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Research on multi-source heterogeneous data cleaning technology based on integrating neural network with fuzzy rules for renewable energy accommodation 4

Research on multi-source heterogeneous data cleaning technol...

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4th IEEE Conference on Energy Internet and Energy System Integration, EI2 2020

作者： Sun, Rongfu Wu, Yuhui Lan, Haibo Wang, Yulin Ding, Ran Xu, Jian Liao, Siyang Hu, Jia Sun, Yamin State Grid Jibei Electric Power Company Limited Dispatch Control Center Beijing China Wuhan University School of Electrical Engineering and Automation Wuhan China State Grid Jibei Electric Power Research Institute Smart Grid and Renewable Energy Beijing China

ISBN: (纸本)9781728196060

In order to avoid conservative results, when calculating the ability of power system to accommodate renewable energy, social data need to be integrated. Data cleaning is an important step in integration. Traditional data cleaning methods rely only on data and ignore the implicit rules. This paper first analyzes the data requirements for renewable energy accommodation, and then proposes a data cleaning technology. This technology digs out the implicit associations between data, converts them into fuzzy rules in the form of Probabilistic soft logic (PSL), and builds a neural network based on this for data cleaning. © 2020 IEEE.

关键词： computer circuits

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Results on sparsity and estimation accuracy in Orthogonal Matching Pursuit with application to optimal input design

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Automatica 2025年 179卷

作者： Javad Parsa Cristian R. Rojas Håkan Hjalmarsson Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm 10044 Sweden Centre for Advanced Bio Production KTH Royal Institute of Technology Stockholm 10044 Sweden

Appropriate excitation conditions are essential for reliably identifying sparse models. In regression problems, these conditions are often characterized by properties of the regressor matrix, with mutual coherence, the maximum correlation between regressors, playing a central role in enabling sparse recovery. However, obtaining sparse estimates is rarely the sole objective, as estimation accuracy is also important. When a model is used in an application, e.g. control design, acceptable performance with high probability can be (approximately) ensured by requiring the confidence ellipsoid to be contained in a certain ellipsoid which depends on the performance specifications in the application. However, it is well known that experiments fulfilling such requirements using minimal excitation energy (least-costly experiments) tend to generate highly correlated regressors, in conflict with the requirements for sparsity. Adhering to this setting and focusing on the popular orthogonal matching pursuit algorithm (OMP), we derive conditions for simultaneously ensuring sparsity and having the confidence ellipsoid contained in a pre-specified ellipsoid. We extend this result to a recently proposed two stage sparse estimation method where a linear transformation is used in a pre-processing step before OMP to reduce mutual coherence. A final contribution is to show that our theoretical results are of importance in optimal input design for sparse models. Specifically, we show that the choice of hyperparameters in a recently proposed input design method can be guided by our contributions and we show explicitly how this can be done in this two-stage method.

关键词： System identification Input design Sparse estimation Mutual coherence

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Distributed Formation of Autonomous Underwater Vehicles with Unreliable Switching Topologies and Transmission Delays

Distributed Formation of Autonomous Underwater Vehicles with...

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第三十九届中国控制会议

作者： Chao Ma Wei Wu Yidao Ji Hang Fu School of Mechanical Engineering University of Science and Technology Beijing State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences School of Automation and Electrical Engineering University of Science and Technology Beijing

This paper investigates the formation problem of autonomous underwater vehicles with unreliable switching communication topologies and time-varying transmission delays. More precisely, the switching topologies contains achievable and unachievable sub-topologies, which can better describe the practical underwater communication environment. By performing model transformation and constructing appropriate multiple Lyapunov-function method, sufficient conditions are established based on admissible edge-dependent average dwell time, such that the desired formation configuration can be achieved with transmission delays. Finally, an illustrative example is given at last to verify the effectiveness of the main results.

关键词： Distributed formation Autonomous underwater vehicles Switching topologies Admissible edge-dependent switching

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Open-Set Recognition Model for Distributed Fiber-Optic Vibration Sensor Based on Hierarchical Attention Network with Openmax

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IEEE Sensors Journal 2025年

作者： Zhou, Zhengxian Xie, Tianchang Wang, Xiankun Qu, Jun Shi, Jianping Han, Yashuai Yang, Xinyan Hu, Xing Zhang, Dawei Zhuang, Songlin Anhui Normal University Anhui Province Key Laboratory for Control and Applications of Optoelectronic Information Materials Wuhu241002 China University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China

Recent advancements in deep learning have improved the signal recognition capabilities of distributed fiber-optic vibration sensors. However, most existing research focuses on closed-set scenarios, assuming that all potential events are known and accounted for during training. In practical applications, the environmental complexity surrounding sensing fibers makes it impractical to anticipate and train on every intrusion event. To address this, we propose a novel Hierarchical Attention Network with Openmax (HANO) model that integrates a hierarchical attention mechanism with the Openmax algorithm. This model effectively extracts and emphasizes discriminative features at multiple levels, enhancing the recognition of both known and unknown events. Incorporating the Openmax layer enhances the classifier’s ability to manage unknown classes, thereby facilitating open-set recognition. To validate the effectiveness of the HANO model, we conducted experiments involving human intrusion events within a fenced area. The network parameters were optimized accordingly, and HANO’s performance was compared against the Swin Transformer, as well as models utilizing Softmax and Sigmoid layers. Additionally, we benchmarked it against other open-set recognition algorithms. The experimental results demonstrate that the HANO model achieves an overall recognition accuracy exceeding 95%, outperforming the comparative methods. These results demonstrate that the HANO model enhances the recognition of open-set events, indicating its strong potential for practical applications, particularly in scenarios requiring precise intrusion detection, such as security monitoring and vibration detection. © 2001-2012 IEEE.

关键词： Convolutional neural networks

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A Vector control-based Design of Multi-port Interline DC Power Flow controller for HVDC System

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Electric Power Systems Research 2025年 248卷

作者： Khan, Muhammad Qasim Khan, Muhammad Mansoor Nawaz, Arshad Wang, Haibing Chengmin, Wang School of Electrical Engineering and Key Laboratory of Control of Power Transmission and Transformation Ministry of Education of China Shanghai Jiao Tong University Shanghai China School of Automation and Key Laboratory of System Control Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai China University of Shanghai for Science and Technology Shanghai China

The Interline DC Power Flow controller (IDCPFC) represents a significant advancement in power distribution and control within Multi-Terminal High Voltage DC (MT-HVDC) systems. Traditional IDCPFC control without external voltage support suffers from coupling issues due to coupled dynamics, which limits transient response in multi-legged converter-based IDCPFC systems. This paper introduces a novel approach that employs vector-based decomposition to address this issue by decoupling current regulation from DC-link voltage regulation, thereby efficiently managing DC power flow across multiple transmission lines in the grid. Furthermore, the concept of zero-sequence control for IDCPFC is introduced. The incorporation of the zero-sequence algorithm increases modulation depth by up to 25%, enhancing DC link voltage utilization and improving system efficiency by reducing switching losses by up to 12%. The proposed method undergoes a detailed analysis encompassing network topology, operational principles, and decoupled control strategies. Simulations and experimental results validate the method's effectiveness under various grid scenarios. © 2025

关键词： Decouple control Interline DC Power Flow controller Multi-port HVDC Zero-sequence Algorithm

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Scheme for Turn-to-Turn Fault Protection of Stepped controllable Shunt Reactor control Winding 5th

Scheme for Turn-to-Turn Fault Protection of Stepped Controll...

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International Top-Level Forum on engineering Science and Technology Development Strategy and 5th PURPLE MOUNTAIN FORUM on Smart Grid Protection and control, PMF2020

作者： Zhu, Liwei Wu, Tonghua Dai, Wei Zhang, Kunxian Zhu, Xiang Ni, Saisai State Key Laboratory of Smart Grid Protection and Control Nanjing Jiangsu211106 China Nari Group Corporation Nanjing Jiangsu211106 China NARI School of Electrical and Automation Engineering Nanjing Normal University Nanjing Jiangsu210023 China State Grid Gansu Electric Power Company Lanzhou Gansu730000 China Electric Power Research Institute of State Grid Gansu Electric Power Company Lanzhou Gansu730000 China

ISBN: (纸本)9789811597459

This paper analyzes the negative-sequence network diagram of stepped controllable shunt reactor (SCSR) in case of turn-to-turn short-circuit fault and turn-to-ground fault of the SCSR control winding, as well as the line fault out of the SCSR area and open-phase running of the system based on the "T"-type equivalent circuit, thus deducing the discriminant of the negative-sequence power direction. The paper also configures the negative-sequence power directional protection for the SCSR control winding and derives the relationship of negative-sequence current between line side winding and control winding. The occurrence of fault is identified by the negative-sequence current of the control winding and the position of fault is determined by the negative-sequence power direction angle. For small turn-to-turn fault, the paper proposes a negative-sequence current amplitude adaptive starting threshold detection method based on the capacity to determine the starting threshold of the negative-sequence current. It also puts forward a negative-sequence phase angle smoothing method based on the dynamic mean to deal with the large phase angle fluctuations of the negative-sequence voltage during the small turn-to-turn short-circuit. The simulation results verify that the method can judge the turn-to-turn fault of the SCSR control winding reliably and effectively. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Equivalent circuits

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Data-Driven Distributionally Robust control for Interacting Agents under Logical Constraints

arXiv

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arXiv 2025年

作者： Kordabad, Arash Bahari Vlahakis, Eleftherios E. Lindemann, Lars Gros, Sebastien Dimarogonas, Dimos V. Soudjani, Sadegh The Max Planck Institute for Software Systems Kaiserslautern Germany The Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden The Thomas Lord Department of Computer Science the Ming Hsieh Department of Electrical and Computer Engineering Viterbi School of Engineering University of Southern California Los Angeles United States Trondheim Norway

In this paper, we propose a distributionally robust control synthesis for an agent with stochastic dynamics that interacts with other agents under uncertainties and constraints expressed by signal temporal logic (STL). We formulate the control synthesis as a chance-constrained program (CCP) with STL specifications that must be satisfied with high probability under all uncertainty tubes induced by the other agents. To tackle the CCP, we propose two methods based on concentration of measure (CoM) theory and conditional value at risk (CVaR) and compare the required assumptions and resulting optimizations. These approaches convert the CCP into an expectation-constrained program (ECP), which is simpler to solve than the original CCP. To estimate the expectation using a finite set of observed data, we adopt a distributionally robust optimization (DRO) approach. The underlying DRO can be approximated as a robust data-driven optimization that provides a probabilistic under-approximation to the original ECP, where the probability depends on the number of samples. Therefore, under feasibility, the original STL constraints are satisfied with two layers of designed confidence: the confidence of the chance constraint and the confidence of the approximated data-driven optimization, which depends on the number of samples. We then provide details on solving the resulting robust data-driven optimization numerically. Finally, we compare the two proposed approaches through case studies. Copyright © 2025, The Authors. All rights reserved.

关键词： Constrained optimization

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Multi-sensor Measurement Information Fusion for Maneuvering Target Recognition and Tracking

Multi-sensor Measurement Information Fusion for Maneuvering ...

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第40届中国控制会议

作者： Zheng Zhang Liang Han Xiwang Dong Qingdong Li Zhang Ren School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control Laboratory Beihang University School of Sino-French Engineer Beihang University Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University) Ministry of Education of ChinaChongqing University

This paper proposes multi-sensor information fusion algorithm for maneuvering target recognition and tracking based on mobile sensor networks（MSNs）. In order to improve target recognition accuracy, the modified evidence combination rule is designed to obtain the identity information of target based on the Dempster-Shafer（DS） algorithm. The target recognition process is designed by two stages, namely, sensing stage and fusion stage. During the first stage, each sensor node in the MSNs can obtain the feature information of the target, which makes up the measurement information. In the information fusion stage,each sensor combine the information exchanged from neighboring nodes with local measurement information to obtain the highprecise identity information of the maneuvering target based on the improved DS algorithm. Then, the identity information can be used to obtain the accurate state estimation of the maneuvering target. For the purpose of obtaining the accurate state estimation of target, the target tracking process is designed by local filtering stage and fusion stage. Finally, a numerical simulation is designed to illustrate the effectiveness of the algorithm.

关键词： Information Fusion Target Recognition and Tracking Mobile Sensor Network

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Battlefield Situation Deduction and Maneuver Decision Using Deep Q-Learning

Battlefield Situation Deduction and Maneuver Decision Using ...

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第40届中国控制会议

作者： Minghui Shi Xiwang Dong Liang Han Qingdong Li Zhang Ren School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control Laboratory Beihang University Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University) Ministry of Education of ChinaChongqing University School of Sino-French Engineer Beihang University

As the pace and complexity of modern warfare accelerates, it is of great significance to apply intelligent technology to defense decision-making. Due to the high dynamics and randomness of the aircraft, traditional methods are difficult to solve the optimal control strategy. The characteristics of reinforcement learning match the difficulty of the problem. In this paper, the situation of hypersonic aircraft is deduced, and the deep reinforcement learning method is used to make autonomous penetration decisions in the reentry phase. The model of aircraft and environment is established, and the maneuvering decision-making model is established based on deep Q-learning and its optimization algorithm. Through a large number of simulation training,this method can effectively give the real-time decision output of the agent and make a good prediction of the situation. It has the ability of short-range accurate operation and long-term planning and prediction. This method can improve the probability of successful penetration, and can be used as the decision-making basis of glider penetration.

关键词： Hypersonic aircraft situational deduction maneuver decision-making reinforcement learning deep Q-learning

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Manifold-aware and sparsity discriminant collaborative representation model and its application to multimode process monitoring

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Knowledge-Based Systems 2025年 324卷

作者： Fu, Yuanjian Xia, Fubing Ding, Jinliang Xu, Xue School of Control Science and Engineering Tiangong University Tianjin 300387 China Tianjin Key Laboratory of Intelligent Control of Electrical Equipment Tiangong University Tianjin 300387 China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang 110819 China School of Artificial Intelligence Tiangong University Tianjin 300387 China

Industrial processes are generally characterized by multiple operating modes from several factors, such as changes in market demand or raw material fluctuations, which pose challenges to traditional process monitoring. Existing multimode process monitoring primarily learns the distinct characteristics in each mode without collaboratively capturing the connections encapsulated across different modes. In this study, a novel multimode process monitoring approach, called the manifold-aware and sparsity discriminant collaborative representation model is proposed, which effectively captures the collaborative representation between and among modes, providing deeper insights into multimodal processes. The between-mode and within-mode affinity matrices were designed by considering the local information of the data to characterize the correlation between modes and the uniqueness of each mode, respectively. To faithfully reveal the characteristics of multimodal processes, a shared representation matrix was constructed to learn the common information among the different modes. Furthermore, the L1/2 norm was imposed on the projection matrix, enabling the proposed approach effective in selecting the key variables for monitoring tasks. On the basis of these distinct model characterizations, the proposed approach is powerful in learning informative representations of data, further enhances the process monitoring capability. In addition, contrastive clustering was introduced to divide the historical modes and identify the running-on modes of the new samples, thereby reducing the online identification time. The monitoring results for both the Tennessee Eastman process and three-phase flow facility demonstrate the effectiveness of the proposed approach, in which fault detection rate was improved by 6 %-26 % in most cases.

关键词： Discriminant analysis Multimode process monitoring Representation learning Sparse regularization

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