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Reinforcement Learning for Stability-Guaranteed Adaptive Optimal Primary Frequency Control of Power Systems Using Partially Monotonic Neural Networks

IEEE Open Access Journal of Power and Energy

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IEEE Open Access Journal of Power and energy 2025年 12卷 259-269页

作者： Alduaij, Hamad Weng, Yang School of Electrical Computer and Energy Engineering Arizona State University Tempe AZ USA

Deepening the deployment of distributed energy resources requires the large-scale integration of inverter-based resources, which can deteriorate the frequency stability. Recent studies propose using neural Lyapunov-based reinforcement learning for control. While this method can be trained offline with performance guarantees, it is only optimal for specific values of system parameters, as it omits critical modeling factors like decreasing inertia and damping variation over time. To maintain the performance at varying operation points, we consider an adaptive neural Lyapunov framework that adapts the controller’s output in the presence of varying parameters. Neural networks require flexibility to maximize adaptive control performance, while stability demands monotonicity, creating an inherent conflict. In this paper, we design a partially monotonic controller that maintains stability with maximal representation capacity for parameter adaptation. Stability is ensured by having monotonicity retained for frequency while non-monotonicity is allowed for the system parameters, such as damping and inertia. The structural form of partially monotonic neural networks is used for the controller design to that end. Flexibility is allowed by the design when adaptation to changes to the system parameters is made, while the Lyapunov stability guarantee is retained. The non-monotonic layers are chosen through an adaptive layer that is designed for damping and inertia based on their relationship to control in the system equation, by which optimized output for different operating conditions is allowed. © 2020 IEEE.

关键词： Frequency stability

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Research Progress in Solar Flare Prediction Methods

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Research in Astronomy and Astrophysics 2025年第3期25卷 280-309页

作者： Ke Han Zhen Liu Xian-Yi Zhao Yi-Fei Li De-Quan Zheng Jie Wan School of Computer and Information Engineering Harbin University of Commerce Faculty of Computing Harbin Institute of Technology School of Energy Science and Engineering Harbin Institute of Technology

Solar flares are one of the strongest outbursts of solar activity,posing a serious threat to Earth’s critical infrastructure,such as communications,navigation,power,and ***,it is essential to accurately predict solar flares in order to ensure the safety of human ***,the research focuses on two directions:first,identifying predictors with more physical information and higher prediction accuracy,and second,building flare prediction models that can effectively handle complex observational *** terms of flare observability and predictability,this paper analyses multiple dimensions of solar flare observability and evaluates the potential of observational parameters in *** flare prediction models,the paper focuses on data-driven models and physical models,with an emphasis on the advantages of deep learning techniques in dealing with complex and high-dimensional *** reviewing existing traditional machine learning,deep learning,and fusion methods,the key roles of these techniques in improving prediction accuracy and efficiency are *** prevailing challenges,this study discusses the main challenges currently faced in solar flare prediction,such as the complexity of flare samples,the multimodality of observational data,and the interpretability of *** conclusion summarizes these findings and proposes future research directions and potential technology advancement.

关键词： (Sun:) sunspots magnetohydrodynamics (MHD) Sun: activity Sun: flares Sun: magnetic fields

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An 18-44 GHz Power Amplifier in 90-nm GaN Using Active Impedance Matching

IEEE Journal of Microwaves

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IEEE Journal of Microwaves 2025年第3期5卷 702-710页

作者： Der, Adam Barton, Taylor University of Colorado Boulder Department of Electrical Computer and Energy Engineering BoulderCO80309 United States

This work presents an active impedance matching technique to extend the bandwidth of a power amplifier (PA). The architecture is similar to that of the load-modulated balanced amplifier, but employs phase-only control to avoid the complexity and gain compression characteristics associated with millimeter-wave load-modulated PAs. To evaluate the effectiveness of the technique and fully explore the design space, four monolithic microwave integrated circuit (MMIC) PA variants are fabricated and compared through measurements. Specifically, load-pull characterization of the best-case performance of the single-ended PA used in the active match balanced amplifier is compared to the overall active match architecture to identify and understand the sources of losses in the system. The primary active match design demonstrates a wideband operation from 18 to 44 GHz, with an output power of 28-31 dBm and a power-added efficiency (PAE) of 12-31% in continuous-wave (CW) measurements. © 2025 IEEE.

关键词： III-V semiconductors

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A two-stage method with twin autoencoders for the degradation trajectories prediction of lithium-ion batteries

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Journal of energy Chemistry 2025年第4期103卷 759-772页

作者： Lei Cai Jing Yan Haiyan Jin Jinhao Meng Jichang Peng Bin Wang Wei Liang Remus Teodorescu School of Computer Science and Engineering Xi'an University of Technology School of Electrical Engineering Xi'an Jiaotong University Smart Grid Research Institute Nanjing Institute of Technology Department of Energy Aalborg University

To predict the lithium-ion(Li-ion) battery degradation trajectory in the early phase,arranging the maintenance of battery energy storage systems is of great ***,under different operation conditions,Li-ion batteries present distinct degradation patterns,and it is challenging to capture negligible capacity fade in early *** the data-driven method showing promising performance,insufficient data is still a big issue since the ageing experiments on the batteries are too slow and *** this study,we proposed twin autoencoders integrated into a two-stage method to predict the early cycles' degradation *** two-stage method can properly predict the degradation from course to *** twin autoencoders serve as a feature extractor and a synthetic data generator,***,a learning procedure based on the long-short term memory(LSTM) network is designed to hybridize the learning process between the real and synthetic *** performance of the proposed method is verified on three datasets,and the experimental results show that the proposed method can achieve accurate predictions compared to its competitors.

关键词： Autoencoder Battery degradation trajectory Early prediction Synthetic data generation

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Data-Driven Decision Support by Utilizing Machine Learning to Predict Passenger Flow for Route and Station Optimization

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Journal of The Institution of Engineers (India): Series B 2025年 1-20页

作者： Patel, Madhuri Patel, Samir B. Swain, Debabrata Department of Computer Engineering Pandit Deendayal Energy University Gandhinagar India Department of Information Technology L D College of Engineering Ahmedabad India

A smart public transportation system with reliable services addresses urban challenges like traffic congestion, infrastructure maintenance, travel costs, and pollution. As part of smart city initiatives, urban public transport systems have adopted advanced data collection methods, enabling data analytics and predictive model development. This study evaluates optimal machine learning models for predicting passenger flow across routes and stations within the Thane Municipal Transport (TMT) network using real historical data from March 2021 to May 2022. Key spatial and temporal features such as weekday patterns, route distance and direction, public holidays, COVID-19 impacts, and weather conditions were identified through exploratory data analysis and incorporated into the models. Several algorithms, including Decision Trees, XGBoost, Extra Tree Regressor, and Random Forest, were implemented to predict daily passenger commute on an hourly basis. Random Forest emerged as the most accurate model, achieving an MAE of 114.55, RMSE of 236.71, and an R2 of 0.9516, leveraging its ensemble approach to capture complex, non-linear patterns and ensure robustness to variability, including disruptions caused by the pandemic. These findings provide actionable insights for optimizing route design, bus allocation, maintenance scheduling, and overall transport system efficiency, enabling stakeholders to achieve sustainable and adaptive public transport management. © The Institution of Engineers (India) 2025.

关键词： Traffic congestion

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>3kV NiO/Ga2O3 Heterojunction Diodes With Space-Modulated Junction Termination Extension and Sub-1V Turn-On

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IEEE Journal of the Electron Devices Society 2025年 13卷 373-377页

作者： Gilankar, Advait Katta, Abishek Das, Nabasindhu Kalarickal, Nidhin Kurian Arizona State University School of Electrical Computer and Energy Engineering TempeAZ85287 United States

This work demonstrates high-performance vertical NiO/Ga2O3 heterojunction diodes (HJDs) with a 2-step space-modulated junction termination extension. Distinct from the current state-of-the-art Ga2O3 HJDs, we achieve breakdown voltage exceeding 3 kV with a low turn on voltage (VON) of 0.8V, estimated at a forward current density (IF) of 1 A-cm-2. The measured devices exhibit excellent turn-on characteristics achieving 100 A-cm-2 current density at a forward bias of 1.5V along with a low differential specific on-resistance (Ron,sp) of 4.4 mΩ-cm2. The SM-JTE was realized using concentric NiO rings with varying widths and spacing that approximates a gradual reduction in JTE charge. The unipolar figure of merit (FOM) calculated exceeds 2 GW-cm2 and is among the best reported for devices with a sub-1V turn-on. The fabricated devices also displayed minimal change in forward I-V characteristics post reverse bias stress of 3 kV applied during breakdown voltage testing. © 2013 IEEE.

关键词： Heterojunctions

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PANTHER: A Power-Optimized and Accurate Positioning, Navigation, and Timing With High Efficiency and Reliability

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2025年 6卷 3265-3279页

作者： Siraj, Md Sadman Atencio, Joshua R. Tsiropoulou, Eirini Eleni Arizona State University School of Electrical Computer and Energy Engineering TempeAZ85281 United States University of New Mexico Department of Electrical and Computer Engineering AlbuquerqueNM87131 United States

Positioning, Navigation, and Timing (PNT) systems are vital in both military and civilian domains to provide essential support for navigation, situational awareness, and coordinated operations. However, in GPS-denied environments, such as contested military zones or disaster areas, existing PNT solutions face significant limitations, such as signal interference and jamming. To address these challenges, we propose the PANTHER framework, which combines matching theory, coalition game models, and power optimization techniques to deliver a ground-based PNT solution tailored for dynamic and complex operational scenarios. The PANTHER framework introduces a novel ground-based solution designed to support search-and-rescue missions and military operations by using ad-hoc developed anchor nodes to assist the targets’ positioning when GPS signals are unavailable. Initially, we develop the Approximate PANTHER (A-PANTHER) framework, utilizing matching theory to enable the anchor node selection for targets located at a Forward Operating Base (FOB). For operations extending beyond the FOB, we present the Accurate PANTHER (Acc-PANTHER) framework, which leverages the coalition game theory to facilitate collaborative selection of anchor nodes by targets, with proven convergence of Nash-individually stable coalitions. Additionally, we optimize the anchor nodes’ transmission power using a non-cooperative game-theoretic approach, maximizing their utility with respect to the PNT services, while improving positioning errors for the targets. The existence and uniqueness of a Pure Nash Equilibrium for power levels are demonstrated. Through extensive simulations, the PANTHER framework demonstrates scalability and effectiveness across various military formations, such as line, echelon, column, and wedge, providing a practical and reliable PNT solution for mission-critical scenarios. © 2020 IEEE.

关键词： Global positioning system

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Personalizing Human Gait Entrainment: A Reinforcement Learning Approach to Optimizing Magnitude of Periodic Mechanical Perturbations

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IEEE Robotics and Automation Letters 2025年第6期10卷 5673-5680页

作者： Save, Omik Zhong, Junmin Joglekar, Suhrud Si, Jennie Lee, Hyunglae Arizona State University School for Engineering for Matter Transport and Energy TempeAZ85281 United States Arizona State University School of Electrical Computer and Energy Engineering TempeAZ85281 United States

The feasibility of gait entrainment to periodic mechanical perturbations varies with perturbation magnitude in neurotypical individuals. Effective design of gait entrainment studies thus requires a systematic approach to personalize periodic perturbation parameters. However, current studies still rely on manually selecting the perturbation magnitude, a practice that is neither efficient nor optimal for individual users. This study proposes a new reinforcement learning (RL) method to personalize the minimum magnitude of periodic perturbation to hip flexion that ensures successful entrainment. The method entails offline learning and in situ adaptation (OLAP), where offline learning involves training a deep Q-network (DQN), which is subsequently used in situ to guide the adaptive selection of an optimal perturbation magnitude for individuals. This study recruited thirteen healthy participants, with entrainment characteristics data from seven participants used for offline DQN training. The remaining six participants performed in situ adaptation to identify their personalized optimal perturbation parameters. Results demonstrate that the OLAP agent effectively tailored a minimum perturbation magnitude for each of the six participants in the adaptation group, leveraging generalization from the DQN policy. All adaptation group participants achieved a 100% entrainment success rate at their personalized perturbation magnitude during a 3-trial post-evaluation session, highlighting the agent's effectiveness. The efficiency and robustness of our approach underscore its significance in designing future optimal gait entrainment studies for diverse population groups. © 2016 IEEE.

关键词： Reinforcement learning

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Demonstrating the Transient System Impact of Cyber-Physical Events Through Scalable Transmission and Distribution (T&D) Co-Simulation

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CSEE Journal of Power and energy Systems 2025年第1期11卷 329-339页

作者： Mengmeng Cai Wenbo Wang Xin Fang Anthony R.Florita Michael Ingram Dane Christensen National Renewable Energy Laboratory GoldenCO15013USA Department of Electrical and Computer Engineering Mississippi State UniversityUSA

Modern power systems become more vulnerable to cyber threats due to their growing interconnectivity, interdependence, and complexity. Widespread deployment of distributed energy resources (DERs) further expands the threat landscape to the grid edge, where fewer cybersecurity protections exist. In this article, a systematic cyber-physical events demonstration, enabled by an integrated transmission, distribution, and communication co-simulation framework, is performed. It analyzes cyber risks to power grid under DER-enabled automatic generation control from different angles. Unlike existing works, the simulation captures millisecond-to-minutes frequency and voltage transient dynamics at a cross-region system scale.

关键词： Cyber-physical events distributed energy resources T&D dynamic co-simulation

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Metaheuristic Approaches in Path Finding and Optimization

Metaheuristic Approaches in Path Finding and Optimization

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Trivedi, Shikhar Gupta, Rajeev Bharti, Santosh Pandit Deendayal Energy University Department of Computer Science and Engineering India

ISBN: (纸本)9798331512965

Navigation planning is a critical component in the operation of mobile systems such as robots, autonomous vehicles, and unmanned aerial vehicles (UAVs). As the complexity of these systems increases, the need for robust and efficient pathfinding algorithms becomes more significant. Traditional heuristic approaches often struggle in dynamic environments, prompting the adoption of metaheuristic algorithms inspired by natural phenomena. This study evaluates five widely used metaheuristic algorithms: Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), Firefly Algorithm (FA), Moth Flame Optimization (MFO), and Tabu Search (TS). The performance of these algorithms is compared across multiple scenarios, including static shortest-path calculations, the Traveling Salesperson Problem (TSP), and randomized environment. Results indicate that ACO consistently identifies optimal paths but incurs relatively high computational time. Tabu Search offers a balanced trade-off between computational efficiency and solution quality, while ABC delivers suboptimal paths with faster execution times. In contrast, MFO and FA exhibit excessive exploration, leading to higher costs and inefficiencies in specific scenarios. © 2025 IEEE.

关键词： Ant colony optimization

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