ASTREAM: Data-stream-driven scalable anomaly detection with accuracy guarantee in IIoT environment
Intrusion detection exerts a crucial influence on securing the IIoT driven by anomaly
detection approaches. Dissimilar with the static data, the intrusion detection data is in the …
detection approaches. Dissimilar with the static data, the intrusion detection data is in the …
Real-Time Synchrophasor Data Anomaly Detection and Classification Using Isolation Forest, KMeans, and LoOP
Power grid operators assess situational awareness using time-tagged measurements from
phasor measurement units (PMUs) placed at multiple locations in a network. However …
phasor measurement units (PMUs) placed at multiple locations in a network. However …
Spatial-temporal recurrent graph neural networks for fault diagnostics in power distribution systems
Fault diagnostics are extremely important to decide proper actions toward fault isolation and
system restoration. The growing integration of inverter-based distributed energy resources …
system restoration. The growing integration of inverter-based distributed energy resources …
DPMU-based multiple event detection in a microgrid considering measurement anomalies
A microgrid may be subjected to various unexpected events, such as sudden tripping of a
generator/load, line outages due to faults, sudden switching of large capacitor banks, etc …
generator/load, line outages due to faults, sudden switching of large capacitor banks, etc …
[HTML][HTML] UInDeSI4. 0: An efficient Unsupervised Intrusion Detection System for network traffic flow in Industry 4.0 ecosystem
Abstract In an Industry 4.0 ecosystem, all the essential components are digitally
interconnected, and automation is integrated for higher productivity. However, it invites the …
interconnected, and automation is integrated for higher productivity. However, it invites the …
Unsupervised machine learning-based multi-attributes fusion dim spot subtle sandstone reservoirs identification utilizing isolation forest
Subtle sandstone reservoirs are difficult to identify due to their weak seismic responses.
Here, we propose to identify subtle sandstone reservoirs by an unsupervised machine …
Here, we propose to identify subtle sandstone reservoirs by an unsupervised machine …
Voltage stability monitoring based on disagreement-based deep learning in a time-varying environment
The traditional learning based static voltage stability monitoring methods require manual
labeling of a large number of training samples. Getting these labeled training sets is …
labeling of a large number of training samples. Getting these labeled training sets is …
A PMU-based data-driven approach for enhancing situational awareness in building a resilient power systems
S Das, BK Panigrahi - IEEE Transactions on Industrial …, 2022 - ieeexplore.ieee.org
A data-driven approach for improving wide-area situational awareness (SA) in modern
power systems for building a more resilient grid is proposed in this article. SA is of …
power systems for building a more resilient grid is proposed in this article. SA is of …
Free Probability Theory Based Event Detection for Power Grids using IoT-Enabled Measurements
Taking advantage of Internet of Things (IoT) enabled measurements, this paper formulates
the event detection problem as an information-plus-noise model, and detects events in …
the event detection problem as an information-plus-noise model, and detects events in …
Distributed finite-time observer for multiple line outages detection in power systems
Y Chen, ZW Liu, G Wen… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Fast detection of power line outages is critical for maintaining the stable operation of the
power system. The aim of this article is to address the real time detection problem of multiple …
power system. The aim of this article is to address the real time detection problem of multiple …