Multi-Fault Detection and Classification of Wind Turbines Using Stacking Classifier

Byun, Yung-Cheol

doi:10.3390/s22186955

Cited by 19 publications

(4 citation statements)

References 40 publications

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“…SVMs are useful for fault classification and localization, assisted by supervision to find the hyperplane for separating data point types [23]. They may considerably improve fault classification and localization processes to find the best hyperplane in n dimensions [24,25].…”

Section: Proposed Methodologymentioning

confidence: 99%

Improved Fault Classification and Localization in Power Transmission Networks Using VAE-Generated Synthetic Data and Machine Learning Algorithms

Khan,

Asad,

Vaimann

et al. 2023

Machines

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The reliable operation of power transmission networks depends on the timely detection and localization of faults. Fault classification and localization in electricity transmission networks can be challenging because of the complicated and dynamic nature of the system. In recent years, a variety of machine learning (ML) and deep learning algorithms (DL) have found applications in the enhancement of fault identification and classification within power transmission networks. Yet, the efficacy of these ML architectures is profoundly dependent upon the abundance and quality of the training data. This intellectual explanation introduces an innovative strategy for the classification and pinpointing of faults within power transmission networks. This is achieved through the utilization of variational autoencoders (VAEs) to generate synthetic data, which in turn is harnessed in conjunction with ML algorithms. This approach encompasses the augmentation of the available dataset by infusing it with synthetically generated instances, contributing to a more robust and proficient fault recognition and categorization system. Specifically, we train the VAE on a set of real-world power transmission data and generate synthetic fault data that capture the statistical properties of real-world data. To overcome the difficulty of fault diagnosis methodology in three-phase high voltage transmission networks, a categorical boosting (Cat-Boost) algorithm is proposed in this work. The other standard machine learning algorithms recommended for this study, including Support Vector Machine (SVM), Decision Trees (DT), Random Forest (RF), and K-Nearest Neighbors (KNN), utilizing the customized version of forward feature selection (FFS), were trained using synthetic data generated by a VAE. The results indicate exceptional performance, surpassing current state-of-the-art techniques, in the tasks of fault classification and localization. Notably, our approach achieves a remarkable 99% accuracy in fault classification and an extremely low mean absolute error (MAE) of 0.2 in fault localization. These outcomes represent a notable advancement compared to the most effective existing baseline methods.

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Section: Proposed Methodologymentioning

confidence: 99%

Improved Fault Classification and Localization in Power Transmission Networks Using VAE-Generated Synthetic Data and Machine Learning Algorithms

Khan,

Asad,

Vaimann

et al. 2023

Machines

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“…Recognizing the intricate relationships inherent in SCADA data streams, Khan and Byun proposed a stacking ensemble classifier that leverages the strengths of AdaBoost, Knearest neighbors, and logistic regression [14]. Their approach yielded enhanced accuracy in anomaly detection within SCADA data.…”

Section: Introductionmentioning

confidence: 99%

Anomaly Detection on Small Wind Turbine Blades Using Deep Learning Algorithms

Altice,

Nazario,

Davis

et al. 2024

Energies

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Wind turbine blade maintenance is expensive, dangerous, time-consuming, and prone to misdiagnosis. A potential solution to aid preventative maintenance is using deep learning and drones for inspection and early fault detection. In this research, five base deep learning architectures are investigated for anomaly detection on wind turbine blades, including Xception, Resnet-50, AlexNet, and VGG-19, along with a custom convolutional neural network. For further analysis, transfer learning approaches were also proposed and developed, utilizing these architectures as the feature extraction layers. In order to investigate model performance, a new dataset containing 6000 RGB images was created, making use of indoor and outdoor images of a small wind turbine with healthy and damaged blades. Each model was tuned using different layers, image augmentations, and hyperparameter tuning to achieve optimal performance. The results showed that the proposed Transfer Xception outperformed other architectures by attaining 99.92% accuracy on the test data of this dataset. Furthermore, the performance of the investigated models was compared on a dataset containing faulty and healthy images of large-scale wind turbine blades. In this case, our results indicated that the best-performing model was also the proposed Transfer Xception, which achieved 100% accuracy on the test data. These accuracies show promising results in the adoption of machine learning for wind turbine blade fault identification.

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“…To demonstrate the efficiency of PV systems and create cost-effective mitigation, soil impact assessments were recommended at different locations and times. Machine learning (ML) is widely used for a variety of purposes in the renewable energy industry 12 – 14 . Some of the applications involve energy forecasting 15 , solar radiation forecasting 16 , locations and sizes of solar 17 , and roof shape classification 18 .…”

Section: Introductionmentioning

confidence: 99%

A stacking ensemble classifier-based machine learning model for classifying pollution sources on photovoltaic panels

Byun

Jeong

2023

Sci Rep

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Solar energy is a very efficient alternative for generating clean electric energy. However, pollution on the surface of solar panels reduces solar radiation, increases surface transmittance, and raises the surface temperature. All these factors cause photovoltaic (PV) panels to be less efficient. To address this problem, a stacking ensemble classifier-based machine learning model is proposed. In this study, different sources of pollution on each solar panel are used, and their power generation is recorded. The proposed model includes gradient boost, extra tree, and random forest classifiers, with the extra tree classifier serving as a meta-learner. The model takes into account various weather features during the training process, including irradiance and temperature, aiming to increase its accuracy and robustness in identifying pollution sources on the PV panel. Moreover, the proposed model is evaluated using various methods in order to examine performance metrics such as accuracy, F1 score, and precision. Results show that the model can achieve an accuracy score of 97.37%. The model’s performance is also compared to state-of-the-art machine learning models, demonstrating its superiority in accurately classifying pollution sources on PV panels. By utilizing different sources of pollution and weather features during training, the model can accurately classify different pollution sources, resulting in increased power generation efficiency and the longevity of PV panels. The main results of this study can be used to manage and maintain PV panels since the model can identify PV modules that need to be cleaned to keep producing the most power. Furthermore, the efficiency, reliability, and sustainability of PV panels can be further enhanced by the proposed model.

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Multi-Fault Detection and Classification of Wind Turbines Using Stacking Classifier

Cited by 19 publications

References 40 publications

Improved Fault Classification and Localization in Power Transmission Networks Using VAE-Generated Synthetic Data and Machine Learning Algorithms

Improved Fault Classification and Localization in Power Transmission Networks Using VAE-Generated Synthetic Data and Machine Learning Algorithms

Anomaly Detection on Small Wind Turbine Blades Using Deep Learning Algorithms

A stacking ensemble classifier-based machine learning model for classifying pollution sources on photovoltaic panels

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