Advanced Energy Technologies
Hassan Z. Al Garni; Arunachalam Sundaram; Anjali Awasthi; Rahul Chandel; Salwan Tajjour; Shyam Singh Chandel
Abstract
A major design challenge for a grid-integrated photovoltaic power plant is to generate maximum power under varying loads, irradiance, and outdoor climatic conditions using competitive algorithm-based controllers. The objective of this study is to review experimentally validated advanced maximum power ...
Read More
A major design challenge for a grid-integrated photovoltaic power plant is to generate maximum power under varying loads, irradiance, and outdoor climatic conditions using competitive algorithm-based controllers. The objective of this study is to review experimentally validated advanced maximum power point tracking algorithms for enhancing power generation. A comprehensive analysis of 14 of the most advanced metaheuristics and 17 hybrid homogeneous and heterogeneous metaheuristic techniques is carried out, along with a comparison of algorithm complexity, maximum power point tracking capability, tracking frequency, accuracy, and maximum power extracted from PV systems. The results show that maximum power point tracking controllers mostly use conventional algorithms; however, metaheuristic algorithms and their hybrid variants are found to be superior to conventional techniques under varying environmental conditions. The Grey Wolf Optimization, in combination with Perturb & Observe, and Jaya-Differential Evolution, is found to be the most competitive technique. The study shows that standard testing and evaluation procedures can be further developed for comparing metaheuristic algorithms and their hybrid variants for developing advanced maximum power point tracking controllers. The identified algorithms are found to enhance power generation by grid-integrated commercial solar power plants. The results are of importance to the solar industry and researchers worldwide.
Renewable Energy Resources and Technologies
Zaiba Ishrat; Ankur Kumar Gupta; Seema Nayak
Abstract
Solar power energy continues to be a renewable and sustainable source of energy in the coming year due to its cleaner nature and abundant availability. Maximum Power Point Tracking (MPPT) is a technique used in solar power systems to extract maximum power from photovoltaic (PV) modules by tracking the ...
Read More
Solar power energy continues to be a renewable and sustainable source of energy in the coming year due to its cleaner nature and abundant availability. Maximum Power Point Tracking (MPPT) is a technique used in solar power systems to extract maximum power from photovoltaic (PV) modules by tracking the operating point of the modules. MPPT is essential for achieving optimal power output from a solar panel, particularly in variable weather conditions. Traditional MPPT techniques are subject to limitations in handling the partial shading conditions (PSC). To ensure the tracking of maximum power point while boosting the MPPT's overall efficacy and performance, Machine Learning must be integrated into MPPT. As per the reviewer work, ML techniques have the potential to play a crucial role in the development of advanced MPPT systems for solar power systems operating under partial shading conditions and to compare the performance of existing ML-MPPT in terms of accuracy, response time, and efficacy. These review papers technically analyze the result of ML-MPPT techniques and suggest the optimum ML-MPPT tactics that are Q learning, Bayesian Regularization Neural Network (BRNN), and Multivariate Linear Regression Model (MLIR) to achieve optimum outcomes in MPPT under PSC. Further, these techniques can offer efficiency greater than 95%, tracking duration less than 1sec, and error threshold of 0.05. In the future, the reviewer may propose simulation work to compare the optimal algorithms.
Renewable Energy Resources and Technologies
Sameer Hanna Khader; Abdel-Karim Khalid Daud
Abstract
This study proposes a novel approach to fast and direct determination of the Maximum Power Point (MPP) at any value of solar irradiation and cell temperature, without applying further mathematical processing to operate at that point. The current approach aims to reduce algorithm complexity, time consumption ...
Read More
This study proposes a novel approach to fast and direct determination of the Maximum Power Point (MPP) at any value of solar irradiation and cell temperature, without applying further mathematical processing to operate at that point. The current approach aims to reduce algorithm complexity, time consumption during the iteration, and oscillation to reach the point at which the panel generates maximum possible power. For avoiding or eliminating these drawbacks, the chopper duty cycle (D) at which the panel-generated power should be the maximum is determined using the panel datasheet with respect to voltage and power at different irradiation rates (G). Mathematical equations are derived for MPP voltage and power at any value of solar irradiation using the manufacturer Photovoltaic (PV) specification. The simulation results obtained by MATLAB/SIMULINK platform showed that the power had a linear change, while the voltage had a nonlinear one with narrow variations. The yield duty cycle controls the Modified Single Ended Primary Converter (MSEPIC) that regulates the load voltage through a wide range below and above the rated panel voltage. The simulation results showed the fast response of chopper operation with a negligible starting time required by the MPPT algorithm, no duty cycle oscillation, and shorter iteration time. Furthermore, the conducted approach is validated based on the data published in a reputed journal, and the obtained results gave rise to new aspects that helped reduce dependency on conventional MPPT algorithms and, consequently, enhance the system response, efficiency and cost reduction.
Renewable Energy Resources and Technologies
Md. Tamim Hossain; Md. Atiqur Rahman; Suman Chowdhury
Abstract
In the context of increasing emission of greenhouse gasses in the environment due to fossil fuel burning, this paper attempts to describe the significance of Maximum Power Point Tracking (MPPT) by investigating the power performance of photovoltaic modules with MATLAB simulation. MPPT algorithm was employed ...
Read More
In the context of increasing emission of greenhouse gasses in the environment due to fossil fuel burning, this paper attempts to describe the significance of Maximum Power Point Tracking (MPPT) by investigating the power performance of photovoltaic modules with MATLAB simulation. MPPT algorithm was employed to secure maximum power from PV module. The boost converter whose pulse is linked to MPPT algorithm restricts the flow of load power and controls the current and voltage of PV panels. The whole design of the solar model, boost converter, and MPPT controlled algorithms was done in the SIMULINK to prioritize the system in simulation. The main concept employed in this paper was to develop a power generation process with MPPT algorithm and to provide information for future use. In this paper, all simulations along with the PV power generation process were done in MATLAB. This research could potentially play a vital role in mitigating the world fuel crisis.
