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Applied Sciences
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Fault Diagnosis and Control Design Applications of Energy Systems
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Fault Diagnosis and Control Design Applications of Energy Systems

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy Science and Technology".

Deadline for manuscript submissions: closed (10 May 2021) | Viewed by 24786

Special Issue Editor

Prof. Dr. Jordi Cusido

E-Mail Website
Guest Editor
School of Industrial, Aeronautica and Audivisuall Engineering of Terrassa (ESEIAAT), Universitat Politècnica de Catalunya (UPC), ES08226 Terrassa, Spain
Interests: fault diagnosis; signal processing; mechatronics; control theory; condition monitoring; system modeling; advanced control theory; system dynamics modeling; data fusion; nonlinear dynamics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Wind and solar energy are extensive renewable energy sources. Both contribute to solving some of the environmental problems caused by climate change. However, the operation and maintenance (O&M) of distributed renewable energy sources is currently a challenge, moving from preventive, corrective, and inspection-based maintenance to data analytics and predictive maintenance.

This Special Issue aims to address the current state-of-the-art technology on data fusion, artificial intelligence, and control applied to optimize the O&M on distributed renewable plants. Papers are invited that investigate innovative methodologies to monitor, diagnose, prognose, and control the performance of renewable assets. Topics may include but are not limited to studies on data-based modelling and supervised and unsupervised algorithms applied to real-time data. Case studies describing real-life applications of novel technologies are also welcome.

Prof. Dr. Jordi Cusido
Guest Editor

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 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

  • Deep learning
  • Machine learning
  • Fault tolerant control
  • Renewables
  • Predictive maintenance
  • Decision support systems
  • Information fusion
  • Text mining

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Published Papers (6 papers)

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Research

21 pages, 71249 KiB  
Article
Using UAV to Detect Solar Module Fault Conditions of a Solar Power Farm with IR and Visual Image Analysis
by Kuo-Chien Liao and Jau-Huai Lu
Appl. Sci. 2021, 11(4), 1835; https://doi.org/10.3390/app11041835 - 19 Feb 2021
Cited by 31 | Viewed by 5032
Abstract
In recent years, solar energy has been regarded as one of the most important sustainable energy sources. Under the rapid and large-scale construction of solar farms, the maintenance and inspection of the health conditions of solar modules in a large solar farm become [...] Read more.
In recent years, solar energy has been regarded as one of the most important sustainable energy sources. Under the rapid and large-scale construction of solar farms, the maintenance and inspection of the health conditions of solar modules in a large solar farm become an important issue. This article proposes a method for detecting solar cell faults with unmanned aerial vehicle (UAV) equipped with a thermal imager and a visible light camera, and providing a fast and reliable detection method. The detection process includes a new concept of real-time monitoring of the detected area and analysis of the health of solar panels. An image process is proposed that may quickly and accurately detect the abnormality of a solar module. The whole process includes grayscale conversion, filtering, 3-D temperature representation, probability density function, and cumulative density function analysis. Ten cases in real fields have been studied with this process, including large scale solar farms and small size solar modules installed on buildings. Results show that the cumulative density function is a convenient way to determine the health status of the solar panel and may provide maintenance personnel a basis for determining whether replacement of solar cells is necessary for improving the overall power generation efficiency and simplify the maintenance process. It is worth noting that image recognition can increase the clarity of IR images and the cumulative chart can judge the defect rate of the cell. These two methods were combined to provide an instant, fast and accurate defect judgment. Full article
(This article belongs to the Special Issue Fault Diagnosis and Control Design Applications of Energy Systems)
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20 pages, 6002 KiB  
Article
Wind Turbine Prognosis Models Based on SCADA Data and Extreme Learning Machines
by Pere Marti-Puig, Alejandro Blanco-M., Moisès Serra-Serra and Jordi Solé-Casals
Appl. Sci. 2021, 11(2), 590; https://doi.org/10.3390/app11020590 - 9 Jan 2021
Cited by 22 | Viewed by 3220
Abstract
In this paper, a method to build models to monitor and evaluate the health status of wind turbines using Single-hidden Layer Feedforward Neural networks (SLFN) is presented. The models are trained using the Extreme Learning Machines (ELM) strategy. The data used is obtained [...] Read more.
In this paper, a method to build models to monitor and evaluate the health status of wind turbines using Single-hidden Layer Feedforward Neural networks (SLFN) is presented. The models are trained using the Extreme Learning Machines (ELM) strategy. The data used is obtained from the SCADA systems, easily available in modern wind turbines. The ELM technique requires very low computational costs for the training of the models, and thus allows for the integration of a grid-search approach with parallelized instances to find out the optimal model parameters. These models can be built both individually, considering the turbines separately, or as an aggregate for the whole wind plant. The followed strategy consists in predicting a target variable using the rest of the variables of the system/subsystem, computing the error deviation from the real target variable and finally comparing high error values with a selection of alarm events for that system, therefore validating the performance of the model. The experimental results indicate that this methodology leads to the detection of mismatches in the stages of the system’s failure, thus making it possible to schedule the maintenance operation before a critical failure occurs. The simplicity of the ELM systems and the ease with which the parameters can be adjusted make it a realistic option to be implemented in wind turbine models to work in real time. Full article
(This article belongs to the Special Issue Fault Diagnosis and Control Design Applications of Energy Systems)
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15 pages, 15471 KiB  
Article
Solar Cell Cracks and Finger Failure Detection Using Statistical Parameters of Electroluminescence Images and Machine Learning
by Harsh Rajesh Parikh, Yoann Buratti, Sergiu Spataru, Frederik Villebro, Gisele Alves Dos Reis Benatto, Peter B. Poulsen, Stefan Wendlandt, Tamas Kerekes, Dezso Sera and Ziv Hameiri
Appl. Sci. 2020, 10(24), 8834; https://doi.org/10.3390/app10248834 - 10 Dec 2020
Cited by 30 | Viewed by 6081
Abstract
A wide range of defects, failures, and degradation can develop at different stages in the lifetime of photovoltaic modules. To accurately assess their effect on the module performance, these failures need to be quantified. Electroluminescence (EL) imaging is a powerful diagnostic method, providing [...] Read more.
A wide range of defects, failures, and degradation can develop at different stages in the lifetime of photovoltaic modules. To accurately assess their effect on the module performance, these failures need to be quantified. Electroluminescence (EL) imaging is a powerful diagnostic method, providing high spatial resolution images of solar cells and modules. EL images allow the identification and quantification of different types of failures, including those in high recombination regions, as well as series resistance-related problems. In this study, almost 46,000 EL cell images are extracted from photovoltaic modules with different defects. We present a method that extracts statistical parameters from the histogram of these images and utilizes them as a feature descriptor. Machine learning algorithms are then trained using this descriptor to classify the detected defects into three categories: (i) cracks (Mode B and C), (ii) micro-cracks (Mode A) and finger failures, and (iii) no failures. By comparing the developed methods with the commonly used one, this study demonstrates that the pre-processing of images into a feature vector of statistical parameters provides a higher classification accuracy than would be obtained by raw images alone. The proposed method can autonomously detect cracks and finger failures, enabling outdoor EL inspection using a drone-mounted system for quick assessments of photovoltaic fields. Full article
(This article belongs to the Special Issue Fault Diagnosis and Control Design Applications of Energy Systems)
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15 pages, 2092 KiB  
Article
Wind Fleet Generator Fault Detection via SCADA Alarms and Autoencoders
by Mattia Beretta, Juan José Cárdenas, Cosmin Koch and Jordi Cusidó
Appl. Sci. 2020, 10(23), 8649; https://doi.org/10.3390/app10238649 - 3 Dec 2020
Cited by 13 | Viewed by 3317
Abstract
A hybrid health monitoring system for wind turbine generators is introduced. The novelty of this research consists in approaching a 115-wind turbine fleet by using the fusion of multiple sources of information. Analog SCADA data is analyzed through an autoencoder which allows to [...] Read more.
A hybrid health monitoring system for wind turbine generators is introduced. The novelty of this research consists in approaching a 115-wind turbine fleet by using the fusion of multiple sources of information. Analog SCADA data is analyzed through an autoencoder which allows to identify anomalous patterns within the input variables. Alarm logs are processed and merged to the anomaly detection output, creating a reliable health estimator of generator conditions. The proposed methodology has been tested on a fleet of 115 wind turbines from four different manufacturers located in various locations around Europe. The solution has been compared with other existing data modeling techniques offering impressive results on the fleet. An accuracy of 82% and a Kappa of 56% were obtained. The detailed methodology is presented using one of the available windfarms, composed of 13 onshore wind turbines rated 2 MW power. The rigorous evaluation of the results, the utilization of real data and the heterogeneity of the dataset prove the validity of the system and its applicability in an online operating scenario. Full article
(This article belongs to the Special Issue Fault Diagnosis and Control Design Applications of Energy Systems)
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23 pages, 1534 KiB  
Article
Damage Diagnosis for Offshore Wind Turbine Foundations Based on the Fractal Dimension
by Ervin Hoxha, Yolanda Vidal and Francesc Pozo
Appl. Sci. 2020, 10(19), 6972; https://doi.org/10.3390/app10196972 - 5 Oct 2020
Cited by 18 | Viewed by 3425
Abstract
Cost-competitiveness of offshore wind depends heavily in its capacity to switch preventive maintenance to condition-based maintenance. That is, to monitor the actual condition of the wind turbine (WT) to decide when and which maintenance needs to be done. In particular, structural health monitoring [...] Read more.
Cost-competitiveness of offshore wind depends heavily in its capacity to switch preventive maintenance to condition-based maintenance. That is, to monitor the actual condition of the wind turbine (WT) to decide when and which maintenance needs to be done. In particular, structural health monitoring (SHM) to monitor the foundation (support structure) condition is of utmost importance in offshore-fixed wind turbines. In this work a SHM strategy is presented to monitor online and during service a WT offshore jacket-type foundation. Standard SHM techniques, as guided waves with a known input excitation, cannot be used in a straightforward way in this particular application where unknown external perturbations as wind and waves are always present. To face this challenge, a vibration-response-only SHM strategy is proposed via machine learning methods. In this sense, the fractal dimension is proposed as a suitable feature to identify and classify different types of damage. The proposed proof-of-concept technique is validated in an experimental laboratory down-scaled jacket WT foundation undergoing different types of damage. Full article
(This article belongs to the Special Issue Fault Diagnosis and Control Design Applications of Energy Systems)
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17 pages, 7369 KiB  
Article
An Optimal Power Control Strategy for Grid-Following Inverters in a Synchronous Frame
by Juan F. Patarroyo-Montenegro, Jesus D. Vasquez-Plaza, Fabio Andrade and Lingling Fan
Appl. Sci. 2020, 10(19), 6730; https://doi.org/10.3390/app10196730 - 25 Sep 2020
Cited by 7 | Viewed by 2923
Abstract
This work proposes a power control strategy based on the linear quadratic regulator with optimal reference tracking (LQR-ORT) for a three-phase inverter-based generator (IBG) using an LCL filter. The use of an LQR-ORT controller increases robustness margins and reduces the quadratic value of [...] Read more.
This work proposes a power control strategy based on the linear quadratic regulator with optimal reference tracking (LQR-ORT) for a three-phase inverter-based generator (IBG) using an LCL filter. The use of an LQR-ORT controller increases robustness margins and reduces the quadratic value of the power error and control inputs during transient response. A model in a synchronous reference frame that integrates power sharing and voltage–current (V–I) dynamics is also proposed. This model allows for analyzing closed-loop eigenvalue location and robustness margins. The proposed controller was compared against a classical droop approach using proportional-resonant controllers for the inner loops. Mathematical analysis and hardware-in-the-loop (HIL) experiments under variations in the LCL filter components demonstrate fulfillment of robustness and performance bounds of the LQR-ORT controller. Experimental results demonstrate accuracy of the proposed model and the effectiveness of the LQR-ORT controller in improving transient response, robustness, and power decoupling. Full article
(This article belongs to the Special Issue Fault Diagnosis and Control Design Applications of Energy Systems)
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