Context
Power quality conditions and grid incidents affect operational continuity in electrical distribution and transmission grids and in industrial distribution grids.
Challenge
Power quality disturbances can lead to outages, equipment damage, and associated costs across distribution and transmission grids, including industrial distribution networks.
Continuous monitoring is needed to detect and diagnose power‑quality incidents early and support faster expert evaluation.
AI Solution
The solution performs permanent monitoring of power quality data and applies machine learning based outlier detection, deep learning based pattern recognition, an expert system based diagnostics layer, and a retrieval augmented generation (RAG) reasoning capability. Outputs are delivered through a dashboard for evaluation and deep dive, fast pre-classification of anomalies and signals, automatically generated reports and bulletins, and access to domain experts. The operating model works as an assistant that keeps a power quality engineer in the evaluation loop, including cross-check during operation.
Impact & evidence
Impact is evidenced through the number of early alerts generated and improvements in reliability indicators such as System Average Interruption Duration Index (SAIDI) and System Average Interruption Frequency Index (SAIFI).
Implementation / Adoption
Implementation requires power quality (PQ) meters to be installed at the most relevant points of the grid, with sufficient harmonic resolution and dynamic and transient triggering capabilities, and supported by external access to the meters.
Deployment involves installation of the meters and establishment of secure remote VPN connectivity based on a comprehensive quick‑start guide, or integration via a REST API with existing monitoring systems such as ElectrificationX .
Data
The solution requires on data in the form of steady-state, dynamic and transient recordings generated by PQ or protection devices.
Future enhancements
Future enhancements include signal set augmentation with a digital twin, evaluating application of deep learning solutions to streams, introducing MCP agents for reasoning purposes, and providing APIs for integration into third party applications.
“Thanks to this warning, we could see that we had a problem with a capacitor battery and a 5th harmonic filter. This warning was very important for us.”
“The Power Quality Analytics service provided by Siemens PTI has helped us to identify the problem. Without these measurements and consulting support we would not have noticed the first disturbances which would possibly have damaged our system in the long term.”