Optimal control and implementation of energy management strategy for a DC microgrid

A critical review and performance comparisons of swarm-based optimization algorithms in maximum power point tracking of photovoltaic systems under partial shading conditions

Orthogonal learning-based Gray Wolf Optimizer for identifying the uncertain parameters of various photovoltaic models

Parameter identification and accurate photovoltaic (PV) modeling from basic I–V information are necessary for simulation, optimization, and control of the PV systems. Therefore, this paper proposes an Improved-African Vultures Optimization (I-AVO) algorithm, which combines the general Opposition-Based Learning (OBL) and Orthogonal Learning to extract the unknown parameters of the solar Photovoltaic (PV) modules accurately and effectually. The proposed I-AVO algorithm is developed from the basic version of the recently proposed African Vultures Optimization (AVO) algorithm. The solar PV parameters estimation problem is considered to be a complex optimization problem with the characteristics such as multi-dimensional, nonlinear, Transcendental, and multi-modal. Therefore, the basic variant of AVO struggles to produce the optimal and is stuck at local optima when it handles this complex optimization problem. Therefore, the I-AVO is formulated by combining the features of OL and OBL, along with the AVO, to generate the optimal solution. Out of various PV models, Three-Diode Model has been considered to determine the parameters. Furthermore, Newton–Raphson (NR) technique is discussed to solve the chaotic behavior of the I–V curve relation. The obtained results proved that the proposed I-AVO along with NR, called I-AVO-NR, can accurately obtain the optimal solution. The superiority of the proposed algorithm is proved to be better than other advanced algorithms based on the obtained results and their comparison. Based on the statistical test value obtained from Friedman’s test, the proposed algorithm stood first among eight algorithms with the ranking value of 1.542 for two case studies.

Parameter estimation of three-diode solar photovoltaic model using an Improved-African Vultures optimization algorithm with Newton–Raphson method

The photovoltaic (PV) systems must work at the maximum power point (MPP) to derive the highest possible power with the higher performance during a change in operating conditions. The primary object...

https://www.tandfonline.com/doi/pdf/10.1080/00051144.2020.1834062

J. Pradeep

Papers

A novel salp swarm assisted hybrid maximum power point tracking algorithm for the solar photovoltaic power generation systems

An Effective Solar Photovoltaic Module Parameter Estimation Technique for Single-Diode Model