scholarly journals Exergetic Optimization of a Solar Photovoltaic Array

2009 ◽  
Vol 2009 ◽  
pp. 1-11 ◽  
Author(s):  
Faramarz Sarhaddi ◽  
Said Farahat ◽  
Hossein Ajam ◽  
Amin Behzadmehr
Keyword(s):  
Short Circuit ◽  
Exergy Efficiency ◽  
Maximum Power ◽  
Electrical Performance ◽  
Design Parameters ◽  
Solar Photovoltaic ◽  
Maximum Power Point ◽  
Photovoltaic Array ◽  
Pv Array ◽  
Power Point

An exergetic optimization is developed to determine the optimal performance and design parameters of a solar photovoltaic (PV) array. A detailed energy and exergy analysis is carried out to evaluate the electrical performance, exergy destruction components, and exergy efficiency of a typical PV array. The exergy efficiency of a PV array obtained in this paper is a function of climatic, operating, and design parameters such as ambient temperature, solar radiation intensity, PV array temperature, overall heat loss coefficient, open-circuit voltage, short-circuit current, maximum power point voltage, maximum power point current, and PV array area. A computer simulation program is also developed to estimate the electrical and operating parameters of a PV array. The results of numerical simulation are in good agreement with the experimental measurements noted in the previous literature. Finally, exergetic optimization has been carried out under given climatic, operating, and design parameters. The optimized values of the PV array temperature, the PV array area, and the maximum exergy efficiency have been found. Parametric studies have been also carried out.

scholarly journals A Reinforcement Learning-Based Maximum Power Point Tracking Method for Photovoltaic Array

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Roy Chaoming Hsu ◽  
Cheng-Ting Liu ◽  
Wen-Yen Chen ◽  
Hung-I Hsieh ◽  
Hao-Li Wang
Keyword(s):  
Reinforcement Learning ◽  
Maximum Power Point Tracking ◽  
Maximum Power ◽  
Weather Data ◽  
Maximum Power Point ◽  
Photovoltaic Array ◽  
Pv Array ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Power Point

A reinforcement learning-based maximum power point tracking (RLMPPT) method is proposed for photovoltaic (PV) array. By utilizing the developed system model of PV array and configuring the environment for the reinforcement learning, the proposed RLMPPT method is able to observe the environment state of the PV array in the learning process and to autonomously adjust the perturbation to the operating voltage of the PV array in obtaining the best MPP. Simulations of the proposed RLMPPT for a PV array are conducted. Experimental results demonstrate that, in comparison to an existing MPPT method, the RLMPPT not only achieves better efficiency factor for both simulated weather data and real weather data but also adapts to the environment much fast with very short learning time.

Optimization of Photovoltaic Array Configurations in Photovoltaic System

2014 ◽  
Vol 1070-1072 ◽  
pp. 48-51
Author(s):  
Wen Ting Jia ◽  
Xue Ye Wei ◽  
Jun Hong Zhang ◽  
Yi Fei Meng
Keyword(s):  
Output Power ◽  
Photovoltaic Cell ◽  
Energy Conversion Efficiency ◽  
Maximum Power ◽  
Photovoltaic System ◽  
Pv System ◽  
Maximum Power Point ◽  
Photovoltaic Array ◽  
Pv Array ◽  
Power Point

Closely related to the actual output power and the light intensity, the temperature of the photovoltaic cell panel and the load of the PV array or the like. In the case of the external environment is stable and load conditions change, the output power of the PV modules exist Maximum Power Point, in order to improve the self-tracking PV system energy conversion efficiency, maximum power point tracking method may ensure the system running at maximum power points. Photovoltaic power generation system, optimize allocation method of PV array are also discussed in this paper.

scholarly journals Optimal Hybrid PV Array Topologies to Maximize the Power Output by Reducing the Effect of Non-Uniform Operating Conditions

Electronics ◽  
2021 ◽  
Vol 10 (23) ◽  
pp. 3014
Author(s):  
Suneel Raju Pendem ◽  
Suresh Mikkili ◽  
Shriram S. Rangarajan ◽  
Sudhakar Avv ◽  
Randolph E. Collins ◽  
...  
Keyword(s):  
Power Output ◽  
Power Loss ◽  
Short Circuit ◽  
Local Maximum ◽  
Operating Conditions ◽  
Maximum Power ◽  
Global Maximum ◽  
Maximum Power Point ◽  
Pv Array ◽  
Power Point

The photovoltaic (PV) system center inverter architecture comprises various conventional array topologies such as simple-series (S-S), parallel (P), series-parallel (S-P), total-cross-tied (T-C-T), bridge-linked (B-L), and honey-comb (H-C). The conventional PV array topologies under non-uniform operating conditions (NUOCs) produce a higher amount of mismatching power loss and represent multiple maximum-power-points (M-P-Ps) in the output characteristics. The performance of T-C-T topology is found superior among the conventional topologies under NUOCs. However, T-C-T topology’s main limitations are higher redundancy, more number of electrical connections, higher cabling loss, poor performance during row-wise shading patterns, and more number of switches and sensors for the re-configuration of PV modules. This paper proposes the various optimal hybrid PV array topologies to overcome the limitations of conventional T-C-T array topology. The proposed hybrid topologies are such as series-parallel-cross-tied (S-P-C-T), bridge-link-cross-tied (B-L-C-T), honey-comb-cross-tied (H-C-C-T), series-parallel-total-cross-tied (S-P-T-C-T), bridge-link-total-cross-tied (B-L-T-C-T), honey-comb-total-cross-tied (H-C-T-C-T), and bridge-link-honey-comb (B-L-H-C). The proposed hybrid topologies performance is evaluated and compared with the conventional topologies under various NUOCs. The parameters used for the comparative study are open-circuit voltage, short-circuit current, global-maximum-power-point (GMPP), local-maximum-power-point (LMPP), number of LMPPs, and fill factor (FF). Furthermore, the mismatched power loss and the conversion efficiency of conventional and hybrid array topologies are also determined. Based on the results, it is found that the hybrid array topologies maximize the power output by mitigating the effect of NUOCs and reducing the number of LMPPs.

An MPPT method using hybrid radial movement optimization with teaching-learning based optimization under fluctuating atmospheric conditions

2021 ◽  
pp. 1-10
Author(s):  
Imran Pervez ◽  
Adil Sarwar ◽  
Afroz Alam ◽  
Mohammad ◽  
Ripon K. Chakrabortty ◽  
...  
Keyword(s):  
Maximum Power ◽  
Metaheuristic Algorithms ◽  
Solar Pv ◽  
Maximum Power Point ◽  
Local Optima ◽  
Pv Array ◽  
Radial Movement ◽  
Teaching Learning Based Optimization ◽  
Power Point ◽  
Teaching Learning

Due to its clean and abundant availability, solar energy is popular as a source from which to generate electricity. Solar photovoltaic (PV) technology converts sunlight incident on the solar PV panel or array directly into non-linear DC electricity. However, the non-linear nature of the solar panels’ power needs to be tracked for its efficient utilization. The problem of non-linearity becomes more prominent when the solar PV array is shaded, even leading to high power losses and concentrated heating in some areas (hotspot condition) of the PV array. Bypass diodes used to eliminate the shading effect cause multiple peaks of power on the power versus voltage (P-V) curve and make the tracking problem quite complex. Conventional algorithms to track the optimal power point cannot search the complete P-V curve and often become trapped in local optima. More recently, metaheuristic algorithms have been employed for maximum power point tracking. Being stochastic, these algorithms explore the complete search area, thereby eliminating any chance of becoming trapped stuck in local optima. This paper proposes a hybridized version of two metaheuristic algorithms, Radial Movement Optimization and teaching-learning based optimization (RMOTLBO). The algorithm has been discussed in detail and applied to multiple shading patterns in a solar PV generation system. It successfully tracks the maximum power point (MPP) in a lesser amount of time and lesser fluctuations.

New Photovoltaic Module Model And A Comparative Study of MPPT Control Techniques Based On Neural Networks

2021 ◽  
pp. 69-76
Author(s):  
Mourad Talbi ◽  
Nawel Mensia ◽  
Hatem Ezzaouia
Keyword(s):  
Renewable Energy ◽  
Comparative Study ◽  
Temporal Variations ◽  
Operating Conditions ◽  
Maximum Power ◽  
Energy Resources ◽  
Solar Photovoltaic ◽  
Maximum Power Point ◽  
Mppt Controller ◽  
Power Point

Nowadays, renewable energy resources play an important role in replacing conventional fossil fuel energy resources. Solar photovoltaic (PV) energy is a very promising renewable energy resource, which rapidly grew in the past few years. The main problem of the solar photovoltaic is with the variation of the operating conditions of the array, the voltage at which maximum power can be obtained from it likewise changes. In this paper, is first performed the modelling of a solar PV panel using MATLAB/Simulink. After that, a maximum power point tracking (MPPT) technique based on artificial neural network (ANN) is applied in order to control the DC-DC boost converter. This MPPT controller technique is evaluated and compared to the “perturb and observe” technique (P&O). The simulation results show that the proposed MPPT technique based on ANN gives faster response than the conventional P&O technique, under rapid variations of operating conditions. This comparative study is made in terms of temporal variations of the duty cycle (D), the output power ( out P ), the output current ( out I ), the efficiency, and the reference current ( ref I ). The efficiency, D, out P , and out I are the output of the boost DC-DC, and ref I is itsinput. The different temporal variations of the efficiency, D, ref I , out P , and out I (for the two cases: the first case, when T = 25°C and G =1000 W/m2 and the second case, when T and G are variables), show negligible oscillations around the maximum power point. The used MPPT controller based on ANN has a convergence time better than conventional P&O technique.

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