The analysis and application of solar energy PV power

Liu, Jianhua; Wang, Jin; Tao, Zhi; Meng, Ying; Xu, Xiaping

doi:10.1109/apap.2011.6180758

Cited by 8 publications

(1 citation statement)

References 3 publications

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“…In this study, we define a factor that reduces the absorptivity of a solar panel as a defect of solar-cell panels [21]. Approximately ten years ago, several approaches were proposed to detect defects in solar-cell panels.…”

Section: Related Work 21 Deep Learning For the Classification Of Defe...mentioning

confidence: 99%

LIRNet: A Lightweight Inception Residual Convolutional Network for Solar Panel Defect Classification

2023

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Solar-cell panels use sunlight as a source of energy to generate electricity. However, the performances of solar panels decline when they degrade, owing to defects. Some common defects in solar-cell panels include hot spots, cracking, and dust. Hence, it is important to efficiently detect defects in solar-cell panels and repair them. In this study, we propose a lightweight inception residual convolutional network (LIRNet) to detect defects in solar-cell panels. LIRNet is a neural network model that utilizes deep learning techniques. To achieve high model performance on solar panels, including high fault detection accuracy and processing speed, LIRNet draws on hierarchical learning, which is a two-phase solar-panel-defect classification method. The first phase is the data-preprocessing stage. We use the K-means clustering algorithm to refine the dataset. The second phase is the training of the model. We designed a powerful and lightweight neural network model to enhance accuracy and speed up the training time. In the experiment, LIRNet improved the accuracy by approximately 8% and performed ten times faster than EfficientNet.

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Section: Related Work 21 Deep Learning For the Classification Of Defe...mentioning

confidence: 99%

LIRNet: A Lightweight Inception Residual Convolutional Network for Solar Panel Defect Classification

2023

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Solar Energy-Based Battery Charger and Motion Sensing Alarm Using Arduino

Singh¹,

Ansari²,

Singh³

et al. 2022

Lecture Notes in Electrical Engineering

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Solar power plant utilization on mass rapid transit based public transportation company

Ramdhani

Sudiarto²

2020

IOP Conf. Ser.: Earth Environ. Sci.

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Nowadays, the growth of population and business activities in a city is creating a new problem, which is the movement of the people itself. Mass Rapid Transit (MRT), which included in public transportation with a dedicated lane continues to prove that this kind of public transportation is effective and efficient to solve that problem by reducing congestion and creating a greener environment. However, MRT requires electrical power in a relatively big number. To meet this requirement and support a greener environment, a study is conducted. The method of this study is to simulate on how much potentially a solar power plant can be built on Jakarta MRT facility and conduct an analysis on engineering, economic, regulation, and strategic of the Company itself. The result is a 451.2 kWp Solar Power Plant could be built on the Workshop Building on Jakarta MRT, has NPV of US$ 44,706.70, and payback period in 12.82 years if the implementation in year 2022. The Cost of Capacity Charge could be avoided by giving the Power Utility Company of Indonesia a Business to Business (B2B) scheme.

show abstract

The analysis and application of solar energy PV power

Cited by 8 publications

References 3 publications

LIRNet: A Lightweight Inception Residual Convolutional Network for Solar Panel Defect Classification

LIRNet: A Lightweight Inception Residual Convolutional Network for Solar Panel Defect Classification

Solar Energy-Based Battery Charger and Motion Sensing Alarm Using Arduino

Solar power plant utilization on mass rapid transit based public transportation company

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