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 for the single-diode model and 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.

/pdf/parameter-estimation-of-the-photovoltaic-system-using-bald-16kasl2dyq.pdf

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

A learning style that focuses on individual learning is one of the most important aspects of any learning environment. Each learner has a unique manner of understanding, retaining, processing, and interpreting new information based on their learning styles. The ability of an e-learning system to automatically determine a student's learning style has become more essential. For learning events, the evolution of e-learning platforms provides students with higher opportunities online. In this paper, we proposed a Convolutional Neural Network-based Levy Flight Distribution (CNN-LFD) algorithm for learning style prediction. An adaptive e-learning system is divided into two sections: automatic learning style prediction and classification based on the number of learning styles included. Initially, the student logs in with their user ID, and the data is saved in the database. The features such as questionnaire score, login credentials (session ID, learner ID, and course ID), and login time (location, session ID) are extracted along with the sequence of the user's learning behavior. After that, the CNN-LFD algorithm predicts the learning styles of the learners namely Active/reflective, Sensing/intuitive, visual/verbal, sequential/global based on the extracted features. The dataset information are gathered from a Massive Open Online Course (MOOC), and the proposed model is built in JAVA software. The experimental results demonstrate higher classification accuracy during learning style prediction. The proposed CNN-LFD algorithm accomplishes 97.09% accuracy, 94.76% specificity, 92.12% sensitivity, and 97.56%, precision values than other methods.

Adaptive learning style prediction in e-learning environment using levy flight distribution based CNN model

The reliability of the photovoltaic models is strongly reliant on their parameters, which are primarily determined by the optimization algorithm and the objective function. As a result, obtaining the parameters under different environmental conditions is critical for increasing their performance, reliability and significantly lowering cost. Many optimization techniques are reported to address this problem based on the complexity. As a result, an enhanced version of the recently reported Hunger Games Search Optimizer (HGSO) method called Gaussian and Cauchy Mutation-based HGSO (GCMHGSO) algorithm for defining the requirements of the Three-Diode equivalent Model (TDeM) by utilizing multiple representations in the algorithm along with an efficient objective function. The Cauchy mutation increases the exploration ability, and Gaussian mutation increases the exploitation ability of the basic HGSO. Furthermore, an Enhanced Newton–Raphson Method (ENRM) is presented to effectively solve the behaviour of the current–voltage relation of the TDeM. The robust optimization is also considered to demonstrate the impact of the measurement error. Comparing the GCMHGSO-ENRM to other competitors reveals that the proposed GCMHGSO-ENRM can accurately find the best solution, and its effectiveness is verified in many statistical parameters. It is found that the GCMHGSO-ENRM algorithm is stable and robust compared to other competitors. 

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1049/rpg2.12475

Constraint estimation in three‐diode solar photovoltaic model using Gaussian and Cauchy mutation‐based hunger games search optimizer and enhanced Newton–Raphson method

A Novel Chaotic-Driven Tuna Swarm Optimizer with Newton-Raphson Method for Parameter Identification of Three-Diode Equivalent Circuit Model of Solar Photovoltaic Cells/Modules

In the modeling and designing of PhotoVoltaic (PV) systems, parameter characterization in PV cell/module models remains a crucial field of research. Diode-based models, such as single-diode model (SDM), double-diode model (DDM), and the three-diode model, are frequently employed, and SDM and DDM are the most significant models. As a result, the difference between the estimated and experimental current can be minimized by using an objective function to solve the parameter characterization of such models. Metaheuristic optimization algorithms have recently been employed to get around the difficulty of finding accurate and highly reliable outcomes quickly. As a result, this research modifies the fundamental SDM and DDM and considers an objective function based on the modified models. Additionally, an improved version of a novel metaheuristic algorithm called White Shark Optimizer (WSO) is proposed by modifying the force control parameters of the WSO, and a chaotic generator is infused to improve the exploitation ability of WSO. The modified algorithm is named IWSO and applied to extract the PV parameters. This paper uses the new objective function to compare the conventional and the modified PV models. The outcomes of the experiment demonstrated IWSO's dominance over competing algorithms. With an average Freidman's ranking test value of 1.171, the proposed IWSO is superior to all selected algorithms. The average accuracy of modified SDM and DDM is 12% better than the traditional PV models. According to the findings, IWSO's estimated parameter values are the best, with the smallest difference between estimated and experimental current. 

Optimal parameter characterization of an enhanced mathematical model of solar photovoltaic cell/module using an improved white shark optimization algorithm

Solar photovoltaic (PV) systems have recently attracted researcher’s attention as a clean source of energy. Thus, the importance to design appropriately the photovoltaic cells highly raises. The main problems faced in the design process are first, the development of a useful model describing the characteristics of the current vs. voltage able to simulate the real solar cells behaviours and then, the precise estimation of photovoltaic cells parameter values. This paper employs an improved version of Salp Swarm Algorithm called Chaotic Salp Swarm Algorithm (CSSA) for the parameters estimation of solar cells in both single and double diode models. CSSA approach benefits from chaotic maps proprieties, and has the advantage of providing good equilibrium between exploration and exploitation mechanisms as well. Performance of the proposed CSSA is compared to fourteen known algorithms. Experimental results demonstrate that the proposed algorithm has the ability to find the optimal solutions with an accurate estimation of parameters for the courant vs voltage characteristics of real solar cell with high performance.

/pdf/estimation-of-photovoltaic-cells-parameters-using-chaos-48y1t8x5dz.pdf

Estimation of Photovoltaic Cells Parameters Using Chaos Embedded Salp Swarm Algorithm

This work proposes a new improved version of the nature-inspired multi-objective grasshopper optimization algorithm (MOGOA), based on a Levy flight method and called the Levy flight trajectory-based multi-objective grasshopper optimization algorithm (LMOGOA). It is worth mentioning that, the Levy flight trajectory is applied for the first time to enhance MOGOA algorithm by increasing the diversity of solution, avoiding premature convergence and local optima stagnation. The main advantage of LMOGOA is fast convergence speed to the true Pareto-optimal front while maintaining good diversity of solutions. To benchmark the performance of the proposed algorithm, a set of diverse standard multi-objective test problems is utilized. Results show that the proposed LMOGOA significantly outperforms the standard MOGOA algorithm.

A new Levy Flight Trajectory-Based Grasshopper optimization Algorithm for Multi-objective optimization Problems

Recently, solar energy as a clean source is widely used. In this way, the design of photovoltaic cells has attracted the attention of researchers over the world. Two main problems are faced in this field: having a useful model to characterize the solar cells and the absence of data about photovoltaic cells. This situation even affects the performance of the photovoltaic modules (panels). The characteristics of the current vs. voltage are used to describe the behaviour of solar cells. Two metaheuristic algorithms are proposed to identify the parameters of the photovoltaic cells and panels namely Ant Lion Optimizer (ALO) and particle swarm optimization (PSO). Experimental results demonstrate the high performance of the proposed ALO method compared to PSO.

Diab Mokeddem

Papers

Estimation of Photovoltaic Cells Parameters Using Chaos Embedded Salp Swarm Algorithm

A new Levy Flight Trajectory-Based Grasshopper optimization Algorithm for Multi-objective optimization Problems

Ant Lion Optimizer for the Estimation of Photovoltaic (PV) Cells Parameters