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Recent Advances in computer Science and Communications 2021年第3期14卷 941-951页

作者： Liang, Weixuan Zhu, Youchan Li, Guoliang School of Control and Computer Engineering North China Electric Power University Baoding China

Background: As the "three-type two-net, world-class" strategy is proposed, the key issues to be addressed are that the number of cloud resources in power grid continues to grow and there is a large amount of data to be filed every day. The long-term preservation of data, using backup data for the operation and maintenance, fault recovery, fault drill and tracking of cloud platform are essential. The traditional compression algorithm faces severe challenges. Methods: In this case, this paper proposes the deep-learning method for data compression. First, a more accurate and complete grid cloud resource status data is gathered through data cleaning, correction, and standardization, the preprocessed data is then compressed by SaDE-MSAE. Results: Experiments show that the SaDE-MSAE method can compress data faster. The data compression ratio based on neural network is basically between 45% and 60%, which is relatively stable and stronger than the traditional compression algorithm. Conclusion: The paper can compress the data quickly and efficiently in a large amount of power data. Improve the speed and accuracy of the algorithm while ensuring that the data is correct and complete, and improve the compression time and efficiency through the neural network. It gives better compression schemes and cloud resource data grid. © 2021 Bentham Science Publishers.

关键词： Electric power transmission networks

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Error-Based Active Disturbance Rejection control for Maximum Power Point Tracking in Photovoltaic System 17th

Error-Based Active Disturbance Rejection Control for Maximum...

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17th Chinese Intelligent Systems Conference, CISC 2021

作者： Ke, Yin Hou, Guolian School of Control and Computer Engineering North China Electric Power University Beijing102206 China

ISBN: (纸本)9789811663239

With the recent focus marked on conversion efficiency and renewable energy, more research is being devoted to the high-performance maximum power point tracking technology for photovoltaic applications. However, the intermittence and naturalness of solar energy bring a series of severe challenges to photovoltaic system, such as overload and overvoltage. In addition, photovoltaic system is faced with various parameter uncertainties and disturbances from irradiance. Traditional maximum power point tracking methods, such as perturb & observe and incremental conductance, are limited to tracking speed and tracking accuracy. To this end, a novel maximum power point tracking scheme based on the error-based active disturbance rejection control approach and perturb & observe method is proposed to obtain higher efficiency. In the face of various irradiance changes, disturbances can be eliminated rapidly. Finally, the superior performance of proposed scheme is validated by extensive simulation results. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Errors

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An improved neural network model for battery smarter state-of-charge estimation of energy-transportation system

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Green Energy and Intelligent Transportation 2023年第2期2卷 56-65页

作者： Bingzhe Fu Wei Wang Yihuan Li Qiao Peng School of Control and Computer Engineering North China Electric Power UniversityBeijing102206China Group of Information Technology Analytics&OperationsQueen's University BelfastBelfastBT95EEUK

The safety and reliability of battery storage systems are critical to the mass roll-out of electrified transportation and new energy *** achieve safe management and optimal control of batteries,the state of charge(SOC)is one of the important *** machine-learning based SOC estimation methods of lithium-ion batteries have attracted substantial interests in recent ***,a common problem with these models is that their estimation performances are not always stable,which makes them difficult to use in practical *** address this problem,an optimized radial basis function neural network(RBF-NN)that combines the concepts of Golden Section Method(GSM)and Sparrow Search Algorithm(SSA)is proposed in this ***,GSM is used to determine the optimum number of neurons in hidden layer of the RBF-NN model,and its parameters such as radial base center,connection weights and so on are optimized by SSA,which greatly improve the performance of RBF-NN in SOC *** the experiments,data collected from different working conditions are used to demonstrate the accuracy and generalization ability of the proposed model,and the results of the experiment indicate that the maximum error of the proposed model is less than 2%.

关键词： Battery management SOC estimation Data science Neural network Golden section method Sparrow search algorithm

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Distributed Power Sharing control of AC Microgrid Based on State Observer Approach

Distributed Power Sharing Control of AC Microgrid Based on S...

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International Conference on Guidance, Navigation and control, ICGNC 2020

作者： Zhang, Yanjing Yu, Mei Wei, Bo Xiao, Feng School of Control and Computer Engineering North China Electric Power University Beijing102206 China

ISBN: (纸本)9789811581540

This paper proposes a consensus-based distributed algorithm to solve both active and reactive sharing problems, which involves alternative current (AC) microgrids and spatially concentrated dispatchable distributed generators (DDGs). The output feedback control method and the leader-follower scheme are designed for each generator to achieve the consistency of the output proportion. Compared with other control strategies, the controller based on state observer only uses local output information and a set of dynamic communicated compensator. In addition, the protocol can solve the proportional consistency with different parameters, which also meets the balance of supply and demand in power system. Numerical examples verify the effectiveness of this method. © 2022, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Numerical methods

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Partial Discharge Classification with Transformer Neural Networks 14

Partial Discharge Classification with Transformer Neural Net...

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14th International Conference on the Properties and Applications of Dielectric Materials, ICPADM 2024

作者： Cheypoca, Thepjit Promphanich, Wiboon Thway, Aung Ye Hankae, Angelina Phimpissada Jeenmuang, Siwakorn Pattanadech, Norasage School of Engineering King Mongkut's Institute of Technology Ladkrabang Instrumentation and Control Engineering Dept. Bangkok Thailand School of Engineering King Mongkut's Institute of Technology Ladkrabang Computer Engineering Dept. Bangkok Thailand School of Engineering King Mongkut's Institute of Technology Ladkrabang Electrical Engineering Dept. Bangkok Thailand School of Engineering King Mongkut's Institute of Technology Ladkrabang Mechatronics Engineering Dept. Bangkok Thailand

ISBN: (纸本)9798350374605

This paper introduces an approach with the Transformer Neural Networks model for partial discharge patterns classification, that consists of corona discharge, internal discharge and surface discharge. The PD measuring circuit suggested in IEC 60270:2000 is used to record Partial discharge signals. Independent parameters such as phase and charge of PD patterns were recorded. The phase value will be encoded into the charge array and Transformer Neural Network is constructed using Positional Embedding and Transformer Encoder Layer. 80% of the recorded data will be used as a training data and 20% recorded data was used for testing of the classification models. Impacts of neuron numbers and network architecture on the PD classification performance will be observed © 2024 IEEE.

关键词： Electric corona

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Solid-Fluid Interaction Simulation System Based on SPH Unified Particle Framework

Solid-Fluid Interaction Simulation System Based on SPH Unifi...

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2022 International Conference on 3D Immersion, Interaction and Multi-Sensory Experiences, ICDIIME 2022

作者： Shao, Xuqiang Zhang, Xin Yang, Shihe Jin, Yizhong School of Control and Computer Engineering North China Electric Power University Hebei Baoding China

ISBN: (数字)9781665490092

ISBN: (纸本)9781665490092

Solid-fluid Interaction Simulation is the important research point in the field of fluid simulation. Existing researches mainly focus on the phenomena such as motion, deformation, infiltration, erosion, etc., and less attention is paid to the solid fracture caused by fluid impact. The simulation solutions used by the related researches have weakly coupling with the SPH solid-fluid interaction framework because their solid fracture methods need the geometric form for representing and processing the solid model. For the same reason, their methods have difficulty to parallelize and achieve the balance between Realism and real-time performance. To deal with these problems, a full process parallelization simulation framework is proposed for parallelize each key stage of the simulation system. The framework is based on the SPH unified particle framework and has the advantage of high parallelism of the SPH method because of the particle form of solid used to describe and solve solid field quantities. For solving the solid fracture, a physics-geometry hybrid method with the high parallelism implementation is proposed. This method is also based on particle form to solve the solid field quantities so that have strong coupling with SPH framework. In term of neighborhood particle search, a spatial three-level index sorting based on the unified grid method is proposed to optimize neighborhood search processes including solid particles. Because the spatial characteristics of the solid fracture method is considered, the two methods can be closely combined with each other. In the stage of fluid rendering, in order to avoid the communication between GPU and CPU caused by surface extraction, the Screen Space Fluid Rendering method is adopted. In SSFR, the particle data is directly input into the rendering pipeline on GPU for rendering without transfer to CPU for extracting fluid mesh. For verifying the stability of the parallelized framework, performance and Realism of the simulation

关键词： Optimization

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Design of Intelligent Epidemic Prevention Vehicle control System Based on Single-Chip Microcomputer 4

Design of Intelligent Epidemic Prevention Vehicle Control Sy...

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4th IEEE International Conference on Civil Aviation Safety and Information Technology, ICCASIT 2022

作者： Li, Xiaolin North China Electric Power University School of Control and Computer Engineering Beijing102206 China

ISBN: (数字)9781665467667

ISBN: (纸本)9781665467667

Nowadays, the COVID-19 epidemic continues to repeat, and the novel coronaviruses are highly contagious. In order to solve the difficulties of information collection and cargo transportation in the process of epidemic prevention and control, and reduce the work intensity of epidemic prevention personnel. In this paper, a multi-functional intelligent epidemic prevention vehicle control system based on single-chip microcomputer is proposed to realize the goal of replacing manual control with intelligent vehicles. This design uses the stm32 single-chip microcomputer as the control core, for the work demand of the epidemic prevention site. First of all, the design realizes the intelligent vehicle tracking navigation and path planning function to realize the contactless distribution. Secondly, the function of face recognition and intelligent temperature measurement is completed, which can upload information remotely, process images, measure the temperature for diagnostics, and strengthen better interaction with the doctor terminal. At last, by wireless transmission, human-computer interaction is realized. The intelligent epidemic prevention vehicle control system can basically complete the designed function through many simulation experiments. Among them, the path planning algorithm can be completed within 5 s, and the success rate of face recognition is as high as 97 %, which achieves a good simulation effect and has a certain value in the current environment. © 2022 IEEE.

关键词： Face recognition

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Construction and application of a novel WGAN-CNN-based predicting approach for dust concentration at underground coal mine working faces

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Environmental Science and Pollution Research 2024年第27期31卷 39271-39284页

作者： Zhou, Banghao Qin, Botao Zhou, Qun Sun, Daowei Chen, Pengpeng Yang, Kai Pan, Qingyan Li, Huizhen Key Laboratory of Gas and Fire Control for Coal Mines China University of Mining and Technology Ministry of Education Jiangsu Xuzhou221116 China School of Safety Engineering China University of Mining and Technology Jiangsu Xuzhou221116 China School of Computer Science & amp Technology China University of Mining and Technology Jiangsu Xuzhou221116 China

To enhance the real-time monitoring and early-warning capabilities for dust disasters in underground coal mine, this paper presents a novel WGAN-CNN-based prediction approach to predict the dust concentration at underground coal mine working faces. Dust concentration, wind speed, temperature, and methane concentration were collected as the original data due to their nonlinear relationship. The consistency between the generated and original data distributions was verified through PCA dimensionality reduction analysis. The predictive performance of this approach was assessed using five metrics (R2, EVS, MSE, RMSE, and MAE) and compared with three other algorithms (Random Forest Regressor, MLP Regressor, and LinearSVR). The findings indicate that a majority of the generated data falls within the distribution range of the real dataset, exhibiting reduced levels of volatility and dispersion. The R2 values of prediction results are all above 98%, and the MSE values are between 0.0007 and 0.0106. The proposed approach exhibits superior predictive accuracy and robust model generalization capabilities compared to alternative algorithms, thereby enhancing the real-time monitoring and early-warning level of dust disasters in underground coal mine. This will facilitate the realization of advanced prevention and control measures for dust disasters, showcasing a wide range of potential applications. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Disasters

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A Strengthened Primal-Dual Decomposition Algorithm for Solving Electricity Market Pricing With Revenue-Adequacy and FFR Constraints

IEEE Transactions on Energy Markets, Policy and Regulation

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IEEE Transactions on Energy Markets, Policy and Regulation 2024年第3期2卷 379-391页

作者： Goudarzi, Hamed Hesamzadeh, Mohammad Reza Bunn, Derek Fotuhi-Firuzabad, Mahmud Shahidehpour, Mohammad Sharif University of Technology Center of Excellence in Power System Management and Control Tehran11365-11155 Iran Kth Royal Institute of Technology School of Electrical Engineering and Computer Science Stockholm10044 Sweden London Business School Department of Management Science and Operations LondonNW1 4SA United Kingdom Illinois Institute of Technology Electrical and Computer Engineering Department ChicagoIL60616 United States

This paper develops a new decomposition algorithm for solving Electricity Market Pricing (EMP) problem, taking into account both revenue-adequacy and Fast Frequency Reserve (FFR) constraints. Due to revenue-adequacy constraint, a bilevel model of the EMP problem is introduced (BL-EMP). The upper level of the BL-EMP model represents the non-convex unit commitment (UC) decisions as well as the revenue-adequacy constraints of the market participants (generators, loads, and battery-storage owner). The lower level is a convex economic dispatch model with FFR constraint. To tackle the computational complexity of the considered BL-EMP model, this paper develops, tests, and proposes a Strengthened Primal-Dual Decomposition (SPDD) algorithm, which takes benefits from both Benders-like and Lagrange Dual-like algorithms. The new SPDD algorithm has a series of interesting computational properties, which are theoretically discussed in the paper. The SPDD algorithm has better computational performance than standard Benders decomposition algorithm and it also does not need tuning of the Big-M (or disjunctive) parameters for solving the proposed BL-EMP problem. Results from the modified IEEE 24-bus, the IEEE 118-bus, and the IEEE 300-bus system show the superiority of proposed SPDD algorithm over the classic Benders algorithm. © 2023 IEEE.

关键词： Power markets

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A Class of Perimeter Defense Strategy via Relative Time and Maximum Matching

A Class of Perimeter Defense Strategy via Relative Time and ...

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2024 China Automation Congress, CAC 2024

作者： Yang, Chengqian Du, Ziyi Lin, Lixiong Xu, Guangtong Huang, Bomin School of Control Engineering Northeastern University at Qinhuangdao Qinhuangdao China Department of Precision Instrument Tsinghua University Beijing China School of Ocean Information Engineering Jimei University Xiamen China Huzhou Institute Zhejiang University Huzhou China College of Computer Engineering Jimei University Xiamen China

ISBN: (纸本)9798350368604

This paper studies a class of perimeter defense problems for large-scale multi-robot systems. A perimeter defense strategy is designed via relative time calculations and maximum matching methods. The strategy is simple, effective, and easy to implement for large-scale perimeter defense problems. Some simulations are made to verify the effectiveness of the strategy. © 2024 IEEE.

关键词： Nanorobots

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