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IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Jing He Yubo Zhang Songhao Yang Chunyan Li Zhiguo Hao State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems China Electric Power Research Institute Beijing China State Key Laboratory of Electrical Insulation and Power Equipment Xi'an Jiaotong University Xi'an China

Wind farms play an increasingly important role in frequency control of the power system, but the identical wind speed assumption is oversimplified for the actual wind speed distribution difference in large-scale wind farms. This paper proposes a method of designing optimal frequency control parameters for wind farms considering wind turbine grouping. Firstly, the influence of wind speed on the frequency control capability of the wind turbine is analyzed and the principle of wind turbine grouping is proposed. Then, the equivalent frequency response model of grouped wind turbines is derived based on the capacity weighted method, which significantly reduces the calculation burden. Finally, to maximize the frequency nadir of the power system, the optimal frequency control parameters of the grouped wind turbines are solved based on the particle swarm optimization algorithm. The simulation results verify the effectiveness of the proposed parameter optimization method, which effectively improves the frequency nadir of the system under the major power shortage disturbance.

关键词： Wind speed simulation Design methodology Optimization methods System integration Wind farms Frequency response

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A multi-source data collection and information fusion method for distribution network based on IOT protocol 3

A multi-source data collection and information fusion method...

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3rd International Conference on Green Energy and Sustainable Development, GESD 2020

作者： Cai, Yongxiang Xiao, Xiaobing Tian, Hao Fu, Yu Wu, Peng He, Hongliu Electric Power Research Institute of Guizhou Power Grid Co. Ltd. Guiyang China State Key Lab of Control and Simulation of Power Systems and Generation Equipments Dept. of Electrical Engineering Tsinghua University Beijing China Binjiang College Nanjing University of Information Science and Technology Wuxi China

With the construction of Global Energy Internet, Intelligent distribution network has more and more characteristics of multi scene, multi service and changeable measurement and control objects. In order to realize data collection and information exchange among the intelligent terminal and energy, power grid, load, energy storage and other control resources in distribution network, we should research an architecture which can adapt to various data sources Based on dynamic self describing protocol. © Published under licence by IOP Publishing Ltd.

关键词： Digital storage

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Ultra-short-term solar PV power forecasting based on cloud displacement vector using multi-channel satellite and NWP data

Ultra-short-term solar PV power forecasting based on cloud d...

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第40届中国控制会议

作者： Rui Gao Xuemin Zhang Zhao Zhen Shengwei Mei State Key Lab of Control and Simulation of Power Systems and Generation Equipments （Dept. of Electrical Engineering Tsinghua University）

Satellite cloud image data plays an important role in the ultra-short-term photovoltaic（PV） power forecasting. By calculating the cloud displacement vectors of two adjacent cloud images, it is possible to calculate the cloud cluster’s occlusion of the PV power plant for a period of time in the future, thereby forecasting the PV power. The satellite cloud image data provided by FY-4 A Satellite has large and unequal sampling time intervals, which brings difficulties to cloud displacement vector calculation. This paper proposes a method for calculating the displacement vector of the cloud edge block based on the cosine similarity and the adaptive adjustment of the matching spatial scale. The parameter equation is used to establish the relationship between the relative position of the cloud cluster and the PV plant over time, and to predict the future position. On this basis, this paper proposes an ultra-short-term forecasting method of PV power based on cloud displacement vector. The performance of the proposed model is verified through case analysis.

关键词： ultra-short-term prediction photovoltaic power generation cloud displacement vector multi-channel satellite data

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Noise Reduction of Partial Discharge in power Cable Insulation Based on Deep Learning 7

Noise Reduction of Partial Discharge in Power Cable Insulati...

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7th IEEE International Conference on High Voltage Engineering and Application, ICHVE 2020

作者： Bin, Ding Jingwen, Liu Jianning, Chen Yuanxiang, Zhou Xuewei, Wang Jiamin, Ge Xiong'an Power Supply Company State Grid Baoding China Tsinghua University China State Key Laboratory of Control and Simulation of Power Systems and Generation Equipment Department of Electrical Engineering Beijing China Xinjiang University Department of Electrical Engineering Urumqi China

ISBN: (纸本)9781728155111

With the rapid development of ultra-high voltage (UHV) transmission technology in China, insulation diagnosis for power equipment has been applied widely to improve the reliability of the giant power system. Partial discharge (PD) detection, with the advantages of high precision and convenient deployment, plays an important role in defect detection, location, and classification. However, the low signal to noise ratio (SNR) of PD signals and poor electromagnetic environment make it difficult to extract useful features from PD signals. To distinguish the PD signals from noise, a noise reduction method based on deep learning is proposed in this paper. With short-time Fourier transform (STFT) technology, time-domain PD signals with various noise were first transformed into time-frequency domain patterns. After preprocessing the raw pattern, a deep convolutional neural network (CNN) was built, trained, and tuned for noise reduction of PD signals. Finally, the noise reduction model was verified and compared with the traditional method based on wavelet analysis. The results demonstrate that noise reduction mothed based on deep learning can restore the PD signals with more than 90% noise reduced. Given the advantage that the convolution kernel could sense both the time-related information and the frequency-related information in a spectrum simultaneously, the DnCNN model performs better than wavelet analysis. The noise reduction model proposed in this paper is based on new generation artificial intelligence, providing a new approach for PD signal restoration, which promotes the reliability of the power system. © 2020 IEEE.

关键词： Partial discharges

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Open Architecture Design of Universal Terminal for Multiple Measurement and control Scenarios of Distribution Network 3

Open Architecture Design of Universal Terminal for Multiple ...

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3rd International Conference on Green Energy and Sustainable Development, GESD 2020

作者： Zhang, Ruifeng Xiao, Xiaobing Tian, Hao Cai, Yongxiang Fu, Yu He, Hongliu Electric Power Research Institute of Guizhou Power Grid Co. Ltd. Guiyang China State Key Lab of Control and Simulation of Power Systems and Generation Equipments Dept. of Electrical Engineering Tsinghua University Beijing China Binjiang College Nanjing University of Information Science and Technology Wuxi China

Introduce the IoT platform technology based on SDN (Software Defined Network), cloud-based, flexible management and configuration of smart terminals, and support access to millions of terminal devices and smart operation and maintenance management. The traditional monitoring system is integrated with the Internet of Things platform, and on the basis of realizing the general SCADA system monitoring function, through the side-cloud collaboration, the lean management of the distribution station area is realized. © Published under licence by IOP Publishing Ltd.

关键词： SCADA systems

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From distributed energy resources to virtual power plants: A cyber-physical system solution for integrating demand-side in smart grid 4

From distributed energy resources to virtual power plants: A...

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

作者： Tian, Liting Cheng, Lin Wan, Yuxiang Yuan, Kai Liu, Rui Wu, Kuihua Tsinghua University State Key Laboratory of Control and Simulation of Power Systems and Generation Equipment Beijing China State Grid Economic Technology Research Institute Beijing China Institute State Grid Shandong Electric Power Company Economic Technology Research Jinan China

ISBN: (纸本)9781728196060

Distributed Energy Resources(DERs) will be an important part of the future power system under the spread of distributed renewable energy generation, electric vehicles, demand response and other technologies. Cyber-physical system (CPS) is one of the development directions for the future smart grid to integrate the widespread DERs. First, this paper presents the basic architecture and operation process of virtual power plant (VPP). Then the solution of communication structure and software platform are proposed. It is pointed out that virtual power plants have good applicability in information and communication infrastructure, power market and DER integration. Finally, a demonstration program in China is introduced. The research and application of virtual power plant is currently in its embryonic form. It provides a solution for the transmission from centralized power production to the distributed energy utilization, and is an adequate way to integrate geographical dispersion, heterogeneous DERs. © 2020 IEEE.

关键词： Virtual power Plants

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Boosting Co2 Absorption and Desorption of Biphasic Solvent by Nanoparticles for Efficient Carbon Dioxide Capture

SSRN

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SSRN 2023年

作者： Yuan, Bingling Zhan, Guoxiong Xing, Lei Li, Yuchen Huang, Zhoulan Chen, Zhen Wang, Lidong Li, Junhua MOE Key Laboratory of Resources and Environmental Systems Optimization Department of Environmental Science and Engineering North China Electric Power University Beijing102206 China State Key Joint Laboratory of Environment Simulation and Pollution Control School of Environment Tsinghua University Beijing100084 China

Introduction of nanoparticles (NPs) in the solvent was treated an efficient route to enhance mass and heat transfer, therefore, it was a promising strategy to boost carbon dioxide (CO2) absorption and desorption, especially for the biphasic solvent with weak absorption rate and low heat capacity. Herein, powder TiO2 and SiO2 with nanoscale was selected to prepare nanofluid for CO2 capture, based on DMPA-NHD biphasic solvent. The stability of nanofluid and the effect of NPs on CO2 absorption-desorption, phase-splitting was comprehensively explored in this work. Experimental results revealed that 0.06wt% 60nm TiO2 presented excellent performance for CO2 absorption. The liquid film resistance decreased by 11% after introducing NPs, therefore, the maximum mass transfer coefficient is 1.7×10-10 mol·cm-2·s-1·Pa (at 40 °C), increasing by 27% than the blank. In addition, 0.06wt% 50nm SiO2 leaded to significant promotion for CO2 desorption, with the maximum desorption rate at 3.9 mmol/min, and the desorption energy consumption reduced by 9.5% compared with blank case. Importantly, introduction of NPs had no significant effect on the phase splitting behavior, according to the time-profile phase-splitting measurements. The influence of NPs on biphasic solvent behavior was firstly explored to provide a reference for subsequent practice application. © 2023, The Authors. All rights reserved.

关键词： Carbon dioxide

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A Novel YJQR-LSTM Model for Nonparametric Probabilistic Sustainable Agriculture Wind power Forecasting Based on Intelligent IoT

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IEEE Internet of Things Journal 2024年

作者： Wang, Jie Jiang, Junhui Chen, Xinlong Cai, Defu Wu, Yue Lu, Renzhi School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Harbin Engineering University College of Computer Science and Technology Harbin150001 China State Grid Hubei Electric Power Research Institute Hubei Wuhan430079 China School of Technology State Key Laboratory of Efficient Production of Forest Resources Key Laboratory of National Forestry and Grassland Administration on Forestry Equipment and Automation Beijing Forestry University Beijing100083 China Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Industrial Internet of Things and Networked Control Ministry of Education Chongqing400065 China State Key Laboratory of Mechanical Transmission for Advanced Equipment Chongqing University Chongqing400044 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China

Energy costs associated with the consumption of non-renewable energy sources have become an important issue in improving the international competitiveness of agriculture. Wind power, as a renewable energy source, can replace non-renewable energy sources to reduce energy costs and improve the sustainability of agricultural. However, the inherent intermittency, randomness, and volatility within weather conditions and wind speed presents a substantial challenge in accurately predicting wind power generation. This work proposes a novel YJQR-LSTM algorithm that leverages Yeo-Johnson quantile regression (YJQR) with a long short-term memory (LSTM) network for nonparametric probabilistic forecasting of wind power generation via the Intelligent Internet of Things. First, an improved YJQR model based on the YJ transformation is designed to obtain a more precise characterization of wind uncertainty, providing a more flexible probability density function for wind power generation. Then, utilizing the unique structure of the LSTM network to learn the parameters of the YJQR model, temporal features can be extracted from time series data. To mitigate the impact of outliers in the raw data on accuracy and improve computational efficiency, a novel logarithmic likelihood function is developed as the loss function utilized in the training phase. The effectiveness of the proposed algorithm is validated using a real-world dataset from five wind farms from the Global Energy Forecasting Competition. Numerical results demonstrate that the algorithm provides more accurate wind power prediction results in complex wind power data environments, which is important for making full use of wind energy and thus reducing the consumption of non-renewable energy in agriculture. © 2024 IEEE.

关键词： Wind forecasting

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Joint Optimization of Wind Farm Layout Considering Optimal control

arXiv

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arXiv 2021年

作者： Chen, Kaixuan Lina, Jin Qiu, Yiwei Liu, Feng Song, Yonghua State Key Laboratory of Control and Simulation of Power Systems and Generation Equipment Department of Electrical Engineering Tsinghua University Beijing100087 China Department of Electrical and Computer Engineering University of Macau 999078 China

The wake effect is one of the leading causes of energy losses in offshore wind farms (WFs). Both turbine placement and cooperative control can influence the wake interactions inside the WF and thus the overall WF power production. Traditionally, greedy control strategy is assumed in the layout design phase. To exploit the potential synergy between the WF layout and control so that a system-level optimal layout can be obtained with the greatest energy yields, the layout optimization should be performed with cooperative control considerations. For this purpose, a novel two-stage WF layout optimization model is developed in this paper. Cooperative WF control of both turbine yaw and axis-induction are considered in the WF layout design. However, the integration of WF control makes the layout optimization much more complicated and results in a large-scale nonconvex problem, hindering the application of current layout optimization methods. To increase the computational efficiency, we leverage the hierarchy and decomposability of the joint optimization problem and design a decomposition-based hybrid method (DBHM). Instead of manipulating all the variables simultaneously, the joint optimization problem is decomposed into several subproblems and their coordination. Case studies are carried out on different WFs. It is shown that WF layouts with higher energy yields can be obtained by the proposed joint optimization compared to traditional separate layout optimization. Moreover, the computational advantages of the proposed DBHM on the considered joint layout optimization problem are also demonstrated. © 2021, CC BY.

关键词： Electric utilities

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The parameter design method of smoothing inductance based on MMC-MTDC with R-type superconducting fault current limiter 16

The parameter design method of smoothing inductance based on...

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16th IET International Conference on AC and DC power Transmission, ACDC 2020

作者： Guo, Peiqian Yuan, Zhichang Liu, Guowei Zhao, Yuming Shi, Zinan State Key Laboratory of Control and Simulation of Power Systems and Generation Equipment Department of Electrical Engineering Tsinghua University Beijing China Shenzhen Power Supply Corporation Shenzhen China Sichuan Energy Internet Research Institute Tsinghua University Chengdu China

ISBN: (纸本)9781839533914

The rapid increase of voltage sourced converter brings the great development of the multi-terminal DC technology, both in the DC transmission and DC distribution fields. However, because of the weak impedance characteristic of the VSC-HVDC system, how to suppress and clear DC fault currents becomes a challenge. Considering the stability, the response speed and the system structure, it is not possible to apply excessive inductance at the DC side, hence, the current limiting effectiveness is relatively restricted. On the one hand, with the effective properties, Resistive type Superconducting Fault Current Limiter (R-SFCL) becomes an effective device to solve the problem of VSC-HVDC system under DC faults. On the other hand, because of dynamic energy-impedance characteristics of R-SFCL, the fixed impedance selection method at a certain time to carry out dynamic current limiting analysis is no longer applicable. Therefore, the DC fault current research and R-SFCL characteristic are comprehensively analyzed firstly in this paper. Based on that, a smoothing inductance determination method is proposed to cut off the DC fault current by DC breaker and ensure the continuous operation of remaining parts. Finally, based on a multi-terminal DC system simulation, the improvement and effectiveness of the proposed method are validated. © 2020 Institution of Engineering and Technology. All rights reserved.

关键词： Inductance

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