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Matching Synchronous Machine Control for Improving Active Support of Grid-forming PV systems with Enhanced DC Voltage Dynamics

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Journal of Modern power systems and Clean Energy 2025年第1期13卷 179-189页

作者： Zizhen Guo Wenchuan Wu State Key Laboratory of Power Systems Department of Electrical EngineeringTsinghua UniversityBeijing 100084China

With photovoltaic(PV)sources becoming more prevalent in the energy generation mix,transitioning grid-connected PV systems from grid-following(GFL)mode to gridforming(GFM)mode becomes essential for offering self-synchronization and active support *** numerous GFM methods have been proposed,the potential of DC voltage control malfunction during the provision of the primary and inertia support in a GFM PV system remains insufficiently *** fill the gap,some main GFM methods have been integrated into PV systems featuring detailed DC source *** conduct a comparative analysis of their performance in active support and DC voltage *** GFM methods such as virtual synchronous machine(VSM)face a significant risk of DC voltage failure in situations like alterations in solar radiation,leading to PV system tripping and jeopardizing local system *** the case of DC GFM methods such as matching control(MC),the active support falls short due to the absence of an accurate and dispatchable droop *** address the issue,a matching synchronous machine(MSM)control method is developed to provide dispatchable active support and enhance the DC voltage dynamics by integrating the MC and VSM control *** active support capability of the PV systems with the proposed method is quantified analytically and verified by numerical simulations and field tests.

关键词： Active support DC voltage dynamics grid-forming(GFM) matching control photovoltaic(PV) virtual synchronous machine

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DistFlow Safe Reinforcement Learning Algorithm for Voltage Magnitude Regulation in Distribution Networks

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Journal of Modern power systems and Clean Energy 2025年第1期13卷 300-311页

作者： Shengren Hou Aihui Fu Edgar Mauricio Salazar Duque Peter Palensky Qixin Chen Pedro P.Vergara Intelligent Electrical Power Grids(IEPG)Group Delft University of TechnologyDelft 2628CDThe Netherlands Electrical Energy Systems(EES)Group Eindhoven University of TechnologyEindhovenThe Netherlands State Key Laboratory of Power Systems Department of Electrical EngineeringTsinghua UniversityBeijing 100084China

The integration of distributed energy resources(DERs)has escalated the challenge of voltage magnitude regulation in distribution ***-based approaches,which rely on complex sequential mathematical formulations,cannot meet the real-time *** reinforcement learning(DRL)offers an alternative by utilizing offline training with distribution network simulators and then executing online without ***,DRL algorithms fail to enforce voltage magnitude constraints during training and testing,potentially leading to serious operational *** tackle these challenges,we introduce a novel safe-guaranteed reinforcement learning algorithm,the Dist Flow safe reinforcement learning(DF-SRL),designed specifically for real-time voltage magnitude regulation in distribution *** DF-SRL algorithm incorporates a Dist Flow linearization to construct an expert-knowledge-based safety ***,the DF-SRL algorithm overlays this safety layer on top of the agent policy,recalibrating unsafe actions to safe domains through a quadratic programming *** results show the DF-SRL algorithm consistently ensures voltage magnitude constraints during training and real-time operation(test)phases,achieving faster convergence and higher performance,which differentiates it apart from(safe)DRL benchmark algorithms.

关键词： Voltage regulation distribution network safe reinforcement learning energy management

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power System Reliability Evaluation Based on Sequential Monte Carlo Simulation Considering Multiple Failure Modes of Components

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Journal of Modern power systems and Clean Energy 2025年第1期13卷 202-214页

作者： Wei Huang Bo Hu Changzheng Shao Wei Li Xiaozhe Wang Kaigui Xie C.Y.Chung State Key Laboratory of Power Transmission Equipment Technology School of Electrical EngineeringChongqing UniversityChongqingChina China Southern Power Grid GuangzhouChina Department of Electrical and Computer Engineering McGill UniversityMontrealCanada Department of Electrical and Electronic Engineering Hong Kong Polytechnic UniversityHong KongChina

The component aging has become a significant concern worldwide,and the frequent failures pose a serious threat to the reliability of modern power *** light of this issue,this paper presents a power system reliability evaluation method based on sequential Monte Carlo simulation(SMCS)to quantify system reliability considering multiple failure modes of ***,a three-state component reliability model is established to explicitly describe the state transition process of the component subject to both aging failure and random failure *** this model,the impact of each failure mode is decoupled and characterized as the combination of two state duration variables,which are separately modeled using specific probability ***,SMCS is used to integrate the three-state component reliability model for state transition sequence generation and system reliability ***,various reliability metrics,including the probability of load curtailment(PLC),expected frequency of load curtailment(EFLC),and expected energy not supplied(EENS),can be *** ensure the applicability of the proposed method,Hash table grouping and the maximum feasible load level judgment techniques are jointly adopted to enhance its computational *** studies are conducted on different aging scenarios to illustrate and validate the effectiveness and practicality of the proposed method.

关键词： power system reliability evaluation aging failure sequential Monte Carlo simulation

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Quantitative analysis on the separated double schottky barrier for ZnO-based ceramic nanowire

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Ceramics International 2025年第10期51卷 13537-13544页

作者： Yang, Xiao Zhang, Han Qi, Bo Yu, Zexuan Li, Chuanyang Hu, Jun He, Jinliang State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing102206 China State Key Laboratory of Power Systems Department of Electrical Engineering Tsinghua University Beijing100084 China

ZnO based ceramic nanowires or whiskers have been reported to possess good nonlinear conducting property similar with ZnO ceramic varistor. The energy band structure and nonlinear conduction process of the ceramic nanowire are different from the traditional back-to-back double schottky barrier structure while a model description has been absent. This paper has taken ceramic nanowire based on ZnO coated with Bi, Co oxide as an instance and proposed a separated double schottky model to quantitatively characterizes the process of interface charges at the reverse and forward bias side which are separately modulated by the corresponding voltage bias and barrier height. Computation result based on the model agree well with the measured nonlinear conducting behavior of a single ZnO ceramic nanowire and its nonlinear mechanism has been analyzed. On this basis, quantitative correlation between the property of a single ZnO ceramic nanowire and the overall nonlinear conducting behavior of its composite has been established. © 2025

关键词： II-VI semiconductors

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A Data-Driven Adaptive Control Approach for Enhancing the Dynamic Response of VSGs in Varying Grid Conditions

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IEEE Transactions on power Delivery 2025年第3期40卷 1421-1433页

作者： Fahad, Shah She, Buxin Yin, Junjie Li, Fangxing Cui, Hantao Bo, Rui Department of Electrical and Computer Engineering Missouri University of Science and Technology Rolla MO USA Senior Power Systems Engineer ETAP Irvine CA USA Optimization & Control Group Parcific Northwest National Laboratory Richland USA Department of Electrical Engineering and Computer Science University of Tennessee at Knoxville Knoxville TN USA Department of Electrical and Computer Engineering North Carolina State University Raleigh NC USA

Conventionally, a virtual synchronous generator (VSG) is designed for islanded mode (IM) operation to meet specific operational requirements such as the rate of change of frequency (RoCoF). However, the operation of VSG designed for IM may not meet the operational and control criteria in grid connected mode (GCM) when the grid conditions vary. In addition, conventional VSG control technology does not consider the influence of the presynchronization scheme when connected to a weak grid, which degrades the RoCoF in IM. To overcome the aforementioned challenges, the proposed study presents a twin-delayed deep deterministic policy gradient (TD3) algorithm to improve the seamless transition performance of VSG from IM to GCM and vice versa. In the first step, the VSG-based power system model is used as a foundation for problem formulation of the proposed TD3 algorithm. Secondly, a reward function is designed according to the performance requirements, i.e., frequency and RoCoF requirements, of the VSG in order to guide the training of the agent in varying load, power reference, and grid conditions. Finally, the superiority of the proposed algorithm over existing methods is validated in MATLAB/SIMULINK and RTDS based real-time simulation environment. © 1986-2012 IEEE.

关键词： Deep reinforcement learning

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Adversarial machine learning: a review of methods, tools, and critical industry sectors

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Artificial Intelligence Review 2025年第8期58卷 1-87页

作者： Pelekis, Sotiris Koutroubas, Thanos Blika, Afroditi Berdelis, Anastasis Karakolis, Evangelos Ntanos, Christos Spiliotis, Evangelos Askounis, Dimitris Decision Support Systems Laboratory School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Superbo AI Athens Greece

The rapid advancement of Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), has produced high-performance models widely used in various applications, ranging from image recognition and chatbots to autonomous driving and smart grid systems. However, security threats arise from the vulnerabilities of ML models to adversarial attacks and data poisoning, posing risks such as system malfunctions and decision errors. Meanwhile, data privacy concerns arise, especially with personal data being used in model training, which can lead to data breaches. This paper surveys the Adversarial Machine Learning (AML) landscape in modern AI systems, while focusing on the dual aspects of robustness and privacy. Initially, we explore adversarial attacks and defenses using comprehensive taxonomies. Subsequently, we investigate robustness benchmarks alongside open-source AML technologies and software tools that ML system stakeholders can use to develop robust AI systems. Lastly, we delve into the landscape of AML in four industry fields –automotive, digital healthcare, electrical power and energy systems (EPES), and Large Language Model (LLM)-based Natural Language Processing (NLP) systems– analyzing attacks, defenses, and evaluation concepts, thereby offering a holistic view of the modern AI-reliant industry and promoting enhanced ML robustness and privacy preservation in the future. © The Author(s) 2025.

关键词： Deep learning

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Stable Stochastic MPC for Uncertain Wind Energy Conversion System Over Whole Operating Regions

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IEEE Transactions on Energy Conversion 2025年第1期40卷 515-528页

作者： Liu, Xiangjie Guo, Shifan Ma, Lele Kong, Xiaobing Lee, Kwang Y. North China Electric Power University State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources Beijing102206 China Baylor University Department of Electrical and Computer Engineering WacoTX76798-7356 United States

The stochastic uncertainty of wind speed presents a great challenge for achieving reliable power control in wind energy conversion system (WECS). Due to the excellence in handling the uncertainties based on probabilistic descriptions, stochastic model predictive control (SMPC) has been regarded as a promising strategy for WECS. However, owing to fluctuation characteristic of wind speed, operating points commonly change in WECS. Thus, an important limitation of applying SMPC to WECS stems from the fact that feasibility and stability of the traditional SMPC can only be ensured at one single predesigned operating point. Regarding this issue, a stable SMPC strategy for uncertain WECS is proposed in this paper to ensure the feasibility and stability of WECS under changing operating points over the whole operating regions. A Luenberger observer is employed to estimate model-plant mismatch introduced by linearization process, while a tube-based control framework is deployed to cope with stochastic wind speed disturbance. The feasibility of SMPC is ensured by incorporating artificial steady targets as optimization variables, while the stability of WECS is guaranteed by modifying the cost function and extending the terminal constraint. The effectiveness of the proposed strategy is validated through simulations and experiments under different scenarios. © 1986-2012 IEEE.

关键词： Stochastic systems

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Improved Adaptive Speed Observer of Permanent Magnet Linear Synchronous Motor With Transient Characteristics

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IEEE Journal of Emerging and Selected Topics in power Electronics 2025年第2期13卷 2330-2340页

作者： Sun, Zhaolong Ding, Anmin Mao, Yinhao Huang, Chuibing Xu, Wei Naval University of Engineering School of Electrical Engineering Wuhan430033 China Institute of Electrical Engineering Chinese Academy of Sciences State Key Laboratory of High Density Electromagnetic Power and Systems Beijing100190 China University of Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering Beijing101408 China

To ensure accurate closed-loop control of the transient motor system, the eddy current position sensors are generally adopted for obtaining motor operation signals. However, the transient operating characteristics of the motor will degrade the accuracy and reliability of position sensors and even worse result in abnormal operation. To address these issues, one improved adaptive speed observer with high accuracy is proposed in this work, thus ensuring the safe and stable operation of the dual three-phase permanent magnet linear synchronous motor (DTP-PMLSM) under the aperiodic transient operation. In this improved observer, an observation model is constructed that considers the effect of stochastic noise, and the expectation of the observation error is derived. In addition, the minimization of the expectation is achieved via a linear quadratic regulator (LQR), which can get higher observation accuracy with a simplified method. Moreover, a speed adaptive law is designed, which can dynamically adjust to changes in the system. Comprehensive simulations and experiments based on a 4-kW prototype have fully demonstrated the effectiveness and the superiority of the improved observer. © 2013 IEEE.

关键词： Permanent magnets

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A Substation Automation System Architecture for Migrating From Decentralized to Centralized Protection and Control in Smart Substations

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IEEE Transactions on Industry Applications 2025年第3期61卷 5173-5186页

作者： Mandal, Anand Muttaqi, Kashem M. Islam, Md. Rabiul Sutanto, Danny Rahman, Anisur Yilmaz, Orhan Vaze, Omkar University of Wollongong Australian Power and Energy Research Institute School of Electrical Computer and Telecommunications Engineering WollongongNSW2522 Australia ABB Australia Digital Systems Electrification Team Australia

The protection of the power system component is crucial for the healthy and reliable operation of the entire grid. Depending upon the equipment being protected and the type of fault it may encounter, various protection functions need to be employed. The paper presents a detailed overview of the evolution of the SAS structure in smart substations facilitating the centralization of multiple protection and control functions. The description detailing the evolution of the substation automation system (SAS), driven by the IEC61850 standard, enabling the transition from conventional substations to digital substations has been highlighted in this paper. The centralized protection and control (CPC) technique, representing an innovative strategy that consolidates all protection and control functions within a single device at the substation level, has been presented in detail. Moreover, the guidelines on the implementation of the CPC architecture in substations along with the approach involved in migration from a decentralized protection scheme to CPC architecture have been proposed considering the existing substation in a power grid. The protection functions employed in the line and transformer bays of the substation have been mathematically described using the actual parameters of the substation equipment. Their effectiveness has been confirmed by analyzing disturbance reports of faults that occurred in both the line and transformer feeders. © 1972-2012 IEEE.

关键词： Transformer substations

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Integrated Framework of Multisource Data Fusion for Outage Location in Looped Distribution systems

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IEEE Transactions on Smart Grid 2025年第3期16卷 2635-2646页

作者： Liu, Liming Yuan, Yuxuan Wang, Zhaoyu Yao, Yiyun Ding, Fei Iowa State University Department of Electrical and Computer Engineering AmesIA50011 United States National Renewable Energy Laboratory Power System Engineering Center GoldenCO80214 United States

Accurate outage location is essential for expediting post-outage power restoration, minimizing outage duration, and enhancing the resilience of distribution networks. With the advent of advanced metering infrastructure, data-driven outage location methods have significantly advanced beyond traditional approaches that rely on manual inspections. However, existing methods still face critical challenges, like reliance on single-source data, limited ability to handle partially observable systems or difficulties with loop networks. To the best of our knowledge, no single approach has comprehensively addressed all of these challenges at once. To this end, this paper proposes a comprehensive multisource data fusion framework for outage locations via probabilistic graph networks. The framework consists of three key phases. First, a novel method for reconstituting distribution networks with loops is developed, transforming looped networks into multiple radial subnetworks that retain all outage causalities of the original network. Second, Bayesian network (BN) models are established for each subnetwork, integrating multiple data sources and network structures. Finally, a joint Gibbs sampling mechanism, featuring forward and backward information flow, is designed to merge data from separate BN models and maximize the utilization of limited evidence, ensuring accurate outage location identification. The framework was validated on two modified public test systems, and comparative studies confirmed its effectiveness. © 2010-2012 IEEE.

关键词： Soft sensors Data models Topology Probabilistic logic Data integration Accuracy Renewable energy sources Observability Government Support vector machines

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