A Parameter Estimation Method for a Photovoltaic Power Generation System Based on a Two-Diode Model

Abstract

This study presents a parameter estimation method that uses an enhanced gray wolf optimizer (EGWO) to optimize the parameters for a two-diode photovoltaic (PV) power generation system. The proposed method consists of three stages. The first stage converts seven parameters for the two-diode model into 17 parameters for different environmental conditions, which provides more precise parameter estimation for the PV model. A PV power generation model is then established to represent the nonlinear relationship between inputs and outputs. The second stage involves a parameter sensitivity analysis and uses the overall effect method to remove the parameters that have smaller effect on the output. The final stage uses an enhanced GWO that is associated with measurement data to optimally estimate the parameters that are selected in the second stage. When the parameters are estimated, the predicted value for the PV power output is calculated for specific values of solar irradiation and module temperature. The proposed method is verified on a 200 kWp PV power generation system. To confirm the feasibility of the proposed method, the parameter estimation before and after optimization are compared, and these results are compared with other optimization algorithms, as well as those for a single-diode PV model.

A MATLAB/GUI for Photovoltaic Modules Performance Simulations Based on Two-Diode Model

“…In this block, the hourly data of P maxC , T c , G and η is also exported to a .txt file. In order to assess the robustness of the two-diode model, the Mean Absolute Error (MAE) was calculated using Equation (16). The internal parameters are compared with the datasheet parameters and also those obtained by PVsyst.…”

“…In this way, the calculation of the I pv , I o1 , I o2 , n 1 and n 2 parameters is expedited, leading to decreased computation time. Replacing Equation (9) in Equation ( 3), gives Equation ( 10) that simplifies the two-diode model [4,16,41,[45][46][47].…”

“…In order to extract these internal parameters of c-Si PV cells, mathematical models based on the one-diode equivalent circuit (that ideally consists of a diode, resistors, and a current source) [12], with its variants of two-diode and three-diode have considered [13][14][15]. Two models for photovoltaic cell modeling are often used: the one-diode model and the two-diode model with five and seven internal parameters respectively [16]. Using these models it is possible to obtain the characteristic current-voltage (V-I) curves, which are very important for monitoring, controlling and evaluating the performance of PV systems.…”

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

“…Metaheuristic methods, on the other hand, are algorithms inspired by nature to solve optimization problems for model parameter estimation. Artificial Bee Colony (ABC) algorithm is in [12], Rat swarm optimizer (RSO) in [13], adaptive differential evolution (DE) algorithm in [14], a performance-guided JAYA (PGJAYA) algorithm in [15], a cat swarm optimization (CSO) algorithm in [16], a flexible particle swarm optimization (FPSO) in [17], a modified simplified swarm optimization (MSSO) algorithm in [18], an improved chaotic whale optimization (CWO) algorithm in [19], an improved opposition-based whale optimization algorithm (WOA) in [20], an improved cuckoo search algorithm (ImCSA) in [21], an improved sine cosine algorithm (ISCA) in [22], an improved Lozi-map chaotic optimization algorithm in [23], an improved teaching-learning-based optimization (TLO) in [24], a coyote optimization algorithm (COA) in [25], a slime mould algorithm (SMA) in [26], a grey wolf optimizer (GWO) in [27] and [28], a sine cosine algorithm (SCA) in [29], an ant lion optimizer (ALO) in [30] and [31], an improved moth-flame optimization (MFO) algorithm in [32] and [33], an improved lion swarm optimization in [34], northern goshawk optimization algorithm (NGO) [10], a multiverse optimizer (MVO) in [35], modified whale optimization algorithm (MWOA) [36], an enhanced adaptive butterfly optimization algorithm (EABOA) in [37], and a manta ray foraging optimization (MRFO) in [38] have been applied for solving the PV parameter estimation.…”

“…The error function (e.g. RMSE) is represented by (13). The objective function (O.F ) is defined such that to minimize our error function concerning 𝜃 as depicted in (14).…”