Comparative Analysis of Power Output, Fill Factor, and Efficiency at Fixed and Variable Tilt Angles for Polycrystalline and Monocrystalline Photovoltaic Panels—The Case of Sukkur IBA University

Hua, Lyu-Guang; Memon, Qasir Ali; Shaikh, Muhammad Fawad; Shaikh, Shoaib Ahmed; Rahimoon, Rehan Ali; Shah, Syed Hadi Hussain; Qadir, Abdul

doi:10.3390/en15113917

Cited by 14 publications

(5 citation statements)

References 34 publications

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“…It can be calculated using the equation shown below [8]. The shading is caused by a variety of factors, including clouds, trees, high structures in front of the panels, and other obstructions that stop the sun's rays from reaching the panel's surface uniformly [9]. This study uses a PV 60 W/17.2 V, as shown in Table 1.…”

Section: Pv Module/arraymentioning

confidence: 99%

Modeling and Simulation of Photovoltaic Panel Using Simulink and Proteus Simulation

Fateh,

Djalal,

Ammar

2024

J. Nano- Electron. Phys.

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This study uses MATLAB/Simulink and Proteus so as to emulate solar photovoltaic (PV) for a model Jarrett 60 W -17.2 V. Our objectives are to identify the nonlinear characteristics of the current-versusvoltage (I-V) and power-versus-voltage (P-V), then compare the results with the manufacturer's data sheet, therefore using all data obtained from Matlab/Simulink on Proteus Simulation. The Proteus used a SPICE model for a PV cell that depends on mathematical equations and is explained using an equivalent circuit that includes a current source Iph, a diode D, and two resistors Rs and Rsh. The Proteus is utilized to simulate a PV module/Array under varying conditions, such as irradiation, temperature, series and shunt resistances effects, and shading effects. To reach the greatest power output, it is required to understand the location of the maximum power point (MPP). Using Arduino, an algorithm is applied to determine the voltage Vmp and current Imp of PV cells. The paper is highly useful for describing the basis and characteristics of a PV module and Array in straightforward terms. This research can also be applied as an instructional methodology for teaching PV panels at different levels of study, which demonstrates how to use the prototyping and modeling/simulation software at a reduced cost (Proteus and Matlab/Simulink) to be able to approximately achieve the formation objective for students. This is especially helpful for schools that have difficulty with a lack of materials or don't have the resources to buy them.

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Section: Pv Module/arraymentioning

confidence: 99%

Modeling and Simulation of Photovoltaic Panel Using Simulink and Proteus Simulation

Fateh,

Djalal,

Ammar

2024

J. Nano- Electron. Phys.

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“…Memon and L.G. Hua 7 , 8 presented a numerical analysis using MATLAB/Simulink to find the OTA for a 1 MW PV system that was previously installed at 15° TA at Sukkur IBA university in Pakistan that has latitude of 27.73° N. The OTA was found to be 29.5°. Although is OTA is very near to the LA, but the authors of the current study are sure that the OTA is slightly less (not greater) than that of the LA, for example for Suez city that has latitude of 29.95° the OTA is calculated a 28.445°.…”

Section: Introductionmentioning

confidence: 99%

Maximizing solar radiations of PV panels using artificial gorilla troops reinforced by experimental investigations

Abdelaal,

Alhamahmy,

Attia

et al. 2024

Sci Rep

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This article's main objective is to maximize solar radiations (SRs) through the use of the gorilla troop algorithm (GTA) for identifying the optimal tilt angle (OTA) for photovoltaic (PV) panels. This is done in conjunction with an experimental work that consists of three 100 W PV panels tilted at three different tilt angles (TAs). The 28°, 30°, and 50° are the three TAs. The experimental data are collected every day for 181-day and revealed that the TA of 28° is superior to those of 50° and 30°. The GTA calculated the OTA to be 28.445°, which agrees with the experimental results, which show a TA of 28°. The SR of the 28o TA is 59.3% greater than that of the 50° TA and 4.5% higher than that of the 30° TA. Recent methods are used to compare the GTA with the other nine metaheuristics (MHTs)—the genetic algorithm, particle swarm, harmony search, ant colony, cuckoo search, bee colony, fire fly, grey wolf, and coronavirus disease optimizers—in order to figure out the optimal OTA. The OTA is calculated by the majority of the nine MHTs to be 28.445°, which is the same as the GTA and confirms the experimental effort. In only 181-day, the by experimentation it may be documented SR difference between the TAs of 28° and 50° TA is 159.3%. Numerous performance metrics are used to demonstrate the GTA's viability, and it is contrasted with other recent optimizers that are in competition.

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“…The suggested approach entails gathering information on electricity usage and training an SVM model to distinguish between regular and irregular consumption patterns. The classification of electricity usage data and the detection of electricity theft are subsequently performed using the SVM mode [11], [12]. Our research focuses on assessing the performance of the suggested SVM-based strategy in identifying electricity theft and contrasting it with different kernel functions.…”

Section: Introductionmentioning

confidence: 99%

Machine learning-based electricity theft detection using support vector machines

Abro,

Hua,

Laghari

et al. 2024

IJECE

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Electricity theft is a serious issue that many nations face, especially in developing areas where non-technical losses can make up a significant percentage of the overall losses sustained by utilities. Electricity theft detection (ETD) is a very challenging task because it frequently introduces irregularities in customer electricity consumption patterns. In recent times, machine learning (ML) techniques have been investigated as a potential solution for ETD. In this research, author propose electricity theft detection based on four kernel functions of support vector machines (SVM). The proposed method analyzes the electricity consumption patterns and then predicts the category of the user. The kernel functions utilized includes polynomial, sigmoid, radial basis function (RBF) and linear kernel function. For experimentation and model training, a dataset of Pakistani utility company is used, which contains the electricity consumption information. The results highlight SVM method works well for accurate ETD. The detection accuracy of the various kernel functions of SVM is 83%, 79%, 80%, and 76% for RBF, polynomial, sigmoid, and linear kernel functions, respectively, demonstrating the effectiveness of the proposed SVM-based method for theft detection. By leveraging these ML-based methods, utility companies can strengthen their ability to detect and prevent electricity theft, leading to improved revenue management and dependability of services.

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Comparative Analysis of Power Output, Fill Factor, and Efficiency at Fixed and Variable Tilt Angles for Polycrystalline and Monocrystalline Photovoltaic Panels—The Case of Sukkur IBA University

Cited by 14 publications

References 34 publications

Modeling and Simulation of Photovoltaic Panel Using Simulink and Proteus Simulation

Modeling and Simulation of Photovoltaic Panel Using Simulink and Proteus Simulation

Maximizing solar radiations of PV panels using artificial gorilla troops reinforced by experimental investigations

Machine learning-based electricity theft detection using support vector machines

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