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Autonomous Monitoring and Analysis of Photovoltaic Systems

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A2: Solar Energy and Photovoltaic Systems".

Deadline for manuscript submissions: closed (1 February 2022) | Viewed by 20189

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Dr. Mohammadreza Aghaei

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1. Department of Ocean Operations and Civil Engineering, Norwegian University of Science and Technology (NTNU), 6009 Alesund, Norway
2. Solar Energy Engineering Program, Department of Sustainable Systems Engineering (INATECH), Albert Ludwigs University of Freiburg, 79110 Freiburg, Germany
Interests: energy transition; renewable energy; photovoltaics; smart grid; enabling technologies; artificial intelligence (AI); unmanned aerial vehicle (UAV)
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Dear Colleagues,

The installation of utility-scale photovoltaic (PV) systems is dramatically increasing and a huge amount of data are generated through PV systems. The large PV systems are creating challenges in terms of monitoring the performance and diagnosing failures during the operation of various systems.

PV system monitoring is essential to assure a energy performance and the long-term reliability of PV systems. Conventionally, monitoring systems collect required data from PV systems and transmit the data to a control center where an expert evaluates the data and assesses the functional quality of PV systems by considering several performance indicators. Therefore, a suitable monitoring method should efficiently, quickly and precisely detect the malfunctions and faults in PV systems. Early failure detection plays a significant role in optimizing PV systems' performance during their operation. The rapid recognition of failures in PV components increases the reliability and durability of PV systems.

Currently, the increasing number of PV installations as well as produced volumes of data collected from energy meters and sensors highlight the importance of developing new monitoring technologies and procedures that can handle such large volumes of systems and data. Existing technology and techniques for controlling PV systems have not yet been centralized and monitoring and analysis procedures are performed in several separate steps. This can be achieved more efficiently and intelligently.

Autonomous monitoring and analysis is a novel concept for integrating various techniques, devices, systems, and platforms to enhance the accuracy of PV monitoring, thereby improving the performance, reliability, and service life of PV systems.

The aim of this Special Issue is to collect scientific manuscripts on the practical aspects and simulation models associated with autonomous monitoring and analysis of PV systems. The key focus is to describe the emerging developments and advances in order to mitigate the challenges for automating the PV monitoring procedure in upcoming years. The topics may include, but are not limited to, the following:

Autonomous monitoring systems
Big data analytics (BDA) techniques for PV monitoring
Big data transmission and storage methods
Automatic failure detection and classification
Internet of Things (IoT) applications in PV monitoring
Unmanned aerial vehicle (UAV) applications in PV monitoring
Smart and predictive monitoring
Service life prediction
Performance and reliability evaluation

Dr. Mohammadreza Aghaei
Guest Editor

Manuscript Submission Information

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Keywords

Photovoltaic (PV)
Autonomous monitoring and analysis
Big data analysis (BDA)
Failure diagnosis and analysis
Unmanned aerial vehicle (UAV)
Internet of Things (IoT)
Reliability evaluation

Published Papers (6 papers)

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Editorial

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6 pages, 390 KiB

Open AccessEditorial

Autonomous Monitoring and Analysis of Photovoltaic Systems

by Mohammadreza Aghaei

Energies 2022, 15(14), 5011; https://doi.org/10.3390/en15145011 - 08 Jul 2022

Cited by 6 | Viewed by 1732

Abstract

At the beginning of 2022, photovoltaic (PV) installation exceeded 1 TWp which was an impressive milestone in the solar energy industry [...] Full article

(This article belongs to the Special Issue Autonomous Monitoring and Analysis of Photovoltaic Systems)

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Research

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25 pages, 8487 KiB

Open AccessArticle

Cloud Computing and IoT Based Intelligent Monitoring System for Photovoltaic Plants Using Machine Learning Techniques

by Masoud Emamian, Aref Eskandari, Mohammadreza Aghaei, Amir Nedaei, Amirmohammad Moradi Sizkouhi and Jafar Milimonfared

Energies 2022, 15(9), 3014; https://doi.org/10.3390/en15093014 - 20 Apr 2022

Cited by 28 | Viewed by 3400

Abstract

This paper proposes an Intelligent Monitoring System (IMS) for Photovoltaic (PV) systems using affordable and cost-efficient hardware and also lightweight software that is capable of being easily implemented in different locations and having the capability to be installed in different types of PV power plants. IMS uses the Internet of Things (IoT) platform for handling data as well as Interoperability and Communication among the devices and components in the IMS. Moreover, IMS includes a personal cloud server for computing and storing the acquired data of PV systems. The IMS also consists of a web monitor system via some open-source and lightweight software that displays the information to multiple users. The IMS uses deep ensemble models for fault detection and power prediction in PV systems. A remarkable ability of the IMS is the prediction of the output power of the PV system to increase energy yield and identify malfunctions in PV plants. To this end, a long short-term memory (LSTM) ensemble neural network is developed to predict the output power of PV systems under different environmental conditions. On the other hand, the IMS uses machine learning-based models to detect numerous faults in PV systems. The fault diagnostic of IMS is based on the following stages. Firstly, major features are elicited through an analysis of Current–Voltage (I–V) characteristic curve under different faulty and normal events. Second, an ensemble learning model including Naive Bayes (NB), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM) is used for detecting and classifying fault events. To enhance the performance in the process of fault detection, a feature selection algorithm is also applied. A PV system has been designed and implemented for testing and validating the IMS under real conditions. IMS is an interoperable, scalable, and replicable solution for holistic monitoring of PV plant from data acquisition, storing, pre-and post-processing to malfunction and failure diagnosis, performance and energy yield assessment, and output power prediction. Full article

(This article belongs to the Special Issue Autonomous Monitoring and Analysis of Photovoltaic Systems)

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18 pages, 3746 KiB

Open AccessArticle

IoT-Based PV Array Fault Detection and Classification Using Embedded Supervised Learning Methods

by Mojgan Hojabri, Samuel Kellerhals, Govinda Upadhyay and Benjamin Bowler

Energies 2022, 15(6), 2097; https://doi.org/10.3390/en15062097 - 13 Mar 2022

Cited by 15 | Viewed by 4651

Abstract

Faults on individual modules within a photovoltaic (PV) array can have a significant detrimental effect on the power efficiency and reliability of the entire PV system. In addition, PV module faults can create risks to personnel safety and fire hazards if they are not detected quickly. As IoT hardware capabilities increase and machine learning frameworks mature, better fault detection performance may be possible using low-cost sensors running machine learning (ML) models that monitor electrical and thermal parameters at an individual module level. In this paper, to evaluate the performance of ML models that are suitable for embedding in low-cost hardware at the module level, eight different PV module faults and their impacts on PV module output are discussed based on a literature review and simulation. The faults are emulated and applied to a real PV system, allowing the collection and labelling of panel-level measurement data. Then, different ML methods are used to classify these faults in comparison to the normal condition. Results confirm that NN obtain 93% classification accuracy for seven selected classes. Full article

(This article belongs to the Special Issue Autonomous Monitoring and Analysis of Photovoltaic Systems)

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20 pages, 2361 KiB

Open AccessArticle

Quantitative Comparison of Infrared Thermography, Visual Inspection, and Electrical Analysis Techniques on Photovoltaic Modules: A Case Study

by Leonardo Cardinale-Villalobos, Carlos Meza, Abel Méndez-Porras and Luis D. Murillo-Soto

Energies 2022, 15(5), 1841; https://doi.org/10.3390/en15051841 - 02 Mar 2022

Cited by 7 | Viewed by 2397

Abstract

This paper compares multiple techniques to detect suboptimal conditions in the PV system. Detection of suboptimal conditions in the PV system is required to achieve optimal photovoltaic (PV) systems. Therefore, maintenance managers need to choose the most suitable techniques objectively. However, there is a lack of objective information comparing the effectiveness of the methods. This article calculates and compares the effectiveness of Infrared thermography (IRT), visual inspection (VI), and electrical analysis (EA) in detecting soiling, partial shadows, and electrical faults experimentally. The results showed that the VI was the best at detecting soiling and partial shading with 100% of effectiveness. IRT and EA had an effectiveness of 78% and 73%, respectively, detecting the three types of conditions under study. It was not possible to achieve maximum detection using only one of the techniques, but that VI must be combined with IR or EA. This research represents a significant contribution by achieving an objective comparison between techniques for detecting suboptimal conditions, being very useful to guide PV system maintainers and designers of fault detection techniques. Full article

(This article belongs to the Special Issue Autonomous Monitoring and Analysis of Photovoltaic Systems)

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19 pages, 9365 KiB

Open AccessArticle

Automated Fault Management System in a Photovoltaic Array: A Reconfiguration-Based Approach

by Luis D. Murillo-Soto and Carlos Meza

Energies 2021, 14(9), 2397; https://doi.org/10.3390/en14092397 - 23 Apr 2021

Cited by 5 | Viewed by 1823

Abstract

This work proposes an automated reconfiguration system to manage two types of faults in any position inside the solar arrays. The faults studied are the short-circuit to ground and the open wires in the string. These faults were selected because they severely affect power production. By identifying the affected panels and isolating the faulty one, it is possible to recover part of the power loss. Among other types of faults that the system can detect and locate are: diode short-circuit, internal open-circuit, and the degradation of the internal parasitic serial resistance. The reconfiguration system can detect, locate the above faults, and switch the distributed commutators to recover most of the power loss. Moreover, the system can return automatically to the previous state when the fault has been repaired. A SIMULINK model has been built to prove this automatic system, and a simulated numerical experiment has been executed to test the system response to the faults mentioned. The results show that the recovery of power is more than 90%, and the diagnosis accuracy and sensitivity are both 100% for this numerical experiment. Full article

(This article belongs to the Special Issue Autonomous Monitoring and Analysis of Photovoltaic Systems)

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Review

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24 pages, 9418 KiB

Open AccessFeature PaperEditor’s ChoiceReview

Automatic Inspection of Photovoltaic Power Plants Using Aerial Infrared Thermography: A Review

by Aline Kirsten Vidal de Oliveira, Mohammadreza Aghaei and Ricardo Rüther

Energies 2022, 15(6), 2055; https://doi.org/10.3390/en15062055 - 11 Mar 2022

Cited by 20 | Viewed by 5083

Abstract

In recent years, aerial infrared thermography (aIRT), as a cost-efficient inspection method, has been demonstrated to be a reliable technique for failure detection in photovoltaic (PV) systems. This method aims to quickly perform a comprehensive monitoring of PV power plants, from the commissioning phase through its entire operational lifetime. This paper provides a review of reported methods in the literature for automating different tasks of the aIRT framework for PV system inspection. The related studies were reviewed for digital image processing (DIP), classification and deep learning techniques. Most of these studies were focused on autonomous fault detection and classification of PV plants using visual, IRT and aIRT images with accuracies up to 90%. On the other hand, only a few studies explored the automation of other parts of the procedure of aIRT, such as the optimal path planning, the orthomosaicking of the acquired images and the detection of soiling over the modules. Algorithms for the detection and segmentation of PV modules achieved a maximum F1 score (harmonic mean of precision and recall) of 98.4%. The accuracy, robustness and generalization of the developed algorithms are still the main issues of these studies, especially when dealing with more classes of faults and the inspection of large-scale PV plants. Therefore, the autonomous procedure and classification task must still be explored to enhance the performance and applicability of the aIRT method. Full article

(This article belongs to the Special Issue Autonomous Monitoring and Analysis of Photovoltaic Systems)

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