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We propose a novel unsupervised anomaly detection and diagnosis algorithm in power electronic networks. Since most anomaly detection and diagnosis algorithms in the literature are based on supervised methods that can hardly be generalized to broader scenarios, we propose unsupervised algorithms. Our algorithm extracts the Time-Frequency Domain (TFD) features from the three-phase currents and three-phase voltages of the point of coupling (PCC) nodes to detect anomalies and distinguish between different types of anomalies, such as cyber-attacks and physical faults. To detect anomalies through TFD features, we propose a novel Informative Leveraging for Anomaly Detection (ILAD) algorithm. The proposed unsupervised ILAD algorithm automatically extracts noise-reduced anomalous signals, resulting in more accurate anomaly detection results than other score-based methods. To assign anomaly types for anomaly diagnosis, we apply a novel Multivariate Functional Principal Component Analysis (MFPCA) clustering method. Unlike the deep learning methods, the MFPCA clustering method does not require labels for training and provides more accurate results than other deep embedding-based clustering approaches. Furthermore, it is even comparable to supervised algorithms in both offline and online experiments. To the best of our knowledge, the proposed unsupervised framework accomplishing anomaly detection and anomaly diagnosis tasks is the first of its kind in power electronic networks.

The conventional dyeing process which is practiced mostly in our textile industry to dye cotton has a high amount of water consumption and consequently releases a massive amount of wastewater which leads to economic cost, and environmental impact associated with the removal of reactive dye from the effluent. Nanocellulose gel, when combined with a reactive dye, can be applied as a coating to cotton fabric. This novel dyeing approach significantly reduces water usage, generation of wastewater, and treatment costs. The study suggests that the application of nanocellulose gel-dye coating to cotton fabrics improves the colorfastness of the fabrics. However, dyenanocellulose gel coating also lightens the fabric color. Therefore, the gel to dye ratio, in grams/milliliter, must be retained roughly under 0.40 to yield the same color as fabric dyed directly without a nanocellulose coating. [ABSTRACT FROM AUTHOR]