Fault detection and identification of solar panels using Bluetooth

Padmavathi, N.; Chilambuchelvan, A.

doi:10.1109/icecds.2017.8390096

Cited by 9 publications

(6 citation statements)

References 2 publications

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“…Each convolutional block is comprised of predefined filters followed by feature aggregation via Max-pooling, with the result feeding into the ReLu activation function. The selection of ReLu was again in line with our research theme, i.e., lightweight footprint, as it simply implied a Max operation, as expressed in (3).…”

Section: Proposed Architecturementioning

confidence: 92%

“…It is crucial to have a dependable inspection process as production is automated to meet demand. These panels may face challenges, like soiling, harsh environments, and damage, which can lower their performance [1,[3][4][5]. These defects may be in the form of micro-cracks, which can be hard to visually identify [6], and their manual detection is subject to human error and thus susceptible to low efficiency, high labor costs, high rates of false detection, as well as a high scrap rate [7]; hence, there is a need to develop an automated process for easy detection.…”

Section: Introductionmentioning

confidence: 99%

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Automated Micro-Crack Detection within Photovoltaic Manufacturing Facility via Ground Modelling for a Regularized Convolutional Network

Animashaun,

Hussain

2023

Sensors

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The manufacturing of photovoltaic cells is a complex and intensive process involving the exposure of the cell surface to high temperature differentials and external pressure, which can lead to the development of surface defects, such as micro-cracks. Currently, domain experts manually inspect the cell surface to detect micro-cracks, a process that is subject to human bias, high error rates, fatigue, and labor costs. To overcome the need for domain experts, this research proposes modelling cell surfaces via representative augmentations grounded in production floor conditions. The modelled dataset is then used as input for a custom ‘lightweight’ convolutional neural network architecture for training a robust, noninvasive classifier, essentially presenting an automated micro-crack detector. In addition to data modelling, the proposed architecture is further regularized using several regularization strategies to enhance performance, achieving an overall F1-score of 85%.

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Section: Proposed Architecturementioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Automated Micro-Crack Detection within Photovoltaic Manufacturing Facility via Ground Modelling for a Regularized Convolutional Network

Animashaun,

Hussain

2023

Sensors

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“…An automatic defect detection system is prescribed in [3], applying a Simple Linear Iterative Clustering scheme in thermal images of solar systems to detect hotspots and generate real-time alerts. Towards detecting the cracks and micro-cracks in solar panels the Halcon-based approach deep learning approach [4] and Bluetooth-based inspection system [5] are presented. The system gets updated with the dynamic images with the support of Bluetooth of android devices.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning-Based Model for Defect Detection and Localization on Photovoltaic Panels

Prabhakaran¹,

Uthra²,

Preetharoselyn³

2023

Computer Systems Science and Engineering

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The Problem of Photovoltaic (PV) defects detection and classification has been well studied. Several techniques exist in identifying the defects and localizing them in PV panels that use various features, but suffer to achieve higher performance. An efficient Real-Time Multi Variant Deep learning Model (RMVDM) is presented in this article to handle this issue. The method considers different defects like a spotlight, crack, dust, and micro-cracks to detect the defects as well as localizes the defects. The image data set given has been preprocessed by applying the Region-Based Histogram Approximation (RHA) algorithm. The preprocessed images are applied with Gray Scale Quantization Algorithm (GSQA) to extract the features. Extracted features are trained with a Multi Variant Deep learning model where the model trained with a number of layers belongs to different classes of neurons. Each class neuron has been designed to measure Defect Class Support (DCS). At the test phase, the input image has been applied with different operations, and the features extracted passed through the model trained. The output layer returns a number of DCS values using which the method identifies the class of defect and localizes the defect in the image. Further, the method uses the Higher-Order Texture Localization (HOTL) technique in localizing the defect. The proposed model produces efficient results with around 97% in defect detection and localization with higher accuracy and less time complexity.

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“…However, the maintenance of optimal performance in solar panels and the maximisation of energy output require continuous monitoring and analysis of data [3]. In response to this crucial necessity, contemporary technical advancements have led to the developing of the solar panel wireless monitoring system, an innovative solution that leverages WiFi technology to facilitate instantaneous monitoring and data analysis [4]- [6]. The wireless monitoring system utilises WiFi as its predominant communication channel, enabling uninterrupted and immediate transmission of data between solar panels and the control centre [7], [8] also has a strong and reliable connection that facilitates prompt analysis of essential electrical characteristics, including voltage, current, temperature, and solar panel power output [9], [10].…”

Section: Introductionmentioning

confidence: 99%

Wireless internet of things solutions for efficient photovoltaic system monitoring via WiFi networks

Yacine,

Boufeldja

2024

IJEECS

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<div align="center"><span>The imperative for sustainable energy production has necessitated the significant expansion of renewable energy sources, particularly photovoltaic (PV) systems. The utilization of real-time monitoring and data analysis is imperative to enhance the efficiency and performance of photovoltaic systems. This abstract presents developing and deploying a wireless monitoring system for a photovoltaic system. The system utilizes a Raspberry Pi device connected to a WiFi network and an SD card for data storage to enable remote monitoring and management of PV systems. The proposed monitoring system comprises a Raspberry Pi equipped with sensors to measure various parameters such as voltage, current, temperature, and the ambient conditions of the solar panels; the monitoring system can be remotely accessible through the wireless capabilities of the Raspberry Pi, which are activated by establishing a connection to an existing WiFi network. The proposed configuration facilitates the placement of the monitoring station in any desired location, hence eliminating the requirement for intricate wiring connections. These real-time data enable solar system managers to quickly identify anomalies, anticipate breakdowns, and optimise energy production. The paper presents a wireless monitoring system with a cost-effective and scalable solution for monitoring photovoltaic systems.</span></div>

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Fault detection and identification of solar panels using Bluetooth

Cited by 9 publications

References 2 publications

Automated Micro-Crack Detection within Photovoltaic Manufacturing Facility via Ground Modelling for a Regularized Convolutional Network

Automated Micro-Crack Detection within Photovoltaic Manufacturing Facility via Ground Modelling for a Regularized Convolutional Network

Deep Learning-Based Model for Defect Detection and Localization on Photovoltaic Panels

Wireless internet of things solutions for efficient photovoltaic system monitoring via WiFi networks

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