Comparative analysis of various P &amp; O MPPT algorithm for PV system under varying radiation condition

Manna, Saibal; Akella, Ashok Kumar

doi:10.1109/icpee50452.2021.9358690

Cited by 23 publications

(11 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…have thrown light on the drift free perturb and observe method which incorporates current in addition to voltage and power as used in conventionally used perturb and observe. The drift algorithm is named so because it efficiently solves the problem of drift which is caused due to the altering environmental conditions mainly sudden to change in insulation levels during cloudy days and hence the authors have performed a test of both the algorithms for variation in insulation level and proved the percentage increase in power during the time of drift in the method discussed which improves its efficiency and accuracy levels [13].…”

Section: S Manna Et Al (2021)mentioning

confidence: 99%

Mathematical Modelling Non Linearity Characteristic Analysis and Efficiency Enhancement Strategies for Hybrid Solar Photovoltaic Energy System

Naval Kishor Jain

2024

cana

View full text Add to dashboard Cite

As the world continues to grapple with the challenges posed by climate change and the depletion of conventional energy sources, renewable energy systems such as solar photovoltaic (PV) technology have gained significant prominence. Hybrid solar PV systems, which combine multiple energy sources and storage solutions, offer a promising avenue to improve the reliability and efficiency of renewable energy generation. This research focuses on the mathematical modeling, non-linearity characteristic analysis, and the development of efficiency enhancement strategies for hybrid solar PV energy systems. The study begins with the development of comprehensive mathematical models that accurately represent the complex interactions within hybrid solar PV systems. These models consider various factors, including solar irradiance, temperature variations, load profiles, and the dynamic behavior of energy storage components. The incorporation of non-linear characteristics, often overlooked in conventional models, allows for a more realistic representation of system performance. To enhance the efficiency of hybrid solar PV systems, a range of strategies are proposed and evaluated. These strategies encompass advanced control algorithms, optimized sizing and placement of energy storage elements, and the integration of emerging technologies such as artificial intelligence and machine learning. The goal is to mitigate non-linear effects, maximize energy utilization, and improve system response to dynamic operating conditions. The findings from this research provide valuable insights into the design, operation, and performance optimization of hybrid solar PV energy systems. By addressing non-linearity characteristics and developing efficient strategies, this study contributes to the advancement of renewable energy technologies and fosters the transition towards a more sustainable and resilient energy future.

show abstract

Section: S Manna Et Al (2021)mentioning

confidence: 99%

Mathematical Modelling Non Linearity Characteristic Analysis and Efficiency Enhancement Strategies for Hybrid Solar Photovoltaic Energy System

Naval Kishor Jain

2024

cana

View full text Add to dashboard Cite

show abstract

“…The advantages of this method include its ease of implementation. Its principle is based on the systematic disturbance of the solar generator voltage or current, followed by observation of the direction of the resulting power change [20]. P&O aims to keep the generator close to the MPP by following the slope of the power-voltage curve.…”

Section: Perturb and Observe (Pando)mentioning

confidence: 99%

Performance Analysis of Three MPPT Controls Under Partial Shading: P&O, PSO and ANN

Atillah,

Stitou,

Boudaoud

et al. 2023

E3S Web of Conf.

View full text Add to dashboard Cite

Photovoltaic energy has gained an important place among renewable energies. Given its weather-dependent efficiency, Maximum Power Point Tracking (MPPT) techniques are essential to maximize the energy extracted from the panels. Partial shading is a phenomenon that degrades the efficiency of photovoltaic panels, by distorting the power vs. voltage curve, which consequently has several peaks, one of which is a global maximum. The aim of this work is to evaluate the ability of MPPT methods to find the global maximum power point in partial shading conditions. To achieve this, the work focuses on simulating and discussing the performance of three MPPT methods, the Perturb and Observe (P&O) command, a command based on particle swarm optimization (PSO), and a command based on artificial neural networks (ANN). By comparing the results of simulations of the three controllers under different partial conditions, we can see that the P&O controller loses accuracy in partially shaded conditions, while the PSO controller is more accurate but with a very slow response time, and lastly, the ANN-based controller offers the best precision and speed performance.

show abstract

“…To guarantee a maximum power extraction from PV cells, a maximum power point tracking (MPPT) algorithm is used to effectively solve this optimization problem. Several works have addressed the problem of finding the operating point to get the maximum energy from PV modules using different MPPT methods, among these methods we mention the widely used perturb and observe (P&O) (Saibal and Ashok, 2021; Salman et al, 2018) and incremental conductance (IC) MPPT controller (Hamam et al, 2019; Liqun et al, 2020). These classical methods are simple, easy to implement and can guarantee a good tracking of the maximum power point (MPP) efficiently in normal conditions.…”

Section: Introductionmentioning

confidence: 99%

A new MPPT controller based on a modified multiswarm PSO algorithm using an adaptive factor selection strategy for partially shaded PV systems

Ben Regaya,

Farhani,

Hamdi

et al. 2024

Transactions of the Institute of Measurement and Control

View full text Add to dashboard Cite

Maximum power point tracking (MPPT) controller is the main element in photovoltaic (PV) systems, which is used to ensure maximum power extraction under different meteorological conditions. A MPPT controller can guarantee good performance criteria even in the presence of climatic changes. To achieve this goal, several techniques have been proposed in the literature to improve robustness of the PV system control, such as artificial intelligence and multiswarm particle swarm optimization (MSPSO) algorithm. Previous research on classical MSPSO has shown that the algorithm search behavior cannot find the optimal solution for certain problems. In this context, we investigate the design of a new MPPT controller based on a modified version of heterogeneous multiswarm particle swarm optimization algorithm using an adaptive factor selection strategy (FMSPSO) for PV systems. The proposed FMSPSO can improve the tracking capability with high accuracy, less oscillations, and high robustness. To validate the proposed solution, a simulation and experimental benchmarking of a PV system are presented and analyzed. The obtained results show the effectiveness of the proposed solution compared with the classical MSPSO, fuzzy logic, and perturb and observe (P&O) control presented in other recent works.

show abstract

Comparative analysis of various P & O MPPT algorithm for PV system under varying radiation condition

Cited by 23 publications

References 7 publications

Mathematical Modelling Non Linearity Characteristic Analysis and Efficiency Enhancement Strategies for Hybrid Solar Photovoltaic Energy System

Mathematical Modelling Non Linearity Characteristic Analysis and Efficiency Enhancement Strategies for Hybrid Solar Photovoltaic Energy System

Performance Analysis of Three MPPT Controls Under Partial Shading: P&O, PSO and ANN

A new MPPT controller based on a modified multiswarm PSO algorithm using an adaptive factor selection strategy for partially shaded PV systems

Contact Info

Product

Resources

About