Feb 1, 2024 · To address some of the gaps in the aforementioned studies, this paper proposes a cyber–physical anomaly detector-based autoencoder to effectively detect any cyber and physical abnormalities in an inverter-based microgrid.

The proposed cyber anomaly detection technique is implemented on a microgrid’s load frequency control (LFC) subject to cyber intrusions on measurements. The simulation results prove the efficacy of the proposed detection method for an islanded microgrid.

Oct 19, 2023 · The objective of this paper is to develop an anomaly detection framework for the smart microgrid system at MCAS Miramar to enhance its cyber-resilience. We implement predictive analytics using machine learning to deal with cyber-uncertainties and threats within the microgrid environment.

We implement predictive analytics using machine learning to deal with cyber-uncertainties and threats within the microgrid environment. An autoencoder neural network is implemented to classify and identify specific cyber-attacks against this infrastructure.

We implement predictive analytics using machine learning to deal with cyber-uncertainties and threats within the microgrid environment. An autoencoder neural network is implemented to...

Oct 19, 2023 · We implement predictive analytics using machine learning to deal with cyber-uncertainties and threats within the microgrid environment. An autoencoder neural network is implemented to classify...

unsupervised deep recurrent autoencoder anomaly detection scheme in DC microgrids offers su. erior detection performance compared with other benchmarks. The autoencoder i. trained on benign data generated from a multi-source DC microgrid model. (ii) Fusing current and voltage data for training offers a 14.7% im.

Due to the tem-poral characteristics in most of the power grid datasets, we ex-plore a long short-term memory-variational autoencoder-based deep generative model that can tolerate the moderate presence of anomalous data during training instead of standard data.

5 days ago · The performance metrics in Table 1 indicate that the LSTM autoencoder model is 77% accurate. However, this is a factor of the high precision score balancing a very low recall rate. ... Musgrave, P., Edmond, A., Seville, J.: Anomaly detection in a smart microgrid system using cyber-analytics: a case study. Energies 16(20), 7151 (2023). https ...

Aug 2, 2024 · This work presents an approach for anomaly detection using autoencoders and wavelets to identify arc faults in a DC power system, where Cassie arc model is used for synthetic arc fault generation. The system uses a deep learning technique called an autoencoder to detect anomalies in the signal.