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IEEE PES Asia-Pacific Power and Energy engineering Conference (APPEEC)

作者： Qi Wang Ning Li Xin Gu Nianchu Zhang Yonghua Chen Guoxin HE School of Electrical and Automation Engineering Nanjing Normal University Nanjing China Laboratory of Integrated Energy System Simulation and Operation Control NARI Group Corporation Nanjing China

ISBN: (数字)9781728157481

ISBN: (纸本)9781728157498

Energy interconnection system integrating “Internet+” has become a new developing form of energy industry with the increase of Chinese urbanization level. It features the characteristics of multiple energy entities interconnection and independent decision. The decision-making structure, requirements of massive data processing, real-time communication and privacy security prevent the further application of centralized optimization method based on independent decision-making entities. Additionally, the difference of optimization among the energy entities cannot be ignored in the distributed optimization application. Integrating the concepts of “cloud supervision” and “weak centralization”, this paper proposes a centralized distributed collaborative regulation and control architecture, and studies the decision-making method under this architecture to accelerate the local and global optimization. Based on this, this paper analyzes and summarizes the key technologies involved, including edge-cloud collaborative deployment mechanism, engineering data processing and service technology for energy interconnection system in new-type towns. The purpose is to provide a way to realize the optimal dispatch of energy interconnection system in new-type towns, and provide some suggestions for the development of energy interconnection system in new-type towns.

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Predicting the Robustness of Undirected Network controllability

Predicting the Robustness of Undirected Network Controllabil...

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第三十九届中国控制会议

作者： Yang Lou Yaodong He Lin Wang Kim Fung Tsang Guanrong Chen Department of Electrical Engineering City University of Hong Kong Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education

Robustness of the network controllability reflects how well a networked system can maintain its controllability against destructive attacks. The measure of the network controllability robustness is quantified by a sequence of values that record the remaining controllability of the network after a sequence of node-removal or edge-removal attacks. Traditionally, the controllability robustness is determined by attack simulations, which is computationally time consuming. In this paper, an improved method for predicting the controllability robustness of undirected networks is developed based on machine learning using a convolutional neural network. This approach is motivated by the following observations: 1) there is no clear correlation between the topological features and the controllability robustness of a general undirected network, 2) the adjacency matrix of a network can be represented as a gray-scale image, 3) the convolutional neural network technique has proved successful in image processing without human intervention. In the new framework, preprocessing and filtering are embedded, and a sufficiently large number of training datasets generated by simulations are used to train several convolutional neural networks for classification and prediction,respectively. Extensive experimental studies were carried out, which demonstrate that the proposed framework for predicting the controllability robustness of undirected networks is more accurate and reliable than the conventional single convolutional neural network predictor.

关键词： Complex network Convolutional neural network controllability Robustness Performance prediction

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A New Method of Fault Detection and Diagnosis for the Tennessee Eastman Process

A New Method of Fault Detection and Diagnosis for the Tennes...

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第32届中国过程控制会议(CPCC2021)

作者： Pengbin Zhang Tao Zou Jingyang Wang Zhijia Yang State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Key Laboratory of Networked Control System Chinese Academy of Sciences Shenyang China. Institutes for Robotics and Intelligent ManufacturingChinese Academy of Sciences University of Chinese Academy of Sciences College of Mechanical and Electrical Engineering Guangzhou University State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of Sciences Key Laboratory of Networked Control System Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences

This paper proposes a new fault detection and diagnosis(FDD) method for the Tennessee Eastman(TE) large-scale chemical *** method is built integrated with a curve-type recognizer and a dynamic Bayesian network(DBN).The fault detection is carried out utilizing the curve-type recognizer,while the diagnosis is completed using the ***,the recognizer extracts fault symptoms from different times by distinguishes the curves' ***,the DBN is constructed to handle these time-related fault symptoms *** DBN will give the probability of each fault more and more accurately as time goes *** whole procedure consists of 1) deciding monitoring variables using chemical and control engineering knowledge 2) extracting fault symptoms,3) building a *** advantage over other data-driven FDD methods in reasonability and interpretability is also *** last,the simulation results demonstrate the availability of the proposed method.

关键词： A New Method of Fault Detection and Diagnosis for the Tennessee Eastman Process

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A Regional Wind Power Probabilistic Forecast Method Based on Deep Quantile Regression

A Regional Wind Power Probabilistic Forecast Method Based on...

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Annual Meeting Industrial and Commercial Power systems Technical Conference

作者： Yixiao Yu Xueshan Han Ming Yang Yumin Zhang Key Laboratory of Power System Intelligent Dispatch and Control Shandong University Jinan China College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao China

ISBN: (数字)9781728171951

ISBN: (纸本)9781728171968

Different from individual wind power forecast (IWPF), regional wind power forecast (RWPF), one of the most significative researches for alleviating negative influence on power systems, aims to estimate the output of multiple wind farms in the specific region, which is a valuable complement of the wind power forecast. This paper proposes a nonparametric probabilistic method for regional wind power forecast, a quantile regression neural network (QRNN), incorporating nonlinear mapping and massive data dealing abilities of the neural network (NN) and nonparametric probabilistic forecast ability of quantile regression (QR). On this basis the deep quantile regression (DQR) is proposed to improve the performance of the QRNN. In this approach, local connection is applied to the input layer of NN for tackling the challenge of the massive data. A ramp function is designed to avoid quantile plane crossing problem. To improve the model's generalization capability, a smoothing method is used to loss function for achieving differentiable everywhere. By properly constructing model, the approach provides a specific solution for regional wind power forecast with massive input information. Test results on a region with 10 wind farms demonstrate the effectiveness of the proposed approach.

关键词： Wind power generation Wind forecasting Wind farms Artificial neural networks Probabilistic logic Power systems

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A Learning Convolutional Neural Network Approach for Network Robustness Prediction

arXiv

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arXiv 2022年

作者： Lou, Yang Wu, Ruizi Li, Junli Wang, Lin Li, Xiang Chen, Guanrong The Department of Computing and Decision Sciences Lingnan University Hong Kong The Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China The College of Computer Science Sichuan Normal University Chengdu610066 China The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Institute of Complex Networks and Intelligent Systems Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201210 China The Department of Control Science and Engineering Tongji University Shanghai200240 China The Department of Electrical Engineering City University of Hong Kong Hong Kong

Network robustness is critical for various societal and industrial networks again malicious attacks. In particular, connectivity robustness and controllability robustness reflect how well a networked system can maintain its connectedness and controllability against destructive attacks, which can be quantified by a sequence of values that record the remaining connectivity and controllability of the network after a sequence of node- or edge-removal attacks. Traditionally, robustness is determined by attack simulations, which are computationally very time-consuming or even practically infeasible. In this paper, an improved method for network robustness prediction is developed based on learning feature representation using convolutional neural network (LFR-CNN). In this scheme, higher-dimensional network data are compressed to lower-dimensional representations, and then passed to a CNN to perform robustness prediction. Extensive experimental studies on both synthetic and real-world networks, both directed and undirected, demonstrate that 1) the proposed LFR-CNN performs better than other two state-of-the-art prediction methods, with significantly lower prediction errors;2) LFR-CNN is insensitive to the variation of the network size, which significantly extends its applicability;3) although LFR-CNN needs more time to perform feature learning, it can achieve accurate prediction faster than attack simulations;4) LFR-CNN not only can accurately predict network robustness, but also provides a good indicator for connectivity robustness, better than the classical spectral measures. © 2022, CC BY.

关键词： Complex networks

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Hexagonally ordered microbowl arrays decorated with ultrathin CuInS_(2) nanosheets for enhanced photoelectrochemical performance

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Journal of Energy Chemistry 2020年第12期29卷 134-142页

作者： Ming Li Le Chen Yanjie Su Huan Yin Kexiang Hu State Key Laboratory of Transducer Technology Shanghai Institute of Microsystem and Information TechnologyChinese Academy of SciencesShanghai 200050China School of Electronic Engineering and Automation Guilin University of Electronic TechnologyKey Laboratory of Optoelectronic Information Processing of Guangxi Colleges and Uniy/ersitiesGuilin 541004GuangxiChina Key Laboratory for Thin Film and Microfabrication of the Ministry of Education Department of Micro/Nano ElectronicsSchool of ElectronicsInformation and Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240China National and Local Joint Engineering Laboratory of Internet Technology on Mine School of Information and Control EngineeringChina University of Mining and TechnologyXuzhou 221008JiangsuChina School of Physics and Telecommunication Engineering Guangxi Colleges and Universities Key Lab of Complex System Optimization and Big Data ProcessingYulin Normal UniversityYulin 537400GuangxiChina

This paper demonstrates the design and fabrication of three-dimensional(3 D) hexagonally ordered microbowl arrays(MBAs) decorated with Cu In S2 nanosheets for enhanced photoelectrochemical(PEC) performance. The 3 D MBAs are fabricated by a micro-fabrication technique. The ultrathin CuInS2 nanosheets are grown on the 3 D electrodes by solvothermal transformation of Cu film. The photocurrent density of 3 D photocathode(CuInS2@MBAs) is about two times higher than that of the planar counterpart(CuInS2@Planar). The improved PEC performance can be ascribed to the elevated light trapping ability and the increased surface area for loading photocatalysts. In addition, CdS quantum dots as cocatalysts are modified onto the Cu In S2 nanosheets to further enhance the PEC activity because the formed p-n heterojunction can accelerate the separation of photogenerated carriers. As a result, the 3 D photocathode of CuInS2/CdS@MBAs shows an optimal incident photon to current efficiency of 10% at the wavelength of400 nm. It is believed that this work can be generalized to design other hierarchical 3 D photoelectrodes for improved solar water splitting.

关键词： Microbowl array CuInS_(2) Nanosheet Photocathode Photoelectrochemical performance

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Research on the Strategy of Energy Storage system Participating in the Secondary Frequency Regulation of Power Grid Based on Fuzzy control

Research on the Strategy of Energy Storage System Participat...

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第33届中国控制与决策会议

作者： Ya Zeng Yan Zhao Tieyan Zhang Yuri Wang Qianzhi Shao Yuanzhuo Du School of Electric Power Shenyang Institute of Engineering School of Renewable Energy Shenyang Institute of Engineering Key Laboratory of Regional Multi-energy System Integration and Control Liaoning Province School of Automation and Electrical Engineering Shenyang Ligong University Industrial Branch State Grid Liaoning Electric Power Supply Co.Ltd. Fengman Training Center State Grid Xinyuan Company

The continuous development of science and technology has led to great changes in the demand for loads in the power system,placing higher demands on the grid for frequency *** disadvantages of traditional FM generators such as slow response speed and dead zone in frequency modulation are becoming more and more ***,the energy storage system has timely tracking,reversible charging and discharging,and rapid *** auxiliary grid frequency modulation through the energy storage system helps to maintain the active power balance of the power system,improve and increase *** frequency adjustment capability of the system provides reliable support for the safe and efficient operation of the power *** paper first analyses the mechanism of secondary frequency regulation of energy storage assisted power grid and establishes an equivalent model of secondary frequency regulation of thermal power units assisted by energy storage *** addition,an energy storage system-assisted grid secondary frequency regulation control strategy based on double-layer fuzzy control is designed to address the shortcomings of the existing energy storage assisted grid frequency regulation control ***,the simulation analysis of the frequency regulation effect is carried out to verify the rationality and effectiveness of the proposed control strategy.

关键词： Fuzzy control Energy Storage system Secondary Frequency Modulation

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Knowledge-based prediction of network controllability robustness

arXiv

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arXiv 2020年

作者： Lou, Yang He, Yaodong Wang, Lin Tsang, Kim Fung Chen, Guanrong The Department of Electrical Engineering City University of Hong Kong Hong Kong The Department of Automation Shanghai Jiao Tong University China The Key Laboratory of System Control and Information Processing

Network controllability robustness reflects how well a networked system can maintain its controllability against destructive attacks. Its measure is quantified by a sequence of values that record the remaining controllability of the network after a sequence of node-removal or edge-removal attacks. Traditionally, the controllability robustness is determined by attack simulations, which is computationally time consuming or even infeasible. In the present paper, an improved method for predicting the network controllability robustness is developed based on machine learning using a group of convolutional neural networks (CNNs). In this scheme, a number of training data generated by simulations are used to train the group of CNNs for classification and prediction, respectively. Extensive experimental studies are carried out, which demonstrate that 1) the proposed method predicts more precisely than the classical single-CNN predictor;2) the proposed CNN-based predictor provides a better predictive measure than the traditional spectral measures and network heterogeneity. © 2020, CC BY.

关键词： Complex networks

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What is the cost and potential of low carbon electricity transition in the MENA region?

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Energy Strategy Reviews 2025年 59卷

作者： Bamisile, Olusola Xing, Yankai Acen, Caroline Cai, Dongsheng Zhang, Guangdou Adun, Humphrey Staffell, Iain Electrical Engineering Department College of Nuclear Science and Automation Engineering Chengdu University of Technology Sichuan China Centre for Environmental Policy Faculty of Natural Science Imperial College London United Kingdom Energy and Environmental Science Division CEPMLP University of Dundee United Kingdom Department of Electrical Engineering State Key Laboratory of Power System Operation and Control Tsinghua University China Operational Research Centre in Healthcare Near East University TRNC Mersin 10 Nicosia99138 Turkey

Replacing fossil fuels with renewable energy sources, including wind and solar power, is a validated approach for mitigating carbon emissions. To maximize renewable energy potential and account for costs and impacts of low carbon energy transitions, research models or modelling tools must provide an accurate representation of the technological and economic capabilities of renewable energy technologies. Hence, this study assesses the electricity generation potential, and costs associated with onshore and offshore wind power, and solar photovoltaic (PV) system, in the Middle East and North Africa (MENA) region based on wind and solar atlas datasets. Unlike existing works in literature, the assessment of offshore wind energy potential and the calculation of the levelized cost of electricity (LCOE) in this study incorporates factors like water depth, distance from shore, and wind speed. Also, the potential of onshore wind energy and solar photovoltaic systems is evaluated by considering variables such as population density, availability of suitable land area, and geographical location, for every grid cell in the MENA region. We found that the average LCOE can be as low as US$40/MWh for solar PV and US$45/MWh for wind power, achieved in Saudi Arabia and Kuwait respectively. This work contributes to existing literature by providing reliable LCOE, capacity factor, and productivity data for solar and wind energy potential estimation in the MENA region. © 2025 The Authors

关键词： Solar fuels

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Distributed cooperative guidance for multi-missile system with time-varying delays and switching topologies

Distributed cooperative guidance for multi-missile system wi...

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第三十九届中国控制会议

作者： Siquan Zhou Jie Ren Yong Heng Xiwang Dong Qingdong Li School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control Laboratory Beihang University Beijing Aerospace System Engineering Research Institute Beijing Institute of Electronic System Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University

Distributed cooperative guidance problems for multi-missile system with time-varying delays and switching topologies are investigated. Firstly, considering the complex communication environment, a two-step cooperative guidance strategy is proposed to achieve the simultaneous attack under time-varying delays and switching topologies. In the first step, a distributed cooperative guidance law using neighboring information is designed to coordinate range-to-go and leading angle based on feedback linearization and consensus theory. The second step starts while multi missiles disconnect from each other and hit the target using PNG（Proportional Navigation Guidance）. Then, the algorithm with three steps is presented to select the parameters in the cooperative guidance law. And the stability of the multi-missile system is proven using Lyapunov-Krasovskii theory. Finally, a numerical simulation is given to demonstrate the effectiveness of the proposed cooperative guidance strategy.

关键词： Cooperative guidance Distributed strategy Consensus theory Time-varying delays Switching topologies

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