Dynamic Voltage Stiffness Control Technique for a Virtual Oscillator-Based Grid- Forming Controller

Ghosh, Ritwik; Tummuru, Narsa Reddy; Rajpurohit, Bharat Singh

doi:10.1109/tie.2023.3288186

Cited by 1 publication

(2 citation statements)

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“…Q-learning uses a Q-value function to estimate the cumulative reward of actions, while Deep Q Network uses deep neural networks for more complex state spaces. Policy gradients parameterize agents' policies to maximize rewards [50][51][52].…”

Section: Reinforcement Learning Based Controlmentioning

confidence: 99%

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Reviewing Control Paradigms and Emerging Trends of Grid-Forming Inverters—A Comparative Study

Rahman,

Hashimoto,

Orihara

et al. 2024

Energies

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Grid-forming inverters (GFMs) have emerged as crucial components in modern power systems, facilitating the integration of renewable energy sources and enhancing grid stability. The significance of GFMs lies in their ability to autonomously establish grid voltage and frequency, enabling grids to form and improve system flexibility. Discussing control methods for grid-forming inverters is paramount due to their crucial role in shaping grid dynamics and ensuring reliable power delivery. This paper explores the fundamental and advanced control methods employed by GFMs, explaining their operational principles and performance characteristics. Basic control methods typically involve droop control, voltage and frequency regulation, and power-balancing techniques to maintain grid stability under varying operating conditions. Advanced control strategies encompass predictive control, model predictive control (MPC), and adaptive control, which influence advanced algorithms and real-time data for enhanced system responsiveness and efficiency. A detailed analysis and performance comparison of different control methods for GFM is presented, highlighting their strengths, limitations, and suitability for diverse grid environments. Through comprehensive studies, this research interprets the ability of various control strategies to mitigate grid disturbances, optimize power flow, and enhance overall system stability.

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Section: Reinforcement Learning Based Controlmentioning

confidence: 99%

“…17, x FOR PEER REVIEW 17 ofDefine the sensor input (SI) and the emotional signal (ES)Choose suitable values of coefficients, α,β With zero initial conditions, define the BEL output u from(51) …”

mentioning

confidence: 99%