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1. An Enhanced Federated Learning Framework for Condition Monitoring of Distributed Wind Systems

   https://doi.org/10.1115/ES2025-156364  

   Mayfield, William; Li, Gang (July 2025, American Society of Mechanical Engineers)

   Abstract This paper presents a trusted execution environment (TEE)-enhanced federated learning (FL) framework for condition monitoring of distributed wind systems (DWSs). DWSs have become a topic of interest with the increased energy demand. Technological advancements in wind turbine technology has paved the way for DWSs to make a massive impact on the power grid. Due to underdeveloped security, malicious groups and individuals can target individual turbines, gain control of wind farms, and ultimately threaten the overall power grid. TEE-enhanced FL offers a solution, however, there are some challenges to their implementation. The remainder of this paper will discuss the challenges further and present solutions to their respective challenges. These solutions have been validated through experimentation and confirm an effective FL framework balancing both practicality and security in DWSs. 

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2. Foundations of Quantum Federated Learning Over Classical and Quantum Networks

   https://doi.org/10.1109/MNET.2023.3327365  

   Chehimi, Mahdi; Chen, Samuel Yen-Chi; Saad, Walid; Towsley, Don; Debbah, Mérouane (January 2024, IEEE Network)

   Quantum federated learning (QFL) is a novel framework that integrates the advantages of classical federated learning (FL) with the computational power of quantum technologies. This includes quantum computing and quantum machine learning (QML), enabling QFL to handle high-dimensional complex data. QFL can be deployed over both classical and quantum communication networks in order to benefit from informationtheoretic security levels surpassing traditional FL frameworks. In this paper, we provide the first comprehensive investigation of the challenges and opportunities of QFL. We particularly examine the key components of QFL and identify the unique challenges that arise when deploying it over both classical and quantum networks. We then develop novel solutions and articulate promising research directions that can help address the identified challenges. We also provide actionable recommendations to advance the practical realization of QFL. 

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3. Security of a Power System Under High Penetration of Wind Energy Considering Contingencies and Stability Margins

   https://doi.org/10.1109/SCOReD53546.2021.9652753  

   Dorile, Pierre O.; Jagessar, Daniel R.; McCann, Roy A. (November 2021, IEEE 19th Student Conference on Research and Development (SCOReD))

   Security is a well-known function to any transmission operator and system planner. As the world is moving toward the decarbonization of the power industry, it is more complicated for the system operators to maintain an acceptable level of security in the power system operation. More large-scale wind farms are being incorporated into the grid, and thus, the voltage stability concern is increasing. In practice, several contingencies are imagined by the system operators to assess the reliability of the grid. Since voltage stability is one of the major menaces that can trigger voltage instability in a power system, this paper is attempting to present to the transmission system planners and operators a dedicated methodology to facilitate the incorporation of large-scale wind farms into a transmission grid under high penetration of wind power. the stability of a wind-dominated power system is discussed based on Q-V and P-V methodologies and some N-1 contingencies with the Remedial Action Schemes (RAS). Furthermore, a methodology to rank the worst contingencies and to predict the voltage collapse during the highest wind penetration level is presented. Simulations have been, extensively, carried out to examine the methodology and have provided valuable information about the static security of the wind-dominated power system. The results can be used by the transmission system operator to anticipate voltage instability or voltage collapse in the power system during high wind penetration levels. 

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4. Time Synchronization Techniques in the Modern Smart Grid: A Comprehensive Survey

   https://doi.org/10.3390/en18051163  

   Liu, Yu; Sun, Biao; Wu, Yuru; Zhang, Yongxin; Yang, Jiahui; Wang, Wen; Thotakura, Naga Lakshmi; Liu, Qian; Liu, Yilu (March 2025, Energies)

   In modern smart grids, accurate and synchronized time signals are essential for effective monitoring, protection, and control. Various time synchronization methods exist, each tailored to specific application needs. Widely adopted solutions, such as GPS, however, are vulnerable to challenges such as signal loss and cyber-attacks, underscoring the need for reliable backup or supplementary solutions. This paper examines the timing requirements across different power grid applications and provides a comprehensive review of available time synchronization mechanisms. Through a comparative analysis of timing methods based on accuracy, flexibility, reliability, and security, this study offers insights to guide the selection of optimal solutions for seamless grid integration. 

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5. Review of Control Techniques for Wind Energy Systems

   https://doi.org/10.3390/en13246666  

   Ghaffarzadeh, Hooman; Mehrizi-Sani, Ali (December 2020, Energies)

   null (Ed.)

   In recent years, penetration of renewable energy resources into the power grid has increased significantly. Wind, as a renewable, clean, and abundantly available source of energy, has an important share in the energy mix. However, increasing the penetration of wind power in the power grid can adversely affect the power quality and introduce new operational challenges. This paper discusses issues related to the integration of wind farms in the power system, such as maximum power point tracking, fault ride-through capabilities, interarea and subsynchronous oscillations, and voltage flicker, and provides a review of the existing control strategies to address these issues in Types I, II, III, and IV wind turbines. This paper also identifies challenges and opportunities ahead. 

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