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

Abstract

To meet the exponentially growing demand for clean and green energy, the solar photovoltaic (PV) system’s importance is increasing day by day, for which PV modeling is considered to be one of the most important work in the current state-of-the-art methods. To effectively model a PV system, accurate PV parameter estimation is of the utmost importance. In line with this, although the values of some of the parameters are provided in the manufacturer’s datasheet, the values of unknown parameters, such as shunt resistance, series resistance, the diode ideality factor, photo-generated current and diode saturation current, are not provided. To estimate these values a lot of algorithms are already reported in the literature. After careful observation of all the reported algorithms, a few best-reported algorithms are identified and their performances are compared with respect to accuracy, convergence issues, computational complexity and thermal stability. All kind of algorithms, such as numerical, analytical and evolutionary algorithms, are considered in this study, and only the best reported algorithms are considered for the comparison.

An Effective Approach for Extracting the Parameters of Solar PV Models Using the Chaotic War Strategy Optimization Algorithm With Modified Newton Raphson Method

“…Generally, in PV parameter estimation problem the calculation of current RMSE between the measured current and calculated current is done with the approximation 𝐼 𝐿 = 𝐼 𝐿,𝑚𝑒𝑠 in the equation-( 5) using the equations from (8) to (9). For the objective function to be solved precisely, this approximation does not work well.…”

“…B. Numerical methods [9] For parameter estimation problem these methods use the curve fitting techniques which is better than the analytical, but these models take a huge amount of the measured data samples. They obtain the solution easily but not the accurate enough because these are generally gradient-based algorithms that are easy to fall into the local optima.…”

A reliable optimization framework for parameter identification of single‐diode solar photovoltaic model using weighted velocity‐guided grey wolf optimization algorithm and Lambert‐W function

“…The following algorithms are a few examples utilized to handle the parameter estimation problem. The algorithms such as Particle Swarm Optimizer (PSO), Differential Evolutionary (DE) algorithm, Genetic Algorithm (GA), Artificial Bee Colony (ABC) algorithm, Ant Colony Optimization (ACO), Bacterial Foraging Algorithm (BFA), Shuffled Frog-Leaping Algorithm (SFLA), Biogeography optimizer, firefly algorithm, firework algorithm, branch and bound global optimization algorithm, pattern search algorithm, harmony search algorithm, simulated annealing, cuckoo search algorithm, interior search algorithm, Teaching Learning-Based Optimization (TLBO), Jaya algorithm, Rao algorithm, political optimizer, supply-demand-based optimizer, Grey Wolf Optimizer (GWO), Salp Swarm Optimizer (SSO), Whale Optimizer (WO), Moth Flame Optimizer (MFO), Multi-Verse Optimizer (MVO), Dragon Fly Algorithm (DFA), red deer algorithm, honey badge algorithm, Aquila optimizer, Arithmetic Optimization Algorithm (AOA), Slime Mould Algorithm (SMA), Hunger Games Search Algorithm (HGSA), Runge-Kutta Algorithm (RKA), Gradient-Based Optimizer (GBO), Marine Predator Algorithm (MPA), white shark optimizer, golden jackal algorithm, plasma generation algorithm, thermal exchange algorithm, heat transfer search algorithm etc., are proposed for numerical and real-time optimization algorithm [6,27,28]. When compared to their conventional counterparts, these strategies have shown themselves to be more accurate and less sensitive to the initial guess.…”

Parameter Identification of Solar Photovoltaic Systems Using an Augmented Subtraction-Average-Based Optimizer

“…Because of the ongoing growth in power consumption and the swiftly diminishing supply of petroleum, coal and natural gas, scientists all over the world are forced to seek new sources of energy [1] through the use of small-scale distributed generation of electricity and energy storage facilities [2]. Their massive incorporation into the electrical network would raise the regulatory burden and have an impact on the main grid's security operation [3]. A microgrid may operate independently or in conjunction with the main system.…”

“…In general, diode-based similar circuit models are employed. In light of this, the PVSD [14] and PVDD [15] varieties are particularly popular [3]. The current voltage (I-V) characteristics are similarly described by the three-diode design.…”