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International Conference on High Voltage Engineering and Application, ICHVE

作者： Yunxiao Zhang Chenning Yan Kai Wu Yuanxiang Zhou Tianfeng Zhang College of Electrical Engineering and Automation Fuzhou University Fuzhou Fujian China Department of Electrical Engineering State Key Laboratory of Control and Simulation of Power Systems and Generation Equipment Tsinghua University Beijing China The Wind Solar Storage Division of State Key Laboratory of Power System and Generation Equipment School of Electrical Engineering Xinjiang University Urumqi China Electric Power Research Institute Anhui Electric Power Company of State Grid Hefei China

ISBN: (数字)9781665407502

ISBN: (纸本)9781665407519

Silicone rubber (SIR) has been widely used in the insulation of cable accessories due to its excellent performance. In this paper, pure SIR and nano $\boldsymbol{\text{Al}_2 \mathrm{O}_3 / \text{SIR}}$ composites are thermally aged for 0–80 days at $\boldsymbol{200^{\circ} \mathrm{C}}$ . The effects of long-term thermal aging on the space charge transport are analyzed. The results show that with the increase of thermal aging time, the accumulation of heteropolar charge of pure SIR and nano $\boldsymbol{\text{Al}_{2}\mathrm{O}_{3}/\text{SIR}}$ composites gradually decreases and changes to the accumulation of homopolar charge. Compared with pure SIR, the space charge accumulation of $\boldsymbol{\text{Al}_{2}\mathrm{O}_{3}/\text{SIR}}$ composites decreases significantly and changes little with the increase of thermal aging time. It is believed that when doped nano $\boldsymbol{\text{Al}_{2}\mathrm{O}_{3}}$ in SIR, the increase of carrier mobility and the introduction of metal oxides reacting with fallen free radicals to form stable compounds both lead to less space charge accumulation of $\boldsymbol{\text{Al}_{2}\mathrm{O}_{3}/\text{SIR}}$ composites.

关键词： Nanocomposites Impurities Metals High-voltage techniques Aging Space charge Thermal analysis

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Fast Monte Carlo simulation of Dynamic Power systems Under Continuous Random Disturbances

Fast Monte Carlo Simulation of Dynamic Power Systems Under C...

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IEEE General Meeting Power& Energy Society

作者： Yiwei Qiu Jin Lin Xiaoshuang Chen Feng Liu Yonghua Songy State Key Laboratory of Control and Simulation of Power Systems and Generation Equipment Tsinghua University Beijing China The University of Macau Macau China

ISBN: (数字)9781728155081

ISBN: (纸本)9781728155098

Continuous-time random disturbances from the renewable generation pose a significant impact on power system dynamic behavior. In evaluating this impact, the disturbances must be considered as continuous-time random processes instead of random variables that do not vary with time to ensure accuracy. Monte Carlo simulation (MCs) is a nonintrusive method to evaluate such impact that can be performed on commercial power system simulation software and is easy for power utilities to use, but is computationally cumbersome. Fast samplings methods such as Latin hypercube sampling (LHS) have been introduced to speed up sampling random variables, but yet cannot be applied to sample continuous disturbances. To overcome this limitation, this paper proposes a fast MCs method that enables the LHS to speed up sampling continuous disturbances, which is based on the Itô process model of the disturbances and the approximation of the Itô process by functions of independent normal random variables. A case study of the IEEE 39-Bus System shows that the proposed method is 47.6 and 6.7 times faster to converge compared to the traditional MCs in evaluating the expectation and variance of the system dynamic response.

关键词： Monte Carlo methods Uncertainty System dynamics Power system dynamics Hypercubes Software Random variables

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Ways to Improve the Intelligence of Algorithms for Optimal Load Distribution at CHP Plants With Complex Equipment Composition

Ways to Improve the Intelligence of Algorithms for Optimal L...

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International Conference on Management of Large-Scale System Development (MLSD)

作者： E. K. Arakelyan F. F. Pashchenko A. V. Andryushin A. A. Kosoy Y. Y. Yagupova M. S. Leonov National Research University "Moscow Power Engineering Institute" Moscow Russia Laboratory of Intelligent control and simulation systems V.A. Trapeznikov Institute of Control Sciences of RAS Moscow Russia

ISBN: (数字)9781728110943

ISBN: (纸本)9781728110950

In this work, the research team reviewed problems and presented solutions integration methods, deep learning and artificial intelligence libraries, in modern software and hardware complexes for solving problems of optimum allocation of the current heat and power production in CHP plants with complex equipment connected with the necessity of considering a large number of constraints and handling large numbers of parameters. A variant of building an intelligent system is proposed, which, in addition to a number of intelligent functions, aims to evaluate immeasurable or poorly measured parameters quantitatively, identify the current technical condition of the equipment and recommend either a solution available in the database, or conduct new calculations.

关键词： Machine learning algorithms Cogeneration Software algorithms Software Thermal loading Resource management Task analysis

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Model predictive control with guarantees for discrete linear stochastic systems subject to additive disturbances with chance constraints

Model predictive control with guarantees for discrete linear...

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American control Conference

作者： Johanna Bethge Shuyou Yu Rolf Findeisen Laboratory for Systems Theory and Automatic Control Otto-von-Guericke University Magdeburg Magdeburg Germany State Key Laboratory of Automobile Dynamic Simulation Jilin University Changchun China

ISBN: (数字)9781538682661

ISBN: (纸本)9781538682678

We propose a stochastic model predictive control scheme for linear discrete-time and time-invariant systems with chance constraints and additive normal distributed disturbance with known covariance. The proposed approach allows to balance between performance optimization and robustness by adjusting the probability p of the chance constraints. The controllability Gramian and the Riccati inequality are used to determine a set that contains all disturbances with a probability p. The proposed approach guarantees asymptotic stability of the nominal system, probabilistic convergence of the real system as well as constraint satisfaction and recursive feasibility in terms of probability. A proof of concept example considers the control of a wind turbine, where the wind acts as a normal distributed disturbance.

关键词： Optimal control Stochastic processes Robustness Convergence Asymptotic stability controllability Probabilistic logic

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Optimal internal boundary control of lane-free automated vehicle traffic

arXiv

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arXiv 2020年

作者： Malekzadeh, Milad Papamichail, Ioannis Papageorgiou, Markos Bogenberger, Klaus Dynamic Systems and Simulation Laboratory Technical University of Crete Chania73100 Greece Technical University of Munich Department of Traffic Engineering and Control Munich Germany

A recently proposed paradigm for vehicular traffic in the era of CAV (connected and automated vehicles), called TrafficFluid, involves lane-free vehicle movement. Lane-free traffic implies that incremental road widening (narrowing) leads to corresponding incremental increase (decrease) of capacity;and this opens the way for consideration of real-time internal boundary control on highways and arterials, in order to flexibly share the total (both directions) road width and capacity among the two directions in dependence of the bi-directional demand and traffic conditions, so as to maximize the total (two directions) flow efficiency. The problem is formulated as a convex QP (Quadratic Programming) problem that may be solved efficiently, and representative case studies shed light on and demonstrate the features, capabilities and potential of the novel control action. © 2020, CC BY-NC-ND.

关键词： Roads and streets

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Achieving an accurate random process model for PV power using cheap data: Leveraging the SDE and public weather reports

arXiv

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arXiv 2021年

作者： Qiu, Yiwei Lin, Jin Zhou, Zhipeng Dai, Ningyi Liu, Feng Song, Yonghua College of Electrical Engineering Sichuan University Chengdu610065 China State Key Laboratory of the Control and Simulation of Power Systems and Generation Equipment Tsinghua University Beijing100087 China State Key Laboratory of Internet of Things for Smart City University of Macau 999078 China Department of Electrical Engineering Tsinghua University Beijing100087 China

The stochastic differential equation (SDE)-based random process models of volatile renewable energy sources (RESs) jointly capture the evolving probability distribution and temporal correlation in continuous time. It has enabled recent studies to remarkably improve the 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 SDE model for PV power be obtained that reflects its weather-dependent uncertainty in online operation, especially when high-resolution numerical weather prediction (NWP) is unavailable for many distributed plants? To fill this gap, this article finds that an accurate SDE model for PV power can be constructed by only using the cheap data from low-resolution public weather reports. Specifically, an hourly parameterized Jacobi diffusion process is constructed to recreate the temporal patterns of PV volatility during a day. Its parameters are mapped from the public weather report using an ensemble of extreme learning machines (ELMs) to reflect the varying weather conditions. The SDE model jointly captures intraday and intrahour volatility. Statistical examination based on real-world data collected in Macau shows the proposed approach outperforms a selection of state-of-the-art deep learning-based time-series forecast methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Random processes

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Communication-Efficient Federated Learning for Connected Vehicles with Constrained Resources

Communication-Efficient Federated Learning for Connected Veh...

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Shuaiqi Shen Chong Yu Kuan Zhang Xi Chen Huimin Chen Song Ci University of Nebraska-Lincoln Omaha NE USA Beijing Electric Power Research Institute State Grid Corporation of China State Key Laboratory for Safety Control and Simulation of Power Systems and Large Power Generation Equipment Tsinghua University Beijing P. R. China

With the upcoming next generation wireless network, vehicles are expected to be empowered by artificial intelligence (AI). By connecting vehicles and cloud server via wireless communication, federated learning (FL) allows vehicles to collaboratively train deep learning models to support intelligent services, such as autonomous driving. However, the large number of vehicles and increasing size of model parameters bring challenges to FL-empowered connected vehicles. Since communication bandwidth is insufficient to upload full-precision local models from numerous vehicles, model compression is usually conducted to reduce transmitted data size. Nevertheless, conventional model compression methods may not be practical for resource-constrained vehicles due to the increasing computational overhead for FL training. The overhead for downloading global model can also be omitted by existing methods since they are originally designed for centralized learning instead of FL. In this paper, we propose a ternary quantization based model compression method on communication-efficient FL for resource-constrained connected vehicles. Specifically, we firstly propose a ternary quantization based local model training algorithm that optimizes quantization factors and parameters simultaneously. Then, we design a communication-efficient FL approach that reduces overhead for both upstream and downstream communications. Finally, simulation results validate that the proposed method demands the lowest communication and computational overheads for FL training, while maintaining desired model accuracy compared to existing model compression methods.

关键词： Training Quantization (signal) Connected vehicles Computational modeling simulation Wireless networks Collaborative work

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Frequency support scheme based on parametrized power curve for de-loaded wind turbine under various wind speed

arXiv

引用

arXiv 2021年

作者： Zhong, Cheng Lv, Yueming Li, Huayi Chen, JiKai Li, Yang Key Laboratory of Modern Power System Simulation and Control Renewable Energy Technology （Ministry of Education） Northeast Electric Power University Jilin132012 China Department of Electronic Electrical and Systems Engineering University of Birmingham BirminghamB15 2TT United Kingdom

With increased wind power penetration in modern power systems, wind plants are required to provide frequency support similar to conventional plants. However, for the existing frequency regulation scheme of wind turbines, the control gains in the auxiliary frequency controller are difficult to set because of the compromise of the frequency regulation performance and the stable operation of wind turbines, especially when the wind speed remains variable. This paper proposes a novel frequency regulation scheme (FRS) for de-loaded wind turbines. Instead of an auxiliary frequency controller, frequency support is provided by modifying the parametrized power versus rotor speed (Pw-ωr) curve, including the inertia power versus rotor speed curve and the droop power versus rotor speed curve. The advantage of the proposed scheme is that it does not contain any control gains and generally adapts to different wind speeds. Further, the proposed scheme can work for the whole section of wind speed without wind speed measurement information. The compared simulation results demonstrate the scheme improves the system frequency response while ensuring the stable operation of doubly-fed induction generators (DFIGs)-based variable-speed wind turbines (VSWTs) under various wind conditions. Furthermore, the scheme prevents rotor speed overdeceleration even when the wind speed decreases during frequency regulation control. Copyright © 2021, The Authors. All rights reserved.

关键词： Wind

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Experimental Investigation of Fuel Starvation on Industrial-Sized Solid Oxide Fuel Cells Using Segmented Cathodes and Area Specific Resistances

SSRN

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SSRN 2022年

作者： Zhang, Jihao Guo, Huding Lei, Libin Shen, Shuanglin Zheng, Keqing Han, Minfang Laboratory of Hydrogen Energy and Fuel Cells School of Low-Carbon Energy and Power Engineering China University of Mining and Technology Xuzhou221116 China Xuzhou Huatsing Jingkun Energy Co. Ltd. Xuzhou221005 China Smart Energy Research Center School of Materials and Energy Guangdong University of Technology Guangzhou510006 China State Key Laboratory of Control and Simulation of Power Systems and Generation Equipment Department of Energy and Power Engineering Tsinghua University Beijing100084 China

Fuel starvation needs to be timely detected to avoid anode oxidation, especially for industrial-sized Solid oxide fuel cells (SOFCs). In this work, a novel and useful segmented cell measurement method is developed to detect fuel starvation occurring in local regions. For this developed method, the cathode of SOFCs is segmented and only the voltage signal is measured in the segments. Then, the local current-voltage (I-V) curve of each segment is determined using the measured voltage together with the global I-V curve. To validate the data reliability, area specific resistances (ASRs) are calculated and compared among different segments. Varieties of current loading rates and fuel flow rates are correspondingly investigated. Furthermore, the occurrence of fuel starvation is identified using the rapid voltage drop in I-V curves as well as the rapid ASR increment in I-ASR curves. The results show that compared to the voltage drop, the intrinsic ASR increment is a more sensitive and robust criterion to identify the occurrence of fuel starvation. This work will considerably benefit the progress of segmented cell measurements and local fuel starvation identifications. © 2022, The Authors. All rights reserved.

关键词： Cathodes

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Optimization of Heterogeneous Resources Combination using Virtual Power Plant based on Modern Portfolio Theory

Optimization of Heterogeneous Resources Combination using Vi...

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Sustainable Power and Energy Conference (iSPEC)

作者： Qiuchen Yun Liting Tian Fang Zhang Lin Cheng School of Electrical Engineering Beijing Jiaotong University Beijing China Key Laboratory of Control and Simulation of Power Systems and Generation Equipment Tsinghua University Beijing China

ISBN: (数字)9781728191645

ISBN: (纸本)9781728191652

In recent years, with the development of smart grid technologies, wind turbines, photovoltaics and other renewable energy sources as well as demand-side loads have participated in grid dispatching, resulting in increased complexity of energy flows in the grid. Distributed energy resources (DERs) differ in many dimensions, such as technical attributes, operating modes, and business objectives, and its management authority also belongs to different entities, making it more difficult for grids to manage DERs. A management mode, virtual power plant (VPP), being enabled by information and communication technology, could effectively overcome the challenges with decentralized DERs and help realize the aggregation of DERs. In this paper, the interaction between customers and VPP is modeled, where VPP divides the owners of DERs into three categories according to the willingness and responsiveness of customers and balances budget costs and risks based on the modern portfolio theory. simulation with the CIGRE 20kV distribution network benchmark demonstrates that VPP could greatly meet the power consumption demand of multiple participants during operation. This paper also provides a basis for the selection of DERs aggregated by VPP. 1 1This paper is sponsored by the National Key Research and Development Program (No.2017YFB0903300) and National Natural Science Foundation of China (No.51807107) 2 2This paper is sponsored by the Young Talents Support Project of the Chinese Society for Electrical Engineering (CSEE) titled “Research on key technologies for optimal operation and control of transportation energy systems”.

关键词： Power demand Transportation Benchmark testing Wind turbines Smart grids Portfolios Optimization

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