Date of Award
Open Access Dissertation
The increasing penetration of wind turbines, photovoltaics (PV), fuel cells, microturbines, cogeneration, energy storage systems, and other Distributed Energy Resources (DER) presents both challenges and opportunities for distribution systems. A deep understanding of the characteristics of those devices, as well as accurate modeling, are essential to plan, design, and control modern distribution grids.
The objective of this research is to define data-driven modeling techniques that allow capitalizing on the results of Hardware In the Loop (HIL) and Power Hardware In the Loop (PHIL) testing by creating models of the devices under test (DUT) –also for closed-source, proprietary systems– using the collected data. For the development of data-driven models, we used Artificial Neural Networks (ANN) and Fast Fourier Transform (FFT) to parameterize pre-defined model structures. We demonstrated the proposed approach using three PV micro-inverters, the proposed approach handles the nonlinearity of a full range grid voltage (0.88-1.10 p.u), not just under the normal grid voltage, including burst mode. No prior knowledge of internal components, structure, and control algorithm is assumed in developing the model. Results show the effectiveness of the approach, which is particularly suitable to model DERs.
As a part of this research, we also would develop an approach to model DERs during abnormal grid conditions. The model will be parameterized by a set of automated PHIL tests. We verified the viability of our approach characterizing micro-inverters from three different manufacturers. It was found that it is possible to develop an approach to mimic the behavior of the internal protection system of microinverter during abnormal grid voltage, without prior knowledge of intimate control algorithms or hardware configuration.
Almukhtar, H. D.(2020). Data-Driven Modeling Through Power Hardware in the Loop Experiments: A PV Micro-Inverter Example. (Doctoral dissertation). Retrieved from https://scholarcommons.sc.edu/etd/5687
Available for download on Tuesday, May 18, 2021