Early Anomaly-Onset Detection based on Wigner--Ville Distribution Slice Spectra: A Transmission-Grid Test Case
arXiv:2606.15856v1 Announce Type: cross Abstract: Operational disturbance monitoring in power networks requires decisions to be made from waveform windows as they arrive, rather than from completed records after the event. This study evaluates full-vector Wigner--Ville Distribution Slice (WVDS) spectra for sequential anomaly-onset detection in high-voltage grid-voltage waveforms. The approach keeps the bilinear midpoint interaction structure of the Wigner--Ville distribution and represents each 128-sample voltage window by a 128-dimensional slice spectrum, avoiding manually selected fault-freq
The increasing complexity and fragility of power grids, coupled with advancements in AI and signal processing, are driving the need for more sophisticated real-time anomaly detection systems.
This development allows for earlier and more precise identification of potential issues in critical infrastructure like power networks, which can prevent widespread disruptions and improve grid resilience.
The ability to detect anomalies at their onset, rather than after an event, shifts power grid monitoring from reactive to proactive, enhancing operational stability.
- · Power grid operators
- · Smart grid technology providers
- · AI/ML algorithm developers
- · Legacy grid monitoring systems
- · Regions with aging grid infrastructure
Improved reliability and reduced downtime for high-voltage power networks.
Increased investment in real-time sensor technologies and data analytics for critical infrastructure.
Potential for autonomous grid management systems that can self-heal or reroute power in response to detected anomalies.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG