Advances in perturb and observe based MPPT algorithm

2020 ◽  
pp. 21-27
Author(s):  
Sampurna Panda ◽  
Manoj Gupta ◽  
CS Malvi
Keyword(s):  
Weather Conditions ◽  
Maximum Power ◽  
Generation System ◽  
Maximum Power Point ◽  
Maximum Power Point Tracker ◽  
Stability Performance ◽  
Pv Panel ◽  
Perturb And Observe ◽  
Electronic Parameters ◽  
Power Point

The significant expense of PV panel and its change over efficiency are downsides of PV Plants. Due to varying weather conditions the PV energy generation system fails to operate reliably and efficiently. Maximum Power Point Tracker can support yield of PV Panel. Regardless of irradiance condition, temperature and load, these techniques give maximum power. Perturb and Observe (P & O) method is a traditional method which is simple and effective with easy implementation. But this technique fails to operate under dynamic conditions. In this paper the later modifications and add on are discussed which are done to P&O technique to improve its stability, performance and fast convergence. Review shows that Artificial Intelligence with P&O gives promising improvement in Efficiency. Extensive researches have been carried out on modifying and improving the Perturb & Observe method most of them are discussed in this paper with a sight to other electronic parameters.

High-Speed Maximum Power Point Tracker for Photovoltaic Systems Using Online Learning Neural Networks

2010 ◽  
Vol 14 (6) ◽  
pp. 677-682 ◽  
Author(s):  
Yasushi Kohata ◽  
◽  
Koichiro Yamauchi ◽  
Masahito Kurihara ◽  
Keyword(s):  
Neural Network ◽  
Online Learning ◽  
Solar Radiation ◽  
High Speed ◽  
Maximum Power ◽  
Maximum Power Point ◽  
Feed Forward Control ◽  
Maximum Power Point Tracker ◽  
Pv Panel ◽  
Power Point

Photo Voltaic (PV) devices have a Maximum Power Point (MPP) at which they generate maximum power. Because the MPP depends on solar radiation and PV panel temperature, it is not constant over time. A Maximum Power Point Tracker (MPPT) is widely used to continuously obtain maximum power, but if the solar radiation changes rapidly, the efficiency of most classic MPPT (e.g., the Perturbation and Observation (P&O) method) reduces. MPPT controllers using neural network respond quickly to rapidly changing solar radiation but must usually undergo prelearning using PV-specific data, so we propose MPPT that handles both online learning of PV properties and feed-forward control of the DC-DC converter with a neural network. Both simulation results and actual device performance using our proposed MPPT showed great efficiency even under rapidly changing solar radiation. Our proposal is implemented using a small microcomputer using low computational power.

Optimization of the perturb and observe maximum power point tracker for a distributed photovoltaic system

INMIC ◽  
2013 ◽  
Author(s):  
Ali F Murtaza ◽  
Hadeed Ahmed Sher ◽  
Marcello Chiaberge ◽  
Diego Boero ◽  
Mirko De Giuseppe ◽  
...  
Keyword(s):  
Maximum Power ◽  
Photovoltaic System ◽  
Maximum Power Point ◽  
Maximum Power Point Tracker ◽  
Perturb And Observe ◽  
Power Point

scholarly journals Grid connected photovoltaic system efficiency and quality improvement using fuzzy-incond MPPT

2020 ◽  
Vol 11 (3) ◽  
pp. 1536
Author(s):  
Amara Yasmine ◽  
Bradai Rafik ◽  
Boukenoui Rachid ◽  
Mellit Adel
Keyword(s):  
Weather Conditions ◽  
Maximum Power ◽  
Photovoltaic System ◽  
Maximum Power Point ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Perturb And Observe ◽  
Power Point ◽  
Improved Technique ◽  
Tracking Response

Maximum Power Point Tracking (MPPT) techniques are developed to harvest and supply maximum power to the load. This depends on the power generated and the MPPT accuracy. Under quick-changing weather conditions, Incremental Conductance (InCond) and numerous different algorithms may fail to track the exact Maximum Power Point (MPP) which may result in significant power loss. Fuzzy Logic (FL) based MPPT is quick and accurate in tracking the MPP, but the high complexity and the implementation difficulty are their main disadvantages. A novel FL-InCond MPPT improved technique is developed based on the features of InCond and FL techniques to overcome their drawbacks.The newly developed approach can automatically adjust the variation of the duty cycle for tracking the MPP with accuracy. The obtained results are compared with conventional Perturb and observe (P&O) and InCond MPPTs for grid-connected mode under fast weather conditions. It is demonstrated that the developed method outperforms the aforementioned MPPT techniques in terms of tracking response, efficiency and the delivered current quality.

scholarly journals Comparison of Different MPPT Algorithms for PV System

2018 ◽  
Vol 7 (1.8) ◽  
pp. 158
Author(s):  
Richa Verma ◽  
B Bhargav ◽  
Srinivasa Varma P
Keyword(s):  
Pso Algorithm ◽  
Optimization Method ◽  
Maximum Power ◽  
Pv System ◽  
Maximum Power Point ◽  
Swarm Optimization ◽  
Pv Panel ◽  
Incremental Conductance ◽  
Perturb And Observe ◽  
Power Point

This paper helps us analyze three different MPPT techniques like Perturb and Observe, Incremental Conductance and Particle Swarm Optimization method. As the output characteristic depends on temperature and irradiance, therefore the maximum power point (MPPT) is not always constant. Hence it is necessary to ensure that the PV panel is operating at its maximum power point. There are many different MPPT techniques but, the confusion lies in selecting which MPPT technique is best as every algorithm has its own merit and demerit. In order to extract maximum power from PV arrangement, PSO algorithm is proposed. Algorithms are implemented using the DC-DC converter as well as SEPI converter. Results of simulations are presented in order to demonstrate the effectiveness of PSO algorithm, when compared to Perturb and Observe (P&O) and Incremental Conductance (INC). To simulate the proposed system MATLAB/SIMULINK power system tool box is used. 

scholarly journals Hybrid and Adaptive P&O Maximum Power Point Tracking Techniques for PV Generation Systems

2021 ◽  
Vol 12 (6) ◽  
pp. 1694-1707
Author(s):  
Eman Eltaher, Abdel-Raheem Youssef, Essam E. M. Mohamed
Keyword(s):  
Maximum Power Point Tracking ◽  
Weather Conditions ◽  
Maximum Power ◽  
Step Size ◽  
Maximum Power Point ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Perturb And Observe ◽  
Power Point ◽  
Pv Generation

Maximum power point tracking (MPPT) techniques work to track the maximum power from the PV cell. A lot of conventional MPPT techniques, such as the perturb and observe (P&O), succeed in catch the maximum power point (MPP) with a good performance. However, they suffer many problems during fast varying weather conditions, where slow time response and high oscillations are dominant. Also, it is difficult to select the right direction for new steps. This article illustrates two new P&O MPPT techniques for PV generation systems. They operate on the power-voltage (P-V) curve under different weather conditions. The first is an adaptive perturb and observe (A-PO) technique, which changes the perturbation step-size adeptly to deal with the rapidly varying weather conditions. The second is a hybrid perturb and observe technique (H-PO), which uses a variable step-size according to the location of the operating point relative to the MPP. The MATLAB/SIMULINK software is used to study the truth of the proposed techniques. The results demonstrate that both techniques attain the MPP faster than the conventional techniques and at a reduced oscillation rate.

scholarly journals Maximum Power Point Tracking of PV System Based on Machine Learning

Energies ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 692 ◽  
Author(s):  
Maen Takruri ◽  
Maissa Farhat ◽  
Oscar Barambones ◽  
José Antonio Ramos-Hernanz ◽  
Mohammed Jawdat Turkieh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Weather Conditions ◽  
Boost Converter ◽  
Maximum Power ◽  
Support Vector ◽  
Variable Load ◽  
External Disturbances ◽  
Maximum Power Point ◽  
Pv Panel ◽  
Power Point

This project studies the conditions at which the maximum power point of a photovoltaic (PV) panel is obtained. It shows that the maximum power point is very sensitive to external disturbances such as temperature and irradiation. It introduces a novel method for maximizing the output power of a PV panel when connected to a DC/DC boost converter under variable load conditions. The main contribution of this work is to predict the optimum reference voltage of the PV panel at all-weather conditions using machine learning strategies and to use it as a reference for a Proportional-Integral-Derivative controller that ensures that the DC/DC boost converter provides a stable output voltage and maximum power under different weather conditions and loads. Evaluations of the proposed system, which uses an experimental photovoltaic dataset gathered from Spain, prove that it is robust against internal and external disturbances. They also show that the system performs better when using support vector machines as the machine learning strategy compared to the case when using general regression neural networks.

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