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2nd Big Data Analytics for Cyber-Physical system in Smart City, BDCPS 2020

作者： Tao, Lei Huang, Yunhao Liu, Dong Ma, Xinxin Wu, Shuzhou Cui, Can Beijing Key Laboratory of Research and System Evaluation of Power Dispatching Automation Technology State Key Laboratory of Power Grid Security and Energy Saving China Electric Power Research Institute Haidian District Beijing100192 China

ISBN: (纸本)9789813345737

With the continuous development of the container technology, the scale of the dispatching and control cloud built by it is expanding continuously. At the same time, the application portability of cloud platform based on virtualization is poor, the man-made operation and maintenance mode of the dispatching and control cloud is inefficient and the error rate is high. It is difficult to guarantee the safe and high-quality operation of power grid and the lean and efficient operation of the dispatching management. Using container technology to containerize cloud platform applications and relying on the Continuous Integration and Continuous Deployment (CI/CD) platform which can achieve efficient and accurate maintenance of the dispatching and control cloud. In this paper, the CI/CD platform which consists of the Jenkins,Kubernetes and Docker is introduced to complete the operation and maintenance tasks of the dispatching and control cloud,such as automatically agile construction, automatic publishing. Finally, through the analysis of the dispatching and control cloud, it is verified that the results show that the platform has a certain effect in automation construction, publishing efficiency, gray publishing update, operation and maintenance quality. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Integration

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Research on Dynamic Resource Scheduling technology of Dispatching and Control Cloud Platform Based on Container 2nd

Research on Dynamic Resource Scheduling Technology of Dispat...

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2nd Big Data Analytics for Cyber-Physical system in Smart City, BDCPS 2020

作者： Liu, Dong Huang, Yunhao Wang, Jiaqi Xia, Wenyue Li, Dapeng Feng, Qiong Beijing Key Laboratory of Research and System Evaluation of Power Dispatching Automation Technology State Key Laboratory of Power Grid Security and Energy Saving China Electric Power Research Institute Haidian District Beijing100192 China

ISBN: (纸本)9789813345737

With the continuous advancement of dispatching and control cloud, traditional platform systems and virtualization technologies have been well supported in resource scheduling, flexible scaling, rapid migration deployment, the future availability and maintainability of these business systems will face enormous challenges. The rapid development of Docker container technology has solved the shortcomings of traditional PaaS platform based on virtual machine. In this paper, Docker container technology is used to build PaaS platform services of dispatching and control cloud. Through the analysis and verification of the flexible scaling, resource scheduling, container response and other performance aspects of the container, the experiment shows that the Docker-based PaaS platform has a good effect in the rational scheduling, rapid response and flexible scaling of resources. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Containers

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Research on Digital Twin Modeling Method for Combustion Process Based on Model Reduction

SSRN

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SSRN 2024年

作者： Zhang, Yue Li, Jiale Department of Automation North China Electric Power University Hebei Baoding071000 China Hebei Technology Innovation Center of Simulation & Optimized Control for Power Generation North China Electric Power University China Baoding Key Laboratory of State Detection and Optimization Regulation for Integrated Energy System China

In response to the difficulty in obtaining combustion information within coal-fired boiler furnaces, a method is proposed in this study to improve the reduced-order model using clustering segmentation. This approach aims to rapidly predict the combustion temperature field inside the furnace by establishing a twin model of the combustion temperature field. Initially, a finite element method is employed to analyze the combustion system of a 600MW subcritical boiler under various operating conditions. Subsequently, cross-sectional data from burner nozzle positions at each operating condition are extracted. These data are subjected to Proper Orthogonal Decomposition (POD), Spectral Proper Orthogonal Decomposition (SPOD), and Wavelet Transform-POD (WT-POD) for dimensionality reduction to obtain modal data. Comparative analyses are conducted on the modal data obtained from different methods. Furthermore, based on modal data analysis, a Support Vector Machine (SVM) regression model is selected to reconstruct the temperature field. The average absolute error of the reconstructed temperature fields from three methods under different operating conditions is then compared. Finally, the model is refined using clustering segmentation, resulting in an improvement of approximately 0.6% in reconstruction accuracy. This enhancement demonstrates that the clustered POD-SVR-GA model achieves higher accuracy in reconstructing combustion temperature fields after clustering-based improvements. © 2024, The Authors. All rights reserved.

关键词： Support vector machines

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Robust Optimal power Flow Model of electricity-Gas Integrated energy system Considering the Uncertainty of Roof Photovoltaic: ∗note: Sub-titles are not captured in Xplore and should not be used

Robust Optimal Power Flow Model of Electricity-Gas Integrate...

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2020 IEEE Sustainable power and energy Conference, iSPEC 2020

作者： Qin, Zhengfeng Bai, Xiaoqing School of Electrical Engineering Guangxi University Guangxi Key Laboratory of Power System Optimization and Energy Technology Nanning China

ISBN: (纸本)9781728191645

With more and more distributed roof photovoltaic generators connected to the grid and their complementarity with gas turbines, which providing many possibilities for the coordinated operation of electricity-gas integrated energy system (IES). In this paper, aiming at minimize the total generation costs, a robust optimal power flow (ROPF) model was proposed for the electricity-gas IES connecting roof photovoltaic (PV) generators in the distribution network. Then, the proposed ROPF model is transformed into the mixed-integer second-order conic programming (MISOCP) model, which can be solved by Gurobi. The numerical simulations are carried on IG-30 system and IG-118 system to verify the applicability, safety and economy of the proposed ROPF model. © 2020 IEEE.

关键词： electric load flow

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Control of Doubly Fed Induction Generator-Based Wind Turbine for the Supplementary Frequency Regulation 20

Control of Doubly Fed Induction Generator-Based Wind Turbine...

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20th IEEE International Conference on Communication technology, ICCT 2020

作者： Chen, Xiaobin Liu, Hui Xie, Haimin Qin, Risheng Guangxi University Guangxi Key Laboratory of Power System Optimization and Energy Technology Nanning China Yunnan Power Grid Corporation Ltd Electric Power Research Institute Kunming China

ISBN: (纸本)9781728181417

To enhance the frequency stability and reduce the reserve of active power, a control strategy is proposed for doubly fed induction generator-based wind turbine (DFIG-WT) to participate in supplementary frequency regulation (SFR) in this paper. A control framework is developed for wind farm to implement the regulation in real time. While the operating conditions of wind turbines are uploaded to the control center, the capacity for regulation of wind farm is calculated in the control center. At the same time, the task for regulation is assigned from the control center to wind turbines, and the power distribution method is proposed for DFIG-WT to participate in SFR within their capacity. Simulations on an interconnected two-area power system show that the proposed strategy can effectively enhance the frequency stability of the system and simultaneously improve the capacity of the power grid for wind power absorption. © 2020 IEEE.

关键词： Wind turbines

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Research on Path Analysis Method and Damping Characteristics of Subsynchronous Oscillation in Direct-Drive Wind Farm with VSC-HYDC system

SSRN

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SSRN 2022年

作者： Gao, Benfeng Liu, Yangyang Shao, Bingbing Ran, Huijuan State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Baoding071003 China Anhui Province Key Laboratory of Renewable Energy Utilization and Energy Saving Hefei University of Technology Hefei230009 China

Subsynchronous oscillation (SSO) exists in direct-drive wind farm with VSC-HVDC (DDWFV) system. In the process of mechanism analysis, the existing analysis methods are difficult to reveal the disturbance transfer process and the coupling relationship between direct-drive wind farm and VSC-HVDC. In this paper, a path analysis method is proposed to study the mechanism of SSO. Firstly, the linearization model of the DDWFV is established, and the transfer path of subsynchronous disturbances between direct-drive wind farm and VSC-HVDC is revealed based on the transfer function block diagram. Then, the damping characteristics of the SSO in DDWFV are studied, in which the damping of interaction can be separated by damping reconstruction. Finally, the influence of the controller parameters on the damping characteristics is analyzed, and the correctness of theoretical analysis is verified by time-domain simulation. The results show that the disturbance transfer path between direct-drive wind farm and VSC-HVDC presents a four-loop coupling relationship, which can cause the occurrence of SSO. By optimizing the controller parameters, the SSO mode damping can be increased, thereby improving the stability of the system. © 2022, The Authors. All rights reserved.

关键词： Regression analysis

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Short-term Load Forecasting Based on R-SDBN 3

Short-term Load Forecasting Based on R-SDBN

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3rd IEEE Conference on energy Internet and energy system Integration, EI2 2019

作者： Jiang, Xiongfeng Wei, Hua Lyu, Zhongliang Lian, Chunjie Guangxi University Key Laboratory of Guangxi Electric Power System Optimization and Energy-saving Technology Nanning China

ISBN: (纸本)9781728131375

This paper proposes a self-organizing deep belief network with robust locally weighted scatterplot smoothing method (R-SDBN) for short-term load forecasting, which can not only organize the structure of short-term load forecasting automatically but also denoise the input load data. The deep belief network (DBN) integrates deep learning and feature learning. It can quickly analyze a large number of data and has a strong data fitting ability. And the robust locally weighted scatterplot smoothing (RLWSS) method is introduced to weaken the noise and correct the outlies of load data. However, the optimal structure of DBN and the optimal span of RLWSS are different given specific applications. In the proposed model, the structure of DBN and the span of RLWSS are selforganized with minimum root mean square error as objective, not selected by experience, during the training process, and they are modified after each forecasting. Taking the actual load of a certain city in China as an example, the root mean square error (RMSE), the mean absolute percentage error (MAPE), and the maximum absolute error (MAE) of the 24hour forecasting is 4.38MW, 6.54%, and 12.48MW respectively. Compared with the backward propagation neural network, support vector machine (SVM), and DBN, R-SDBN has remarkable advantages. © 2019 IEEE.

关键词： Forecasting

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Subsynchronous Oscillation Detection Using Synchrosqueezing Wavelet Transforms and K-means Clustering

Subsynchronous Oscillation Detection Using Synchrosqueezing ...

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power and electrical Engineering (ACPEE), 5th Asia Conference on

作者： Jianluo Lu Yude Yang Guangxi Key Laboratory of Power System Optimization and Energy-Saving Technology Guangxi University Nanning China

ISBN: (数字)9781728152813

ISBN: (纸本)9781728152820

For the non-stationary and nonlinear characteristics of the Subsynchronous Oscillation (SSO) of the power system, the existing detection methods are sensitive to noise, and it is difficult to obtain the characteristic and development trend of oscillation. In this paper, Synchrosqueezing Wavelet Transforms (SWT) combined with K-means Clustering is introduced into subsynchronous oscillation harmonic detection and analysis. The strong anti-mode aliasing ability and anti-noise ability of SWT are utilized to clearly and intuitively represent the oscillation modes contained in the signal under the background of noise. Frequency domain slicing is used to extract and reconstruct the modes that need attention from the power signals containing multiple oscillation modes for parameter identification. Combined with the feature of SWT that squeezes the wavelet coefficients to the center frequency, the K-means clustering method is used to accurately locate the center frequency of the oscillating mode contained in the signal before reconstruction, and the frequency slice interval of the signal reconstruction can be automatically selected. Finally, the simulation results verify the validity and accuracy of the proposed method.

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Docker-Based energy Management system Development and Deployment Methods

Docker-Based Energy Management System Development and Deploy...

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power and energy Engineering (ICPEE), International Conference on

作者： Zongsheng Li Hua Wei Chunjie Lian Shengchao Qin Guangxi Key Laboratory of Power System Optimization and Energy Saving Technology (Guangxi University) Nanning China

ISBN: (数字)9781728191256

ISBN: (纸本)9781728191263

This paper proposes a Docker-based energy management system (DEMS) architecture to solve the problems of slow deployment and low flexibility of traditional energy management system (TEMS). Compared with TEMS, DEMS uses containerized packaging, the deployment environment and the number of Web Service releases are reduced from at least three times to one, and the workload is reduced by about two times. When the system has high concurrency, the application can be elastically expanded by adding or subtracting instances to calmly cope with peak traffic. DEMS applications use a mirrored development method. During the update process, only the Dockerfile needs to be modified first, and then the container instance is regenerated according to the new Dockerfile, which speeds up and simplifies the application update process. On the other hand, Docker virtualization is more lightweight and convenient than VMs, effectively reducing virtualization costs. This architecture has been applied to the upgrade and transformation of an actual power system EMS in China, which has good actual operating results.

关键词： Containers Virtualization Computer architecture Cloud computing power systems Reliability Virtual machining

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Unlocking new possibilities in ionic thermoelectric materials: a machine learning perspective

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National Science Review 2025年第1期 195-206页

作者： Yidan Wu Dongxing Song Meng An Cheng Chi Chunyu Zhao Bing Yao Weigang Ma Xing Zhang Key Laboratory for Thermal Science and Power Engineering of Ministry of Education Department of Engineering Mechanics Tsinghua University Key Laboratory of Process Heat Transfer and Energy Saving of Henan Province School of Mechanics and Safety Engineering Zhengzhou University College of Mechanical and Electrical Engineering Shaanxi University of Science and Technology Key Laboratory of Power Station Energy Transfer Conversion and System of Ministry of Education School of Energy Power and Mechanical Engineering North China Electric Power University School of Materials and Chemical Engineering Xuzhou University of Technology

The high thermopower of ionic thermoelectric(i-TE) materials holds promise for miniaturized waste-heat recovery devices and thermal sensors. However, progress is hampered by laborious trial-and-error experimentations, which lack theoretical underpinning. Herein, by introducing the simplified molecular-input line-entry system, we have addressed the challenge posed by the inconsistency of i-TE material types, and present a machine learning model that evaluates the Seebeck coefficient with an R2of0.98 on the test dataset. Using this tool, we experimentally identify a waterborne polyurethane/potassium iodide ionogel with a Seebeck coefficient of 41.39 m V/K. Furthermore, interpretable analysis reveals that the number of rotatable bonds and the octanol-water partition coefficient of ions negatively affect Seebeck coefficients, which is corroborated by molecular dynamics simulations. This machine learning-assisted framework represents a pioneering effort in the i-TE field, offering significant promise for accelerating the discovery and development of high-performance i-TE materials.

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