Advances in the Monitoring and Diagnosis of Faults in Wind Power Plants

Topic Information

Dear Colleagues,

The generation of electrical power in wind farms is currently a key technology in the energy transition to a system based on renewable sources, free from greenhouse gas emissions, and therefore essential in the fight against climate change.

Although wind power generation is currently a mature technology, one of the inherent problems it presents is the difficulty of accessing the generator units, which complicates maintenance and repair work in case of breakdowns. This problem worsens with the aging of the wind farms and also with the installation of new ones, where the power of the units (and consequently the height of the towers and the difficulty of access) continuously increases. For all these reasons, the importance of monitoring and early fault detection techniques for wind power plants is growing every day. These techniques allow the development of predictive maintenance systems that minimize operating costs and production losses due to unexpected shutdowns.

The objective of this topic is to provide a multidisciplinary overview of the most recent advances in the field of monitoring and early detection of faults in wind power units. Works related to fault detection in any of the elements or systems of wind turbine units (turbine, powertrain, bearings, gearbox, electrical generator, electronic controller, yaw system, pitch system, etc.) are of interest in this section. These works may be based on the use of any type of sensor or data provided by the SCADA system, using signal analysis techniques, model-based methods, data-driven based methods, or AI-based methods. In particular, contributions suited for this topic include, but are not limited to:

• New diagnostic techniques based on conventional sensors for wind turbine fault diagnosis
• Development and application of smart sensors for wind turbine fault diagnosis
• Use of electrical drive as a sensor for fault detection in wind turbines; embedded diagnostic systems in pre-existing controllers
• Fault diagnosis based on SCADA data processing
• Diagnostic techniques in wind power units, based on the integration of information provided by different types of sensors
• Application of AI to wind turbine fault diagnosis.
• Development of deep learning based methods to fault detection in wind turbines.
• Development of models for fault detection. Methodologies based on digital twins.

Prof. Dr. Javier Martinez-Roman
Prof. Dr. Manuel Pineda-Sanchez
Prof. Dr. Martin Riera-Guasp
Prof. Dr. Angel Sapena-Bano
Prof. Dr. Jordi Burriel-Valencia
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Actuators actuators	2.2	3.9	2012	16.5 Days	CHF 2400	Submit
Applied Sciences applsci	2.5	5.3	2011	17.8 Days	CHF 2400	Submit
Energies energies	3.0	6.2	2008	17.5 Days	CHF 2600	Submit
Machines machines	2.1	3.0	2013	15.6 Days	CHF 2400	Submit
Sensors sensors	3.4	7.3	2001	16.8 Days	CHF 2600	Submit

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Published Papers (5 papers)

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19 pages, 6618 KiB  

Open AccessArticle

Leading Edge Erosion Classification in Offshore Wind Turbines Using Feature Extraction and Classical Machine Learning

by Oscar Best, Asiya Khan, Sanjay Sharma, Keri Collins and Mario Gianni

Energies 2024, 17(21), 5475; https://doi.org/10.3390/en17215475 - 1 Nov 2024

Viewed by 519

Abstract

Leading edge (LE) erosion is a type of damage that inhibits the aerodynamic performance of a wind turbine, resulting in high operation and maintenance (O&M) costs. This paper makes use of a small dataset consisting of 50 images of LE erosion and healthy [...] Read more.

Leading edge (LE) erosion is a type of damage that inhibits the aerodynamic performance of a wind turbine, resulting in high operation and maintenance (O&M) costs. This paper makes use of a small dataset consisting of 50 images of LE erosion and healthy blades for feature extraction and the training of four types of classifiers, namely, support vector machine (SVM), random forest, K-nearest neighbour (KNN), and multi-layer perceptron (MLP). Six feature extraction methods were used with these classifiers to train 24 models. The dataset has also been used to train a convolutional neural network (CNN) model developed using Keras. The purpose of this work is to determine whether classical machine learning (ML) classifiers trained with extracted features can produce higher-accuracy results, train faster, and classify faster than deep learning (DL) models for the application of LE damage detection of wind turbine blades. The oriented fast and rotated brief (ORB)-trained SVM achieved an accuracy of 90% ± 0.01, took 80.4 s to train, and achieved inference speeds of 63 frames per second (FPS), compared to the CNN model, which achieved an accuracy of 79.4% ± 2.07, took 4667.4 s to train, and achieved an inference speed of 1.3 FPS. These results suggest that classical ML models can be more accurate and efficient than DL models if the appropriate feature extraction method is used. Full article

(This article belongs to the Topic Advances in the Monitoring and Diagnosis of Faults in Wind Power Plants)

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26 pages, 10503 KiB  

Open AccessArticle

Wireless Data Acquisition System with Feedback Function

by Anatoliy Manukovsky, Aigerim Sagyndyk, Aleksandr Kislov, Olzhas Talipov and Alexey Manukovsky

Appl. Sci. 2024, 14(13), 5553; https://doi.org/10.3390/app14135553 - 26 Jun 2024

Viewed by 1198

Abstract

When operating solar–wind power plants (SWPPs) located in populated areas, cases of premature failure of expensive batteries and other power equipment often occur. The purpose of this study is to develop a wireless data acquisition system (DAS) for the operation of an SWPP [...] Read more.

When operating solar–wind power plants (SWPPs) located in populated areas, cases of premature failure of expensive batteries and other power equipment often occur. The purpose of this study is to develop a wireless data acquisition system (DAS) for the operation of an SWPP with a feedback function to prevent material damage from the failure of power equipment and to increase the efficiency of natural energy use. The principles of constructing a DAS, free from some of the disadvantages of analogues, are described in this paper. Intelligent wireless current and voltage sensors and a device for receiving and recording data with an additional feedback function have been developed, providing real-time feedback when the measured parameters go outside the norm. Measurement data are displayed on the laptop screen and alphanumeric display and stored on the hard drive along with timestamps and current event messages. An example of using a reverse communication channel to implement the functions of backup battery protection and to switch SWPP loads is described. The principles and methods proposed in this article are suitable for constructing systems for remote measurements of any physical quantities; therefore, the scope of application of the described system can be significantly expanded. Full article

(This article belongs to the Topic Advances in the Monitoring and Diagnosis of Faults in Wind Power Plants)

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14 pages, 3203 KiB  

Open AccessArticle

Real-Time Monitoring of Wind Turbine Bearing Using Simple Neural Network on Raspberry Pi

by Tianhao Wang, Hongying Meng, Rui Qin, Fan Zhang and Asoke Kumar Nandi

Appl. Sci. 2024, 14(7), 3129; https://doi.org/10.3390/app14073129 - 8 Apr 2024

Cited by 1 | Viewed by 1204

Abstract

Wind turbines are a crucial part of renewable energy generation, and their reliable and efficient operation is paramount in ensuring clean energy availability. However, the bearings in wind turbines are subjected to high stress and loads, resulting in faults that can lead to [...] Read more.

Wind turbines are a crucial part of renewable energy generation, and their reliable and efficient operation is paramount in ensuring clean energy availability. However, the bearings in wind turbines are subjected to high stress and loads, resulting in faults that can lead to costly downtime and repairs. Fault detection in real time is critical to minimize downtime and reduce maintenance costs. In this work, a simple neural network model was designed and implemented on a Raspberry Pi for the real-time detection of wind turbine bearing faults. The model was trained to accurately identify complex patterns in raw sensor data of healthy and faulty bearings. By splitting the data into smaller segments, the model can quickly analyze each segment and generate predictions at high speed. Additionally, simplified algorithms were developed to analyze the segments with minimum latency. The proposed system can efficiently process the sensor data and performs rapid analysis and prediction within 0.06 milliseconds per data segment. The experimental results demonstrate that the model achieves a 99.8% accuracy in detecting wind turbine bearing faults within milliseconds of their occurrence. The model’s ability to generate real-time predictions and to provide an overall assessment of the bearing’s health can significantly reduce maintenance costs and increase the availability and efficiency of wind turbines. Full article

(This article belongs to the Topic Advances in the Monitoring and Diagnosis of Faults in Wind Power Plants)

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29 pages, 10690 KiB  

Open AccessArticle

Compound Fault Characteristic Analysis for Fault Diagnosis of a Planetary Gear Train

by Yulin Ren, Guoyan Li, Xiong Li, Jingbin Zhang, Runjun Liu and Sifan Shi

Sensors 2024, 24(3), 927; https://doi.org/10.3390/s24030927 - 31 Jan 2024

Cited by 2 | Viewed by 1098

Abstract

The carrier eccentricity error and gear compound faults are most likely to occur simultaneously in an actual planetary gear train (PGT). Various faults and errors are coupled with each other to generate a complex dynamic response, which makes the diagnosis of PGT faults [...] Read more.

The carrier eccentricity error and gear compound faults are most likely to occur simultaneously in an actual planetary gear train (PGT). Various faults and errors are coupled with each other to generate a complex dynamic response, which makes the diagnosis of PGT faults difficult in practice. In order to analyze the joint effect of the error and the compound faults in a PGT, a carrier eccentricity error model is proposed and incorporated into the TVMS model by considering the time-varying center distance, line of action (LOA), meshing angle, and contact ratio. Then, the TVMS of the cracked gear is derived based on the potential energy method. On this basis, the dynamic model of a PGT with both the carrier eccentricity error and compound gear cracks as internal excitations are established. Furthermore, the meshing characteristics and dynamic responses of the PGT are simulated to investigate the compound fault features. A series of experiments are conducted to further analyze the influence of the compound fault on the vibration response. The relevant conclusions can provide a reference for the compound fault diagnosis of a PGT in practice. Full article

(This article belongs to the Topic Advances in the Monitoring and Diagnosis of Faults in Wind Power Plants)

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29 pages, 4365 KiB  

Open AccessArticle

Data Fusion and Ensemble Learning for Advanced Anomaly Detection Using Multi-Spectral RGB and Thermal Imaging of Small Wind Turbine Blades

by Majid Memari, Mohammad Shekaramiz, Mohammad A. S. Masoum and Abdennour C. Seibi

Energies 2024, 17(3), 673; https://doi.org/10.3390/en17030673 - 31 Jan 2024

Cited by 9 | Viewed by 2045

Abstract

This paper introduces an innovative approach to Wind Turbine Blade (WTB) inspection through the synergistic use of thermal and RGB imaging, coupled with advanced deep learning techniques. We curated a unique dataset of 1000 thermal images of healthy and faulty blades using a [...] Read more.

This paper introduces an innovative approach to Wind Turbine Blade (WTB) inspection through the synergistic use of thermal and RGB imaging, coupled with advanced deep learning techniques. We curated a unique dataset of 1000 thermal images of healthy and faulty blades using a FLIR C5 Compact Thermal Camera, which is equipped with Multi-Spectral Dynamic Imaging technology for enhanced imaging. This paper focuses on evaluating 35 deep learning classifiers, with a standout ensemble model combining Vision Transformer (ViT) and DenseNet161, achieving a remarkable 100% accuracy on the dataset. This model demonstrates the exceptional potential of deep learning in thermal diagnostic applications, particularly in predictive maintenance within the renewable energy sector. Our findings underscore the synergistic combination of ViT’s global feature analysis and DenseNet161’s dense connectivity, highlighting the importance of controlled environments and sophisticated preprocessing for accurate thermal image capture. This research contributes significantly to the field by providing a comprehensive dataset and demonstrating the efficacy of several deep learning models in ensuring the operational efficiency and reliability of wind turbines. Full article

(This article belongs to the Topic Advances in the Monitoring and Diagnosis of Faults in Wind Power Plants)

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