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Multi-pulse parallel demodulation for high-precision measurement in Raman-distributed optical fiber sensing

Journal of Lightwave Technology 2025年第11期43卷 5260-5269页

作者： Xu, Yang Li, Jian Chai, Mingyuan Zhang, Mingjiang Taiyuan University of Technology Key Laboratory of Advanced Transducers and Intelligent Control System Ministry of Education Taiyuan030024 China Taiyuan University of Technology College of Physics Taiyuan030024 China Shanxi-Zheda Institute of Advanced Materials and Chemical Engineering Taiyuan030032 China

Raman distributed temperature sensors enables distributed measurement of temperature along the sensing fiber, which is widely used in production. Due to the limitation of the optical time-domain reflection principle, the temperature measurement accuracy of the sensors will be seriously affected when the length of the temperature variation region is shorter than the pulse width. In this work, the relationship between pulse width, fiber under test (FUT) length and FUT temperature is firstly studied. Then, a multi-pulse parallel demodulation scheme is proposed. The experiments depict that a temperature measurement accuracy is optimized from over 30°C to 1.55 °C with a 0.2 m temperature variation region over 5.6 km. The scheme is based on multi-pulse parallel measurements combined with numerical simulations to achieve equivalent short pulse measurements of the small-region temperature variation without changing the hardware structure of the sensors. This scheme reduces the hardware requirements of the sensors and avoids the problems of sensing distance reduction and signal-to-noise ratio deterioration caused by shortening the pulse width. © 2025 IEEE. All rights reserved.

关键词： Demodulation

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Distributed Stochastic Security Constrained Unit Commitment for Coordinated Operation of Transmission and Distribution system

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CSEE Journal of Power and Energy systems 2021年第4期7卷 708-718页

作者： Aamir Nawaz Hongtao Wang Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong UniversityJinan 250000China

With the high penetration of renewable energies in modern power systems,deterministic coordination algorithms are facing two major problems:one is degradation in accuracy if fewer scenarios are utilized for uncertainty evaluation while second is the high computational time if a high number of scenarios are considered for better accuracy・In both cases,the efficiency of the algorithm is degraded・To solve these problems in coupled transmission system and distribution systems(TSDS),probabilistic coordination algorithms are adopted to solve with less *** this paper,a TSDS probabilistic coordination model is proposed to solve the coordinated security-constrained unit commitment problem.A mean and standard deviation matching based probabilistic analytical target cascading algorithm has been utilized for evaluation of TSDS coordination *** of solving each scenario as a separate problem,the proposed algorithm considers a single coordination problem with probabilistic characteristics as shared variables and hence,achieves fast convergence・Different case studies are performed to prove the efficacy of the proposed *** verify that the proposed algorithm reduces computational time and resources for large-scale systems.

关键词： Distribution system mean probabilistic analytical standard deviation target Cascading transmission system

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Design of Load Frequency controller for Power system Based on Equivalent Input Disturbance Method 4

Design of Load Frequency Controller for Power System Based o...

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4th IEEE New Energy and Energy Storage system control Summit Forum, NEESSC 2024

作者： Zhang, Jianrong Xu, Hang Wang, Hongzhang Zhang, Chuanke China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education School of Automation Wuhan China

ISBN: (纸本)9798350389012

In the power system, due to the instability of the external environment, safe and stable operation cannot always be guaranteed, as it frequently encounters varying degrees of external disturbances. In order to mitigate the effects of these disturbances on the system, the power system can operate safely and stably, In this paper, a power system load frequency control (LFC) based on Equivalent Input Disturbance (EID) method is proposed from the perspective of active disturbance suppression. Firstly, the disturbances present in the environment are unified as external disturbances, and then a single area the electric power system model with external disturbance was constructed. Then, a controller algorithm based on EID was designed using EID equivalent processing method and linear matrix inequality method to address the issue of external interference in the power system. Finally, a numerical example is used to validate the effectiveness and accuracy of the controller by simulating with two aspects, fixed perturbation and random perturbation, respectively. © 2024 IEEE.

关键词： Linear matrix inequalities

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An Optimal control-Based Distributed Reinforcement Learning Framework for A Class of Non-Convex Objective Functionals of the Multi-Agent Network

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IEEE/CAA Journal of Automatica Sinica 2023年第11期10卷 2081-2093页

作者： Zhe Chen Ning Li Department of Automation Shanghai Jiao Tong UniversityShanghai 200240 Key Laboratory of System Control and Information Processing Ministry of Education of ChinaShanghai 200240 Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240China Department of Automation Tsinghua UniversityBeijing 100084China IEEE

This paper studies a novel distributed optimization problem that aims to minimize the sum of the non-convex objective functionals of the multi-agent network under privacy protection, which means that the local objective of each agent is unknown to others. The above problem involves complexity simultaneously in the time and space aspects. Yet existing works about distributed optimization mainly consider privacy protection in the space aspect where the decision variable is a vector with finite dimensions. In contrast, when the time aspect is considered in this paper, the decision variable is a continuous function concerning time. Hence, the minimization of the overall functional belongs to the calculus of variations. Traditional works usually aim to seek the optimal decision function. Due to privacy protection and non-convexity, the Euler-Lagrange equation of the proposed problem is a complicated partial differential ***, we seek the optimal decision derivative function rather than the decision function. This manner can be regarded as seeking the control input for an optimal control problem, for which we propose a centralized reinforcement learning(RL) framework. In the space aspect, we further present a distributed reinforcement learning framework to deal with the impact of privacy protection. Finally, rigorous theoretical analysis and simulation validate the effectiveness of our framework.

关键词： Distributed optimization multi-agent optimal control reinforcement learning(RL)

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Risk-aware Distributed Optimal Power Flow in Coordinated Transmission and Distribution system

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Journal of Modern Power systems and Clean Energy 2021年第3期9卷 502-515页

作者： Aamir Nawaz Hongtao Wang Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong UniversityJinan 250000China

Active distribution grids cause bi-directional power flow between transmission system(TS)and distribution system(DS),which not only affects the optimal cost but also the secure operation of the power *** paper proposes a hybrid coordination method to solve the risk-aware distributed optimal power flow(RA-DOPF)problem in coordinated TS and *** operation risk evaluation,the weather-based contingencies are considered in both TS and DS.A hybrid coordination method is developed that entails analytical target cascading(ATC)and Benders decomposition(BD).Moreover,the risk-aware optimal power flow(RAOPF)in TS and risk-based security-constrained optimal power flow in DS have been performed using the BD method considering basic optimal power flow as a master problem,whereas N-1 and N-2 contingencies are considered as *** case studies are performed using the IEEE 30-bus system with generation reserves as a TS and a 13-bus system as a *** results demonstrate the efficacy of the proposed method.

关键词： Analytical target cascading Benders decomposition distribution system preventive and corrective contingen-cies risk awareness transmission system

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Deep Multimodal Learning and Fusion Based intelligent Fault Diagnosis Approach

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Journal of Beijing Institute of Technology 2021年第2期30卷 172-185页

作者： Huifang Li Jianghang Huang Jingwei Huang Senchun Chai Leilei Zhao Yuanqing Xia Key Laboratory of Complex System Intelligent Control and Decision Beijing Institute of TechnologyBeijing 100081China

Industrial Internet of Things(IoT)connecting society and industrial systems represents a tremendous and promising paradigm *** IoT,multimodal and heterogeneous data from industrial devices can be easily collected,and further analyzed to discover device maintenance and health related potential knowledge *** data-based fault diagnosis for industrial devices is very helpful to the sustainability and applicability of an IoT *** how to efficiently use and fuse this multimodal heterogeneous data to realize intelligent fault diagnosis is still a *** this paper,a novel Deep Multimodal Learning and Fusion(DMLF)based fault diagnosis method is proposed for addressing heterogeneous data from IoT environments where industrial devices ***,a DMLF model is designed by combining a Convolution Neural Network(CNN)and Stacked Denoising Autoencoder(SDAE)together to capture more comprehensive fault knowledge and extract features from different modal ***,these multimodal features are seamlessly integrated at a fusion layer and the resulting fused features are further used to train a classifier for recognizing potential ***,a two-stage training algorithm is proposed by combining supervised pre-training and fine-tuning to simplify the training process for deep structure models.A series of experiments are conducted over multimodal heterogeneous data from a gear device to verify our proposed fault diagnosis *** experimental results show that our method outperforms the benchmarking ones in fault diagnosis accuracy.

关键词： fault diagnosis deep learning multimodal heterogeneous data multimodal fused features

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Low-Carbon Dispatch Strategy for Virtual Power Plant Based on Bagging Ensemble Learning Forecasting Method 7

Low-Carbon Dispatch Strategy for Virtual Power Plant Based o...

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7th IEEE Student Conference on Electric Machines and systems, SCEMS 2024

作者： Li, Guang Dong, Xin Li, Li Yu, Yue Li, Changgang State Grid Jinan Power Supply Company Jinan China Key Laboratory of Power System Intelligent Dispatch and Control Ministry of Education Shandong University Jinan China

ISBN: (纸本)9798350366945

Virtual power plants (VPPs) struggle with peak-shaving due to unreliable distributed generation (DG) data and the intermittent nature of wind and solar power, impacting carbon emissions. This paper proposes a low-carbon dispatch strategy for VPPs based on a Bagging ensemble forecasting model. Firstly, a VPP dispatch framework based on DG output and load forecasting is established, and a Bagging ensemble forecasting model with feature selection is proposed. Secondly, the control characteristics of flexible industrial, commercial, and residential loads are analyzed to establish a control cost model, which is then integrated with the balanced relationship between wind, photovoltaic, and energy storage, as well as the carbon emission mechanism, to formulate a low-carbon dispatch model. Finally, the simulation results show that the proposed strategy can improve the accuracy of DG output and load forecasting in complex feature environments while realizing the interaction between source-grid-load and reducing the generation and operation costs of the grid. © 2024 IEEE.

关键词： Carbon capture and utilization

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Forecasting-Aided Voltage Estimation for Distribution Network Considering the Integrated Management of Virtual Power Plants Platform 7

Forecasting-Aided Voltage Estimation for Distribution Networ...

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7th IEEE Student Conference on Electric Machines and systems, SCEMS 2024

作者： Jia, Yujian Liu, Zhao Kan, Changtao Yu, Yue Li, Changgang State Grid Jinan Power Supply Company Jinan China Key Laboratory of Power System Intelligent Dispatch and Control Ministry of Education Shandong University Jinan China

ISBN: (纸本)9798350366945

As more distributed energy sources integrate into distribution networks, issues such as voltage fluctuations and reduced observability become increasingly prominent. Therefore, this paper proposes an algorithm for forecasting-aided state estimation (FASE) in virtual power plants' integrated operation management platform. Firstly, a Bidirectional Long Short-Term Memory (BiLSTM)-Extended Kalman Filter (EKF) algorithm for distribution network FASE is introduced. Secondly, the integrated management of virtual power plant platforms (IM-VPPP) enhances the observability of distribution networks. To evaluate the performance of the proposed method, simulations are conducted using the IEEE 33-bus standard distribution system. Results demonstrate significant advantages of the algorithm in terms of accuracy and robustness. © 2024 IEEE.

关键词： Power distribution networks

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The Optimal Duty Cycle Model-Predictive control of PMSM Used for Satellite Payload 1

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17th Annual Conference of China Electrotechnical Society, CES 2022

作者： Li, Jialin Chen, Zhen Hu, Hengzai Liu, Xiangdong Bao, Liang School of Automatic Beijing Institute of Technology Beijing100081 China Key Laboratory of Drive and Control of Servo Motion System Ministry of Industry and Information Technology Beijing100081 China State Key Laboratory of Intelligent Control and Decision for Complex System Beijing100081 China Beijing Aerospace Automatic Control Institute Beijing100854 China

ISBN: (数字)9789819904082

ISBN: (纸本)9789819904075

Model-predictive control is emerging as an advanced control strategy for its feature that the state variables can be predicted according to the math model of the control system. In the PMSM drive application, conventional model-predictive control strategy induces current ripple due to the fact that the optimal voltage vector always acts on the system for the whole control period. In order to restrain the current ripple, the idea of duty cycle control is taken into MPC by inserting a zero vector into the optimal non-zero vector and regulating the duty cycle of non-zero vector. In this paper, the optimal duty cycle FSC-MPC strategy based on two different principles is investigated. Principle A is that the estimation is equal to the reference at the end of control cycle, and principle B is that the area under trace of the estimation is equal to that of reference. The calculation of duty based on the two different principles and the flow chart of the strategy are introduced in detail. Finally, the effectiveness of the optimal duty cycle FSC-MPC is firstly verified by the simulation, and then verified by the experimental results, which is implemented on the PMSM driver of the DQ-1 satellite payload. © 2023, Beijing Paike Culture Commu. Co., Ltd.

关键词： Model predictive control

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Research on Path Planning of Mobile Robot Based on DWA-IMP-A∗ Algorithm 42

Research on Path Planning of Mobile Robot Based on DWA-IMP-A...

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42nd Chinese control Conference, CCC 2023

作者： Liu, Zhi Zhang, Lin Wang, Shoukun Niu, Tianwei Xu, Yongkang Cao, Jin School of Automation Beijing Institute of Technology Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing100081 China School of Automation Beijing Institute of Technology Key Laboratory of Servo Motion System Drive and Control Ministry of Industry and Information Technology Beijing100081 China

ISBN: (纸本)9789887581543

Path planning is one of the most critical links in mobile robots. Its timeliness, security and accessibility are crucial to the development and wide application of mobile robots. However, in solving the problem of path planning, the most popular A∗ algorithm has some problems, such as heuristic function cannot be estimated accurately, node redundancy, path is not smooth, and obstacle avoidance cannot be achieved in real time. To solve these problems, A fusion algorithm of improved A∗ combined with reverse path and dynamic window method (DWA-IMP-A∗) was proposed. The algorithm refines the heuristic function by incorporating the reverse path. The node optimization algorithm is used to further reduce the path length. The generated trajectories are smoothed by cubic spline interpolation. At the same time, it is integrated with the improved DWA algorithm to improve the efficiency and safety of robot path planning. The algorithm takes ROS mobile robot as the carrier and is tested under typical road conditions. Compared with A∗ algorithm, the planning time is reduced by 54.6% and the path length is reduced by 6.37%. Experimental results verify the effectiveness and robustness of the algorithm. The research results have certain reference significance for the path planning of various types of mobile robots and the research of driverless vehicles. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Motion planning

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