are a reliable solution, as they are nonpolluting and can be used almost anywhere on the planet. However, the design and development of more efficient photovoltaic cells and modules require an accurate extraction of their intrinsic parameters. Up to date, metaheuristic algorithms have proven to be the best methods to obtain accurate values of these intrinsic parameters. Hence, to extract these parameters reliably and accurately, this paper presents an optimization method based on the principle of bald eagle search (BES) during fish hunting. This search is divided into three steps: in the first stage (space selection), the eagle selects the space with the largest number of prey; in the second stage (space search), the eagle moves into the selected space to search for prey; in the third stage (dive), the eagle swings from the best position identified in the second stage and determines the best point to hunt. Thus, we used the proposed BES algorithm to determine the parameters of the single-diode model (SDM), the double-diode model (DDM), and the PV modules. This algorithm converges very quickly and gives a root mean square error (RMSE) of 9.8602 e − 04 for the single-diode model and 9.8248 e − 4 for the dual-diode model. The results obtained show that the proposed algorithm is more efficient than the other methods available in the literature, in terms of the better accuracy of the results obtained. The good harmony of the I-V and P-V characteristic curve of the calculated parameters with that of the measured data from a PV module/cell data sheet proves that the proposed BES should be used among the methods provided in the literature for the identification of PV solar cell parameters.

Harris Hawk Optimization Combined with Differential Evolution for the Estimation of Solar Cell Parameters

“…Table 1 gives all the bounds (lower and upper) of this simulation. The lower and upper bounds used here are the same as those used in works found in the literature, such as [6,15,18] and others. Thus, we have the photocurrent which varies between [0 1] (A), the diode's saturation current [0 1] (μA), the ideality factor [1 2], the series resistance [0 0.5] (Ω), and the shunt resistance [0 100] (Ω).…”

“…Execute equation (10) Execute equation ( 11 2019) in [6], Ridha (2020) in [33], and Nicaire et al ( 2021) in [15]. We did not design an experimental bench but just retrieved the data already available to evaluate our proposed method.…”

“…The Manta-ray foraging optimizer method was utilised by El-Hameed et al in [14], to identify the intrinsic characteristics of the three diode model of a solar module. The bald eagle search method was used by Nicaire et al in [15], to extract the intrinsic parameters of the one and two diode model of a solar cell and photovoltaic module. In [16], Ndi et al estimate single and double diode model parameters using the equilibrium optimizer approach, which is inspired by models used to analyze dynamic and equilibrium states.…”

Robust parameter identification strategy of solid oxide fuel cells using bald eagle search optimization algorithm

“…For these reasons, other new MAs may provide better results compared with the published ones. Bald search optimization algorithm (BES) got great results in several complex optimization problems such as DC microgrid energy management 1 and for extracting the PV parameters 38 . The BES can provide excellent performance in terms of convergence and efficiency since its convergence mechanism is based on three stages: select space, search in the space, and swooping stage.…”

“…Bald search optimization algorithm (BES) got great results in several complex optimization problems such as DC microgrid energy management 1 and for extracting the PV parameters. 38 The BES can provide excellent performance in terms of convergence and efficiency since its convergence mechanism is based on three stages: select space, search in the space, and swooping stage. The algorithm evaluates the fitness in each stage and enhances the candidate solutions.…”