Date of Award

8-2024

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

School of Computing

Committee Chair/Advisor

Shuangshuang Jin

Committee Member

Zheyu Zhang

Committee Member

Amy Apon

Committee Member

Rong Ge

Committee Member

High-Performance Computing, Power Electronics, Photovoltaic System (PV), Reliability Assessment, Parallel Computing

Abstract

The rising popularity of renewable energy sources requires advanced, efficient power electronic systems for energy conversion, grid integration, and system management, thereby raising expectations for power electronics in the energy industry. The complexity of modern power electronic systems requires comprehensive simulations and in-depth analysis to predict performance accurately, but this process is impeded by prolonged simulation times. The primary objective of this dissertation is to develop a high-fidelity, high-speed event-driven simulator to tackle challenges related to mass data processing, uncertainty evaluation, as well as modeling and simulation issues in assessing the reliability of power electronics in large-scale Photovoltaic (PV) systems.

Firstly, to meet the demand for accuracy and real-time capability of PV system degradation evaluation, a massive volume data is needed to run high-fidelity and high-efficiency simulations and perform advanced data analysis. However, PV farm operators face a series of difficulties with manipulating PV system data, such as data collection from multiple channels, massive data storage, data management, and massive data analysis. To address these challenges, we have developed an integrated data management platform capable of data acquisition, processing, storage, querying, and performing big data analysis. The platform can also achieve data correctness verification and provide an effective distributed data management solution to retrieve massive amounts of data and establish a connection to distributed computational frameworks.

Secondly, uncertainties involved by various sources need to be addressed to improve the reliability and robustness of decision and control strategies based on simulation or prediction results. Standard methods to deal with this issue, like sensitivity analysis, demand generating a large number of samples to achieve statistically reliable results. If simulation parameters and simulation results accompanied by uncertainties are passed across multiple layers, new levels of uncertainties may be caused. By applying Sobol sensitivity screen and unscented transformation, A sample-reduced strategy is proposed to better perform the result prediction without sacrificing accuracy by generating fewer but more representative samples.

Then, compared to switching model simulation, average modeling requires much less computational load to represent converter behavior for controller design; it compromises the thermal information and switching ripples/harmonics, which are vital for power electronics design, operation, and reliability analysis. The average-to-switching (A2S) method, an innovative approach combining a low-fidelity average model with parallel computing to expedite switching model-based simulations, is proposed to achieve high fidelity. The foundational concept and methodology of the A2S method are presented. A case study is implemented to validate the capability of the A2S model.

Finally, we propose an event-driven parallel computing-based simulator. The proposed simulator applies high-performance computing techniques and other accessory optimization techniques—including cluster merging, adaptive model updates, and steady-state identification, and sample-reduced parameter variation—to make reliability assessments for PV system components under given input mission profiles and operating conditions with high efficiency and high fidelity. The main idea of the simulator and its workflow are introduced. For performing simulations on real-world year-long mission profiles, the proposed simulator reduces the simulation time from years to minutes while maintaining acceptable accuracy.

In summary, the dissertation designs and implements a comprehensive state-of-the-art event-driven simulation solution in power electronics. Modeling power electronics systems by combining the domains of power electronics and computer science requires thoughtful integration strategies that combine electrical engineering fundamentals with computer science techniques. This interdisciplinary integration not only addresses the specific challenges in power electronics but also opens up new perspectives for using computer science to enhance the efficiency, accuracy, and scalability of engineering solutions in renewable energy and beyond.

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