Print Email Facebook Twitter Improved Anomaly Detection and Localization Using Whitening-Enhanced Autoencoders Title Improved Anomaly Detection and Localization Using Whitening-Enhanced Autoencoders Author Wang, C. (TU Delft Intelligent Electrical Power Grids) Tindemans, Simon H. (TU Delft Intelligent Electrical Power Grids) Palensky, P. (TU Delft Intelligent Electrical Power Grids) Date 2024 Abstract Anomaly detection is of considerable significance in engineering applications, such as the monitoring and control of large-scale energy systems. This article investigates the ability to accurately detect and localize the source of anomalies, using an autoencoder neural network-based detector. Correlations between residuals are identified as a source of misclassifications, and whitening transformations that decorrelate input features and/or residuals are analyzed as a potential solution. For two use cases, regarding spatially distributed wind power generation and temporal profiles of electricity consumption, the performance of various data processing combinations was quantified. Whitening of the input data was found to be most beneficial for accurate detection, with a slight benefit for the combined whitening of inputs and residuals. For localization of anomalies, whitening of residuals was preferred, and the best performance was obtained using standardization of the input data and whitening of the residuals using the zero-phase component analysis (ZCA) or zero-phase component analysis-correlation (ZCA-cor) whitening matrix with a small additional offset. To reference this document use: http://resolver.tudelft.nl/uuid:8711ee3f-3ad0-43e2-9f20-7d6c101ef3c0 DOI https://doi.org/10.1109/TII.2023.3268685 Embargo date 2024-01-03 ISSN 1941-0050 Source IEEE Transactions on Industrial Informatics, 20 (1), 659-668 Bibliographical note Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. Part of collection Institutional Repository Document type journal article Rights © 2024 C. Wang, Simon H. Tindemans, P. Palensky Files PDF Improved_Anomaly_Detectio ... coders.pdf 3.75 MB Close viewer /islandora/object/uuid:8711ee3f-3ad0-43e2-9f20-7d6c101ef3c0/datastream/OBJ/view