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Annual Meeting Industrial and Commercial power systems Technical Conference

作者： Mostafa Nazemi Payman Dehghanian Mohannad Alhazmi Fei Wang Department of Electrical and Computer Engineering George Washington University Washington DC USA State Key Laboratory of Alternate Electrical Power System With Renewable Energy Sources North China Electric Power University Beijing China

ISBN: (数字)9781728171951

ISBN: (纸本)9781728171968

Following substantial advancements in stochastic classes of decision-making optimization problems, scenario-based stochastic optimization, robust\ distributionally robust optimization, and chance-constrained optimization have recently gained an increasing attention. Despite the remarkable developments in probabilistic forecast of uncertainties (e.g., in renewable energies), most approaches are still being employed in a univariate framework which fails to unlock a full understanding on the underlying interdependence among uncertain variables of interest. In order to yield cost-optimal solutions with predefined probabilistic guarantees, conditional and dynamic interdependence in uncertainty forecasts should be accommodated in power systems decision-making. This becomes even more important during the emergencies where high-impact low-probability (HILP) disasters result in remarkable fluctuations in the uncertain variables. In order to model the interdependence correlation structure between different sources of uncertainty in power systems during both normal and emergency operating conditions, this paper aims to bridge the gap between the probabilistic forecasting methods and advanced optimization paradigms; in particular, perdition regions are generated in the form of ellipsoids with probabilistic guarantees. We employ a modified Khachiyan's algorithm to compute the minimum volume enclosing ellipsoids (MVEE). Application results based on two datasets on wind and photovoltaic power are used to verify the efficiency of the proposed framework.

关键词： Uncertainty Optimization Ellipsoids power systems Stochastic processes Decision making Probabilistic logic

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Adaptive Dynamic Surface Control of Constrained Nonlinear systems

Adaptive Dynamic Surface Control of Constrained Nonlinear Sy...

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第32届中国控制与决策会议

作者： Ailiang Xing Biao Song Yixin Yin School of Automation and Electrical Engineering University of Science and Technology Beijing Key Laboratory of Knowledge Automation for Industrial Processes Ministry of Education Demonstration and Research Department of Network Information System Beijing Institute of Computer Application

This paper presents an adaptive dynamic surface control for uncertain nonlinear strict feedback systems with constraints on both input and *** technology is employed in the controller *** performance method with asymmetric barrier Lyapunov function(BLF) is used to deal with the output *** input saturation is solved by using the Nussbaum *** dynamic surface control(DSC) technique is employed to reduce the complexity of the virtual *** is illustrated that all closed-loop signals are semi-global uniformly ultimately bounded(SGUUB).Finally,a simulation example proves the proposed results.

关键词： Input saturation Output constraints Nussbaum function Dynamic surface control

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A Learning Convolutional Neural Network Approach for Network Robustness Prediction

arXiv

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arXiv 2022年

作者： Lou, Yang Wu, Ruizi Li, Junli Wang, Lin Li, Xiang Chen, Guanrong The Department of Computing and Decision Sciences Lingnan University Hong Kong The Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China The College of Computer Science Sichuan Normal University Chengdu610066 China The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Institute of Complex Networks and Intelligent Systems Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201210 China The Department of Control Science and Engineering Tongji University Shanghai200240 China The Department of Electrical Engineering City University of Hong Kong Hong Kong

Network robustness is critical for various societal and industrial networks again malicious attacks. In particular, connectivity robustness and controllability robustness reflect how well a networked system can maintain its connectedness and controllability against destructive attacks, which can be quantified by a sequence of values that record the remaining connectivity and controllability of the network after a sequence of node- or edge-removal attacks. Traditionally, robustness is determined by attack simulations, which are computationally very time-consuming or even practically infeasible. In this paper, an improved method for network robustness prediction is developed based on learning feature representation using convolutional neural network (LFR-CNN). In this scheme, higher-dimensional network data are compressed to lower-dimensional representations, and then passed to a CNN to perform robustness prediction. Extensive experimental studies on both synthetic and real-world networks, both directed and undirected, demonstrate that 1) the proposed LFR-CNN performs better than other two state-of-the-art prediction methods, with significantly lower prediction errors;2) LFR-CNN is insensitive to the variation of the network size, which significantly extends its applicability;3) although LFR-CNN needs more time to perform feature learning, it can achieve accurate prediction faster than attack simulations;4) LFR-CNN not only can accurately predict network robustness, but also provides a good indicator for connectivity robustness, better than the classical spectral measures. © 2022, CC BY.

关键词： Complex networks

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Review of accumulative failure of winding insulation subjected to repetitive impulse voltages

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High Voltage 2019年第1期4卷 1-11页

作者： Potao Sun Wenxia Sima Xiongwei Jiang Dingfei Zhang Jiahui He Lian Ye State Key Laboratory of Power Transmission Equipment&System Security and New Technology Chongqing UniversityChongqing 400030People's Republic of China Department of Materials Science College of Engineering MaterialsChongqing UniversityChongqing 400030People's Republic of China Department of Electrical and Computer Engineering University of FloridaGainesvilleFL 32611USA

The premature failure phenomenon that occurs in solid insulation under the application of repetitive impulse voltages is known as the accumulative effect of impulse voltages on a solid *** accumulative failure of winding insulation is considered to be a core problem resulting from this *** main problems include the accumulative failure of motor winding insulation under pulse-width modulation impulses and the accumulative failure of transformer winding insulation under the application of overvoltages,and these problems represent new challenges in the safe and stable operation of power *** regards to the above problems,this study reviews the results of recent research on the accumulative failure characteristics and mechanisms of winding insulation when subjected to repetitive impulse *** challenges faced are discussed at the end of this study.

关键词： insulation winding dielectric

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Study on Subsynchronous Resonance Damping Control for Series-Compensated DFIG-Based Wind Farm

Study on Subsynchronous Resonance Damping Control for Series...

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2019 IEEE Sustainable power and Energy Conference, iSPEC 2019

作者： Luo, Chao Guo, Qi Xiao, Xiangning Gole, A.M. Liao, Mengjun Electric Power Research Institute State Key Laboratory of HVDC China CSG Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid Guangzhou China North China Electric Power University State Key Laboratory for Alternate Electrical Power System with Renewable Energy Sources Beijing China University of Manitoba Electrical and Computer Engineering Department Winnipeg Canada

ISBN: (纸本)9781728149301

Subsynchronous Resonance (SSR) is a potential threat for long distance power transmission from doubly fed induction generator (DFIG) based wind farms with fixed series compensation (FSC). In this paper, subsynchronous resonance damping control (SSRDC) strategy based on shunt voltage source converter is proposed. Taking account of different series compensation levels and wind speeds, several potential inputs to the SSRDC are investigated. SSRDC control parameters are determined via root locus techniques to ensure the system stability of subsynchronous modes. Considering implementation feasibility in practical project and stability of supersynchronous modes, an enhanced SSRDC is proposed. Impact of transmission line and DFIG parameters and the reactive power compensation to the mitigation effect is investigated. Time domain simulation using PSCAD/EMTDC are carried out to validate the effectiveness of the SSRDC. © 2019 IEEE.

关键词： Wind farm

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A hybrid correction framework using disentangled seasonal-trend representations and MoE for NWP solar irradiance forecast

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Applied Energy 2025年 397卷

作者： Weijing Dou Kai Wang Shuo Shan Kanjian Zhang Haikun Wei Victor Sreeram State Grid Electric Power Research Institute (NARI Group Corporation) National Key Laboratory of Risk Defense Technology and Equipment for Power Grid Operation Nanjing 211106 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University Nanjing 210096 China Southeast University Shenzhen Research Institute Shenzhen 518063 China Department of Electrical Electronics and Computer Engineering The University of Western Australia Perth Australia

Day-ahead solar irradiance forecast holds important value for optimizing energy utilization within the power system and ensuring stable grid scheduling. The forecast outputs of numerical weather prediction (NWP) are widely acknowledged as one of the indispensable data sources for day-ahead solar irradiance forecast tasks. In previous studies, post-processing methods have generally been employed as correction models to enhance the accuracy of NWP solar irradiance forecasts. However, irradiance sequences contain complex mixed patterns and exhibit various seasonal periodic differences. Based on the analysis of NWP global horizontal irradiance (GHI) error characteristics in this study, errors in NWP GHI forecasts also show obvious seasonal variations. Given these issues, it is challenging for a single correction model to achieve good correction performance and strong seasonal robustness. Therefore, this paper proposes a hybrid model comprising representation learning module, feature sparse activation module, and encoder-decoder-based correction module to address the aforementioned problems. A contrastive-learning-based representation learning module named CoST is introduced to learn disentangled seasonal features and trend features of irradiance sequences. A learnable mixture-of-experts (MoE) layer is adopted to sparsely activate the seasonal-trend features that contribute more to improving correction accuracy. The encoder-decoder-based correction module takes the sparsely activated seasonal-trend features as inputs, achieving the final corrected NWP GHI forecasts. The correction performance of the proposed method was validated on both publicly available datasets and actual field dataset. The results for various datasets show that our proposed CoST-MoELSTM model achieves the highest improvement for NWP forecasts, with increases of 29.82 %, 36.54 %, and 26.58 %. Additionally, we conducted a detailed analysis of the correction performance of CoST-MoELSTM across different

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A two-stage optimization model for floating offshore wind farm layout and control

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Renewable Energy 2025年 253卷

作者： Tao, Siyu Yang, Jisheng Zheng, Gang He, Ruiyang Feijóo-Lorenzo, Andrés E. College of Automation Engineering Nanjing University of Aeronautics and Astronautics No. 29 Jiangjun Road Nanjing211106 China Department of Building Environment and Energy Engineering Hong Kong Polytechnic University No. 102 Pokfulam Road Pok Fu Lam Hong Kong 999077 China State Key Laboratory of Power System Operation and Control Tsinghua University Beijing100084 China State Key Laboratory of Ocean Engineering Shanghai Jiao Tong University Shanghai200240 China State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing102206 China Monash Biomedical Imaging Monash University Clayton VIC3800 Australia School of Engineering Cardiff University CardiffCF243AA United Kingdom Department of Electrical Engineering University of Vigo Vigo36310 Spain

The effective planning of wind turbine (WT) positions in a wind farm (WF) plays a crucial role in capturing wind energy and increasing power generation. Control optimization of WT attitudes can also help reduce wake effects and increase power generation, thereby maximizing the annual electricity production (AEP) of a WF. In this paper, a two-stage optimization model is proposed for planning the stationary layout of floating offshore wind farms (FOWFs), as well as the positions and attitudes of the floating offshore wind turbines (FOWTs) via optimal control. In the first stage, a competitive particle swarm optimizer (CPSO) is employed to generate multiple FOWF layouts. In the second stage, the genetic algorithm (GA) is utilized to optimize the yaw angles and axial induction factors of the FOWTs for operation control. Unlike the existing approaches that address WT layout design and operation control sequentially and separately, the proposed method integrates these aspects to simplify the FOWTs operation and to improve their performances. Additionally, taking the movable characteristics of the FOWTs into consideration, the proposed model incorporates the positional movement of the FOWTs under varying wind conditions, making the power calculations more realistic. Case studies demonstrate that compared with the traditional layout-only optimization, the proposed two-stage optimization model can help increase the AEP of the FOWFs by 4.72 %. © 2025 Elsevier Ltd

关键词： Genetic algorithms

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Compound control method for DC–DC converter

Compound control method for DC–DC converter

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2019 Jiangsu Annual Conference on automation, JACA 2019

作者： Tang, Yongwei Li, Juan Li, Shengquan Lin, Zhongwei Wang, Shuwang Department of Electrical Engineering School of Electrical Energy and Power Engineering Yangzhou University Yangzhou225127 China State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Control and Computer Engineering North China Electric Power University Beijing102206 China

Considering the problems of internal and external disturbances, i.e. time-varying load, inaccurate system model and external uncertainties etc., a compound control strategy based on model predictive control (MPC) and non-linear extended state observer (ESO) are proposed to attenuate the output voltage fluctuation under continuous conduction mode (CCM) mode of the buck circuits. In addition, a non-linear ESO is used to observe all kinds of uncertainties caused by internal and external disturbances and to eliminate their effects by compensating these disturbances in the feed-forward channel. Compared with the pure MPC control, the simulation results show that the proposed composite control method has the better disturbance suppression effect and also can effectively improve the stability of the buck circuit. © 2019 Institution of engineering and Technology. All rights reserved.

关键词： Model predictive control

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Detailed modelling and simulations of an all-DC PMSG-based offshore wind farm

Detailed modelling and simulations of an all-DC PMSG-based o...

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作者： Shi, Tianyu Shi, Libao Yao, Liangzhong Department of Electrical Engineering Automation National Key Laboratory of Power System in Shenzhen Graduate School at Shenzhen Tsinghua University Shenzhen China China Electric Power Research Institute Beijing100912 China

A detailed modelling method and corresponding simulations for an all-DC offshore wind farm equipped with a permanent magnet synchronous generator (PMSG) are elaborately discussed and conducted. This all-DC wind farm is designed in series-connected topology with an offshore DC substation and a voltage-sourced converter−high-voltage direct current (VSC-HVDC) transmission system. Some major components including wind turbine, synchronous generator, VSC, and isolated full bridge converter are all modelled based on the complete electrical models and engineering parameter settings. Containing the start process of the system, the simulations under various wind speed conditions and the specific faults analysis under different situations are carried out in the MATLAB/SimulinkTM environment. Simulation results reveal the comprehensive operating characteristics of this all-DC offshore wind farm and its system response to faults. © 2019 Institution of engineering and *** right reserved.

关键词： Synchronous generators

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Integrated Response to Boost Distribution system Resilience Against Windstorms

Integrated Response to Boost Distribution System Resilience ...

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2019 IEEE Sustainable power and Energy Conference, iSPEC 2019

作者： Wang, Gang Liu, Zhaoxi Wang, Lingfeng Hu, Bo Xie, Kaigui Yan, Jiahao Chongqing University State Key Laboratory of Power Transmission Equipment and System Security College of Electrical Engineering Chongqing400044 China University of Wisconsin-Milwaukee Department of Electrical Engineering and Computer Science MilwaukeeWI53211 United States

ISBN: (纸本)9781728149301

Improving distribution system resilience is one of the urgent tasks for the modern society. This paper proposes a novel integrated preventive and emergency response model for system operators to boost the distribution system resilience considering all possible component damage statuses. Topology reconfiguration and generation redispatch are allowed in both the preventive response (PR) and emergency response (ER), while load curtailment is only deployed in the ER. A two-stage stochastic mixed-integer optimization model is developed for the integrated optimization problem, which is solved by the progressive hedging (PH) algorithm. Case studies on the IEEE 33-bus system and a 69-bus distribution system validate the correctness and effectiveness of the proposed model. Sensitivity analysis is also carried out to evaluate the influence of the weather severity and generator availability. The key conclusions of this paper include the following: 1) integrated response (IR) improves distribution system resilience with the minimum expected cost;and 2) integrated response is preferable comparing with the individual PR or ER. © 2019 IEEE.

关键词： Sensitivity analysis

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