Perform power plant model validation (PPMV) using manual and automated techniques for both traditional generation and renewable energy systems. This approach to PPMV is especially important when required by technical regulations such as NERC MOD-026, MOD-027, and voltage ride through events. Accurate models with reliable active power and reactive power responses remain necessary for grid studies and digital twins.
Explore PPMV as applied to both offline step tests and online performance monitoring of grid events using PMU data and as a workflow that includes both manual adjustments and automated techniques.
Through a utility-scale solar plant case study you will see how to:
- Replay active power and reactive power measured data through your simulations.
- Gain insight into response discrepancies through field data replay
- Use engineering judgment and automated parameter sensitivity to assess and rank the influence of system parameters on system response.
- Fine-tune your system response using both manual adjustments and automated parameter estimation.
Additional templates for traditional generation tests are discussed as well:
- Zero-power factor load rejection tests
- Open circuit voltage step tests
- Online step tests
Read this white paper to learn how you can efficiently perform PPMV with MATLAB® and Simulink®.
- Renewable Energy Model Validation (23:33): Use field data to calibrate a utility-scale solar plant model.
- Traditional Power Plant Model Validation
- Part 1: Introduction (1:02): A three-step process for power plant model validation using MATLAB and Simulink.
- Part 2: Summary (2:49): Learn more on how to apply power plant model validation using online performance monitoring of grid events.
- Part 3: Manual Parameter Tuning (6:07): Gain deeper insight into response discrepancies through both Voltage/Frequency replay and Active and Reactive Power replay. Apply engineering judgement to adjust parameter settings.
- Part 4: Automated Parameter Sensitivity and Parameter Tuning (9:00): Complement engineering judgement with automated parameter sensitivity to assess and rank the influence of system parameters on system response.