Parameter extraction of solar photovoltaic modules using Manta Ray Foraging Optimization (MRFO) Algorithm

In this paper, a new application of Bonobo (BO) metaheuristic optimizer is presented for PV parameter extraction. Its processes depict a reproductive approach and the social conduct of Bonobos. The BO algorithm is employed to extract the parameters of both the single diode and double diode model. The good performance of the BO is experimentally investigated on three commercial PV modules (STM6-40 and STP6-120/36) and an R.T.C. France silicon solar cell under various operating circumstances. The algorithm is easy to implement with less computational time. BO is extensively compared to other state of the art algorithms, manta ray foraging optimization (MRFO), artificial bee colony (ABO), particle swarm optimization (PSO), flower pollination algorithm (FPA), and supply-demand-based optimization (SDO) algorithms. Throughout the 50 runs, the BO algorithm has the best performance in terms of minimal simulation time for the R.T.C. France silicon, STM6-40/36 and STP6-120/36 modules. The fitness results obtained through root mean square (RMSE), standard deviation (SD), and consistency of solution demonstrate the robustness of BO.

This paper presents a critical analysis of the meta-heuristic techniques used in various researches on the optimisation of photovoltaic (PV) parameters, which involves the use of different algorithms in order to extract and improve these parameters from the single diode model (SDM), double diode model (DDM) and three diode model (TDM) respectively. The modelling parameters such as the photon current, saturation current, the series and parallel resistances are investigated to understand the optimum value. It will also equate the results of datasheet values from PV manufactures with experiment values obtained from PV module measurements. The meta-heuristics techniques to be considered include genetic algorithm (GA), particle swarm optimisation (PSO), harmony search (HS), flower pollination algorithm (FPA), simulated annealing (SA), teaching learning based optimisation (TLBO), and other different hybrid solutions to optimize the convergence speed. Root mean square error (RMSE) is used as a performance indicator of each meta-heuristic technique. These optimisation techniques are utilised in extracting the parameters of a 5 W polycrystalline panel at standard testing conditions. The results presented in this paper compared the performances of the mentioned meta-heuristics on the single, double and triple diode models respectively.

In this paper the modelling of the photovoltaic module has been applied through the experimental flash test data of industrial photovoltaic module. The theoretical background of the single diode model and the double diode model of the photovoltaic module have been described and similar procedures for the extraction and estimation of the unknown parameters have been proposed. The Levenberg-Marquardt optimization algorithm has been used to minimize objective function as a set of five nonlinear equations that were written for five equilibrium points on the current-voltage curve of the photovoltaic module. The single diode model and double diode model have then been simulated with the estimated unknown parameters and a very good matching between flash test data and a simulation data.

Perovskite solar cells (PSCs) are third-generation photovoltaic technology that has gained a lot of attention due to its technological and economic advantages. The PSCs are characterized with low cost, high absorption, long carrier diffusion length, and low fabrication temperature. To model the performance of PSCs, a new dynamic model is proposed to track the cells hysteresis by adding a variable voltage capacitor to the single, double, and triple diode models. The aim with adding the variable capacitor is to emulate the effect of charge accumulation at the interfaces of the device. Three modified models are presented based on one or more variable capacitors at single, double, and triple diode models. Where, the triple diode model with third order variable voltage capacitor model is the best among all models by 74.9% enhancement in comparison with single diode model. Also, the Equilibrium optimization algorithm (EOA) is proposed to determine the parameters of the proposed dynamic model based on the triple diode model. In comparison with the experimental data, for both forward and reverse scans, the findings showed that the suggested model accurately reflects cell performance. Added to that, the EOA finds well the optimal model parameters considering the root mean square as primary objective function. Based on the simulation results, it was proved that the proposed model gives very close results to reality.

This research study aims to introduce chaos theory into the Manta Ray Foraging Optimization (MRFO) Algorithm and optimize a real-world design problem through the chaos-enhanced versions of this method. Manta Ray Foraging Optimization algorithm is a bio-inspired swarm intelligence-based metaheuristic algorithm simulating the distinctive food search behaviors of the manta rays. However, MRFO suffers from some intrinsic algorithmic inefficiencies such as slow and premature convergence and unexpected entrapment to the local optimum points in the search domain like most of the metaheuristic algorithms in the literature. Recently, random numbers generated by chaos theory have been incorporated into the metaheuristic algorithms to solve these problems. More than twenty chaotic maps are applied to the base algorithm and ten best performing methods are considered for performance evaluation on high-dimensional optimization test problems. Forty test problems comprising unimodal and multimodal functions have been solved by chaotic variants of MRFO and extensive statistical analysis is performed. Furthermore, thermo-economic design optimization of an air-fin cooler is maintained by the chaotic MRFO variants to assess their optimization capabilities over complex engineering design problems. Ten decisive design variables of an air fin cooler are optimized in terms of total annual cost rates and optimum solutions obtained by five best chaotic MRFO algorithms are compared to the preliminary design. A significant improvement is observed in the objective function values when MRFO with chaotic operators is applied to this considered thermal design problem.

This paper compares two methods for extraction of photovoltaic (PV) module parameters. The numerical method called Gauss-Seidel, used on the single diode model, and the analytical method applied on the double diode model. Both methods are based on the manufacturer’s datasheets to extract the five parameters. These methods are validated using monocrystalline PV module. The modified mathematical modeling equations are classified into models 1 to 5. Only model 2 and model 4 are used for the analytical method. Each model is based on the different combinations of the ideality factor, the series resistance, photocurrent and saturation current plotted under varying temperature and irradiance. The accuracy of PV module based on both methods is evaluated by Absolute Error (AE). In order to evaluate the effectiveness and accuracy of the proposed methods, the equations were implemented in the MATLAB/Simulink environment and the results obtained are compared with extracted experimental data.

It is important to investigate the long-term performances of an accurate modeling of photovoltaic (PV) systems, especially in the prediction of output power, with single and double diode models as the configurations mainly applied for this purpose. However, the use of one configuration to model PV panel limits the accuracy of its predicted performances. This paper proposes a new hybrid approach based on classification algorithms in the machine learning framework that combines both single and double models in accordance with the climatic condition in order to predict the output PV power with higher accuracy. Classification trees, k-nearest neighbor, discriminant analysis, Naïve Bayes, support vector machines (SVMs), and classification ensembles algorithms are investigated to estimate the PV power under different conditions of the Mediterranean climate. The examined classification algorithms demonstrate that the double diode model seems more relevant for low and medium levels of solar irradiance and temperature. Accuracy between 86% and 87.5% demonstrates the high potential of the classification techniques in the PV power predicting. The normalized mean absolute error up to 1.5% ensures errors less than those obtained from both single-diode and double-diode equivalent-circuit models with a reduction up to 0.15%. The proposed hybrid approach using machine learning (ML) algorithms could be a key solution for photovoltaic and industrial software to predict more accurate performances.

This research paper systematically reviewed and investigated single diode model and double diode model of a solar photovoltaic systems in terms of accuracy, differences under major unknown PV parameters, different optimization and fabrication. This research paper reviewed the differences and the similarities between the single diode model and double diode model. From the review, it was clear that single diode model has less computation time and number of unknown parameters compared to double diode model. The double diode model on its own superiority is more accurate under solar shading condition effect than single diode model but single diode model performs better under high insolation levels. None of the two models is superior than the other but the solar photovoltaic modelers/installers should bear the solar irradiance of the environment before installation

Sizing and shape optimization of truss employing a hybrid constraint-handling technique and manta ray foraging optimization

This book chapter begins with an overview of three equivalent circuit models of solar PV such as single diode model (SDM), double diode model (DDM) and three diode model (TDM), as well as critical comparison and analysis of each model. In addition, the extraction and optimisation of the solar PV parameters using various Evolutionary algorithms are discussed in this chapter. The modelling parameters such as the photon current, saturation current, the series and parallel resistances are investigated to understand the optimum value. It will compare the results of datasheet values from PV manufacturers with experimental values obtained from PV module measurements. Furthermore, the meta-heuristics techniques include genetic algorithm (GA), particle swarm optimisation (PSO), harmony search (HS), flower pollination algorithm (FPA), simulated annealing (SA), teaching learning-based optimisation (TLBO), and others will also be investigated.

Virtualization is a transformative technology that enables multiple virtual machines to operate concurrently on a single physical host, thereby rationalizing resource utilization in cloud data centers. These data centers consist of multiple servers and consume a significant amount of energy, which requires cloud providers to have mechanisms that optimize the placement of virtual machines in these servers. An optimal placement allows for a decrease in the energy consumed and an increase in the resource usage rate. In this paper, we introduce a novel algorithm based on a recent bio-inspired metaheuristic called Manta Ray Foraging Optimization (MRFO) to solve the virtual machine placement problem. Although MRFO was recently proposed in order to solve optimization problems for engineering applications, to the best of our knowledge, it has never been used to solve the virtual machines placement problem in the cloud. Accordingly, this paper investigates for the first time an enhanced version of the MRFO algorithm to solve the virtual machine placement problem. The proposed algorithm is evaluated with CloudSim toolkit under various performance metrics, including energy consumption, resource utilization, and number of active servers. Simulation results show that our approach outperforms five well-known metaheuristics widely adopted in this field, namely Simulated Annealing (SA), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Salp Swarm Algorithm (SSA), and Whale Optimization Algorithm (WOA).

A photovoltaic (PV) module is an equipment that converts solar energy to electrical energy. A mathematical model should be presented to show the behavior of this device. The well-known single-diode and double-diode models are utilized to demonstrate the electrical behavior of the PV module. “Matlab/Simulink” is used to model and simulate the PV models because it is considered a major software for modeling, analyzing, and solving dynamic system real problems. In this work, a new modeling method based on the “Multiplexer and Functions blocks” in the "Matlab/Simulink Library" is presented. The mathematical analysis of single and double diodes is conducted on the basis of their equivalent circuits with simple modification. The corresponding equations are built in Matlab by using the proposed method. The unknown internal parameters of the PV panel circuit are extracted by using the PV array tool in Simulink, which is a simple method to obtain the PV parameters at certain weather conditions. Double-diode model results are compared with the single-diode model under various irradiances and temperatures to verify the performance and accuracy of the proposed method. The proposed method shows good agreement in terms of the I–V and P–V characteristics. A monocrystalline NST-120 W PV module is used to validate the proposed method. This module is connected to a variable load and tested for one summer day. The experimental voltage, current, and power are obtained under various irradiances and temperatures, and the I–V and P–V characteristics are obtained.

ABSTRACTThe optimal design and installation of hybrid photovoltaic (PV), wind turbine (WT) distributed generation (DG), and battery energy storage system (BESS) in radial distribution network (RDN) using dynamic arithmetic optimization algorithm (DAOA) is the main purpose of this work. The DAOA algorithm is the improved version of arithmetic optimization algorithm (AOA) which is widely used in the field of Electrical Engineering to solve the different optimization tasks. The simultaneous minimization of active power loss and voltage deviation is taken as the objectives to improve the efficiency of the distribution network using hybrid PV/WT/BESS system. To establish the efficacy of the suggested DAOA method, it is examined on two very well‐known test systems (33‐bus and 69‐bus). The uncertainty of power generation from PV‐ and WT‐based DG due to solar irradiance and wind speed variation also is taken into consideration. The cost analysis of the hybrid PV/WT/BESS system as well as active power loss cost are also assessed in this study. The result comparison shows that the DAOA algorithm gives supreme result in case of active power losses, voltage deviation, and benefits in active power lost cost. This algorithm has good speed of response and solution quality when hybrid PV/WT/BESS system allocation problem of the distribution network with simultaneous power loss reduction and voltage deviation minimization is executed in RDN. The placement of hybrid PV/WT/BESS system in 33‐bus reduces the active power loss by 44.27%, 41.47%, 46.23% using DAOA and 44.26%, 39.87%, 45.18% using AOA for normal loading, 15% increased loading, and 10% decreased loading, respectively. Similar results are also observed for 69‐bus test system. Moreover, if the performance of the DAOA, AOA, immune clone selection algorithm (ICSA), clone selection algorithm (CSA), manta ray foraging optimization (MRFO), and particle swarm optimization (PSO) for PV, WT, BESS‐ based 33‐bus system are observed, it can be noticed that DAOA gives annual savings of 10$, 496$, 505$, 669$, and 554$ as compared to AOA, ICSA, CSA, MRFO, and PSO, respectively. Similarly for PV, WT, BESS‐based 69‐bus system, it is observed that DAOA gives annual savings of 29$, 3147$, 3154$, 3342$, and 3158$ as compared to AOA, ICSA, CSA, MRFO, and PSO, respectively. In case of solution quality, the DAOA method grants its superiority over several optimization methods found in the literature.

An automated metaheuristic-optimized approach for diagnosing and classifying brain tumors based on a convolutional neural network

Knapsack Cipher-based metaheuristic optimization algorithms for cryptanalysis in blockchain-enabled internet of things systems