Optimization of PV Modules through Tilt Angle in Different Cities Of Punjab, Pakistan

Ashraf, Muhammad Adeel; Aaqib, Sheikh Muhammad; Manzoor, Habib Ullah; Asharaf, Muhammad Waqar; Manzor, Tareq; Sethi, Muzammal Hussain

doi:10.1109/inmic50486.2020.9318120

Cited by 4 publications

(2 citation statements)

References 23 publications

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“…Similar work was done in Ref. 16 in Punjab, Pakistan which has LA of 31.1471° N but suggested the same TA of 15 o which far from the value of the LA. Karinka and Upadhyaya in Ref.…”

Section: Introductionsupporting

confidence: 78%

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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“…Similar work was done in Ref. 16 in Punjab, Pakistan which has LA of 31.1471° N but suggested the same TA of 15 o which far from the value of the LA. Karinka and Upadhyaya in Ref.…”

Section: Introductionsupporting

confidence: 78%

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

Abdelaal,

Alhamahmy,

Attia

et al. 2024

Sci Rep

View full text Add to dashboard Cite

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“…An uninterrupted power supply is essential for daily living since interruptions in the electric power supply can cause serious losses for businesses and industrial customers. A lot of research has been carried out to improve power infrastructure and meet the growing energy demand, including efficient energy management systems [1], cost optimization [2], power line optimisation [3], tilt angle optimization of PV modules [4], and renewable energy optimization such as solar cells [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Machine learning‐assisted anomaly detection for power line components: A case study in Pakistan

Basit,

Manzoor,

Akram

et al. 2024

The Journal of Engineering

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A continuous supply of electricity is necessary to maintain an acceptable standard of life, and the power distribution system's overhead line components play a crucial role in this matter. In Pakistan, identifying defective parts often necessitates human involvement. An unmanned aerial vehicle was used to gather a collection of 10,343 photos to automate this procedure. Using supervised and unsupervised machine learning methods, a number of automated anomaly detection systems were created. Support vector machine, random forest, VGG16, and ResNet50 were used as supervised machine learning models, and a convolutional auto‐encoder was used as the unsupervised machine learning model. VGG16 achieved the best accuracy of 99.00% while random forest achieved the worst accuracy of 72.49%. The convolutional auto‐encoder was successful in distinguishing between normal and abnormal components. The aforementioned machine learning models can be put on unmanned aerial vehicles to immediately identify defective parts.

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Swarm‐Optimized ZnO/CdS/CIGS/GaAs Solar Cell for Enhanced Efficiency and Thermal Resilience

Manzoor,

Hussain

et al. 2024

Adv Energy and Sustain Res

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Optimizing solar cell design is vital for boosting efficiency, cutting production costs, and meeting the increasing demand for renewable energy solutions. Through meticulous adjustments in material compositions and device architectures, optimization enhances energy conversion efficiency, making solar power more competitive and adaptable across various applications. This article presents the optimization and efficiency enhancement of a ZnO/CdS/CIGS solar cell with GaAs. The optimization process utilizes the particle swarm optimization algorithm with a step‐by‐step approach. Solar cells are designed using SCAPS‐1D software, and optimization is performed using Python. The optimized ZnO/CdS/CIGS solar cell achieves an efficiency of 32.4%, which rises to 44.7% upon integrating a GaAs layer. Further efficiency gains are observed, reaching 53.2% through back contact optimization, providing a power density of 54 mW cm−2. Optimization also notices a significant improvement in quantum efficiency. The cells are tested under concentrated solar irradiance (1000–10 000 W m−2) and temperatures (300–800 K). Results show that at 10 000 W m−2 and 800 K, the ZnO/CdS/CIGS/GaAs cell requires 53.9% less material than the ZnO/CdS/CIGS cell. Thus, adding GaAs enhances efficiency and thermal resilience, making it ideal for concentrated photovoltaics.

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Optimization of PV Modules through Tilt Angle in Different Cities Of Punjab, Pakistan

Cited by 4 publications

References 23 publications

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

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

Machine learning‐assisted anomaly detection for power line components: A case study in Pakistan

Swarm‐Optimized ZnO/CdS/CIGS/GaAs Solar Cell for Enhanced Efficiency and Thermal Resilience

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