Modernize legacy SCADA infrastructure with real-time data processing
This project blueprint demonstrates how you can build real-time data processing pipelines for modern energy systems. It connects smart meters, IoT sensors, and SCADA systems to enable immediate insights and automated responses. The project includes components for equipment monitoring, consumption analysis, and predictive maintenance - showing practical implementations of stream processing for energy infrastructure.
Main project components
IoT Data Ingestion
Collects telemetry from distributed sensors and modern IoT devices to monitor equipment performance and environmental conditions.
Legacy Data Ingestion
Integrates data from legacy SCADA systems into the modern streaming pipeline.
Load Forecaster
Processes consumption patterns to predict demand and optimize distribution.
Predictive Maintenance
Analyzes sensor data to detect anomalies and predict maintenance needs across energy infrastructure.
Fault Detector
Processes real-time telemetry data to identify distribution network issues, abnormal voltage patterns, and equipment malfunctions. Uses pattern recognition to distinguish between temporary fluctuations and actual faults, enabling faster response times and reduced downtime.
Operational Data Sinks
Transmit the results of the load forecaster, predictive maintenance, and fault detection services to operation systems that can act on or visualize the data in real time.
Data Lake Sink
Stores processed data in a format optimized for long-term analysis and ML model training.
Technologies used
Using this template
This use case illustrates how energy companies can solve several common challenges in modern power distribution. It enables companies to process real-time grid data for fault detection, predictive maintenance, demand response, and dynamic pricing - all running on a single unified system that teams can build upon as needed.