IoT Product Engineering for Grid Monitoring: Edge AI Embedded Systems in High-Voltage Distribution
Introduction: The Challenge of Sensing in High-Voltage Environments
Utilities required reliable distributed sensing for grid health, including partial discharge, insulation degradation, and transient overvoltage detection. This necessitated a module that could overcome severe safety, galvanic isolation, and EMC constraints inherent in high-voltage proximity devices, demanding expert IoT product engineering for a safe smart infrastructure solution.
Solution Overview: Rugged Edge AI for Predictive Grid Maintenance
EurthTech designed a rugged, IEC-compliant high-voltage sensor module for pole-top and substation use. The module captures line voltage, current, and partial-discharge-like transients. A local Edge AI embedded system performs waveform feature extraction and compression. This provides the foundational data for grid stability analytics and a crucial Predictive maintenance AI IoT strategy, significantly reducing reliance on visual inspection.
Technical Implementation: Embedded Systems Development and Signal Chain
The embedded systems development focused on safety, using capacitive voltage dividers, Rogowski coil sensors, and optical isolation to meet IEC/IEEE safety margins. The analog front-end includes selective transient capture paths and high-resolution ADC channels. For partial-discharge events, a separate high-bandwidth channel samples in the MHz range. EMC hardening included multi-stage shielding and PCB layouts adhering to strict creepage and clearance rules.
Results & Impact: AI Engineering and Smart City Solutions
Field validation showed that the sensors reliably detected early insulation degradation events with lead times of weeks to months over traditional methods. Accurate voltage measurement (within 0.5%) and reliable transient capture enabled proactive maintenance scheduling, which reduced emergency repair costs and outage incidents. This high-ROI AI engineering solution confirms our capability as a smart city solutions provider for critical energy infrastructure.
Scalability & GIS Context: The local edge module sends compressed event summaries via cellular/LoRa fallback. All devices include secure identity modules and signed telemetry for audit trails. Integrating these distributed sensors with grid analytics requires precise geospatial engineering services to map and manage the asset fleet, a key competence of our GIS consulting company for establishing a full grid Digital twin smart city model.