Video and Webinar Series

Power Plant Model Validation (PPMV) with MATLAB and Simulink

It has long been recognized in the electric power industry that to have a large-scale simulation of an electric grid that closely matches reality, you need to go through the process of calibrating the individual components of that grid. This process is referred to as power plant model validation (PPMV) and can be performed both with offline step tests and online performance monitoring of grid events. This task can be challenging, particularly when required by technical regulations, such as NERC Standard MOD-026.

This video series explores PPMV as applied to online performance monitoring of grid events using phasor measurement unit (PMU) data through a workflow that includes both manual adjustments and automated techniques. A case study of a gas plant is presented to demonstrate the following workflow elements:

  1. Gain deeper insight into response discrepancies through both voltage/frequency (VF) and active-power/reactive-power (PQ) replay.
  2. Use engineering judgement and automated parameter sensitivity to assess and rank the influence of system parameters on system response.
  3. Fine-tune your system response using both manual adjustments and automated parameter estimation.

Introduction | Power Plant Model Validation (PPMV) with MATLAB and Simulink, Part 1 A three-step process for power plant model validation using MATLAB and Simulink.

Summary | Power Plant Model Validation (PPMV) with MATLAB and Simulink, Part 2 Learn more on how to apply power plant model validation using online performance monitoring of grid events.

Manual Parameter Tuning | Power Plant Model Validation (PPMV) with MATLAB and Simulink, Part 3 Gain deeper insight into response discrepancies through both Voltage/Frequency replay and Active and Reactive Power replay.

Automated Parameter Sensitivity and Parameter Tuning | Power Plant Model Validation (PPMV) with MATLAB and Simulink, Part 4 Complement engineering judgement with automated parameter sensitivity to assess and rank the influence of system parameters on system response.