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10th Frontier Academic Forum of Electrical Engineering, FAFEE 2022

作者： Chen, Jianning Zhou, Yuanxiang Lin, Menglong Pan, Zehua Ge, Jiamin State Key Laboratory of Control and Simulation of Power Systems and Generation Equipment Department of Electrical Engineering Tsinghua University Beijing100084 China

ISBN: (纸本)9789819934034

In order to carry out risk warning for high-voltage cable transmission system and realize differentiated operation and maintenance decision-making, it is necessary to carry out time series prediction for online monitoring state quantities of high-voltage cables such as load current, partial discharge, and grounding circulating current. Therefore, a joint prediction method for multi-state variables of high-voltage cables based on Gate Recurrent Unit (GRU) is proposed. Taking the online monitoring state quantities of a certain area in central China in May as the analysis data set, a GRU network is built to predict the sequence of multi-state quantities, compared with different models. Taking the phase A load current of a node of the cable as an example to carry out state prediction, the results show that compared with the Long Short Term Memory (LSTM) neural network, back-propagation neural network (BPNN), and Auto Regressive Integrated Moving Average (ARIMA) model, the accuracy of the sequence prediction model based on GRU is Higher. When predicting for 5 min, the MAE and RMSE are 1.94 and 2.65 A, respectively, with the MAPE of 0.54%. When predicting for 30 min, the MAE and RMSE are 9.81 and 15.36 A, respectively, with the MAPE of 2.89%. When predicting for 1 h, the MAE and RMSE are 17.05 and 27.47 A, respectively, with the MAPE of 4.91%. The GRU model performs better due to the suppression on over-fitting of training data with simpler model structure. © Beijing Paike Culture Commu. Co., Ltd. 2023.

关键词： Forecasting

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Genetic Algorithm Based Approach for Optimal Scheduling Problem in Agricultural Industry 4

Genetic Algorithm Based Approach for Optimal Scheduling Prob...

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4th International Conference on control systems, Mathematical Modeling, Automation and Energy Efficiency, SUMMA 2022

作者： Pashchenko, Alexander Tordiya, Mark V.A. Trapeznikov Institute of Control Sciences of Ras Laboratory of Intelligent Control Systems and Simulation Moscow Russia

ISBN: (数字)9781665456593

ISBN: (纸本)9781665456593

The paper considers the problem of production planning in the field of agriculture. The structure of the model for searching for the optimal schedule of agricultural works has been developed, and a genetic algorithm for searching for the priority vector has been created. simulation modeling of three experiments of varying complexity was carried out, confirming the effectiveness of the implemented model. © 2022 IEEE.

关键词： Genetic algorithms

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Short-Term Prediction of Wind Power Based on NWP Error Correction with TimeGAN and LSTM-TCN 4th

Short-Term Prediction of Wind Power Based on NWP Error Corre...

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4th International Conference on Energy Power and Automation Engineering, ICEPAE 2023

作者： Li, Shuona Ma, Wei Liu, Zhao Duan, Yuge Tian, Chengwei School of Electrical Engineering Beijing Jiaotong University Beijing China State Key Laboratory of Control and Simulation of Power Systems and Generation Equipments of Electrical Engineering Tsinghua University Beijing China

ISBN: (纸本)9789819988778

The data quality of numerical weather prediction has a great influence on the accuracy of wind power prediction technology. In order to improve the short-term forecasting effect of wind power, we proposed a combined LSTM-TCN forecasting model based on NWP error correction model with TimeGAN. First, minimal redundancy maximal relevance (mRMR) algorithm is used to extract original NWP features and measured meteorological features, respectively. Then, TimeGAN is used to correct initial NWP data according to the internal hidden associations with the measured weather features. After that, the corrected NWP features combined with historical wind power are applied to establish a wind power short-term prediction hybrid model based on LSTM and TCN network. Finally, the performance of the hybrid model is tested by a wind farm dataset in China, which verified effectiveness of this method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Wind power

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Achieving an Accurate Random Process Model for PV Power Using Cheap Data:Leveraging the SDE and Public Weather Reports

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CSEE Journal of Power and Energy systems 2025年第1期11卷 124-135页

作者： Yiwei Qiu Jin Lin Zhipeng Zhou Ningyi Dai Feng Liu Yonghua Song College of Electrical Engineering Sichuan UniversityChengdu610065China State Key Laboratory of the Control and Simulation of Power Systems and Generation Equipment Tsinghua UniversityBeijing 100087China State Key Laboratory of Internet of Things for Smart City University of MacaoMacao999078China Department of Electrical Engineering Tsinghua UniversityBeijing100087China

Stochastic differential equation (SDE)-based random process models of renewable energy sources (RESs) jointly capture evolving probability distribution and temporal correlation in continuous time. It enabled recent studies to remarkably improve performance of power system dynamic uncertainty quantification and optimization. However, considering the non-homogeneous random process nature of PV, there still remains a challenging question: how can a realistic and accurate daily SDE model for PV power be obtained that reflects its weather-dependent and non-Gaussian uncertainty in operation, especially when high-resolution numerical weather prediction (NWP) or sky imager is unavailable for many distributed plants? To fill this gap, this article finds that an accurate SDE model for PV power can be constructed only using the data from low-resolution public weather reports. Specifically, for each day, an hourly parameterized Jacobi diffusion process recreates temporal patterns of PV volatility. Its parameters are mapped from the day's public weather reports to reflect varying weather conditions using a simple learning model. The SDE model jointly captures intraday and intrahour volatility. Statistical examination shows that the proposed approach outperforms a selection of the latest deep learning-based time series models on real-world data collected in Macao.

关键词： Photovoltaic(PV)power random process stochastic differential equation(SDE) stochastic optimization time series modeling uncertainty quantification weather report

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A Novel Modeling Method for Power Flow Calculation of Integrated Enegy Network 13

A Novel Modeling Method for Power Flow Calculation of Integr...

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2022 IEEE International Conference on Power systems Technology, POWERCON 2022

作者： Tian, Liting Zhou, Xuyan Lin, Cheng Yin, Zheng Lin, Ende Equipment Tsinghua University State Key Laboratory of Control and Simulation of Power Systems and Generation Beijing China Institute of Science and Technology China Three Gorges Corporation State Key Laboratory of Control and Simulation of Power Systems Beijing China

ISBN: (数字)9781665417754

ISBN: (纸本)9781665417754

Multi-energy systems may contain integrated energy networks such as electrical, gas and heating networks. The differences in the physical characteristics of each energy network make it difficult to solve power flows of the integrated energy networks. Based on the concept of energy flow, this paper realizes the unified modeling of electrical, gas and heating network to describe the stable equilibrium relationship. The basic equations for energy flow calculation contains Kirchhoff's Current Law equations, Kirchhoff's Voltage Law equations, and energy flow efficiency equations. A weighted directed graph is used to model the total system with networks and energy hubs. The proposed model is used for the joint power flow solution of the multi-energy system with Newton-Raphson algorithm. Finally, the validity and accuracy of the model are verified in a test system. © 2022 IEEE.

关键词： Newton-Raphson method

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Significant potential of Solid Oxide Fuel Cell systems for distributed power generation and carbon neutrality

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Frontiers in Energy 2022年第6期16卷 879-882页

作者： Ziyuan TENG Minfang HAN Fuel Cell and Energy Storage Center Department of Energy and Power EngineeringState Key Laboratory of Control and Simulation of Power Systems and Generation EquipmentTsinghua UniversityBeijing 100084China

Energy is important for human survival and *** September 2020,the Chinese government announced that“China aims to have their CO_(2) emissions peak before 2030 and achieve carbon neutrality before 2060.”As China’s reformation and opening-up proceeds into its fifth decade,this new vision for its future is in line with that of the world’s major economies in terms of the need to reach net zero emissions globally by the mid-21stcentury[1].However,coal-fueled thermal power generation is currently dominant and accounts for more than 70%of the total amount of power generated in China,making it the world’s largest energy consumer and carbon *** on the country’s domestic resources,namely the abundance of coal and scarcity of oil and gas,it would be difficult to fundamentally change this coal-based energy structure in the short *** 2021,the global total energy sector was responsible for 36.3 Gt of CO_(2) emissions,including approximately 12 Gt from China,which thus accounts for one-third of the total global emissions[2].The demand for efficiency,energy savings,and emissions reduction in the power generation industry has become increasingly *** need has arisen to enhance ways in which to use coal cleanly and efficiently,reduce the consumption thereof,and replace coal with other forms of *** addition,carbon emissions need to be lowered by promoting the adoption of alternative power generation technologies that are more energy *** would require transformation away from coal power and the exploration of clean,efficient,flexible,and safe forms of energy as the main future development directions[3].

关键词： neutral carbon replace

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Catalytic urea hydrolysis by composite metal oxide catalyst towards efficient urea-based SCR process:performance evaluation and mechanism investigation

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Frontiers of Environmental Science & Engineering 2023年第5期17卷 67-80页

作者： Yuchen Li Zhen Chen Xiangyu Zhang Kun Yang Lidong Wang Junhua Li MOE Key Laboratory of Resources and Environmental Systems Optimization Department of Environmental Science and EngineeringNorth China Electric Power UniversityBeijing 102206China State Key Joint Laboratory of Environment Simulation and Pollution Control School of EnvironmentTsinghua UniversityBeijing 100084China Xi’an Thermal Power Research Institute Co. Xi’an 710032China

As a promising option to provide gaseous NH_(3) for SCR system,catalytic urea hydrolysis has aroused great attention,and improving surface area and activity of catalysis are the crucial issues to be solved for efficient urea ***,a composite metal oxide(TiO_(2)@Al_(2)O_(3))catalyst was prepared by a simple hydrothermal method,with mesoporous alumina(γ-Al_(2)O_(3))as *** results verify the mesoporous structure and submicron cluster of TiO_(2)@Al_(2)O_(3),with exposed crystal faces of(101)and(400)for TiO_(2)andγ-Al_(2)O_(3),*** electronegativity difference of Ti4+and Al3+changes the charge distribution scheme around the interface,which provides abundant acid/base sites to boost the urea ***,for an optimal proportioning with nano TiO_(2)content at 10 wt.%,the hydrolysis efficiency can reach up to 35.2%at 100℃ in 2 h,increasing by~7.1%than that of the blank experiment.^(13)C NMR spectrum measurements provide the impossible intermediate species during urea *** calculations are performed to clarify the efficient H_(2)O decomposition at the interface of TiO_(2)@Al_(2)O_(3).The result offers a favorable technology for energy-efficiency urea hydrolysis.

关键词： SCR Urea hydrolysis Catalytic Water dissociation Electronegativity

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Time Delay Estimation for Indoor Temperature Prediction by Weather Conditions 15

Time Delay Estimation for Indoor Temperature Prediction by W...

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15th International Conference Management of Large-Scale System Development, MLSD 2022

作者： Rassadin, Yury Pashchenko, Alexander V.A. Trapeznikov Institute of Control Sciences of Ras Laboratory of Discontinuous Control Systems Moscow Russia V.A. Trapeznikov Institute of Control Sciences of Ras Laboratory of Intelligent Control Sysytems and Simulation Moscow Russia

ISBN: (数字)9781665497015

ISBN: (纸本)9781665497015

The problem of indoor temperature prediction for further application in model predictive control synthesis was considered. The introduction of time delay for weather conditions was used to develop a simplified model of the temperature's dynamics that simulates thermal inertness of the building shell. The procedure of numerical estimation of the time delay was proposed on the basis of correlation analysis. Dataset of continuous measurements of indoor temperature and weather conditions was used in the experiment. The data were collected by the wireless network of smart sensors and local meteostation. Splitting the data into training and test samples was used to validate the accuracy of the prediction. © 2022 IEEE.

关键词： Time series analysis

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control, Modeling and Stability Analysis of DRU-based Grid-forming Converter

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Chinese Journal of Electrical Engineering 2025年第1期11卷 83-92页

作者： Wencong Wu Minzhi Wang Liang Tu Haiqing Cai Guanglei Yan Xiaohui Qu Haohan Gu Wei Chen State Key Laboratory of HVDC China Southern Power GridGuangzhou 510663China National Energy Power Grid Technology R&D Center Guangzhou 510663China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power Systems China Southern Power GridGuangzhou 510663China CSG Key Laboratory for Power System Simulation China Southern Power GridGuangzhou 510663China School of Electrical Engineering Southeast UniversityNanjing 210096China

Diode rectifier unit(DRU)-based high-voltage direct current(HVDC)transmissionsystems areeffectivein achieving the stableandeconomical operation of offshore *** theuncontrollable characteristicsof DRUs,a grid-forming(GFM)strategy forwind-turbine converters isnecessary to support offshore AC voltageand ***,the active power-synchronization control in traditional GFM converters is unsuitable for DRU-based GFM ***,the stability issue for DRU-based HVDC systems involving DRU-based GFM and grid-following(GFL)converters has not yet been *** solve these issues,this study begins with the characteristics of a DRU-based HVDC system and presents a control scheme for DRU-based GFM converters for power ***,the dq-frame impedance model of the DRU-based GFM converteris proposed for the stability analysis of the entire HVDC ***,a simulation platform is built to verify the model accuracy and system stability.

关键词： Diode rectifier unit offshore wind-powergeneration grid-forming converter impedance model stabilityanalysis

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Rnbformer: A High-Performance Roadside Noise Barriers Recognition Algorithm 24

Rnbformer: A High-Performance Roadside Noise Barriers Recogn...

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3rd Asia Conference on Algorithms, Computing and Machine Learning, CACML 2024

作者： Zhang, Zirui Wu, Jiaheng Huang, Heqiang Chen, Junzhou Hu, Hongyu Zhang, Ronghui Guangdong Provincial Key Laboratory of Intelligent Transportation System School of Intelligent Systems Engineering Sun Yat-Sen Uniyersity Guangdong Guangzhou510275 China State Key Laboratory of Automotive Simulation and Control Jilin University Changchun Jilin Changchun130022 China

ISBN: (纸本)9798400716416

Traffic noise pollution is a significant source of urban pollution. Roadside noise barriers (RNBs) serve as a primary solution to urban traffic noise pollution, but achieving precise positioning of RNBs requires on-site inspections and manual labeling, which is time-consuming and labor-intensive. Deploying an RNB recognition algorithm on inspection vehicles equipped with cameras and positioning devices is an efficient approach to establishing a high-precision RNB positioning database. We propose a high-accuracy RNB recognition algorithm, RNBFormer. In the model design, we integrate both convolutional and transformer structures to enhance the model's global feature extraction capability while optimizing the representation of local features. Additionally, we incorporate a channel attention mechanism to provide more discriminative features at the channel level. We trained and tested our model on a publicly available dataset, achieving a classification accuracy of 98.93%. © 2024 ACM.

关键词： Convolution

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