The Problem
A renewable energy operator managing a distributed wind turbine fleet had three interconnected operational problems. Monitoring was fragmented: each turbine reported to its own local SCADA system with no aggregated view across the fleet. Identifying underperformers required manually cross-referencing reports from separate data sources — a step that didn’t happen in real time. Maintenance was reactive: component failures were detected after they caused downtime, not from early warning signals in the sensor data. And energy production was not optimized — turbines operated at default parameters regardless of current wind conditions, missing output opportunities when conditions allowed for dynamic adjustment.
Fleet growth compounded all three problems. Adding turbines meant adding more disconnected data sources, more manual coordination, and more coordination overhead across maintenance teams. The system didn’t scale.
The Constraints
Sensor data at IoT scale. Wind turbines generate continuous telemetry from dozens of sensors: rotor speed, blade pitch, nacelle temperature, vibration, power output, wind direction and speed. The data pipeline had to handle high-frequency ingest from a multi-turbine fleet without data loss or sampling.
Predictive models must explain their signals. Operations teams act on maintenance recommendations only if they trust them. A model that flagged “probable bearing failure” without specifying the sensor signal driving that prediction would generate skepticism rather than action. Every alert needed supporting evidence.
SCADA consolidation, not replication. A monitoring interface that simply displayed raw SCADA data on a web dashboard would not eliminate manual cross-referencing work. The platform needed to contextualize data — comparing actual production against expected output given current wind conditions, and surfacing deviations automatically.
Maintenance scheduling required field integration. A maintenance calendar that existed only in the platform would be ignored. The system needed to connect to the field team’s existing communication tools and generate actionable work orders, not just scheduling entries.
Infrastructure must scale with the fleet. Wind energy operators expand as assets are acquired. The architecture had to accommodate new turbines through configuration rather than re-engineering.
Our Approach
IoT sensors on each turbine feed raw telemetry to a data ingestion layer built on AWS — wind speed, torque, output, temperature, and vibration readings captured at 1-second resolution. The platform aggregates this telemetry through standardized data connectors, normalizing heterogeneous data formats from different turbine manufacturers into a unified data model.
The predictive analytics engine applies machine learning models trained on historical failure data and sensor patterns. The models detect anomaly signatures — specific vibration frequency patterns that precede bearing failures, temperature curve deviations that indicate cooling system issues, output drops correlated with blade pitch degradation — and generate prioritized maintenance alerts with the supporting sensor evidence. Operations staff receive actionable recommendations, not raw anomaly scores.
The performance monitoring layer compares actual energy output against a model of expected output given current wind speed, direction, and temperature — surfacing underperforming turbines automatically rather than requiring manual analysis. Alerts are configurable by deviation threshold and urgency level.
Automated real-time optimization adjusts blade pitch and rotor speed parameters on turbines where current wind conditions allow for higher output within the manufacturer’s safe operating range. The optimization runs continuously, compounding across the full fleet.
The maintenance scheduling module manages planned and corrective maintenance workflows: scheduling upcoming inspections against manufacturer recommendations, generating work orders from alert conditions, and tracking completion against the maintenance plan. Integration with field team communication tools ensures work orders reach technicians in their existing workflow rather than requiring them to check a separate system.
The reporting module generates structured energy production reports in the formats required by regulatory bodies and internal stakeholders — pulling from the normalized production database without manual data assembly.
Elasticsearch and Kibana serve the analytics and visualization layer — fleet-wide dashboards showing real-time output, anomaly alerts, maintenance queue, and historical performance trends by turbine and cluster.
The Outcome
- 25% improvement in energy output through continuous real-time parameter optimization
- Predictive maintenance in production — component failures identified from sensor patterns days before failure, enabling planned maintenance rather than reactive repair
- Unified fleet dashboard — all turbines visible from a single interface with real-time status and production data; automated deviation detection eliminates manual cross-referencing
- Maintenance workflows structured and tracked end-to-end — from scheduling through technician completion
- Regulatory reporting generated on-demand in compliant formats — manual report assembly eliminated
- Infrastructure scales with the fleet without re-engineering — new turbines onboarded through configuration
Team
Engagement: 5 months, 4 engineers (1 ML, 2 backend, 1 DevOps).
Stack: React.js, Node.js, Python, PostgreSQL, AWS, Elasticsearch, Kibana, IoT/SCADA Integration