Time-series infrastructure for grid operators

Energy distribution networks generate massive volumes of telemetry — voltage readings, frequency data, transformer temperatures, fault signals. Most utilities in emerging markets either cannot store this data at the precision they need, or cannot query it fast enough to act on it.

Legacy historians were designed for industrial process control, not for the volume and query patterns utilities demand. Operators need sub-second fault detection, historical trend analysis across years, and real-time dashboards — simultaneously.

Without this infrastructure, grid operators manage complexity by adding personnel instead of adding data. Fault detection is manual. Historical analysis requires exporting to spreadsheets. The cost compounds every month.

We implement TDengine as the historian layer — purpose-built for time-series workloads at the scale utilities generate. Ingestion rates of millions of data points per second with retention policies measured in years, not months.

Telemetry collection runs through EMQX for MQTT-based sensors and OPC-UA connectors for SCADA integration. Kafka provides the stream processing layer that enables real-time fault detection rules alongside batch analytics.

Every deployment includes a complete observability stack. Grafana dashboards for operations, Prometheus for infrastructure health, Loki for centralized logging. Alerts are configured per-substation, per-transformer, per-feeder — at whatever granularity the operator needs.