作者机构：
[Cao, Yijia; Huang, Sunhua; Zhou, Yang] Changsha Univ Sci & Technol, Sch Elect & Informat Engn, State Key Lab Disaster Prevent & Reduct Power Grid, Changsha 410114, Peoples R China.;[Huang, Sunhua] Shanghai Jiao Tong Univ, Key Lab Control Power Transmiss & Convers, Minist Educ, Shanghai 200240, Peoples R China.;[Huang, Sunhua] Hong Kong Polytech Univ, Dept Elect & Elect Engn, Hong Kong 999077, Peoples R China.;[Xiong, Linyun] Chongqing Univ, Sch Elect Engn, Chongqing 400044, Peoples R China.;[Li, Yong] Hunan Univ, Coll Elect & Informat Engn, Changsha 410082, Peoples R China.
通讯机构：
[Huang, SH ] S;Shanghai Jiao Tong Univ, Key Lab Control Power Transmiss & Convers, Minist Educ, Shanghai 200240, Peoples R China.
关键词：
Hydro-turbine governing system;Fractional-order calculus;Lyapunov function;Predefined-time stability;Sliding mode control
摘要：
The hydro-turbine governing system (HTGS) holds a crucial position in maintaining overall stability and security of hydropower stations. HTGS characterized by intricate dynamics involving strong coupling, nonlinearity, and non-minimum phase behavior, is responsible for regulating the frequency of the hydropower system. This article proposes a predefined-time fractional-order sliding mode controller (PTFOSMC) aimed at improving the frequency stability of the nonlinear HTGS, accounting for both internal disturbances and external noises within a predefined time. Input/output feedback linearization serves to define the explicit mapping between the HTGS control inputs and its observed outputs. Leveraging fractional calculus principles, a fractional-order controller is designed to mitigate chattering effects associated with the sign function, surpassing conventional sliding mode control (SMC) techniques. Moreover, fractional-order control methods afford greater flexibility in parameter tuning, enhancing the adaptability of controller design. The proposed PTFOSMC guarantees stability of the HTGS within a predefined time, allowing for explicit adjustment as a tuning parameter. Stability analysis of the HTGS under the proposed PTFOSMC utilizes the Lyapunov function. Numerical simulations validate the robustness, effectiveness, and superiority of the PTFOSMC relative to both finite-time control method (FTCM) and integer-order sliding mode controller (IOSMC). 1
The hydro-turbine governing system (HTGS) holds a crucial position in maintaining overall stability and security of hydropower stations. HTGS characterized by intricate dynamics involving strong coupling, nonlinearity, and non-minimum phase behavior, is responsible for regulating the frequency of the hydropower system. This article proposes a predefined-time fractional-order sliding mode controller (PTFOSMC) aimed at improving the frequency stability of the nonlinear HTGS, accounting for both internal disturbances and external noises within a predefined time. Input/output feedback linearization serves to define the explicit mapping between the HTGS control inputs and its observed outputs. Leveraging fractional calculus principles, a fractional-order controller is designed to mitigate chattering effects associated with the sign function, surpassing conventional sliding mode control (SMC) techniques. Moreover, fractional-order control methods afford greater flexibility in parameter tuning, enhancing the adaptability of controller design. The proposed PTFOSMC guarantees stability of the HTGS within a predefined time, allowing for explicit adjustment as a tuning parameter. Stability analysis of the HTGS under the proposed PTFOSMC utilizes the Lyapunov function. Numerical simulations validate the robustness, effectiveness, and superiority of the PTFOSMC relative to both finite-time control method (FTCM) and integer-order sliding mode controller (IOSMC). 1
期刊：
Electric Power Systems Research,2026年250:112102 ISSN：0378-7796
通讯作者：
Yong Li
作者机构：
College of Electrical and Information Engineering, Hunan University, Changsha, 410082, China;Greater Bay Area Institute for Innovation, Hunan University, Guangzhou, 511300, China;[Yijia Cao] School of Electrical & Information Engineering, Changsha University of Science and Technology, Changsha, 410114, China;[Fang Wu; Rui Li; Jiuqing Cai] Wuhan second ship design and research institute, Wuhan, 44227, China;[Yinglong Zhao; Yong Li; Sijia Hu] College of Electrical and Information Engineering, Hunan University, Changsha, 410082, China<&wdkj&>Greater Bay Area Institute for Innovation, Hunan University, Guangzhou, 511300, China
通讯机构：
[Yong Li] C;College of Electrical and Information Engineering, Hunan University, Changsha, 410082, China<&wdkj&>Greater Bay Area Institute for Innovation, Hunan University, Guangzhou, 511300, China
关键词：
Electrified ship;Electric power system;Transformer design;Vibration reduction;Finite element model;Multiple optimization
摘要：
The optimization of transformer vibration and power density in marine electrical power systems poses a challenging task due to the constraints imposed by ship noise and limited space. This paper introduces a multi-optimization design method for transformers based on multi-objective optimization model and finite element model, with the objective of minimizing vibration and improving power density. The proposed approach leverages the merits of both methodologies by initially utilizing a multi-objective optimization technique to attain an optimal transformer preliminary design, featuring optimized volume, loss, and vibration acceleration. Subsequently, based on this preliminary design, a finite element model is constructed to further refine the transformer’s placement configuration and thermal limits, ultimately yielding an optimal design scheme for a transformer that boasts both low vibration and high power density. Experimental results demonstrate that the proposed method effectively reducing transformer vibrations and volume. Compared to previous-generation transformers not utilizing this method, the proposed approach leads to a 55.81% reduction in vibrational acceleration and 44.93% reduction in volume. Additionally, the calculation values of the transformer from the proposed method exhibit high precision compared to actual measurements.
The optimization of transformer vibration and power density in marine electrical power systems poses a challenging task due to the constraints imposed by ship noise and limited space. This paper introduces a multi-optimization design method for transformers based on multi-objective optimization model and finite element model, with the objective of minimizing vibration and improving power density. The proposed approach leverages the merits of both methodologies by initially utilizing a multi-objective optimization technique to attain an optimal transformer preliminary design, featuring optimized volume, loss, and vibration acceleration. Subsequently, based on this preliminary design, a finite element model is constructed to further refine the transformer’s placement configuration and thermal limits, ultimately yielding an optimal design scheme for a transformer that boasts both low vibration and high power density. Experimental results demonstrate that the proposed method effectively reducing transformer vibrations and volume. Compared to previous-generation transformers not utilizing this method, the proposed approach leads to a 55.81% reduction in vibrational acceleration and 44.93% reduction in volume. Additionally, the calculation values of the transformer from the proposed method exhibit high precision compared to actual measurements.
期刊：
High Voltage,2025年10(2):362-373 ISSN：2397-7264
通讯作者：
Wang, FP
作者机构：
[Du, Guoqiang; Pan, Lei; He, Yushuang; Wang, Feipeng; Li, Jian] Chongqing Univ, Sch Elect Engn, State Key Lab Power Transmiss Equipment Technol, Chongqing, Peoples R China.;[Yang, Hongming; He, Yushuang] Changsha Univ Sci & Technol, Sch Elect & Informat Engn, Changsha, Peoples R China.;[Zhang, Xiao] Naval Univ Engn, Natl Key Lab Sci & Technol Vessel Integrated Power, Natl Key Lab Vessel Integrated Power Syst Technol, Wuhan, Peoples R China.;[Zhang, Zhicheng] Xian Jiaotong Univ XJTU, Sch Chem, Xian Key Lab Sustainable Energy Mat Chem, Xian, Peoples R China.;[Wang, Kaizheng] Kunming Univ Sci & Technol, Fac Elect Engn, Kunming, Peoples R China.
通讯机构：
[Wang, FP ] C;Chongqing Univ, Sch Elect Engn, State Key Lab Power Transmiss Equipment Technol, Chongqing, Peoples R China.
摘要：
AbstractMetallised film capacitors (MFCs) are renowned for their unique self‐healing (SH) properties, which bestow them with exceptional reliability and stability in the face of intense electric fields, high voltages, and pulse power applications. Nonetheless, the exploration of SH characteristics concerning single‐layer dielectric film remains insufficient for advancing MFC reliability evaluation. To establish the theoretical correlation of SH characteristics from the device to the film in the MFCs, this work developed a simulation model to analyse the SH dynamic behaviour in the MFCs. The effects of coupling capacitors, arc resistance and insulation resistance on the macroscopic characteristics (voltage drop and pulse current) are focused during the SH process in MFCs. The results indicate that SH is primarily associated with the voltage drop duration rather than the sampling current. Consequently, the SH process in MFC is characterised as an abrupt decrease in voltage to its minimum value. This refinement enhances the SH energy dissipation model of MFC. The quantified relationship between the macroscopic characteristics and microstructure evolution (polypropylene decomposition and aluminium electrode vaporisation) is established in MFCs under diverse SH energy levels. As SH energy and duration increase, the proportion of energy attributed to polypropylene decomposition increases, resulting in multi‐layer ablation and adhesion within the metallised film and a pronounced deterioration in MFC electrical performance. The examination of macro–micro perspectives sheds new light on the intricate mechanisms governing the SH behaviour in MFCs, offering valuable insights for the advancement of their design, reliability evaluation, and performance optimisation in diverse electrical applications.
摘要：
Lithium-ion batteries are now widely used as energy storage units in electric vehicles. Achieving high accuracy in state of charge (SOC) estimation in the battery management system (BMS) is critical for safe operation of electric vehicles. However, accurate SOC estimation remains a challenging task due to the complex dynamics of batteries and the wide range of ambient temperature. Here we propose a new method called ResNet-GRNN for accurate SOC estimation. Our approach combines a Residual network (ResNet) and a gated recurrent neural network (GRNN). Compared to traditional GRNNs, the proposed method can improve the accuracy and generalization of SOC estimation without altering the original GRNN output. The proposed method is tested on datasets collected from two lithium-ion batteries under dynamic drive cycles at different temperatures. The results show that the mean absolute errors (MAEs) of the proposed method is 80% and 56% lower than those of GRNNs and Deep-GRNNs, respectively. Particularly at low temperatures, the ResNet-GRNNs reduce MAEs by 86% and 79%. Moreover, the proposed method achieves low MAEs of 0.51% and 1.14%, respectively, under untrained varying temperatures. Finally, upon testing in a practical BMS, the proposed method achieved the highest level of accuracy while reducing memory consumption by 70%, demonstrating its superiority in practical applications.
Lithium-ion batteries are now widely used as energy storage units in electric vehicles. Achieving high accuracy in state of charge (SOC) estimation in the battery management system (BMS) is critical for safe operation of electric vehicles. However, accurate SOC estimation remains a challenging task due to the complex dynamics of batteries and the wide range of ambient temperature. Here we propose a new method called ResNet-GRNN for accurate SOC estimation. Our approach combines a Residual network (ResNet) and a gated recurrent neural network (GRNN). Compared to traditional GRNNs, the proposed method can improve the accuracy and generalization of SOC estimation without altering the original GRNN output. The proposed method is tested on datasets collected from two lithium-ion batteries under dynamic drive cycles at different temperatures. The results show that the mean absolute errors (MAEs) of the proposed method is 80% and 56% lower than those of GRNNs and Deep-GRNNs, respectively. Particularly at low temperatures, the ResNet-GRNNs reduce MAEs by 86% and 79%. Moreover, the proposed method achieves low MAEs of 0.51% and 1.14%, respectively, under untrained varying temperatures. Finally, upon testing in a practical BMS, the proposed method achieved the highest level of accuracy while reducing memory consumption by 70%, demonstrating its superiority in practical applications.
关键词：
IES-WTE;CCS-P2G;carbon trading;ladder-type GCT;ladder-type CET;low-carbon dispatch;synergistic interaction mechanism;multi-energy system optimization
摘要：
Waste-to-energy (WTE) is considered the most promising method for municipal solid waste treatment. An integrated energy system (IES) with carbon capture systems (CCS) and power-to-gas (P2G) can reduce carbon emissions. The incorporation of a "green-carbon" offset mechanism further enhances renewable energy consumption. Therefore, this study constructs a WTE-IES hybrid system, which conducts multi-dimensional integration of IES-WTP, CCS-P2G, photovoltaic (PV), wind turbine (WT), multiple energy storage technologies, and the "green-carbon" offset mechanism. It breaks through the limitations of traditional single-technology optimization and achieves the coordinated improvement of energy, environmental, and economic triple benefits. First, waste incineration power generation is coupled into the IES. A mathematical model is then established for the waste incineration and CCS-P2G IES. The CO2 produced by waste incineration is absorbed and reused. Finally, the "green-carbon" offset mechanism is introduced to convert tradable green certificates (TGCs) into carbon emission rights. This approach ensures energy demand satisfaction while minimizing carbon emissions. Economic incentives are also provided for the carbon capture and conversion processes. A case study of an industrial park is conducted for validation. The industrial park has achieved a reduction in carbon emissions of approximately 72.1% and a reduction in the total cost of approximately 33.5%. The results demonstrate that the proposed method significantly reduces carbon emissions. The energy utilization efficiency and system economic performance are also improved. This study provides theoretical and technical support for the low-carbon development of future IES.
摘要：
The integration of large-scale wind power clusters significantly reduces the inertia level of the power system, increasing the risk of frequency instability. Accurately assessing the equivalent virtual inertia of wind farms is critical for grid stability. Addressing the dual bottlenecks in existing inertia assessment methods, where physics-based modeling requires full control transparency and data-driven approaches lack interpretability for inertia response analysis, thus failing to reconcile commercial confidentiality constraints with analytical needs, this paper proposes a symbolic regression framework for inertia evaluation in doubly fed wind farms with limited control information constraints. First, a dynamic model for the inertia response of DFIG wind farms is established, and a mathematical expression for the equivalent virtual inertia time constant under different control strategies is derived. Based on this, a nonlinear function library reflecting frequency-active power dynamic is constructed, and a symbolic regression model representing the system's inertia response characteristics is established by correlating operational data. Then, sparse relaxation optimization is applied to identify unknown parameters, allowing for the quantification of the wind farm's equivalent virtual inertia. Finally, the effectiveness of the proposed method is validated in an IEEE three-machine nine-bus system containing a doubly fed wind power cluster. Case studies show that the proposed method can fully utilize prior model knowledge and operational data to accurately assess the system's inertia level with low computational complexity.
关键词：
Computer crime;Location awareness;Hybrid power systems;Prevention and mitigation;Automation;Power distribution;Hardware;Cyber-physical system;cyber security;hybrid attack;bi-level resilient control;feeder automation
摘要：
Distributed feeder automation system (DFAs), as a promising protection technology for power distribution system (PDS) with distributed generation, its vulnerability to cyberattacks and hybrid attacks (contains both physical and cyberattacks) is gradually recognized and haunts utilities, creating potential risks for its large-scale applications. This paper proposes a novel bi-level resilient control solution (BRCS) deployed to DFAs without hardware burden. Two key modules are developed: 1) Lightweight distributed cyberattack detection module (DCDM), deployed into agents of DFAs, based on unsupervised learning to realize the quick detection and reporting of cyberattacks; 2) Robust centralized fault section localization module (CFSLM), installed in DFAs’ host workstation located in control center, achieving the correctly fault section localization and the high dimensionally awareness of attack events in cyberattack and hybrid attack scenarios. By adopting BRCS, outages and load losses caused by cyberattacks can be 100% avoided, and faults caused by physical attacks can be correctly isolated at once. Finally, the effectiveness and performance of the proposal are verified and conducted by the real two-feeder test platform with DFAs. In this process, a digital high-dimensional awareness and control unit is created against cyber and hybrid attacks, contributing to the system-level application of risk management and resilient control.
摘要：
With the expansion of new energy grid connection scale, the penetration rate of wind turbine increases significantly, which leads to the deterioration of power system inertia and damping characteristics, affecting the stable operation of power grid. Aiming at the problem of insufficient dynamic response due to fixed parameters in the virtual synchronization control strategy of doubly-fed wind turbine, this paper proposes a dual-adaptive control strategy based on radial basis function (RBF) neural network for inertia and damping of DFIG-VSG system. Firstly, a small-signal model of DFIG-VSG is established to reveal the influence mechanism of virtual inertia and damping parameters on the stability; subsequently, the local approximation property of Gaussian radial basis function is utilized to accurately model the strong nonlinear coupling relationship of the DFIG-VSG system; at the same time, the dual-input (angular velocity deviation Δω and its rate of change dω/d<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$t$</tex>) and dual-output (virtual inertia <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$J$</tex> and damping coefficient D) architecture is proposed The real-time dynamic adjustment of control parameters is realized. Simulation results show that compared with the traditional VSG control, the proposed strategy reduces the maximum power deviation by 36.4%, the overshooting amount by 52.9%, and the regulation time by 52.9<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup>; in the frequency dynamics, the maximum deviation is reduced by 50%, and the regulation time is accelerated by 63.8%. The RBF neural network, with its strong nonlinear fitting ability, significantly improves the system adaptability and dynamics, and provides effective solutions for the grid-connected stable operation of a high proportion of wind power. The RBF neural network significantly improves the adaptive and dynamic performance of the system by virtue of its strong nonlinear fitting ability, providing an effective solution for the stable operation of high percentage of wind power.
摘要：
Since the voltage amplitude of the arc suppression device is different during the normal operation and single line-to-ground fault, the problems of high cost and low module utilization rate are serious. An integrated grid-connected converter (IGCC) with reactive power compensation and fault regulation ability is proposed. First, the topology and operation mechanism of IGCC are introduced in this article. A common unit combining neutral point clamped (NPC) and cascaded H-bridge is formed by improving the traditional arc suppression device. By adding a fourth leg in the NPC module and connecting with the arc suppression inductance, the integration of the two structures is realized. The access of NPC unit not only reduces the number of modules of traditional arc suppression device, but also provides an integrated port for arc suppression device and reactive power compensation device. Second, the parameters of active and passive part of IGCC are optimally designed to ensure the stable operation of IGCC. In addition, compared with existing schemes, the superiority of IGCC in cost and volume is proved. Finally, the correctness, feasibility and effectiveness of the proposed topology and functions are verified by the simulation and experiment results.
作者机构：
School of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha, 410114, China;School of Robotics, Hunan University, Changsha, 410012, China;Truking Technology Limited, Changsha, 410600, China;[刘优武; 陶岩; 孔森林] 长沙理工大学电气与信息工程学院;[张辉] 湖南大学机器人学院
通讯机构：
[Wang, Q ] N;Nanjing Normal Univ, Sch Elect & Automat Engn, Nanjing 210097, Peoples R China.
关键词：
Game theory;power systems;security defense;zero-sum game;Game theory;power systems;security defense;zero-sum game
摘要：
The power system, as vital national infrastructure, is confronted with increasingly severe external attack threats. Current research has primarily focused on defensive layouts and loss analyses in the context of specific attack levels, neglecting the unpredictability of attacker's capability and lacking an endurance safety assessment for the power system. Compared to mainstream studies, the work reported in this paper presents a novel analytical scenario where a regional power system experiences complete failure due to the physical attack, aiming to develop a holistic vulnerability assessment method for localized power grids facing unknown attacker capability. The proposed method specifically establishes an Attack-Defense game model including two distinct objective functions with interdependent decision-making to maintain the resistance characteristic of the defender. Besides, based on the characteristics of the model, a customized method is proposed to solve and validate the original optimization problem through permutation problem. Simulation results based on the IEEE 14-bus and 118-bus systems verified the correctness of the proposed models. And a comparative analysis of various resource allocation schemes for national territorial defense has been conducted to validate the effectiveness of the assessment methodology.
摘要：
During the battery cathode materials preparation, the temperature correlation, the external environment disturbance, and the system instability caused by control updating widely exist. All these make it difficult to accurately control the temperature of roller kiln. For this reason, an event-triggered decentralized H infinity control method based on adaptive dynamic programming is proposed. First, the temperature interconnection model is established by describing the relationship between temperatures of the atmosphere outlet and each temperature zone. Then, as for temperature interconnection and disturbance, an auxiliary subsystem is introduced and a cost function including upper bound of interconnection term, temperature state, control input, auxiliary control law and disturbance is designed. Next, the event-trigger mechanism is introduced. The event-triggering condition is designed by considering the temperature interconnection, temperature state, control input and disturbance. It is proved that the temperature event-triggered decentralized H infinity control problem can be converted to solve the Hamilton-Jacobi-Isaacs (HJI) equation problem of a set of auxiliary subsystems and the critic learning method is used to solve the HJI equation. The state of the auxiliary subsystem and the pulse dynamic system are proved to be uniformly ultimately bounded. Finally, the proposed control approach is implemented to roller kiln to prove its validity.
期刊：
IET Renewable Power Generation,2025年19(1):e70119 ISSN：1752-1416
通讯作者：
Zhiyi Li
作者机构：
[Yanfu Jiang] Faculty of Engineering, Monash University, Clayton, Victoria, Australia;[Md Tanjid Hossain; Xutao Han; Zhiyi Li] Department of Electrical Engineering, Zhejiang University, Hangzhou, China;[Xingyu Shi] Department of Electrical Engineering, Changsha University of Science and Technology, Changsha, China
通讯机构：
[Zhiyi Li] D;Department of Electrical Engineering, Zhejiang University, Hangzhou, China
摘要：
Seasonal fluctuations and the intermittent nature of photovoltaic (PV) generation create significant challenges for accurate short-term forecasting. This study presents Next Frame Gramian Angular field U-Net (NFGUN), a hybrid deep learning forecasting framework that stands apart from conventional methods by transforming 1D PV time-series data into 2D Gramian Angular Summation Field (GASF) images. Unlike models that rely on direct regression or sky imagery, NFGUN forecasts the next GASF frame using a deep architecture and reconstructs it back into time-series form, effectively capturing nonlinear temporal dynamics. Its uniqueness lies in several key innovations: (1) the integration of Convolutional Long Short-Term Memory 2D (ConvLSTM2D) into a customised U-Net model for better generalisation spatiotemporal features; (2) the incorporation of residual blocks in the bottleneck to preserve deep features while mitigating vanishing gradients and cyclical encoding of time to enrich seasonal patterns; (3) the use of Lanczos interpolation with CIEDE2000 colour difference for high-precision reconstruction from predicted image frames. We evaluate NFGUN against six well-established forecasting methods and measure performance using six accuracy metrics such as MAE, RMSE, and WAPE across all four seasons; NFGUN demonstrates superior performance. Compared to the best-performing benchmark, it achieved improvements in MAE (61.23% winter, 56% spring, 37.45% summer, 59.67% autumn), RMSE (48.34% winter, 64.63% spring, 31.65% summer, 45.83% autumn), and WAPE (49.9% winter, 43.84% spring, 45.83% summer, 48.72% autumn), underscoring its ability to adapt to seasonal variability. These results demonstrate NFGUN's ability to effectively capture complex, seasonal dynamics, making it a robust solution for ultra-short-term PV power forecasting.
摘要：
Time series classification is a significant and complex issue in data mining, it is prevalent across various fields and holds substantial research value. However, enhancing the classification rate of time series data remains a formidable challenge. Traditional time series classification methods often face difficulties related to insufficient feature extraction or excessive model complexity. In this study, we propose a self-optimizing polynomial neural network with a temporal feature enhancement, which is referred to as OPNN-T. Existing classifiers based on polynomial neural networks (PNNs) struggle to achieve high-quality performances when dealing with time series data, primarily due to their inability to extract temporal information effectively. The goal of the proposed classifier is to enhance the nonlinear modeling capability for time series data, thereby improving the classification rate in practical applications. The key features of the proposed OPNN-T include the following: (1) A temporal feature module is employed to capture the dependencies in time series data, providing adaptability and flexibility in handling complex temporal patterns. (2) A polynomial neural network (PNN) is constructed using sub-datasets combined with three types of polynomial neurons, which enhances its nonlinear modeling capabilities across diverse scenarios. (3) A self-optimization mechanism is integrated into iteratively optimized sub-datasets, features, and polynomial types, resulting in significant improvements in the classification rate. The experimental results demonstrate that the proposed method achieves superior performances across multiple standard time series datasets, exhibiting higher classification accuracy and greater robustness than the existing classification models. Our research offers an effective solution for time series classification, and highlights the potential of polynomial neural networks in this field.
摘要：
Process optimization is a highly successful method for achieving optimal efficiency in industrial production. The conventional optimization approach presupposes that the operational parameters should align with the optimization settings. However, it fails to consider that, influenced by the stochastic performance of the control loops, the operating parameters may deviate from the optimal operating settings. Consequently, this results in the violation of constraints in the optimization results and affects production safety. Therefore, this paper proposes an uncertainty optimization method that considers the stochastic performance of control loops to accurately determine the optimal operational performance that can be practically achieved in industrial production. Firstly, a multi-optimization variational mode decomposition strategy is developed to precisely extract the smooth random and trend terms of the control loop output data. Secondly, the random grouping smooths out the random terms and accurately characterizes the uncertainty associated with these terms. Subsequently, a moment uncertainty set with mild mean-zero net condition is then defined to construct an improved distribution robust optimization model considering the stochastic performance of control loops. Finally, the validation of the proposed optimization method in the actual hydrocracking process shows that the optimization error of the proposed method is reduced by more than 10%, and the constraint violation rate is reduced by 14%, which fully proves the effectiveness and applicability of the method.
通讯机构：
[Huang, JC; Peng, ZY ] C;Changsha Univ Sci & Technol, Sch Energy & Power Engn, Key Lab Efficient & Clean Energy Utilizat, Changsha 410111, Peoples R China.
摘要：
Despite the advancements in film fabrication techniques for emerging perovskite solar cells, achieving a high-quality film by solution processing, while maintaining considerable performance remains a significant challenge. To tackle the issue of inferior CsPbI 2 Br perovskite films deposited via solution-based methods, a novel thermal conduction heating approach was devised and implemented, significantly enhancing film uniformity. Crucially, aliphatic amine acetates (3A) were introduced into the precursor solution to regulate the crystallization process and therefore to mitigate defects. Systematic investigation into the impact of 3A molecules featuring varying alkyl chain lengths on defect passivation revealed that the molecular dipole moment of these additives contributed to both defect mitigation and grain size refinement. Notably, the integration of alkyl chains significantly bolstered the hydrophobic properties of the perovskite film. Consequently, an impressive efficiency of 13.50% for HTM-free carbon-based CsPbI 2 Br perovskite solar cells was achieved, and the device exhibited robust stability retaining 92.4% of its initial efficiency at room temperature after being stored in dry air for 5400 h. This research offers profound insights into defect passivation mechanisms and perovskite crystallization dynamics, paving the way for further advancements in the field of perovskite solar cell technology.
作者机构：
[Wenjuan Liu; Dongqi Liu] Department of Electrical and Information Engineering, Changsha University of Science & Technology, Changsha, China
会议名称：
2025 IEEE 8th International Electrical and Energy Conference (CIEEC)
会议时间：
16 May 2025
会议地点：
Changsha, China
会议论文集名称：
2025 IEEE 8th International Electrical and Energy Conference (CIEEC)
关键词：
Resilient control;vertical federal learning;network attack;Microgrid;distributed cooperative control
摘要：
In microgrids, distributed energy resources share information through sensor and communication systems, which are vulnerable to cyber attacks. This article addresses safety and control challenges in microgrid systems, particularly against network attacks like random denial-of-service. It introduces an adaptive gain elastic controller to ensure system stability by disconnecting communication links with excessive delays and restoring connections through optimization. Additionally, the article presents federated learning as a privacy-preserving method that allows microgrids to collaboratively train models without centralizing data, thus reducing system failure risks. A method for optimizing the collaborative operation of multi-agent microgrids using vertical federated learning is described, involving local training, strategy searching in the cloud, and distributed joint training. Testing confirms the method's effectiveness in enhancing privacy protection and network resilience. Future research will focus on improving computing efficiency and assessing each microgrid's contributions to optimize collaborative strategies.
摘要：
The extensive application of the distributed feeder automation system (DFAs) is a prominent feature of the power distribution system (PDS). To enhance the resilience of DFAs to cyberattacks and hybrid attacks (contains both physical and cyber-attacks), this paper proposes an edge resilient control approach (ERCA) for DFAs, which integrates attack detection and mitigation. Two key modules are developed: 1) Edge Cyberattack Detection and Correction Module (ECDCM), achieving quick cyberattack detection and correction based on machine learning and physical constraints; 2) Consensus-based Fault Section Localization Module (CFSLM), which incorporates causality function related to agent overcurrent states and fault location to correctly localize the fault section in hybrid attack scenarios. In addition, the effective boundaries of ERCA are theoretically deduced in this paper, which proves that ERCA can retain resilience to cyberattacks and hybrid attacks even if undetected cyberattacks exist due to the failures of machine learning-based detection submodules. Finally, the proposal's effectiveness and performance are tested and verified using the IEEE 123-bus system and the real two-feeder test platform with DFAs. By adopting ERCA, outages and load losses caused by cyber attacks are 100% avoided, and the fault caused by physical attack is correctly isolated with more than 97.6% reliability in 100,000 times-level Monte Carlo Numerical simulations of hybrid attacks.
作者机构：
Department of Control Science and Engineering, Tongji University, Shanghai, China;State Key Lab of Autonomous Intelligent Unmanned Systems, Beijing Institute of Technology, Beijing, China;[Xin Wang] School of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha, China;Beijing Institute of Technology Chongqing Innovation Center, Chongqing, China;[Yifei Li; Jian Sun; Tao Cai; Gang Wang] State Key Lab of Autonomous Intelligent Unmanned Systems, Beijing Institute of Technology, Beijing, China<&wdkj&>Beijing Institute of Technology Chongqing Innovation Center, Chongqing, China
会议名称：
2025 44th Chinese Control Conference (CCC)
会议时间：
28 July 2025
会议地点：
Chongqing, China
会议论文集名称：
2025 44th Chinese Control Conference (CCC)
摘要：
This paper addresses the self-triggered consensus control (STC) problem for unknown linear multi-agent systems (MASs). Self-triggering mechanisms (STMs) have gained popularity in the context of MASs due to their ability to eliminate the need for continuous monitoring and reduce communication demands. However, conventional STM designs typically rely on explicit model knowledge, which is often difficult to obtain in practical applications. To overcome this limitation, we propose a data-driven framework for synthesizing an STC protocol that integrates an STM with a state feedback control law. First, by interpreting a self-triggered MAS as a switched MAS, we develop a system lifting technique to construct a data-based representation for the MAS. Leveraging Petersen's lemma, a stabilizing controller and the self-triggering matrix are codesigned by formulating a data-based linear matrix inequality (LMI), while ensuring the stability of the closed-loop system. Simulation results validate the effectiveness of the proposed data-driven approach.
摘要：
This paper presents a method of rotor position estimation for switched reluctance motors suitable for saturation. The effects of saturation as well as voltage changes are taken into account at the same time. It is based on the inductance in the unsaturated region. When the phase inductance is equal to the threshold, it is defined as a characteristic point. Meanwhile the characteristic pulse signal is triggered. Different inductance intersection thresholds are determined when the phase current and bus voltage change. The rotor position is estimated by interval speed. Compared with the traditional inductance method, the position estimation error is smaller. Finally, the correctness and effectiveness of the proposed method are verified by simulation and experiments.
