Micro-Fracture Detection in Photovoltaic Cells with Hardware-Constrained Devices and Computer Vision

• URL: http://arxiv.org/abs/2403.05694v1
• Date: Fri, 8 Mar 2024 22:09:10 GMT
• Title: Micro-Fracture Detection in Photovoltaic Cells with Hardware-Constrained Devices and Computer Vision
• Authors: Booy Vitas Faassen, Jorge Serrano, and Paul D. Rosero-Montalvo
• Abstract summary: crystalline silicon is fragile and vulnerable to cracking over time or in predictive maintenance tasks. This work aims to developing a system for detecting cell cracks in solar panels to anticipate and alaert of a potential failure.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Solar energy is rapidly becoming a robust renewable energy source to conventional finite resources such as fossil fuels. It is harvested using interconnected photovoltaic panels, typically built with crystalline silicon cells, i.e. semiconducting materials that convert effectively the solar radiation into electricity. However, crystalline silicon is fragile and vulnerable to cracking over time or in predictive maintenance tasks, which can lead to electric isolation of parts of the solar cell and even failure, thus affecting the panel performance and reducing electricity generation. This work aims to developing a system for detecting cell cracks in solar panels to anticipate and alaert of a potential failure of the photovoltaic system by using computer vision techniques. Three scenarios are defined where these techniques will bring value. In scenario A, images are taken manually and the system detecting failures in the solar cells is not subject to any computationa constraints. In scenario B, an Edge device is placed near the solar farm, able to make inferences. Finally, in scenario C, a small microcontroller is placed in a drone flying over the solar farm and making inferences about the solar cells' states. Three different architectures are found the most suitable solutions, one for each scenario, namely the InceptionV3 model, an EfficientNetB0 model shrunk into full integer quantization, and a customized CNN architechture built with VGG16 blocks.

Related papers

• Unveiling the Invisible: Enhanced Detection and Analysis of Deteriorated Areas in Solar PV Modules Using Unsupervised Sensing Algorithms and 3D Augmented Reality [1.0310343700363547]
  This article presents a groundbreaking methodology for automatically identifying and analyzing anomalies like hot spots and snail trails in Solar Photovoltaic (PV) modules. By transforming the traditional methods of diagnosis and repair, our approach not only enhances efficiency but also substantially cuts down the cost of PV system maintenance. Our immediate objective is to leverage drone technology for real-time, automatic solar panel detection, significantly boosting the efficacy of PV maintenance.
  arXiv  Detail & Related papers  (2023-07-11T09:27:00Z)
• Improving day-ahead Solar Irradiance Time Series Forecasting by Leveraging Spatio-Temporal Context [46.72071291175356]
  Solar power harbors immense potential in mitigating climate change by substantially reducing CO$_2$ emissions. However, the inherent variability of solar irradiance poses a significant challenge for seamlessly integrating solar power into the electrical grid. In this paper, we put forth a deep learning architecture designed to harnesstemporal context using satellite data.
  arXiv  Detail & Related papers  (2023-06-01T19:54:39Z)
• Data-driven soiling detection in PV modules [58.6906336996604]
  We study the problem of estimating the soiling ratio in photo-voltaic (PV) modules. A key advantage of our algorithms is that they estimate soiling, without needing to train on labelled data. Our experimental evaluation shows that we significantly outperform current state-of-the-art methods for estimating soiling ratio.
  arXiv  Detail & Related papers  (2023-01-30T14:35:47Z)
• Computational Solar Energy -- Ensemble Learning Methods for Prediction of Solar Power Generation based on Meteorological Parameters in Eastern India [0.0]
  It is important to estimate the amount of solar photovoltaic (PV) power generation for a specific geographical location. In this paper, the impact of weather parameters on solar PV power generation is estimated by several Ensemble ML (EML) models like Bagging, Boosting, Stacking, and Voting. The results demonstrate greater prediction accuracy of around 96% for Stacking and Voting models.
  arXiv  Detail & Related papers  (2023-01-21T19:16:03Z)
• Low Emission Building Control with Zero-Shot Reinforcement Learning [70.70479436076238]
  Control via Reinforcement Learning (RL) has been shown to significantly improve building energy efficiency. We show it is possible to obtain emission-reducing policies without a priori--a paradigm we call zero-shot building control.
  arXiv  Detail & Related papers  (2022-08-12T17:13:25Z)
• CellDefectNet: A Machine-designed Attention Condenser Network for Electroluminescence-based Photovoltaic Cell Defect Inspection [67.99623869339919]
  A big challenge faced by industry in photovoltaic cell visual inspection is the fact that it is currently done manually by human inspectors. In this work, we introduce CellDefectNet, a highly efficient attention condenser network designed via machine-driven design exploration. We demonstrate the efficacy of CellDefectNet on a benchmark dataset comprising of a diversity of photovoltaic cells captured using electroluminescence imagery.
  arXiv  Detail & Related papers  (2022-04-25T16:35:19Z)
• Feature Construction and Selection for PV Solar Power Modeling [1.8960797847221296]
  Building a model to predict photovoltaic (PV) power generation allows decision-makers to hedge energy shortages. The solar power output is time-series data dependent on many factors, such as irradiance and weather. A machine learning framework for 1-hour ahead solar power prediction is developed in this paper based on the historical data.
  arXiv  Detail & Related papers  (2022-02-13T06:49:28Z)
• SunCast: Solar Irradiance Nowcasting from Geosynchronous Satellite Data [2.285928372124628]
  We propose a Convolutional Long Short-Term Memory Network model that treats solar nowcasting as a next frame prediction problem. Our models can predict solar irradiance for entire North America for up to 3 hours in under 60 seconds on a single machine without a GPU.
  arXiv  Detail & Related papers  (2022-01-17T01:55:26Z)
• A Multi-Stage model based on YOLOv3 for defect detection in PV panels based on IR and Visible Imaging by Unmanned Aerial Vehicle [65.99880594435643]
  We propose a novel model to detect panel defects on aerial images captured by unmanned aerial vehicle. The model combines detections of panels and defects to refine its accuracy. The proposed model has been validated on two big PV plants in the south of Italy.
  arXiv  Detail & Related papers  (2021-11-23T08:04:32Z)
• Optimizing a domestic battery and solar photovoltaic system with deep reinforcement learning [69.68068088508505]
  A lowering in the cost of batteries and solar PV systems has led to a high uptake of solar battery home systems. In this work, we use the deep deterministic policy algorithm to optimise the charging and discharging behaviour of a battery within such a system.
  arXiv  Detail & Related papers  (2021-09-10T10:59:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.