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6th IEEE International Electrical and Energy Conference, CIEEC 2023

作者： Zhong, Jianying Deng, Yuan Yan, Xin Qu, Lu Fan, Naiyuan Feng, Jian Gan, Zhizheng Pinggao Group CO. Ltd Pingdingshan China Tsinghua University State Key Laboratory of Control and Simulation of Power System and Generation Equipment Department of Electrical Engineering Beijing China Dc Research Center Sichuan Energy Internet Research Institute Tsinghua University Chengdu China

ISBN: (纸本)9798350346671

Solid-state DC circuit breakers are key equipment for the protection of medium and low voltage DC systems and are the most effective means of isolating short-circuit faults. With the development of high-power power electronics, the performance of solid-state DC circuit breakers has also improved. In this paper, a new topology of anti-parallel connection is proposed based on the reverse blocking IGCT, which requires fewer switches, lower cost and smaller loss than the anti-series and diode-bridge structure. Besides, a snubber branch design method based on MOV-C is proposed, which can effectively suppress the breaking overvoltage and AC harmonics. Finally, a prototype of low-voltage solid-state DC circuit breaker with rated voltage of 750V was developed, and the breaking test and temperature test were carried out. © 2023 IEEE.

关键词： Electric circuit breakers

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Benefit Assessment Analysis of Electrochemical Energy Storage

Benefit Assessment Analysis of Electrochemical Energy Storag...

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2023 IEEE International Conference on power Science and Technology, ICPST 2023

作者： Ma, Junliang Wang, Zhidong Zhang, Shuming Northeast Electric Power University School of Electrical Engineering Jilin China State Grid Economic and Technological Research Institute Co. Ltd Transmission Grid Planning Center Beijing China Tsinghua University State Key Lab of Control and Simulation of Power Systems and Generation Equipments Beijing China

ISBN: (纸本)9798350311358

Along with the power fluctuation and other problems caused by large-scale grid connection of renewable energy, electrochemical energy storage has been widely concerned by researchers. Firstly, the technical characteristics and application scenarios of important electrochemical energy storage are summarized in this paper. Then the analysis focus on the evaluation indexes of the economic and social benefits of electrochemical energy storage on the generation side, grid side and user side. Finally, in order to adapt to the landing of carbon peaking and carbon neutral targets, and combined with the implementation of time-sharing tariffs, energy storage planning problems that need further in-depth research are proposed in view of the shortcomings in existing energy storage planning studies, which are of great significance for improving the adaptability of subsequent energy storage planning studies. © 2023 IEEE.

关键词： Economic and social effects

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Utilization of Energy Storage and Hydrogen in power and Energy Systems:Viewpoints from Five Aspects

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CSEE Journal of power and Energy Systems 2023年第1期9卷 1-7页

作者： Yonghua Song Mohammad Shahidehpour Saifur Rahman Nigel Brandon Kai Strunz Jin Lin Yuxuan Zhao State Key Laboratory of Internet of Things for Smart City University of MacaoTaipaMacao 999078China Department of Electrical Engineering Tsinghua UniversityBeijing 100084China Galvin Center for Electricity Innovation Illinois Institute of TechnologyChicagoIL 60616USA Department of Advanced Research Institute Virginia Polytechnic Institute and State UniversityArlingtonVA 22203USA Department of Earth Science and Engineering Imperial College LondonLondon SW72AZU.K Department of Sustainable Electric Networks and Sources of Energy Technische Universitt Berlin10587 BerlinGermany State Key Laboratory of Control and Simulation of Power Systems and Generation Equipment Department of Electrical EngineeringTsinghua UniversityBeijing 100084China

Decarbonizing power systems is crucial to mitigating climate change impacts and achieving carbon *** renewable energy supply can reduce greenhouse gas emissions and accelerate the decarbonization ***,renewable energy sources(RESs)such as wind and solar power are characterized by intermittency and often non-dispatchability,significantly challenging their high-level integration into power *** storage is acknowledged as a vital indispensable solution for mitigating the intermittency of renewables such as wind and solar power and boosting the penetrations of *** the CSEE JPES Forum,five well-known experts were invited to give keynote speeches,and the participating experts and scholars had comprehensive exchanges and discussions on energy storage ***,the views on the design,control,performance,and applications of new energy storage technologies,such as the fuel cell vehicle,water electrolysis,and flow battery,in the coordination and operation of power and energy systems were *** experts also provided experience that could be used to develop energy storage for constructing and decarbonizing new power systems.

关键词： Electrolysis electric vehicle energy storage flow battery fuel cell electric vehicle hydrogen energy

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Design and simulation of Adaptive Internal Model control Algorithm 5

Design and Simulation of Adaptive Internal Model Control Alg...

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5th International Conference on power and Renewable Energy, ICPRE 2020

作者： Ming, Xie Chunyu, Gao Baojie, Liu Ze, Dong China Datang Corporation Science and Technology Research Institute Ltd Thermal Power Technology Research Institute Beijing China North China Electric Power University Hebei Technology Innovation Center of Simulation Optimized Control for Power Generation Baoding China

ISBN: (纸本)9781728190266

In the actual industrial control, many objects have the characteristics of large delay and parameter time-varying, which makes the traditional PID control difficult to obtain satisfactory control effect. The internal model control has many advantages, such as intuitive design, strong practicability, simple structure, few on-line adjusting parameters and no need of accurate object model. Especially for the improvement of robustness and anti-interference and the control of the system with large time delay, the control effect is particularly significant. But the system model of internal model control is not easy to get, and it is difficult to get a more accurate internal model. In order to obtain a more accurate internal model, improve the control effect of the internal model control system and improve the dynamic characteristics of the control system, it is necessary to identify the model parameters of the controlled object efficiently. In this paper, the improved particle swarm optimization (PSO) algorithm is used to identify the system online and combined with the internal model control method to control the controlled object. © 2020 IEEE.

关键词： Three term control systems

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Prediction of NOx in SCR Inlet of Thermal power Plant Based on Mutual Information and Long Short-Term Memory Network 5

Prediction of NOx in SCR Inlet of Thermal Power Plant Based ...

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5th International Conference on power and Renewable Energy, ICPRE 2020

作者： Chunyu, Gao Ming, Xie Wei, Jiang Ze, Dong China Datang Corporation Science and Technology Research Institute Ltd Thermal Power Technology Research Institute Beijing China North China Electric Power University Hebei Technology Innovation Center of Simulation Optimized Control for Power Generation Baoding China

ISBN: (纸本)9781728190266

NOx produced by thermal power plant incineration is one of the main sources of atmospheric pollutants. Most thermal power plants use selective catalytic reduction (SCR) technology to denitrate. The current NOx concentration sensor has a large delay and cannot accurately reflect the real-time change of the NOx concentration at the SCR inlet, which will affect the economy and safety of the denitration control. This paper uses balanced mutual information feature selection (BMIFS) for feature extraction. Driven by historical data, a long short-term memory network (LSTM) model of inlet NOx concentration was established. By comparing the error with the field measured data, the accuracy of the prediction model and the effectiveness of the prediction method are verified. The conclusion shows that the balanced mutual information feature selection method can reduce the coupling degree and data dimension between the input variables, and achieve the purpose of improving modeling accuracy and reducing computational complexity. © 2020 IEEE.

关键词： Long short-term memory

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A novel heterogeneous ensemble of extreme learning machinesand its soft sensing application

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Journal of Southeast University(English Edition) 2020年第1期36卷 41-49页

作者： Ma Ning Dong Ze Hebei Technology Innovation Center of Simulation&Optimized Control for Power Generation North China Electric Power UniversityBaoding 071003China

To obtain an accurate and robust soft sensor model in dealing with the increasingly complex industrial modeling data,an effective heterogeneous ensemble of extreme learning machines(HEELM)is ***,the kernel extreme learning machine(KELM)and four common extreme learning machine(ELM)models that have different internal activations are contained in the HEELM for enriching the diversity of *** number of hidden layer nodes of the extreme learning machine is determined by the trial and error method,and the optimal parameters of the kernel extreme learning machine model are determined by cross ***,to obtain the best output of the ensemble model,least squares regression is applied to aggregate the outputs of all individual *** complex data sets of practical industrial processes are used to test the HEELM *** simulation results show that the HEELM has a high prediction *** with the individual ELM models,bagging ELM ensemble model,BP and SVM models,the prediction accuracy of the HEELM model is improved by 4.5%to 8.7%,and the HEELM model can obtain better generalization capability.

关键词： soft sensor extreme learning machine least squares ensemble

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research on Predictive control Based on LongShort-Term Memory Network

Research on Predictive Control Based on LongShort-Term Memor...

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2020 IEEE International Conference on Advances in Electrical engineering and Computer Applications, AEECA 2020

作者： Gao, Chunyu Xie, Ming Jiang, Wei Dong, Ze China Datang Corporation Science and Technology Research Institute Ltd. Thermal Power Technology Research Institute Beijing China North China Electric Power University Hebei Technology Innovation Center of Simulation Optimized Control for Power Generation Baoding China

ISBN: (纸本)9781728165202

Predictive control has the advantages of low model accuracy requirements, ability to handle multivariables, and ability to handle constraints. This paper expounds the basic principles of long-and short-term memory networks, and combines its advantages in modeling time series data with a recursive multi-step model to establish a prediction model. The golden section method is used to dynamically solve the control quantity. Correct the error of the prediction model in real time through feedback correction. The design of predictive control algorithm based on long and short-term memory network is completed. The simulation verifies the feasibility and antidisturbance of the algorithm. © 2020 IEEE.

关键词： Long short-term memory

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Benefit Assessment Analysis of Electrochemical Energy Storage

Benefit Assessment Analysis of Electrochemical Energy Storag...

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power Science and Technology (ICPST), IEEE International Conference on

作者： Junliang Ma Zhidong Wang Shuming Zhang School of Electrical Engineering Northeast Electric Power University Jilin China Transmission Grid Planning Center State Grid Economic and Technological Research Institute Co. Ltd Beijing China State Key Lab of Control and Simulation of Power Systems and Generation Equipments Tsinghua University Beijing China

关键词：

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Joint Multi-stage Planning of Renewable generation, HESS, and AESS for Deeply Decarbonizing power Systems with High-penetration Renewables

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IEEE Transactions on Sustainable Energy 2024年

作者： Yu, Zhipeng Chi, Yingtian Lin, Jin Liu, Feng Li, Jiarong Song, Yonghua Ren, Zhengwei Lu, Chengcheng Ji, Mengbo Tsinghua University State Key Laboratory of Control and Simulation of Power Systems and Generation Equipment Department of Electrical Engineering Beijing100087 China Tsinghua University Sichuan Energy Internet Research Institute Chengdu610213 China Harvard University Harvard John A. Paulson School of Engineering and Applied Sciences Cambridge United States University of Macau Department of Electrical and Computer Engineering Macau999078 China Tsinghua University Department of Electrical Engineering Beijing100087 China China Electric Power Planning & Engineering Institute National Electric Power Planning and Research Center Beijing100087 China Co. Ltd. Beijing100087 China Ordos City Hanxia Renewable Energy Co. Ltd. Ordos014300 China

The further decarbonization of power systems with high renewable energy penetration faces the problem of inter-day intermittence of renewable energy sources (RES) and the seasonal imbalance between RES and load demand, due to the limited regulation ability of conventional units such as thermal generation. Regular solutions based on battery energy storage system (BESS) are too costly to be practical. To address issues above, hydrogen energy storage system (HESS) and ammonia energy storage system (AESS) are introduced to gradually replace thermal generation. Specifically, first, HESS and AESS are incorporated into the multi-stage capacity expansion planning (MSCEP) model with carbon emission reduction constraints. Yearly data with hourly time resolution are utilized for each stage to accurately describe the intermittence of RES. Then, an improved column generation (CG) with Dantzig-Wolfe decomposition (DWD) embedded solution approach is used to efficiently solve the large-scale MSCEP model. Finally, a real-life system in China is studied. The results indicate that the proposed method can guarantee high power supply reliability (PSR) under different renewable energy penetration levels, avoiding the low PSR problem that may be caused by the existing typical scenario-based method (TSM) under high penetration (≥30%). Moreover, HESS and AESS are essential to reduce the cost of decarbonization. Especially under the goal of carbon neutrality, the contribution of HESS and AESS in reducing levelized cost of energy (LCOE) reaches 12.28% and 14.59%, respectively, leading to a levelized cost of carbon reduction (LCOCr) of 998 RMB/t. © 2010-2012 IEEE.

关键词： Hydrogen storage

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LSSVM Boiler Main Steam Temperature Anomaly Detection Algorithm Based on Improved SSA

LSSVM Boiler Main Steam Temperature Anomaly Detection Algori...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Lei Liu Tianmu Qin Li Wang Zheng Wu North China Electric Power Research Institute Co. Ltd. Beijing China North China Electric Power Research Institute Co. Ltd. Xi'an Branch Xi'an China School of Control and Computer Engineering North China Electric Power University Beijing China Hebei Technology Innovation Center of Simulation & Optimized Control for Power Generation North China Electric Power University Baoding China

ISBN: (纸本)9781665466097

To address the problem of blindness in parameter selection of LSSVM model, this paper proposes to use the improved sparrow algorithm (CDE-SSA) for parameter search. First, the CDE-SSA algorithm is designed on the basis of the sparrow algorithm, and the chaos theory is integrated into the sparrow algorithm to establish the chaotic sparrow algorithm model for the shortcomings of the poor global search ability and unstable initialized sparrow distribution of the sparrow algorithm. To address the shortcomings of the way of discoverer position update in the algorithm, a new variational operator is used instead of the original one to improve the practicality of the algorithm. In order to balance the global search ability and local search ability in the first and second stages of the algorithm, the cosine factor is introduced to improve the proportional operator in the sparrow algorithm, and the information of the number of iterations is introduced into the iterative process. The performance of CDE-SSA algorithm is compared with PSO algorithm and SSA algorithm by testing functions, and the results show that CDE-SSA algorithm shows better performance in terms of search accuracy and search stability. Meanwhile, the CDE-SSA algorithm is used to optimize the LSSVM parameters and build the KPCA-CDE-SSA-LSSVM anomaly detection model, which is applied to the main steam temperature system anomaly detection scenario, and the detection accuracy reaches 98%, which proves the rationality of the algorithm proposed in this paper.

关键词： Temperature Machine learning algorithms Search problems Stability analysis Classification algorithms Anomaly detection Thermal stability

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