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Title: Autonomous Control Techniques For Grid-Connected Inverters
Committee:
Dr. Deepak Divan, ECE, Chair, Advisor
Dr. Santiago Grijalva, ECE
Dr. Maryam Saeedifard, ECE
Dr. Mukul Chandorkar, IIT Bombay
Dr. Chanyeop Park, Mississippi State
Abstract: The traditional power grid dominated by synchronous machines is shifting toward a modern power-electronics-based system due to environmental concerns and a significant plunge in the price of solar modules and wind-energy systems. This radical evolution leads to more controllability, sustainability, and higher power conversion efficiency. At the same time, it creates new challenges for the stability and operation of the grid. Due to the high number of inverters deployed at the transmission and distribution level, it is challenging to control all inverters in real-time by a central controller. Therefore, autonomous control techniques are required that enable inverters to work with local information and poor system knowledge. These inverters should manage the transients and support the grid to keep it firm and stable. To improve the resiliency of the grid, they should be capable of working in both grid-connected and grid-isolated modes and seamlessly switch between them. Moreover, as the number of inverters increases, the grid might experience faster transients. Therefore, inverters should be equipped with techniques that enable the measurement of grid parameters and extraction of useful information in a fast and reliable manner. The objective of the proposed research is to develop a universal method that meets these requirements and addresses some of the issues with the existing techniques. The proposed universal controller (UniCon) consists of three elements that all work in harmony: adaptive inertia + damping, current-limiting phase-jump, and adaptive virtual impedance. These elements together allow grid-connected inverters to manage significant frequency transients, ride through faults and connect/disconnect to/from the grid in an ad hoc manner. Moreover, a novel scheme is proposed based on deep neural networks that enables inverters to rapidly track grid variables, such as amplitude and phase, so that inverters can take proper action promptly. The efficacy of the proposed schemes has been validated with simulation and experimental results.
