Consulting Engineer - FACTS
GE Vernova
Senior Research Engineer – Control and System Dynamics
ENODA Ltd
full-time
Posted on:
Origin: • 🇬🇧 United Kingdom
Visit company websiteSalary
💰 £80,000 - £100,000 per year
Job Level
Senior
Tech Stack
Python
About the role
- Experimental Validation: Designing and executing laboratory experiments (scaled and full power) to measure transient response, load step behaviour, and short-circuit dynamics.
- Leading hardware-in-the-loop (HIL) campaigns with real controllers under simulated grid conditions.
- Control Performance Analysis: Investigating and quantifying latency sources in the control loop (sensing, computation, actuation, magnetic dynamics).
- Proposing and prototyping strategies to reduce response times from multi-cycle to sub-cycle levels.
- System Modelling: Developing reduced-order and control-oriented models of system dynamics to guide controller design and validation.
- Collaborating with digital twin specialists to align experimental findings with simulation environments.
- Data Generation: Building internal datasets of operational behaviour (thousands of hours) from controlled experiments to benchmark and improve stability, efficiency, and robustness.
- Cross-functional Collaboration: Working with hardware, firmware, and data science teams to ensure experimental insights translate into practical design improvements and field-ready control strategies.
Requirements
- Power Electronics & Control: Deep understanding of converters, inverters, and grid-interactive control schemes.
- Power Transformer Design: Experience with short-circuit ratio analysis and weak-grid stability.
- Experimental & Lab Skills: Proven ability to design and execute experiments on power electronics hardware.
- Skilled in the use of oscilloscopes, power analysers, and HIL platforms (dSPACE, OPAL-RT).
- Capable of designing safe setups for transient and fault testing.
- Simulation & Modelling: Proficiency in real-time simulation tools (Simulink/PLECS, PSCAD, RTDS, OPAL-RT).
- Strong coding ability in Python or MATLAB for control analysis and reduced-order modelling.
- Familiarity with digital twin approaches is desirable but not the primary focus.
- Data-Driven Methods: Knowledge of time-series analysis, signal processing, and machine learning methods for system identification and forecasting.
- Advantageous: Experience with model predictive control (MPC) or reinforcement learning applied to power converters.
- Background in system identification for complex energy or industrial systems.
- Familiarity with thermal modelling and loss estimation in magnetic/electronic systems.
- Prior experience with grid compliance testing (fault ride-through, harmonics, stability standards).
- Familiarity with digital twin approaches.
