Parameter Estimation of Solar Modules Operating Under Outdoor Operational Conditions Using Artificial Hummingbird Algorithm

Abstract

In this article, an artificial algorithm called hummingbirds optimization method, named AHA algorithm, is settled to extract accurately the parameters of PV modules under outdoor operational conditions. AHA is the main contribution in this work, regarding its efficiency and good performance in terms of standard deviation (StD), root mean square error (RMSE), sum of squared error (SSE), maximum number of iterations (MaxIt), particularly, for extracting parameter from modules operating in real conditions, and under different temperature and irradiance levels. The AHA is applied on a Polycrystalline-solar panel module type 320W-72P at real operating conditions and on a PV array of three polycrystalline PV modules connected in series. PV cells of the same modules are assumed operating under the same conditions where they share the same electric current and voltage values. In this last pattern, eight different scenarios are chosen. In the first five scenarios, the temperatures are (46.97, 44.23, 42.87, 40.59 and 30.60°C), and the irradiance varies, such as (910, 800.57, 614.13, 415.1 and 200.87W/m2), respectively. In the other three testing scenarios, the solar irradiance is equal (803 W/ m2), and the temperature differs, such as T=47. 93 °C, 53.38 °C and 36.62°C. Lower values of root mean square errors (RMSE) are achieved (9.8602 × 10 −4 and 2.572533 × 10 −2 for RTC France PV cell and the 320W-72P module respectively) with 6000 iterations. Moreover, all the eight scenarios are lower than 7. 245916 × 10 −2 for the last case study. Moreover, results show that we could recommend the AHA algorithm as an advanced and efficient method for dealing with real time parameter optimization of photovoltaic modules. In fact, a high closeness between the simulated and the experimental curves is achieved, which indicates the perfectness of this optimization method. Finally, the proposed AHA algorithm can be engaged as tools for the best designing of PV systems.

Evaluation of the Performance of Polycrystalline and Monocrystalline PV Technologies in a Hot and Arid Region: An Experimental Analysis

“…Hence, we selected the AHA optimization technique to extract PV internal parameters due to its ability to solve several optimization problems and its high search capability. The technique is based on three strategies: territory, guided and migration process [22,28].…”

“…Furthermore, the solution can be improved by a two-step nonlinear optimization procedure using the Levenberg-Marquardt algorithm or trust-region reflective algorithm. An optimization algorithm named AHA (Artificial Hummingbird Algorithm) is presented to determine the internal physical PV parameters of a polycrystalline solar module type 320W-72P and a PV array of three polycrystalline PV modules connected in series under real operating conditions [22].…”

A Critical Review on the Estimation Techniques of the Solar PV Cell’s Unknown Parameters

“…Due to existence of various characteristic types ranging from multi-modality, nonlinearity and multi-variability in the voltage current curve of the photovoltaic cell, metaheuristic algorithms are the finest approach. Sofiane et al [83] came up with a hummingbird's optimisation method termed as artificial hummingbird algorithm (AHA). AHA is a meta-heuristic bio-inspired optimisation algorithm in which populations of n humming birds are randomly initialised and placed on n food tables, as in Equation (30).…”

“…Various meta-heuristic techniques, including CPMPSO [47], EHHO [84], EJADE [85], ELBA [86], NMSOLMFO [87], GBO [88], RUN [89], GSK [90], RLGBO [91], DSCSE [92], IMPA [93], CCNMHHO [94], SEDE [95], WLCSODGM [96], SGDE [97], EABOA [98], MTLBO [99] and WHHO [76], were compared in [83] based on squared statistical error (SSE), standard deviation (StD) and Root mean square error (RMSE). Based on the following performance indicator, the proposed AHA stands out as an effective parameter-extraction algorithm.…”

A New Approach for Solar Photovoltaic Parameter Extraction Using Metaheuristic Algorithms From Manufacturer Datasheet

“…Photovoltaic (PV) string model is used to estimate the parameters of the SDM in [13]. Artificial hummingbird algorithm (AHA) [14] and genetic algorithm (GA) [15] are utilized to determine solar cell parameter values and demonstrated the accuracy and efficiency of these methods for identification of parameters of SDM. In [16], authors identified the parameters of DDM applying combination of artificial bee colony (ABC) optimization and PSO.…”