A Deep Learning-Based Framework for Parameters Calibration of Power Plant Models Using Event Playback Approach

Abstract

Accurate and reliable calibration of power plant models in the power system is crucial for maintaining grid stability and preventing power outages. This becomes even more crucial as the power grids become more dynamic and stochastic with the ever-increasing penetration of renewable energy sources, mid-size generators, smart loads, and energy storage. Current practice such as staged-test is costly and time-consuming. Alternatively, recent PMU-based approaches using online measurements without interfering with generators operation have been introduced to provide a scalable and low-cost to meet North American Electric Reliability Corporation (NERC) standards. This research paper proposes a PMU-based framework to validate and calibrate model parameters by a new iterative deep learning approach. The results show that the proposed approach can accurately calibrate power plantmodels from a single disturbance event without the urgent need of prior knowledge of the model’s initial parameters. The proposed method has been validated on real-world and simulated scenarios on a relatively large number of calibrated parameters with an average error of 1.43%.

Keywords

• Parameters calibration
• parameters verification
• deep learning
• WaveNet
• event playback
• power system dynamics