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Extraction and application of intrinsic predictable component in day-ahead power prediction for wind farm cluster

Energy 2025年 328卷

作者： Yang, Mao Jiang, Renxian Yu, Xinnan Wang, Bo Su, Xin Ma, Chenglian The Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems China Electric Power Research Institute Beijing100912 China

With the continuous updating and iteration of artificial intelligence algorithms, prediction models emerge one after another, but the research and utilization of the predictability of wind power is still less. Therefore, this paper proposed a day-ahead power prediction method for Wind Farm Cluster (WFC) based on intrinsic predictable component extraction. Firstly, based on the difference of power distribution between wind farm and WFC, a method of Wind Power Curve (WPC) modeling for WFC is proposed, which provides the basis for establishing the Final Set of Wind Power Curve (FSWPC). Secondly, the Intrinsic Predictable Component (IPC) of wind power is extracted based on the FSWPC, and the Interference Component (IC) of wind power is separated to eliminate the influence of IC on IPC in the process of prediction. Thirdly, the historical similarity matching method with large threshold is used to predict the IC to make up for the numerical deficiency of the IPC. Finally, the proposed method was applied to a WFC in China to verify its effectiveness. Compared with the traditional prediction strategy, the NRMSE, NMAE and MAPE are reduced by 1.59 %, 1.15 % and 6.42 %, respectively. © 2025 Elsevier Ltd

关键词： Iterative methods

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Performance-Aware control of Modular Batteries For Fast Frequency Response

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IEEE Transactions on Sustainable Energy 2025年

作者： He, Yutong Ruan, Guangchun Zhong, Haiwang Tsinghua University State Key Laboratory of Power System Operation and Control Department of Electrical Engineering Beijing100084 China Massachusetts Institute of Technology Lab for Information and Decision Systems CambridgeMA02139 United States

Modular batteries can be aggregated to deliver frequency regulation services for power grids. Although utilizing the idle capacity of battery modules is financially attractive, it remains challenging to consider the heterogeneous module-level characteristics such as dynamic operational efficiencies and battery degradation. In addition, real-time decision making within seconds is required to enable fast frequency response. In order to address these issues, this paper proposes a performance-aware scheduling approach for battery modules to deliver fast frequency response (FFR) support. In particular, the conduction loss and switching loss of battery packs as well as converters are captured within a mix-integer quadratic constrained program (MIQCP). The cycle-based aging model identifies the aging cost of battery modules during frequent cycling by introducing the aging subgradient calculation and linearization. Case studies based on real-world battery data show that the proposed scheduling approach can effectively reduce power loss cost by nearly 28%-57% and battery aging cost by 4%-15% compared to conventional methods, which can also enhance the SoC balance. © 2010-2012 IEEE.

关键词： Battery management systems

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Improving the real-time performance of Ethernet for plant automation(EPA) based industrial networks

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Journal of Zhejiang University-Science C(Computers and Electronics) 2013年第6期14卷 433-448页

作者： Li LU Dong-qin FENG Jian CHU Institute of Cyber-Systems and Control Zhejiang University State Key Laboratory of Industrial Control Technology Zhejiang University

Real-time Ethernet(RTE) control systems with critical real-time requirements are called fast real-time(FRT) *** improve the real-time performance of Ethernet for plant automation(EPA),we propose an EPA-FRT *** minimum macrocycle of EPA networks is reduced by redefining the EPA network frame format,and the synchronization process is modified to acquire higher accuracy.A multi-segmented topology with a scheduling scheme is introduced to increase effective bandwidth utilization and reduce protocol overheads,and thus to shorten the communication cycle *** analysis and practical tests on a prototype system show the effectiveness of the proposed scheme,which achieves the best performance at small periodic payload in large scale systems.

关键词： Ethernet for plant automation （EPA） Fast real-time （FRT） system Real-time Ethernet （RTE） Scheduling scheme171 ~ ~l ~nI~ ]Ui^umanf onrla＊ A ~｜ F numh~＊ TDg02

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Mean square stability for Markov jump Boolean networks

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Science China(Information Sciences) 2020年第1期63卷 190-199页

作者： Liqing WANG Mei FANG Zheng-Guang WU State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and ControlZhejiang University College of Automation Harbin Engineering University

In this paper, one of the stability definitions of Markov jump Boolean networks(MJBNs), called mean square stability(MSS), is investigated. Some necessary and sufficient conditions are presented to guarantee the MSS of such MJBNs. Moreover, one of the necessary and sufficient conditions for MSS is obtained in terms of linear programming, which implies that MSS is equivalent to global stability with probability 1 for MJBNs. Furthermore, the construction of Lyapunov function is given and also another theorem based on the Lyapunov function is derived to ensure the MSS of MJBNs. Finally, a numerical example is provided to illustrate the profits of our results.

关键词： mean square stability MSS Boolean network BN stochastic stability Markovian jump parameters semi-tensor product STP Lyapunov function

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DEEPCON: Improving Distributed Deep Learning Model Consistency in Edge-Cloud Environments via Distillation

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Qian, Bin Qian, Jiaxu Wen, Zhenyu Wu, Di He, Shibo Chen, Jiming Ranjan, Rajiv Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou China Zhejiang University of Technology Institute of Cyberspace Security College of Information Engineering Hangzhou China University of St Andrews School of Computer Science United Kingdom Newcastle University United Kingdom

In a typical distributed Deep Learning (DL) based application, models are configured differently to meet the requirements of resource constraints. For instance, a large ResNet56 model is deployed on the cloud server while a small lightweight MobileNet model is more suitable for the end-user device with fewer computation resources. However, the heterogeneity of the model architectures and configurations may bring a systemic problem -models may produce different outputs when given the same input. This inconsistency problem may cause severe system failure of prediction agreement inside the application. Current research has not studied the systemic design for efficiently detecting and reducing the inconsistency among models in distributed DL applications. With the increasing scale of distributed DL applications, the challenges of inconsistency mitigation should consider both algorithm and system design. To this end, we design and implement DEEPCON, an adaptive deployment system across the edge-cloud layer with over-the-air model updates. We implement ASRS sampling for efficiently sampling data to reveal the real data distribution as well as model prediction inconsistency. Then, we implement DMML-Par, an asynchronous parallel training algorithm for quickly updating the models and reducing inconsistency. implements over-the-air updates with a set of APIS to enable seamless inconsistency detection and reduction in such deep learning applications. Our experiment results on both vision and language tasks demonstrate that DMML could improve the model consistency up to 4%, 7%, and 13% at CIFAR10/100 and IMDB datasets without sacrificing the accuracy of individual models. We also show that the ASRS sampling can save 90% network bandwidth of data transmission and that DMML-Par is up to 60% faster compared to simple synchronous parallel training. 2015 IEEE.

关键词： Distributed cloud

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Resilient Formation control Based on Watermarks for Networked Quadrotors under Deception Attacks

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IEEE Transactions on Vehicular technology 2025年

作者： Zhan, Weiwei Miao, Zhiqiang Zeng, Jianxin Chen, Yanjie Wu, Zheng-Guang He, Wei Wang, Yaonan Hunan University College of Electrical and Information Engineering Changsha410082 China Fuzhou University School of Mechanical Engineering and Automation Fuzhou350108 China National Engineering Research Center of Robot Visual Perception and Control Technology Changsha410082 China Zhejiang University Institute of Cyber-Systems and Control Hangzhou310027 China University of Science and Technology Beijing Key Laboratory of Intelligent Bionic Unmanned Systems Ministry of Education School of Intelligence Science and Technology Institute of Artificial Intelligence Beijing100083 China

This article addresses the problem of formation control of networked UAVs under deception attacks. A lightweight resilient formation control framework based on watermarks is proposed to achieve the desired formation configuration without extra monitoring attack status. Since deception attacks are covert, the shared information is watermarked before transmission to establish the protocol among networked UAVs. To facilitate the resilient formation controller design, a decoder is first developed to decipher the inverse transformation of deception attacks, where the watermark is the criterion for approximating the inverse transformation. The Gauss error function is introduced to eliminate some unrealistic input commands under deception attacks, obtaining the predefined input performance. Then, a resilient formation controller is developed to guarantee the global boundedness of all signals within the inner-loop and outerloop system, while the formation error converges to a small neighborhood of zero by solving the linear matrix inequality. Two challenging deception attack examples in simulations and experiments are conducted to verify the effectiveness and performance of the proposed resilient formation control framework. © 1967-2012 IEEE.

关键词： Inverse transforms

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Target Defense with Multiple Defenders and an Agile Attacker via Residual Policy Learning

arXiv

引用

arXiv 2025年

作者： Tao, Jiyue Shen, Tongsheng Zhao, Dexin Zhang, Feitian Robotics and Control Laboratory Department of Advanced Manufacturing and Robotics College of Engineering and the State Key Laboratory of Turbulence and Complex Systems Peking University Beijing100871 China National Innovation Institute of Defense Technology Beijing100071 China

The target defense problem involves intercepting an attacker before it reaches a designated target region using one or more defenders. This letter focuses on a particularly challenging scenario in which the attacker is more agile than the defenders, significantly increasing the difficulty of effective interception. To address this challenge, we propose a novel residual policy framework that integrates deep reinforcement learning (DRL) with the force-based Boids model. In this framework, the Boids model serves as a baseline policy, while DRL learns a residual policy to refine and optimize the defenders’ actions. Simulation experiments demonstrate that the proposed method consistently outperforms traditional interception policies, whether learned via vanilla DRL or fine-tuned from force-based methods. Moreover, the learned policy exhibits strong scalability and adaptability, effectively handling scenarios with varying numbers of defenders and attackers with different agility levels. Copyright © 2025, The Authors. All rights reserved.

关键词： Adversarial machine learning

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A framework of day-ahead wind supply power forecasting by risk scenario perception

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IEEE Transactions on Sustainable Energy 2025年

作者： Yang, Mao Huang, Yutong Wang, Zhao Wang, Bo Su, Xin Northeast Electric Power University Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Jilin132012 China China Electric Power Research Institute State Key Laboratory of Operation and Contr Ol of Renewable Energy & Storage Systems Beijing100912 China

Wind power forecasting (WPF) systems are essential to maintain the safe and stable operation of the power system in case of large-scale grid-connected wind farms. However, the current forecasting has the problem of disunity between statistical value and application value, that is, it only pays attention to its forecasting accuracy and ignores the risks caused by it in the power system. In order to solve the above problems, this study proposes a framework of wind supply power forecasting (WSPF) for wind farm cluster, which takes into account the risk scenario perception. First of all, aiming at the predicted risk phenomenon in WPF, TimesNet combined with the fluctuation information of Numerical Weather Prediction (NWP) wind speed is used to identify the corresponding risk scenarios. Secondly, the effective consumption area and power supply risk area evaluation index, as well as the accuracy of WSPF are defined, and the optimal forecasting curve correction scheme is fitted according to the index. Thirdly, taking into account the correction scheme and identification results, a variety of predictors are used to verify the WSPF according to the above framework. Finally, the proposed method is applied to a wind farm cluster in Inner Mongolia Autonomous region of China, the average accuracy of WSPF has increased by 37%, which verifies the effectiveness and universality of this method. © 2010-2012 IEEE.

关键词： Wind forecasting

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Brain-Conditional Multimodal Synthesis: A Survey and Taxonomy

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1080-1099页

作者： Mai, Weijian Zhang, Jian Fang, Pengfei Zhang, Zhijun South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Southeast University School of Computer Science and Engineering Nanjing210096 China South China University of Technology Key Library of Autonomous Systems and Network Control Ministry of Education The School of Automation Science and Engineering Guangzhou510640 China Institute for Super Robotics Huangpu Guangzhou510555 China Nanchang University Jiangxi Thousand Talents Plan Nanchang330031 China Jishou University College of Computer Science and Engineering Jishou416000 China Guangdong Artificial Intelligence and Digital Economy Laboratory Pazhou Lab Guangzhou510335 China Shaanxi University of Technology Shaanxi Provincial Key Laboratory of Industrial Automation School of Mechanical Engineering Hanzhong723001 China Changsha Normal University School of Information Science and Engineering Changsha410100 China Guangdong University of Petrochemical Technology School of Automation Science and Engineering Institute of Artificial Intelligence and Automation Maoming525000 China

In the era of artificial intelligence generated content (AIGC), conditional multimodal synthesis technologies (e.g., text-to-image) are dynamically reshaping the natural content. Brain signals, serving as potential reflections of how the brain interprets external information, exhibit a distinctive one-to-many correspondence with various external modalities. This correspondence makes brain signals emerge as a promising guiding condition for multimodal synthesis (e.g., image, text, and audio), which is crucial for developing practical brain–computer interface systems and unraveling complex mechanisms underlying human perception. This survey comprehensively examines the emerging field of brain-conditional multimodal synthesis, termed AIGC-brain, to delineate the current landscape and future directions. To begin, related neuroimaging datasets and generative models are introduced as the foundation of AIGC-brain decoding and analysis. Next, we present a comprehensive taxonomy according to AIGC-brain methodologies, followed by task-specific representative work and implementation details to facilitate in-depth comparison and analysis. Quality assessments are then introduced for both qualitative and quantitative evaluation. Finally, this survey explores insights gained, outlining current challenges and prospects of AIGC-brain. As a pioneering survey, this article paves the way for future advances in AIGC-brain research. © 2020 IEEE.

关键词： Taxonomies

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Modeling of distributed parameter systems based on independent partial derivative-physics-informed neural network

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Neurocomputing 2025年 642卷

作者： Wang, Lijie Lin, Yangshu Yan, Xinrong Shao, Yuhao Xu, Zuhua Yang, Chao Fan, Haidong Xie, Yurong Zheng, Chenghang State Key Lab of Clean Energy Utilization Institute of Carbon Neutrality State Environmental Protection Engineering Center for Coal-Fired Air Pollution Control Zhejiang University Hangzhou310027 China State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou310027 China Zhejiang Baima Lake Laboratory Co. Ltd. Hangzhou310051 China Huadian Electric Power Research Institute Co. Ltd. Hangzhou310030 China

Physics-informed neural networks (PINNs) have attracted considerable interest due to their capacity to incorporate established physical principles, thus facilitating efficient training with a limited amount of observed data. This approach ensures that the results of supervised learning are in accordance with the governing physical laws of the system, thereby enhancing the interpretability of models. However, existing research predominantly employs a single network architecture to simultaneously learn the information of the partial differential equations (PDEs). Such architectures typically require large network sizes to learn the system characteristics, which leads to prolonged training times and inefficiencies. This paper introduces a novel modeling framework, termed the independent partial derivative-physics-informed neural network (IPD-PINN). In contrast to conventional methodologies, IPD-PINN employs a network comprising multiple subnetworks, with each subnetwork dedicated to the learning of individual partial derivatives in the PDEs. The modular network structure allows for flexible and scalable adjustments to the size of each subnetwork, accommodating modeling tasks of varying complexities. Consequently, the entire network is capable of reducing the required training time while maintaining precise modeling accuracy. The efficacy of the proposed IPD-PINN method is demonstrated through two simulation case studies, which highlight its potential to improve the efficiency and accuracy of IPD-PINN modeling. © 2025

关键词： Wave equations

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