In power systems, monitoring, protection, and control are usually model based; an accurate dynamic model for either synchronous generators or power electronic converters is essential. Besides, renewable energies, smart loads, energy storage, and new market behavior add new sources of uncertainty to power systems. Therefore, planning in real-time and developing high-quality models is crucial to adapt to uncertainties. In this thesis, we propose a framework for validating and calibrating power system models using novel methods. At the first step, we developed the nonlinear sensitivity-based method to find the critical parameters. Then, we propose an Approximate Bayesian Computation (ABC) based method which is a simulation-based method. By proposing an adaptive kernel function and a threshold sequence, we reduce the computational complexity of a ABC with a sequential Monte Carlo sampler (ABC-SMC). Using deep learning to improve the estimation accuracy, we overcome the curse of dimensionality in our proposed method. Via event playback, we build the simulations for training our model and using a parallel multi-modal long short-term memory (PM LSTM), we improve the accuracy for the high dimensional cases, but to build a model, we need to have a lot of samples. Our next proposal was a conditional variational autoencoder (CVAE), which has a great performance and requires a much smaller sample for training. The proposed methods are comprehensively evaluated; all results show that the proposed approaches have great performance in time and accuracy. We demonstrate the effectiveness of the proposed models on a synchronous generator with its controllers, a DC-DC buck converter, and a three-port inverter.
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Doctor of Philosophy (Ph.D.)
College of Engineering and Computer Science
Electrical and Computer Engineering
Length of Campus-only Access
Doctoral Dissertation (Campus-only Access)
Khazeiynasab, Seyyed Rashid, "Parameter Calibration and Optimization in Smart Grid for Synchronous Generators and Converters" (2021). Electronic Theses and Dissertations, 2020-. 889.
Restricted to the UCF community until December 2022; it will then be open access.