Imaging Wind Turbine Fault Signatures Based on Power Curve and Self-Organizing Map for Image-Based Fault Diagnosis

Bilendo, Francisco; Badihi, Hamed; Lu, Ningyun; Cambron, Philippe; Jiang, Bin

doi:10.1109/adconip55568.2022.9894155

Cited by 2 publications

(3 citation statements)

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“…During the training process, the weights adjust according to input until maximum iteration is reached. This approach (SOM), for instance, was used in [76] to minimize the squared error generated by local interpolation applicable to datasets with outliers, in [77] it was used for imaging wind turbine fault signatures based on power curve for image-based fault diagnosis, and in [78] due to its ability to cluster data in an unsupervised manner.…”

Section: Clustering Approachmentioning

confidence: 99%

“…For instance, the number of clusters in "DBSCAN" is detected according to the algorithm's principles, whereas the number of clusters in "k-means clustering" algorithm is specified by the operator, and, thus, it can be challenging to attain an optimal number of clusters using the "k-means clustering". With respect to "SOMs", they have not yet been widely explored on power curve based data, however, the results of implementations in [76][77][78] are appealing.…”

Section: Remarks (On Clustering Approach)mentioning

confidence: 99%

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Applications and Modeling Techniques of Wind Turbine Power Curve for Wind Farms—A Review

et al. 2022

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In the wind energy industry, the power curve represents the relationship between the “wind speed” at the hub height and the corresponding “active power” to be generated. It is the most versatile condition indicator and of vital importance in several key applications, such as wind turbine selection, capacity factor estimation, wind energy assessment and forecasting, and condition monitoring, among others. Ensuring an effective implementation of the aforementioned applications mostly requires a modeling technique that best approximates the normal properties of an optimal wind turbines operation in a particular wind farm. This challenge has drawn the attention of wind farm operators and researchers towards the “state of the art” in wind energy technology. This paper provides an exhaustive and updated review on power curve based applications, the most common anomaly and fault types including their root-causes, along with data preprocessing and correction schemes (i.e., filtering, clustering, isolation, and others), and modeling techniques (i.e., parametric and non-parametric) which cover a wide range of algorithms. More than 100 references, for the most part selected from recently published journal articles, were carefully compiled to properly assess the past, present, and future research directions in this active domain.

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Section: Clustering Approachmentioning

confidence: 99%

Section: Remarks (On Clustering Approach)mentioning

confidence: 99%

Applications and Modeling Techniques of Wind Turbine Power Curve for Wind Farms—A Review

et al. 2022

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“…In [30], the authors augmented a base linear regression learner with recurrent neural networks (RNNs) to improve interpretability for time-series forecasting. Using class activation maps for power converter fault diagnosis [23], [31], decoupling position embedding units [32], time-domain attention [33], and self-organizing maps for encoding power curves [34] have improved the interpretability of fault diagnosis. However, the interpretability approach explored in these studies has significant drawbacks.…”

Section: Introductionmentioning

confidence: 99%

Hyperparameter Optimization of Long Short Term Memory Models for Interpretable Electrical Fault Classification

G. M.,

Pillai

2023

IEEE Access

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The reliability of the model significantly affects early detection and accurate classification of electrical faults. In this study, a Long Short Term Memory based fault classification model was developed for the Power System Machine Learning benchmark dataset, focusing on improving reliability by increasing interpretability. First, novel metrics are introduced to measure model interpretability. These interpretability metrics are uniquely defined based on the disentanglement of the fault classification factors. Subsequently, hyperparameter optimization was performed using multi-objective Bayesian Optimization to determine the optimal model architecture. The objective of optimization is to maximize interpretability and classification accuracy. The Pareto-optimal solution presents different model architectures with varying accuracy and interpretability trade-offs. Finally, the manifestation of interpretability in terms of subsequences is studied using Shapley Additive Explanations. The impact of class representation and architectural parameters on interpretability was also analyzed. Furthermore, the most accurate model in the Pareto front achieved highly competitive accuracy for the benchmark data.

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Imaging Wind Turbine Fault Signatures Based on Power Curve and Self-Organizing Map for Image-Based Fault Diagnosis

Cited by 2 publications

References 21 publications

Applications and Modeling Techniques of Wind Turbine Power Curve for Wind Farms—A Review

Applications and Modeling Techniques of Wind Turbine Power Curve for Wind Farms—A Review

Hyperparameter Optimization of Long Short Term Memory Models for Interpretable Electrical Fault Classification

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