Power Plant Controller (PPC) - Inavitas
Developed advanced Power Plant Controller (PPC) as Senior Software Engineer for Inavitas, a precision control system deployed to 20+ renewable energy power plants across 3 continents. Built high-performance, fault-tolerant system managing real-time power generation optimization, grid synchronization, and compliance with international grid codes. Architected hybrid edge-cloud solution using C++ for millisecond-latency control loops processing 10K+ sensor readings per second, with Python microservices for analytics and React dashboard for monitoring. Implemented Dynamic Power Control algorithms automatically adjusting power output based on real-time grid conditions, achieving 15% efficiency improvement and 30% reduction in downtime through predictive maintenance. System ensures 99.999% availability managing MW-scale power generation with automatic failover and redundant communication paths. Integrated with diverse hardware systems using Modbus, IEC 61850, and DNP3 protocols through hardware abstraction layer.
Project Details
Key Features
- Real-time power generation control with <10ms latency processing 10K+ sensor readings/second
- Dynamic Power Control automatically adjusting output based on grid frequency, voltage, and demand
- Active and reactive power control with power factor correction maintaining grid stability
- Automatic grid synchronization with phase matching and frequency regulation
- Compliance automation for international grid codes (IEC 61400, IEEE 1547, VDE-AR-N 4120)
- Predictive maintenance using machine learning models forecasting equipment failures 48h in advance
- SCADA integration with real-time plant monitoring and remote control capabilities
- Hardware abstraction layer supporting Modbus RTU/TCP, IEC 61850, DNP3, and custom protocols
- Edge computing architecture processing control loops locally with cloud analytics
- Redundant control systems with automatic failover ensuring 99.999% availability
- Time-series database storing 1M+ data points daily for trend analysis and reporting
- Web-based dashboard with real-time visualization, alerts, and historical performance analysis
Challenges
- Achieving <10ms deterministic latency for critical control loops in renewable energy systems
- Managing integration with 50+ different equipment manufacturers each with proprietary protocols
- Ensuring zero downtime in mission-critical systems managing MW-scale power generation
- Processing and analyzing 10K+ sensor readings per second while maintaining real-time control
- Implementing predictive maintenance models with limited training data from industrial equipment
- Handling intermittent renewable energy production (solar, wind) with dynamic grid synchronization
- Deploying to harsh industrial environments with temperature extremes and electromagnetic interference
- Meeting strict grid code requirements across different countries and regulatory frameworks
Solutions
- Built C++ real-time control loops running on Linux RT kernel with PREEMPT_RT patches
- Developed hardware abstraction layer with plugin architecture supporting 15+ industrial protocols
- Implemented N+1 redundancy with heartbeat monitoring and sub-second automatic failover
- Designed lock-free data structures and memory pools eliminating GC pauses in control loops
- Deployed machine learning models using Python with feature engineering from historical failure data
- Created adaptive control algorithms using PID with Kalman filtering for renewable intermittency
- Utilized edge computing on industrial-grade hardware with fanless cooling and wide temperature range
- Built configurable grid code compliance engine mapping requirements to control parameters
- Implemented comprehensive monitoring with distributed tracing achieving <5min MTTR
- Designed time-series data pipeline using TimescaleDB with automatic downsampling and retention
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