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Photovoltaic Sensor and Applications
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Photovoltaic Sensor and Applications

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: closed (20 November 2020) | Viewed by 12449

Special Issue Editor

Prof. Dr. Manuel Fuentes Conde

E-Mail Website
Guest Editor
IDEA Research Group, Electronics and Automation Engineering Department, High Polytechnic School of Linares, University of Jaén Campus Científico-Tecnológico, Avd. Universidad s/n, D-113 23700 Jaén, Spain
Interests: low-cost systems for monitoring; measurement devices for PV; development of hybrid solar photovoltaic devices
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, the fast evolution of renewable energies and, particularly, solar photovoltaic (PV) technology, has led to a proliferation of PV installations throughout the world, mainly because of their successful interconnection to the grid, their competitive price, and their efficiency improvement. PV generation systems face challenges, but also new opportunities, in their electronic components and applications.

The field of PV sensors and their applications needs monitoring processes in real time, which can be used in any location, at a low-cost if possible. The application of Internet of things (IoT) can promote the use of this type of sensor. The biggest challenge is to develop PV sensors with the enhaced performance required to enable their widespread penetration.

The scope of the Special Issue is hightlighting advances in the materials, properties, device concepts, development, and the testing and modeling of sensors based or applied on photovoltaics. Potencial topics include, but are not limited to, the following:

  • PV sensors development and analysis
  • IoT-PV sensors and applications
  • Smart PV sensors
  • Enviromental monitoring
  • Clean energy monitoring
  • Advanced PV sensor characterization

Dr. Manuel Fuentes Conde
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

  • PV sensors
  • Self-powering sensors
  • Calibration PV sensors
  • IoT solar applications
  • Accurate and precision analysis
  • Smart PV sensors

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Published Papers (3 papers)

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Research

17 pages, 2714 KiB  
Article
A New Approach to Estimate from Monitored Demand Data the Limit of the Coverage of Electricity Demand through Photovoltaics in Large Electricity Grids
by Francisco Baena, Francisco José Muñoz-Rodriguez, Pedro Gómez Vidal and Gabino Almonacid
Sensors 2020, 20(16), 4390; https://doi.org/10.3390/s20164390 - 6 Aug 2020
Cited by 2 | Viewed by 2225
Abstract
In a traditional large electricity grid without storage, there is a limit to the maximum photovoltaic energy that can be consumed as the demand and generation may not match, either in magnitude or in time. This paper aims to provide a new method [...] Read more.
In a traditional large electricity grid without storage, there is a limit to the maximum photovoltaic energy that can be consumed as the demand and generation may not match, either in magnitude or in time. This paper aims to provide a new method to estimate the limit of the coverage of electricity demand by photovoltaics in large electricity grids. This new method eliminates the random and the periodic variability over time as it is based either on the load duration curve for demand and the output duration curve for PV generation. We will assume there is no energy storage or inter-network exchanges. Moreover, conditions for the best scenario for photovoltaics are provided in order to estimate the upper limit: photovoltaic overgeneration is not considered and a complete system flexibility is assumed. The knowledge of this limit will manage to provide not only a reference for the planning of the energy sector but also to analyze the viability of the integration of future photovoltaic projects in the electrical system. In order to illustrate the method, several large electricity grids have been analysed in order to determine the aforementioned limit. Values between 19.3% and 29.9% have been obtained. Full article
(This article belongs to the Special Issue Photovoltaic Sensor and Applications)
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18 pages, 4964 KiB  
Article
Data Description Technique-Based Islanding Classification for Single-Phase Grid-Connected Photovoltaic System
by Ahteshamul Haque, Abdulaziz Alshareef, Asif Irshad Khan, Md Mottahir Alam, Varaha Satya Bharath Kurukuru and Kashif Irshad
Sensors 2020, 20(11), 3320; https://doi.org/10.3390/s20113320 - 11 Jun 2020
Cited by 13 | Viewed by 3485
Abstract
This paper develops an islanding classification mechanism to overcome the problems of non-detection zones in conventional islanding detection mechanisms. This process is achieved by adapting the support vector-based data description technique with Gaussian radial basis function kernels for islanding and non-islanding events in [...] Read more.
This paper develops an islanding classification mechanism to overcome the problems of non-detection zones in conventional islanding detection mechanisms. This process is achieved by adapting the support vector-based data description technique with Gaussian radial basis function kernels for islanding and non-islanding events in single phase grid-connected photovoltaic (PV) systems. To overcome the non-detection zone, excess and deficit power imbalance conditions are considered for different loading conditions. These imbalances are characterized by the voltage dip scenario and were subjected to feature extraction for training with the machine learning technique. This is experimentally realized by training the machine learning classifier with different events on a 5   kW grid-connected system. Using the concept of detection and false alarm rates, the performance of the trained classifier is tested for multiple faults and power imbalance conditions. The results showed the effective operation of the classifier with a detection rate of 99.2% and a false alarm rate of 0.2%. Full article
(This article belongs to the Special Issue Photovoltaic Sensor and Applications)
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20 pages, 4885 KiB  
Article
On Field Infrared Thermography Sensing for PV System Efficiency Assessment: Results and Comparison with Electrical Models
by Mirco Muttillo, Iole Nardi, Vincenzo Stornelli, Tullio de Rubeis, Giovanni Pasqualoni and Dario Ambrosini
Sensors 2020, 20(4), 1055; https://doi.org/10.3390/s20041055 - 15 Feb 2020
Cited by 22 | Viewed by 3699
Abstract
The evaluation of photovoltaic (PV) system’s efficiency loss, due to the onset of faults that reduce the output power, is crucial. The challenge is to speed up the evaluation of electric efficiency by coupling the electric characterization of panels with information gathered from [...] Read more.
The evaluation of photovoltaic (PV) system’s efficiency loss, due to the onset of faults that reduce the output power, is crucial. The challenge is to speed up the evaluation of electric efficiency by coupling the electric characterization of panels with information gathered from module diagnosis, amongst which the most commonly employed technique is thermographic inspection. The aim of this work is to correlate panels’ thermal images with their efficiency: a “thermal signature” of panels can be of help in identifying the fault typology and, moreover, for assessing efficiency loss. This allows to identify electrical power output losses without interrupting the PV system operation thanks to an advanced PV thermography characterization. In this paper, 12 faulted working panels were investigated. Their electrical models were implemented in MATLAB environment and developed to retrieve the ideal I-V characteristic (from ratings), the actual (operative) I-V characteristics and electric efficiency. Given the curves shape and relative difference, based on three reference points (namely, open circuit, short circuit, and maximum power points), faults’ typology has been evidenced. Information gathered from infrared thermography imaging, simultaneously carried out on panels during operation, were matched with those from electrical characterization. Panels’ “thermal signature” has been coupled with the “electrical signature”, to obtain an overall depiction of panels’ health status. Full article
(This article belongs to the Special Issue Photovoltaic Sensor and Applications)
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