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Condition Monitoring, Field Inspection and Fault Diagnostic Methods for Photovoltaic Systems

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

Deadline for manuscript submissions: closed (31 August 2022) | Viewed by 24965

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Special Issue Editors

Dr. Spataru Sergiu

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Guest Editor

Department of Photonics Engineering, Technical University of Denmark, Kongens Lyngby, Denmark
Interests: PV performance modeling; PV diagnostics; electroluminescence imaging; PV reliability
Special Issues, Collections and Topics in MDPI journals

Dr. Dezso Sera

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Guest Editor

School of Electrical Engineering and Robotics, Queensland University of Technology, George Street 2, 4059 Brisbane, Australia
Interests: modelling; characterisation; diagnostics; power conversion and grid and energy storage integration for photovoltaic systems
Special Issues, Collections and Topics in MDPI journals

Dr. Gisele Alves dos Reis Benatto

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Guest Editor

Technical University of Denmark
Interests: PV reliability; photoluminescence imaging; electroluminescence imaging; PV characterization

Special Issue Information

Dear Colleagues,

With the deployment of millions of solar panels along with the expectation for photovoltaic (PV) systems to operate for 25+ years with minimal maintenance, there is a growing need for more accurate and cost-effective condition monitoring, field inspection, and diagnostic sensors and methods for PV systems. These are necessary to drive down operation and maintenance costs, prolong the useful life of PV systems, and increase investor confidence in solar PV as a safe investment.

Accurate and intelligent condition monitoring sensors and systems are necessary to detect degradation and failures early on by monitoring the electrical performance parameters of the PV system at the panel, string, and array or inverter levels. Such monitoring systems need to be partially or fully automated and consider the size of the PV installation.

The localization and identification of the type of PV failure and its severity is typically time consuming, and requires a high level of expertise. Therefore, more accurate and efficient field inspection methods and tools are necessary to drive down labor costs and inspection times. Infrared thermography and electroluminescence imaging have shown great potential—especially as they can be performed by aerial drones—for the fast inspection of a large number of solar panels. Nonetheless, more research and novel approaches are necessary to improve and automate the inspection and diagnostic process of PV systems.

Dr. Spataru Sergiu
Prof. Dr. Dezso Sera
Dr. Gisele Alves dos Reis Benatto
Guest Editors

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Keywords

condition monitoring
fault detection
fault identification
photovoltaic systems
performance monitoring
diagnostics
field inspection
electroluminescence imaging
infrared thermography
inverter measurements
I–V characteristic
UAV inspection

Published Papers (7 papers)

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Research

31 pages, 3920 KiB

Open AccessArticle

Barnacles Mating Optimizer Algorithm to Extract the Parameters of the Photovoltaic Cells and Panels

by Manoharan Madhiarasan, Daniel T. Cotfas and Petru A. Cotfas

Sensors 2022, 22(18), 6989; https://0-doi-org.brum.beds.ac.uk/10.3390/s22186989 - 15 Sep 2022

Cited by 6 | Viewed by 1208

Abstract

The goal of this research is to accurately extract the parameters of the photovoltaic cells and panels and to reduce the extracting time. To this purpose, the barnacles mating optimizer algorithm is proposed for the first time to extract the parameters. To prove that the algorithm succeeds in terms of accuracy and quickness, it is applied to the following photovoltaic cells: monocrystalline silicon, amorphous silicon, RTC France, and the PWP201, Sharp ND-R250A5, and Kyocera KC200GT photovoltaic panels. The mathematical models used are single and double diodes. Datasets for these photovoltaic cells and panels were used, and the results obtained for the parameters were compared with the ones obtained using other published methods and algorithms. Six statistical tests were used to analyze the performance of the barnacles mating optimizer algorithm: the root mean square error mean, absolute percentage error, mean square error, mean absolute error, mean bias error, and mean relative error. The results of the statistical tests show that the barnacles mating optimizer algorithm outperforms several algorithms. The tests about the computational time were made using two computer configurations. Using the barnacles mating optimizer algorithm, the computational time decreases more than 30 times in comparison with one of the best algorithms, hybrid successive discretization algorithm. Full article

(This article belongs to the Special Issue Condition Monitoring, Field Inspection and Fault Diagnostic Methods for Photovoltaic Systems)

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34 pages, 19453 KiB

Open AccessArticle

Single Diode Solar Cells—Improved Model and Exact Current–Voltage Analytical Solution Based on Lambert’s W Function

by Muhyaddin Rawa, Martin Calasan, Abdullah Abusorrah, Abdullah Ali Alhussainy, Yusuf Al-Turki, Ziad M. Ali, Hatem Sindi, Saad Mekhilef, Shady H. E. Abdel Aleem and Hussain Bassi

Sensors 2022, 22(11), 4173; https://0-doi-org.brum.beds.ac.uk/10.3390/s22114173 - 31 May 2022

Cited by 12 | Viewed by 3554

Abstract

There are three standard equivalent circuit models of solar cells in the literature—single-diode, double-diode, and triple-diode models. In this paper, first, a modified version of the single diode model, called the Improved Single Diode Model (ISDM), is presented. This modification is realized by adding resistance in series with the diode to enable better power loss dissipation representation. Second, the mathematical expression for the current–voltage relation of this circuit is derived in terms of Lambert’s W function and solved by using the special trans function theory. Third, a novel hybrid algorithm for solar cell parameters estimation is proposed. The proposed algorithm, called SA-MRFO, is used for the parameter estimation of the standard single diode and improved single diode models. The proposed model’s accuracy and the proposed algorithm’s efficiency are tested on a standard RTC France solar cell and SOLAREX module MSX 60. Furthermore, the experimental verification of the proposed circuit and the proposed solar cell parameter estimation algorithm on a solar laboratory module is also realized. Based on all the results obtained, it is shown that the proposed circuit significantly improves current–voltage solar cell representation in comparison with the standard single diode model and many results in the literature on the double diode and triple diode models. Additionally, it is shown that the proposed algorithm is effective and outperforms many literature algorithms in terms of accuracy and convergence speed. Full article

(This article belongs to the Special Issue Condition Monitoring, Field Inspection and Fault Diagnostic Methods for Photovoltaic Systems)

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20 pages, 490 KiB

Open AccessArticle

Increasing the Accuracy of Hourly Multi-Output Solar Power Forecast with Physics-Informed Machine Learning

by Daniel Vázquez Pombo, Henrik W. Bindner, Sergiu Viorel Spataru, Poul Ejnar Sørensen and Peder Bacher

Sensors 2022, 22(3), 749; https://0-doi-org.brum.beds.ac.uk/10.3390/s22030749 - 19 Jan 2022

Cited by 18 | Viewed by 2721

Abstract

Machine Learning (ML)-based methods have been identified as capable of providing up to one day ahead Photovoltaic (PV) power forecasts. In this research, we introduce a generic physical model of a PV system into ML predictors to forecast from one to three days ahead. The only requirement is a basic dataset including power, wind speed and air temperature measurements. Then, these are recombined into physics informed metrics able to capture the operational point of the PV. In this way, the models learn about the physical relationships of the different features, effectively easing training. In order to generalise the results, we also present a methodology evaluating this physics informed approach. We present a study-case of a PV system in Denmark to validate our claims by extensively evaluating five different ML methods: Random Forest, Support Vector Machine, Convolutional Neural Networks (CNN), Long-Short Term Memory (LSTM) and a hybrid CNN–LSTM. The results show consistently how the best predictors use the proposed physics-informed features disregarding the particular ML-method, and forecasting horizon. However, also, how there is a threshold regarding the number of previous samples to be included that appears as a convex function. Full article

(This article belongs to the Special Issue Condition Monitoring, Field Inspection and Fault Diagnostic Methods for Photovoltaic Systems)

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14 pages, 2614 KiB

Open AccessArticle

Photovoltaic Panels Classification Using Isolated and Transfer Learned Deep Neural Models Using Infrared Thermographic Images

by Waqas Ahmed, Aamir Hanif, Karam Dad Kallu, Abbas Z. Kouzani, Muhammad Umair Ali and Amad Zafar

Sensors 2021, 21(16), 5668; https://0-doi-org.brum.beds.ac.uk/10.3390/s21165668 - 23 Aug 2021

Cited by 30 | Viewed by 3765

Abstract

Defective PV panels reduce the efficiency of the whole PV string, causing loss of investment by decreasing its efficiency and lifetime. In this study, firstly, an isolated convolution neural model (ICNM) was prepared from scratch to classify the infrared images of PV panels based on their health, i.e., healthy, hotspot, and faulty. The ICNM occupies the least memory, and it also has the simplest architecture, lowest execution time, and an accuracy of 96% compared to transfer learned pre-trained ShuffleNet, GoogleNet, and SqueezeNet models. Afterward, ICNM, based on its advantages, is reused through transfer learning to classify the defects of PV panels into five classes, i.e., bird drop, single, patchwork, horizontally aligned string, and block with 97.62% testing accuracy. This proposed approach can identify and classify the PV panels based on their health and defects faster with high accuracy and occupies the least amount of the system’s memory, resulting in savings in the PV investment. Full article

(This article belongs to the Special Issue Condition Monitoring, Field Inspection and Fault Diagnostic Methods for Photovoltaic Systems)

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22 pages, 2679 KiB

Open AccessEditor’s ChoiceArticle

Anomaly Detection and Automatic Labeling for Solar Cell Quality Inspection Based on Generative Adversarial Network

by Julen Balzategui, Luka Eciolaza and Daniel Maestro-Watson

Sensors 2021, 21(13), 4361; https://0-doi-org.brum.beds.ac.uk/10.3390/s21134361 - 25 Jun 2021

Cited by 15 | Viewed by 2833

Abstract

Quality inspection applications in industry are required to move towards a zero-defect manufacturing scenario, with non-destructive inspection and traceability of 100% of produced parts. Developing robust fault detection and classification models from the start-up of the lines is challenging due to the difficulty in getting enough representative samples of the faulty patterns and the need to manually label them. This work presents a methodology to develop a robust inspection system, targeting these peculiarities, in the context of solar cell manufacturing. The methodology is divided into two phases: In the first phase, an anomaly detection model based on a Generative Adversarial Network (GAN) is employed. This model enables the detection and localization of anomalous patterns within the solar cells from the beginning, using only non-defective samples for training and without any manual labeling involved. In a second stage, as defective samples arise, the detected anomalies will be used as automatically generated annotations for the supervised training of a Fully Convolutional Network that is capable of detecting multiple types of faults. The experimental results using 1873 Electroluminescence (EL) images of monocrystalline cells show that (a) the anomaly detection scheme can be used to start detecting features with very little available data, (b) the anomaly detection may serve as automatic labeling in order to train a supervised model, and (c) segmentation and classification results of supervised models trained with automatic labels are comparable to the ones obtained from the models trained with manual labels. Full article

(This article belongs to the Special Issue Condition Monitoring, Field Inspection and Fault Diagnostic Methods for Photovoltaic Systems)

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25 pages, 7089 KiB

Open AccessArticle

Statistical Methods for Degradation Estimation and Anomaly Detection in Photovoltaic Plants

by Vesna Dimitrievska, Federico Pittino, Wolfgang Muehleisen, Nicole Diewald, Markus Hilweg, Andràs Montvay and Christina Hirschl

Sensors 2021, 21(11), 3733; https://0-doi-org.brum.beds.ac.uk/10.3390/s21113733 - 27 May 2021

Cited by 6 | Viewed by 2577

Abstract

Photovoltaic (PV) plants typically suffer from a significant degradation in performance over time due to multiple factors. Operation and maintenance systems aim at increasing the efficiency and profitability of PV plants by analyzing the monitoring data and by applying data-driven methods for assessing the causes of such performance degradation. Two main classes of degradation exist, being it either gradual or a sudden anomaly in the PV system. This has motivated our work to develop and implement statistical methods that can reliably and accurately detect the performance issues in a cost-effective manner. In this paper, we introduce different approaches for both gradual degradation assessment and anomaly detection. Depending on the data available in the PV plant monitoring system, the appropriate method for each degradation class can be selected. The performance of the introduced methods is demonstrated on data from three different PV plants located in Slovenia and Italy monitored for several years. Our work has led us to conclude that the introduced approaches can contribute to the prompt and accurate identification of both gradual degradation and sudden anomalies in PV plants. Full article

(This article belongs to the Special Issue Condition Monitoring, Field Inspection and Fault Diagnostic Methods for Photovoltaic Systems)

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30 pages, 1929 KiB

Open AccessArticle

A Monitoring System for Online Fault Detection and Classification in Photovoltaic Plants

by André Eugênio Lazzaretti, Clayton Hilgemberg da Costa, Marcelo Paludetto Rodrigues, Guilherme Dan Yamada, Gilberto Lexinoski, Guilherme Luiz Moritz, Elder Oroski, Rafael Eleodoro de Goes, Robson Ribeiro Linhares, Paulo Cézar Stadzisz, Júlio Shigeaki Omori and Rodrigo Braun dos Santos

Sensors 2020, 20(17), 4688; https://0-doi-org.brum.beds.ac.uk/10.3390/s20174688 - 20 Aug 2020

Cited by 53 | Viewed by 7057

Abstract

Photovoltaic (PV) energy use has been increasing recently, mainly due to new policies all over the world to reduce the application of fossil fuels. PV system efficiency is highly dependent on environmental variables, besides being affected by several kinds of faults, which can lead to a severe energy loss throughout the operation of the system. In this sense, we present a Monitoring System (MS) to measure the electrical and environmental variables to produce instantaneous and historical data, allowing to estimate parameters that ar related to the plant efficiency. Additionally, using the same MS, we propose a recursive linear model to detect faults in the system, while using irradiance and temperature on the PV panel as input signals and power as output. The accuracy of the fault detection for a 5 kW power plant used in the test is 93.09%, considering 16 days and around 143 hours of faults in different conditions. Once a fault is detected by this model, a machine-learning-based method classifies each fault in the following cases: short-circuit, open-circuit, partial shadowing, and degradation. Using the same days and faults applied in the detection module, the accuracy of the classification stage is 95.44% for an Artificial Neural Network (ANN) model. By combining detection and classification, the overall accuracy is 92.64%. Such a result represents an original contribution of this work, since other related works do not present the integration of a fault detection and classification approach with an embedded PV plant monitoring system, allowing for the online identification and classification of different PV faults, besides real-time and historical monitoring of electrical and environmental parameters of the plant. Full article

(This article belongs to the Special Issue Condition Monitoring, Field Inspection and Fault Diagnostic Methods for Photovoltaic Systems)

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Condition Monitoring, Field Inspection and Fault Diagnostic Methods for Photovoltaic Systems

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