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Sensor Enabled Smart Energy Solutions
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Sensor Enabled Smart Energy Solutions

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

Deadline for manuscript submissions: 20 June 2025 | Viewed by 11992

Special Issue Editors

Dr. Hamid Reza Shaker

E-Mail Website
Guest Editor
Center for Energy Informatics, University of Southern Denmark, 5230 Odense, Denmark
Interests: fault detection and diagnosis; fault and critical event prediction; proactive and predictive maintenance; digital energy solutions
Special Issues, Collections and Topics in MDPI journals
Dr. Rahman Dashti

E-Mail Website
Guest Editor
Faculty of Intelligent Systems Engineering and Data Science, Persian Gulf University, Bushehr I.R., Iran
Interests: power system protection; fault location; distribution networks; transient analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The global environmental and energy challenges cannot be effectively addressed without digital transformation of the energy sector. Today’s sensors and metering technologies provide the means for such transformation. Globally, increasingly more sensors and smart meters are being deployed due to their benefits for customers, policies, and regulations. To leverage sensors and smart meter data and to enjoy the full potential and benefits of digitalization, it is important to develop ICT-based solutions that enable a reliable, sustainable, and low-carbon energy system. This Special Issue is focused on digital solutions for improving the energy efficiency, reliability and security of the energy supply and the intelligent use of energy within the following two interconnected areas:

  • Smart energy networks, such as smart electricity, gas, district heating, and cooling grids
  • Smart buildings

Dr. Hamid Reza Shaker
Dr. Rahman Dashti
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • fault detection, location, and diagnosis
  • data analytics
  • smart maintenance and renovation planning
  • reliability and risk analysis
  • intelligent control and protection
  • energy management and optimization
  • forecasting

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Published Papers (4 papers)

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Research

19 pages, 4058 KiB  
Article
Enhanced Fault Detection in Photovoltaic Panels Using CNN-Based Classification with PyQt5 Implementation
by Younes Ledmaoui, Adila El Maghraoui, Mohamed El Aroussi and Rachid Saadane
Sensors 2024, 24(22), 7407; https://doi.org/10.3390/s24227407 - 20 Nov 2024
Viewed by 869
Abstract
Solar photovoltaic systems have increasingly become essential for harvesting renewable energy. However, as these systems grow in prevalence, the issue of the end of life of modules is also increasing. Regular maintenance and inspection are vital to extend the lifespan of these systems, [...] Read more.
Solar photovoltaic systems have increasingly become essential for harvesting renewable energy. However, as these systems grow in prevalence, the issue of the end of life of modules is also increasing. Regular maintenance and inspection are vital to extend the lifespan of these systems, minimize energy losses, and protect the environment. This paper presents an innovative explainable AI model for detecting anomalies in solar photovoltaic panels using an enhanced convolutional neural network (CNN) and the VGG16 architecture. The model effectively identifies physical and electrical changes, such as dust and bird droppings, and is implemented using the PyQt5 Python tool to create a user-friendly interface that facilitates decision-making for users. Key processes included dataset balancing through oversampling and data augmentation to expand the dataset. The model achieved impressive performance metrics: 91.46% accuracy, 98.29% specificity, and an F1 score of 91.67%. Overall, it enhances power generation efficiency and prolongs the lifespan of photovoltaic systems, while minimizing environmental risks. Full article
(This article belongs to the Special Issue Sensor Enabled Smart Energy Solutions)
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19 pages, 9889 KiB  
Article
Low-Cost, Open-Source, Emoncms-Based SCADA System for a Large Grid-Connected PV System
by Luqman Ahsan, Mirza Jabbar Aziz Baig and Mohmmad Tariq Iqbal
Sensors 2022, 22(18), 6733; https://doi.org/10.3390/s22186733 - 6 Sep 2022
Cited by 13 | Viewed by 3781
Abstract
This article describes a low-cost Supervisory Control and Data Acquisition (SCADA) system for a PV plant with local data logging. Typically, SCADA systems that are available on the market are proprietary (commercial), which are expensive and individually configured for a particular site. The [...] Read more.
This article describes a low-cost Supervisory Control and Data Acquisition (SCADA) system for a PV plant with local data logging. Typically, SCADA systems that are available on the market are proprietary (commercial), which are expensive and individually configured for a particular site. The main objective of this paper is to design a low-cost and open-source monitoring solution (hardware and software) to meet the requirements. The hardware used for this SCADA consisted of Arduino, Raspberry Pi, sensors, serial communication cables, and an open-source web view platform. This open-source platform manipulates, logs, and visualizes PV and environmental data. Emoncms runs on the Debian operating system. Field instruments were connected to two remote terminal units (RTUs). A PV array provided data to the RTU1, while an inverter output provided data to the RTU2, and the Raspberry Pi received the collected data in JSON format. As these data arrived, Emoncms used Emonhub as its main module, which refines data and then displays it on Emoncms’s WebView. The Raspberry Pi also stores data locally. Data logging was tested for 6 h, but the final results showed that data logging can last much longer. From an hour to a year, the data trend can be viewed on a user-friendly dashboard. Full article
(This article belongs to the Special Issue Sensor Enabled Smart Energy Solutions)
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14 pages, 5153 KiB  
Article
A Localized Transient-Based Fault Location Scheme for Distribution Systems
by Navid Bayati, Lasse Kappel Mortensen, Mehdi Savaghebi and Hamid Reza Shaker
Sensors 2022, 22(7), 2723; https://doi.org/10.3390/s22072723 - 1 Apr 2022
Cited by 6 | Viewed by 2132
Abstract
Many distribution systems have several branches with only one protection system at the upstream system. This characteristic degrades the performance of traditional fault location schemes. In this paper, a localized fault location method based on the transient behavior of fault currents by using [...] Read more.
Many distribution systems have several branches with only one protection system at the upstream system. This characteristic degrades the performance of traditional fault location schemes. In this paper, a localized fault location method based on the transient behavior of fault currents by using local data is proposed. The proposed scheme uses only local current and the voltage of the upstream overcurrent relay as input data of the fault location scheme. The formulation considers fault resistance, loads, and different fault locations. Furthermore, due to the usage of transient fault current data, the proposed method locates the fault within several milliseconds with a suitable range of error. To validate the effectiveness of this method, field measurement data, obtained from a real distribution system in East Jutland, Denmark operated by Dinel A/S, are used, and extensive real-time simulations are performed. The results prove that the proposed method locates different types of faults within an appropriate time and error, which can improve the maintenance and reliability of distribution systems. Full article
(This article belongs to the Special Issue Sensor Enabled Smart Energy Solutions)
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17 pages, 3640 KiB  
Article
Machine Learning-Based Fault Location for Smart Distribution Networks Equipped with Micro-PMU
by Hamid Mirshekali, Rahman Dashti, Ahmad Keshavarz and Hamid Reza Shaker
Sensors 2022, 22(3), 945; https://doi.org/10.3390/s22030945 - 26 Jan 2022
Cited by 39 | Viewed by 4212
Abstract
Faults in distribution networks occur unpredictably, causing a threat to public safety and resulting in power outages. Automated, efficient, and precise detection of faulty sections could be a major element in immediately restoring networks and avoiding further financial losses. Distributed generations (DGs) are [...] Read more.
Faults in distribution networks occur unpredictably, causing a threat to public safety and resulting in power outages. Automated, efficient, and precise detection of faulty sections could be a major element in immediately restoring networks and avoiding further financial losses. Distributed generations (DGs) are used in smart distribution networks and have varied current levels and internal impedances. However, fault characteristics are completely unknown because of their stochastic nature. Therefore, in these circumstances, locating the fault might be difficult. However, as technology advances, micro-phasor measurement units (micro-PMU) are becoming more extensively employed in smart distribution networks, and might be a useful tool for reducing protection uncertainties. In this paper, a new machine learning-based fault location method is proposed for use regardless of fault characteristics and DG performance using recorded data of micro-PMUs during a fault. This method only uses the recorded voltage at the sub-station and DGs. The frequency component of the voltage signals is selected as a feature vector. The neighborhood component feature selection (NCFS) algorithm is utilized to extract more informative features and lower the feature vector dimension. A support vector machine (SVM) classifier is then applied to the decreased dimension training data. The simulations of various fault types are performed on the 11-node IEEE standard feeder equipped with three DGs. Results reveal that the accuracy of the proposed fault section identification algorithm is notable. Full article
(This article belongs to the Special Issue Sensor Enabled Smart Energy Solutions)
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