arXiv subjects

Rahul Rane

Publications and source records attributed to Rahul Rane.

4 recordsLinked to original sources

arXiv2026

Robust Tuning of Model Predictive Control for MMC-Based High-Voltage Power Systems

High-voltage direct current (HDVC) transmission systems based on modular multilevel converters (MMCs) have become a key topology in modern power systems. The dynamics of MMCs exhibit strong multivariable coupling, constraints, and uncertainties, motivating the use of model predictive control (MPC) to enhance current regulation performance. However, MPC tuning is nontrivial and does not inherently guarantee stability or robustness, particularly in the presence of model uncertainties. This paper proposes a MPC tuning method that ensures robust performance under bounded model uncertainties. This method solves a convex linear optimization problem to compute the optimal weighting matrices Q, R, and P ensuring optimality and reproducibility. As a result, robustness is enhanced without increasing the online computation burden. The effectiveness of the method is validated through testing on a real-time digital simulator (RTDS) model of a point-to-point HVDC system. Results demonstrate improved performance compared to conventional LQR-based MPC tuning.

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arXiv2026

Robust Current Regulation of MMC-based MTDC Power Systems based on Lyapunov Inequality

Multi-terminal DC (MTDC) transmission systems based on modular multilevel converters (MMCs) are a key component of the envisioned future energy sector, where sustainability and efficiency are increasingly prioritized. To ensure their reliable operation, MMC currents must be regulated safely and rapidly under a wide range of uncertain operating conditions. Consequently, the design of current controllers faces a fundamental challenge: achieving fast transient response while maintaining robustness against uncertainties. This paper addresses this challenge by proposing a linear matrix inequality (LMI)-based design framework that leverages Lyapunov stability conditions to synthesize a less conservative static state-feedback controller. The proposed design method explicitly accounts for system constraints, including input saturation and overcurrent limits. The proposed method effectiveness is assessed on the CIGRE MT-HVDC benchmark, simulated in RTDS, and compared with existing methods.

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arXiv2025

An OPF-based Control Framework for Hybrid AC-MTDC Power Systems under Uncertainty

The increasing integration of renewable energy, particularly offshore wind, introduces significant uncertainty into hybrid AC-HVDC systems due to forecast errors and power fluctuations. Conventional control strategies typically rely on fixed setpoints and neglect frequency deviations, which can compromise system stability under rapid renewable variations. To address this challenge, this paper presents a forecast-integrated, optimal power flow (OPF)-based adaptive control framework. Wind speed forecasts generated using a Random Forest model are incorporated into a time-coupled OPF to determine baseline converter setpoints in anticipation of wind fluctuations, which are further adjusted in real time based on actual operating conditions. An adaptive droop control scheme is developed that jointly considers DC voltage and AC frequency deviations. The effectiveness of the proposed control framework is validated through hardware-in-the-loop (HIL) simulations, demonstrating its capability to ensure stable and robust operation of hybrid AC-HVDC systems under high penetration of renewable energy.

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arXiv2025

Quantum Hardware-in-the-Loop for Optimal Power Flow in Renewable-Integrated Power Systems

Quantum computing has emerged as a promising computational paradigm to address unresolved challenges in the modeling and control of modern power systems. However, most existing studies focus on offline simulations, and a practical framework for validating quantum algorithms in real-time operational environments remains lacking. This study proposes a quantum hardware-in-the-loop framework that integrates a real-time digital simulator with quantum and quantum-inspired hardware to solve combinatorial power flow and optimal power flow formulations under dynamic operating conditions. The proposed framework is validated using the IEEE 9-bus test system and a modified version with integrated solar and wind farms. The results confirm successful integration and convergence within a predefined tolerance. The study also identifies key limitations and challenges, such as limited access to quantum and digital annealers and current scalability limitations, that must be considered in future developments. Nevertheless, the results highlight the potential of quantum computing to significantly enhance the modeling and control of future power systems with high penetration of renewable energy sources.

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