R.R. Kamat Tarcar

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Master thesis (1)

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Revolutionizing MTDC Networks

Unlocking the Power of FPGA-based MMCs with Grid Forming Control on RSCAD through Model Predictive Control

Master thesis (2023) - R.R. Kamat Tarcar , A. Lekic , M. Popov , M. Ghaffarian Niasar , A.D. Shetgaonkar

Integrating renewable offshore wind generation into global power grids is a critical issue in the energy industry. Modular Multilevel Converter (MMC)-based High Voltage Direct Current (HVDC) grids are the most effective and promising technical solution for the new offshore wind energy power system connections. These grids offer scalability, controllability, and reliability advantages, but their stable and compliant operation requires implementing MMC control strategies and controllers. This thesis investigates the intricacies of HVDC technologies, focusing on MMC control strategies and controllers. The research explores grid following and grid forming converter control strategies, essential for ensuring stable and compliant operation of MMC-based HVDC grids.
The research elaborates on the Model Predictive Control (MPC), which is a promising control strategy for MMC-based HVDC grids. The MPC controller makes the control strategies respond faster, emphasizing constraints
and cost functions. Furthermore, a comparative analysis of Proportional Integral (PI)-based and MPC-based
controllers is conducted across various scenarios, highlighting the advantages of MPC-based controllers over
PI-based controllers regarding response time and stability.
Lastly, a large stability analysis is conducted using the direct Lyapunov method to evaluate the effectiveness of the control strategies and controllers during transient events. This analysis provides insights into the stability and performance of MMC-based HVDC grids under different operating conditions and disturbances.
This thesis illustrates the capabilities of grid-forming control strategy through Model Predictive controllers for
FPGA-based MMC-MTDC networks on RSCAD/RTDS®. The outcomes of this thesis contribute to advancing
HVDC technologies and control strategies, with implications for enhancing the stability and efficiency of MMC-based HVDC grids. The findings of this thesis have significant potential in the energy industry, particularly in integrating renewable offshore wind generation into global power grids.