scholarly journals Comparison of Maximum Power Point (PO & GMPP) Algorithm Under Varying Environmental Factors to Extract Maximum Peak Power from Photovoltaic System

2021 ◽  
Vol 36 (1) ◽  
pp. 391-398
Author(s):  
A. Mohamed Danish Rayan ◽  
Dr.G. Ramya
Keyword(s):  
Environmental Conditions ◽  
Peak Power ◽  
Green Energy ◽  
Maximum Power ◽  
Photovoltaic System ◽  
Global Maximum ◽  
Maximum Power Point ◽  
Data Set ◽  
Maximum Peak ◽  
Power Point

Aim: This paper deals with efficiency enhancement on extraction of maximum peak power under varying environmental conditions from the photovoltaic green energy systems (PV). Materials & Methods: Perturb and Observe (P&O) and Global maximum power point (GMPP) algorithms have been implemented to analyse tracking efficiency. Results: Based on results obtained, GMPP has higher efficiency of about 93.37 % than PO has the efficiency of about 90.71%. Conclusion: GMPP appears to produce more consistent efficiency under varying environmental conditions than P&O MPPT algorithm for the selected data set.

scholarly journals A Novel Ten Check Maximum Power Point Tracking Algorithm for a Standalone Solar Photovoltaic System

Electronics ◽  
2018 ◽  
Vol 7 (11) ◽  
pp. 327 ◽  
Author(s):  
Muhammad Afzal Awan ◽  
Tahir Mahmood
Keyword(s):  
Soft Computing ◽  
Maximum Power ◽  
Photovoltaic System ◽  
Flower Pollination Algorithm ◽  
Global Maximum ◽  
Maximum Power Point ◽  
Partial Shading ◽  
Computing Technique ◽  
Soft Computing Techniques ◽  
Power Point

Optimal energy extraction under partial shading conditions from a photovoltaic (PV) array is particularly challenging. Conventional techniques fail to achieve the global maximum power point (GMPP) under such conditions, while soft computing techniques have provided better results. The main contribution of this paper is to devise an algorithm to track the GMPP accurately and efficiently. For this purpose, a ten check (TC) algorithm was proposed. The effectiveness of this algorithm was tested with different shading patterns. Results were compared with the top conventional algorithm perturb and observe (P&O) and the best soft computing technique flower pollination algorithm (FPA). It was found that the proposed algorithm outperformed them. Analysis demonstrated that the devised algorithm achieved the GMPP efficiently and accurately as compared to the P&O and the FPA algorithms. Simulations were performed in MATLAB/Simulink.

scholarly journals An Intelligent Automatic Adaptive Maximum Power Point Tracker for Photovoltaic Module Arrays

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4775
Author(s):  
Kuei-Hsiang Chao ◽  
Yu-Ju Lai
Keyword(s):  
Digital Signal Processor ◽  
Local Maximum ◽  
Maximum Power ◽  
Photovoltaic System ◽  
Global Maximum ◽  
Photovoltaic Module ◽  
Maximum Power Point ◽  
Maximum Power Point Tracker ◽  
Tlbo Algorithm ◽  
Power Point

In this study, a maximum power point tracker was developed for photovoltaic module arrays by using a teacher-learning-based optimization (TLBO) algorithm to control the photovoltaic system. When a photovoltaic module array is shaded, a conventional maximum power point tracker may obtain the local maximum power point rather than the global maximum power point. The tracker developed in this study was aimed at solving this problem. To prove the viability of the proposed method, a SANYO HIP 2717 photovoltaic module with diverse connection patterns and shading ratios was used. Thus, single-peak, double-peak, triple-peak, and multi-peak power–voltage characteristic curves of the photovoltaic module array were obtained. A simulation of maximum power point tracking (MPPT) was then performed with MATLAB software. With regard to practical testing, a boost converter was used as the hardware structure of the maximum power point tracker and a TMS320F2808 digital signal processor was selected to execute the rules for MPPT. The results of the practical tests verified that the proposed improved TLBO algorithm had a superior accuracy to existing TLBO algorithms. In addition, the proposed improved TLBO algorithm can shorten the tracking time to 1/2 or 1/4, so it can improve the efficiency of power generation by two to three percentage.

An Advanced MPPT Based on Artificial Bee Colony Algorithm for MPPT Photovoltaic System under Partial Shading Condition

2017 ◽  
Vol 8 (2) ◽  
pp. 647 ◽  
Author(s):  
Salmi Hassan ◽  
Badri Abdelmajid ◽  
Zegrari Mourad ◽  
Sahel Aicha ◽  
Baghdad Abdenaceur
Keyword(s):  
Local Maximum ◽  
Maximum Power ◽  
Photovoltaic System ◽  
Solar Irradiation ◽  
Global Maximum ◽  
Maximum Power Point ◽  
Partial Shading ◽  
Pv Array ◽  
Point Tracking ◽  
Power Point

<p>Maximum power point tracking (MPPT) algorithms are employed in photovoltaic (PV) systems to make full utilization of PV array output power, which have a complex relationship between ambient temperature and solar irradiation. The power-voltage characteristic of PV array operating under partial shading conditions (PSC) exhibits multiple local maximum power points (LMPP). In this paper, an advanced algorithm has been presented to track the global maximum power point (GMPP) of PV. Compared with the Perturb and Observe (P&amp;O) techniques, the algorithm proposed the advantages of determining the location of GMPP whether partial shading is present.</p>

scholarly journals Supertwisting Sliding Mode Algorithm Based Nonlinear MPPT Control for a Solar PV System with Artificial Neural Networks Based Reference Generation

Energies ◽  
2020 ◽  
Vol 13 (14) ◽  
pp. 3695 ◽  
Author(s):  
Shahzad Ahmed ◽  
Hafiz Mian Muhammad Adil ◽  
Iftikhar Ahmad ◽  
Muhammad Kashif Azeem ◽  
Zil e Huma ◽  
...  
Keyword(s):  
Dynamic Response ◽  
Environmental Conditions ◽  
Peak Power ◽  
Sliding Mode ◽  
Maximum Power ◽  
Sliding Mode Controller ◽  
Pv System ◽  
Maximum Power Point ◽  
Nonlinear Controllers ◽  
Power Point

The problem of extracting maximum power from a photovoltaic (PV) system with negligible power loss is concerned with the power generating capability of the PV array and nature of the output load. Changing weather conditions and nonlinear behavior of PV systems pose a challenge in tracking of varying maximum power point. A robust nonlinear controller is required to ensure maximum power point tracking (MPPT) by handling nonlinearities of a system and making it robust against changing environmental conditions. Sliding mode controller is robust against disturbances, model uncertainties and parametric variations. It depicts undesirable phenomenon like chattering, inherent in it causing power and heat losses. In this paper, a supertwisting sliding mode algorithm based nonlinear robust controller has been designed for MPPT of a PV system which not only removes the chattering but also enhances the overall system’s dynamic response. Moreover, supertwisting sliding mode controller is robust against changing environmental conditions like change in temperature and irradiance. Noninverting DC-DC Buck-Boost converter has been used as an interface between source and the load. The efficiency of MPPT of a PV system depends upon the accuracy of reference for peak power voltage, therefore an efficient mechanism for reference generation has also been proposed in this work. The reference for peak power voltage has been generated by using a trained artificial neural network, which is to be tracked by proposed nonlinear controllers. Sliding mode controller (SMC) and synergetic controllers have also been designed for MPPT of a PV system in order to compare them with supertwisting sliding mode controller (ST-SMC). Global asymptotic stability of the system has been ensured by using Lyapunov stability criterion. The performance of the proposed nonlinear controllers has been validated in MATLAB/Simulink ODE 45 environment. ST-SMC has also been compared with recently proposed integral backstepping controller and other conventional MPPT controllers given in the literature. The simulation results show the better performance of ST-SMC in terms of best dynamic response and robustness.

scholarly journals Multicore PSO Operation for Maximum Power Point Tracking of a Distributed Photovoltaic System under Partially Shading Condition

2016 ◽  
Vol 2016 ◽  
pp. 1-19 ◽  
Author(s):  
Ru-Min Chao ◽  
Ahmad Nasirudin ◽  
I-Kai Wang ◽  
Po-Lung Chen
Keyword(s):  
Buck Converter ◽  
Maximum Power ◽  
Photovoltaic System ◽  
Peak Power Output ◽  
Global Maximum ◽  
Silicon Thin Film ◽  
Pv System ◽  
Maximum Power Point ◽  
Partial Shading ◽  
Power Point

This paper identifies the partial shading problem of a PV module using the one-diode model and simulating the characteristics exhibiting multiple-peak power output condition that is similar to a PV array. A modified particle swarm optimization (PSO) algorithm based on the suggested search-agent deployment, retracking condition, and multicore operation is proposed in order to continuously locate the global maximum power point for the PV system. Partial shading simulation results for up to 16 modules in series/parallel formats are presented. A distributed PV system consisting of up to 8 a-silicon thin film PV panels and also having a dedicated DC/DC buck converter on each of the modules is tested. The converter reaches its steady state voltage output in 10 ms. However for MPPT operation, voltage, and current measurement interval is set to 20 ms to avoid unnecessary noise from the entire electric circuit. Based on the simulation and experiment results, each core of the proposed PSO operation should control no more than 4 PV modules in order to have the maximum tracking accuracy and minimum overall tracking time. Tracking for the global maximum power point of a distributed PV system under various partial shading conditions can be done within 1.3 seconds.

scholarly journals Robust Integral Backstepping Based Nonlinear MPPT Control for a PV System

Energies ◽  
2019 ◽  
Vol 12 (16) ◽  
pp. 3180 ◽  
Author(s):  
Kamran Ali ◽  
Laiq Khan ◽  
Qudrat Khan ◽  
Shafaat Ullah ◽  
Saghir Ahmad ◽  
...  
Keyword(s):  
Environmental Conditions ◽  
Climatic Conditions ◽  
Maximum Power ◽  
Photovoltaic System ◽  
Pv System ◽  
Maximum Power Point ◽  
Point Tracking ◽  
Controller Performance ◽  
Low Efficiency ◽  
Power Point

A photovoltaic system generates energy that depends on the environmental conditions such as temperature, irradiance and the variations in the load connected to it. To adapt to the consistently increasing interest of energy, the photovoltaic (PV) system must operate at maximum power point (MPP), however, it has the issue of low efficiency because of the varying climatic conditions. To increase its efficiency, a maximum power point technique is required to extract maximum power from the PV system. In this paper, a nonlinear fast and efficient maximum power point tracking (MPPT) technique is developed based on the robust integral backstepping (RIB) approach to harvest maximum power from a PV array using non-inverting DC-DC buck-boost converter. The study uses a NeuroFuzzy network to generate the reference voltage for MPPT. Asymptotic stability of the whole system is verified using Lyapunov stability criteria. The MATLAB/Simulink platform is used to test the proposed controller performance under varying meteorological conditions. The simulation results validate that the proposed controller effectively improves the MPPT in terms of tracking speed and efficiency. For further validation of the proposed controller performance, a comparative study is presented with backstepping controller, integral backstepping, robust backstepping and conventional MPPT algorithms (PID and P&O) under rapidly varying environmental conditions.

Sign in / Sign up

Export Citation Format

Share Document