Photovoltaic (PV) cells are widely used for their clean and sustainable advantages, forcing researchers to accurately model their characteristics. The behavior of PV cells can be derived from their current–voltage characteristics, depending on their unknown circuit model parameters. Due to the simulation, evaluation, control, and optimization of PV systems, it is essential to accurately and reliably extract the parameters of PV models. However, because of the non-linear, multi-variable, and multi-modal characteristics, it is still a very challenging task. With the rapid development of intelligent computing, various meta-heuristic algorithms have been devoted to extracting the parameters of different PV models. The purpose of this paper is to comprehensively review the meta-heuristic algorithms and their related variants that have been used to extract the parameters of different PV models. Different from the existing research works, this paper presents a comprehensive review based on the reliability, robustness, computational resources, and time complexity of the algorithm. These features are essential to design an algorithm for efficient parameter extraction of PV models. Based on the conducted review, some useful recommendations are provided, which have important reference significance when designing the new parameter extraction methods of PV models and are of great significance for further improving the performance, control, and design of PV cells.

A comprehensive survey on meta-heuristic algorithms for parameter extraction of photovoltaic models

A hybrid adaptive teaching–learning-based optimization and differential evolution for parameter identification of photovoltaic models

When discussing the commercial applications of photovoltaic (PV) systems, one of the most critical problems is to estimate the efficiency of a PV system because current (I) – voltage (V) and power (P) – voltage (V) characteristics are highly non-linear. It should be noted that most of the manufacturer’s datasheets do not have complete information on the electrical equivalent parameters of PV systems that are necessary for simulating an effective PV module. Compared to conventional approaches, computational optimization and global research strategies are more acceptable as an effective alternative to parameter estimation of solar PV modules. Recently, a Gradient-based optimizer (GBO) is reported to solve the engineering design optimization problems. However, the basic GBO algorithm is stuck in local optima when handling complex non-linear problems. In this sense, this paper presents a new optimization technique called the Chaotic-GBO (CGBO) algorithm to derive the parameters of PV modules while offering precise I-V and P-V curves. To this end, the CGBO algorithm is based on a chaotic generator to obtain the PV parameters combined with the GBO algorithm. There are five case studies considered to validate the performance of the proposed CGBO algorithm. A quantitative and qualitative performance evaluation reveals that the proposed CGBO algorithm has improved results than other state-of-the-art algorithms in terms of accuracy and robustness when obtaining PV parameters. The average RMSE values and runtime of five case studies are equal to 9.8427E-04, 2.3700E-04, 2.4251E-03, 4.3524E-03 and 1.8349E-03, and 18.44, 17.78, 18.18, 18.28 and 17.97, respectively. The results proved the superiority of the proposed CGBO algorithm over the different selected algorithms. For future research, this study will be backed up with external support at https://premkumarmanoharan.wixsite.com/mysite .

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Identification of Solar Photovoltaic Model Parameters Using an Improved Gradient-Based Optimization Algorithm With Chaotic Drifts

Parameter estimation of PV solar cells and modules using Whippy Harris Hawks Optimization Algorithm

The whale optimization algorithm (WOA) is a powerful swarm intelligence method which has been widely used in various fields such as parameter identification of solar cells and PV modules. In order to better balance the exploration and exploitation of WOA, we propose a novel modified WOA (MWOA) in which both the mutation strategy based on Levy flight and a local search mechanism of pattern search are introduced. On the one hand, Levy flight can make the algorithm get rid of the local optimum and avoid stagnation; thus, it is able to prevent the algorithm from losing diversity and to increase the global search capability. On the other hand, pattern search, a direct search method, has not only high convergence rate but also good stability, which can boost the local optimization ability of the WOA. Therefore, the combination of these two mechanisms can greatly improve the capability of WOA to obtain the best solution. In addition, MWOA may be employed to estimate parameters in single diode model (SDM), double diode model (DDM), and PV modules and to identify unknown parameters of two different types of PV modules under diverse light irradiance and temperature conditions. The analytical results demonstrate the validity and the practicality of MWOA for estimating parameters of solar cells and PV modules.

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Modified Whale Optimization Algorithm for Solar Cell and PV Module Parameter Identification

Parameters identification of photovoltaic models using niche-based particle swarm optimization in parallel computing architecture

A heuristic whale optimization algorithm with niching strategy for global multi-dimensional engineering optimization

: Decision tree algorithms in Educational Data Mining (EDM) emerges as a powerful method for student classification according to their learning station. This article emphasizes the necessity to classify students due to the changing scenario of China's higher education system, which has shifted from elitist to popularization, and lead to university students' differences in learning abilities. For the specialty characteristics of educational data, this study adopts decision tree algorithm based on ID3 to classify students. This article presents an analysis of the application of decision tree algorithm in the course "Hydraulic and Pneumatic Technology Course Design," which is a mandatory course for Mechanical Design Manufacture and Automation Major. This study identifies five splitting features that affect the students' ability to achieve success in this practical course, and build the decision tree model. The learning samples for this algorithm mode are collected from Bachelor students of the University of Shanghai for Science and Technology (USST).

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Xiankun Lin

Papers

Parameters identification of photovoltaic models using niche-based particle swarm optimization in parallel computing architecture

A heuristic whale optimization algorithm with niching strategy for global multi-dimensional engineering optimization

Application of Decision Tree Algorithm in Educational Data Mining