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Enhancing Reliability and Energy Performance of Photovoltaic Modules Using Artificial Intelligence

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 (3 July 2023) | Viewed by 2779

Special Issue Editor

Dr. Mohammadreza Aghaei

E-Mail Website
Guest Editor
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)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Photovoltaic (PV) modules experience different unexpected faults due to human errors, temperature, humidity, mechanical load, UV irradiation, shading, irreversible equipment damage, and environmental impacts, etc. The increasing number of PV installations as well as related massive volumes of data which are collected from energy meters and sensors reveal the importance of developing novel techniques and procedures that can handle such large volumes of systems and data.

Recent advances in software, hardware, and also platforms for large data acquisition, storage, and Big Data Analytics (BDA) aims to recognize the failures, faults, and malfunctions in PV modules and components efficiently, quickly and precisely, as well as increase the reliability and durability of PV systems. In recent years, the evolution of reliable condition monitoring, energy yield assessment, reliability analysis and fault detection techniques based on Artificial Intelligence (AI) techniques has been dramatically initiated. Artificial Intelligence (AI) techniques such as machine/deep learning aim to develop innovative, autonomous, and smart condition monitoring concepts for precise failure detection in PV modules, increasing the service life and reliability.

The aim of this Special Issue is to collect scientific manuscripts on the practical aspects and simulation models associated with Artificial Intelligence-based methods. The key focus is to describe the emerging developments and advances in order to mitigate the challenges, from effective reliability assessment, smart predictive monitoring, autonomous monitoring, reliability assessment and faults detection, to intelligent decision making and remedial actions in upcoming years. This Special Issue aims to address the current and future challenges of enabling PV terawatt transition. The topics may include, but are not limited to, the following:

  • Energy Yield Prediction
  • Conventional PV Technologies (Crystalline Silicon, Thin-Film)
  • Emerging PV Technologies (Dye-sensitized, Organic, Perovskite)
  • Reliability Metrics and Test Methodologies for PV Modules
  • Degradation and Failure Modes
  • Performance and Reliability Assessment
  • Autonomous Monitoring and Analysis
  • Predictive Monitoring
  • Photovoltaics Big Data Analysis (PVBDA)
  • AI-based Methods for Big Data Handling/Transmission/Storage 
  • Databases and AI-based Analysis Tools
  • Machine/Deep Learning Techniques for Failure Diagnosis and Analysis

Dr. Mohammadreza Aghaei
Guest Editor

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. Energies 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.

Published Papers (2 papers)

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Research

17 pages, 4164 KiB  
Article
Weightless Neural Network-Based Detection and Diagnosis of Visual Faults in Photovoltaic Modules
by Naveen Venkatesh Sridharan, Jerome Vasanth Joseph, Sugumaran Vaithiyanathan and Mohammadreza Aghaei
Energies 2023, 16(15), 5824; https://doi.org/10.3390/en16155824 - 05 Aug 2023
Cited by 3 | Viewed by 890
Abstract
The present study introduces a novel approach employing weightless neural networks (WNN) for the detection and diagnosis of visual faults in photovoltaic (PV) modules. WNN leverages random access memory (RAM) devices to simulate the functionality of neurons. The network is trained using a [...] Read more.
The present study introduces a novel approach employing weightless neural networks (WNN) for the detection and diagnosis of visual faults in photovoltaic (PV) modules. WNN leverages random access memory (RAM) devices to simulate the functionality of neurons. The network is trained using a flexible and efficient algorithm designed to produce consistent and precise outputs. The primary advantage of adopting WNN lies in its capacity to obviate the need for network retraining and residual generation, making it highly promising in classification and pattern recognition domains. In this study, visible faults in PV modules were captured using an unmanned aerial vehicle (UAV) equipped with a digital camera capable of capturing RGB images. The collected images underwent preprocessing and resizing before being fed as input into a pre-trained deep learning network, specifically, DenseNet-201, which performed feature extraction. Subsequently, a decision tree algorithm (J48) was employed to select the most significant features for classification. The selected features were divided into training and testing datasets that were further utilized to determine the training, test and validation accuracies of the WNN (WiSARD classifier). Hyperparameter tuning enhances WNN’s performance by achieving optimal values, maximizing classification accuracy while minimizing computational time. The obtained results indicate that the WiSARD classifier achieved a classification accuracy of 100.00% within a testing time of 1.44 s, utilizing the optimal hyperparameter settings. This study underscores the potential of WNN in efficiently and accurately diagnosing visual faults in PV modules, with implications for enhancing the reliability and performance of photovoltaic systems. Full article
(This article belongs to the Special Issue Enhancing Reliability and Energy Performance of Photovoltaic Modules Using Artificial Intelligence)
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16 pages, 3431 KiB  
Article
Load-Following Operation of Small Modular Reactors under Unit Commitment Planning with Various Photovoltaic System Conditions
by Seong-Hyeon Ahn, Jin-Hee Hyun, Jin-Ho Choi, Seong-Geun Lee, Gyu-Gwang Kim, Byeong-Gwan Bhang, Hae-Lim Cha, Byeong-Yong Lim, Hoon-Joo Choi and Hyung-Keun Ahn
Energies 2023, 16(7), 2946; https://doi.org/10.3390/en16072946 - 23 Mar 2023
Viewed by 1425
Abstract
Globally, renewable energies are indispensable resources on account of RE100 and the Paris Agreement. The most developed renewable energies are photovoltaics (PV) and wind energy, and they are continuously expanding. This study aims to optimize and analyze the nuclear power plant (NPP) load-following [...] Read more.
Globally, renewable energies are indispensable resources on account of RE100 and the Paris Agreement. The most developed renewable energies are photovoltaics (PV) and wind energy, and they are continuously expanding. This study aims to optimize and analyze the nuclear power plant (NPP) load-following operation in various PV conditions in a metropolitan region. With theoretically estimated power demand and PV power, a mixed-integer problem (MIP) with ramping cycle constraint (RCC) was constructed for a safe load-following operation and simulated through duck curves under various NPP load-following regions (the extreme, normal, and safe regions). The simulation showed two major results for NPP load-following. Technically, RCC successfully controlled the NPP ramp cycle and was assured to be an optimization tool for NPP operation. Numerically, NPP load-following alleviated PV intermittency to almost 50%, 30%, and 15% depending on the load-following region. However, these effects were restricted when the PV capacity rate was high, especially when it exceeded 60%. Thus, PV system capacity is recommended to be 63% of the maximum power demand in the metropolitan region with NPP load-following, and larger PV systems need more flexibility. Full article
(This article belongs to the Special Issue Enhancing Reliability and Energy Performance of Photovoltaic Modules Using Artificial Intelligence)
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