Cost Optimization of Off Grid Photovoltaic System by Increasing Conversion Efficiency

Abideen, Zain Ul; Aslam, Arooj; Manzoor, Habib Ullah; Manzoor, Tareq; Bashir, Nouman

doi:10.1109/icecce47252.2019.8940692

Cited by 3 publications

(1 citation statement)

References 14 publications

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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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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

Self Cite

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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

Ashraf

Aaqib

Manzoor

et al. 2020

2020 IEEE 23rd International Multitopic Conference (INMIC)

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Cost Optimization of Off Grid Photovoltaic System by Increasing Conversion Efficiency

Cited by 3 publications

References 14 publications

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

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

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

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