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2024 China Automation Congress, CAC 2024

作者： Ren, Xinyu Li, Bo Zhao, Feng Chen, Xiangyong Zhang, Xianhe Jiang, Xiaowei School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation of Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Finance Anhui University of Finance and Economics Bengbu China Key Laboratory of Complex systems and Intelligent Computing in Universities of Shandong Linyi University Linyi China School of Automation and Electrical Engineering Key Laboratory of Complex systems and Intelligent Computing in Universities of Shandong Linyi University Linyi China School of Electrical Engineering and Automation Hubei Normal University Huangshi China

ISBN: (纸本)9798350368604

This paper investigates the optimal tracking performance (OTP) of multiple-input multiple-output (MIMO) discrete networked control systems (NCSs) under the influence of Denial of Service (DoS) attack, channel noise, and bandwidth constraints. The channel noise is assumed to be additive Gaussian white noise (AWGN). Due to the randomness of DoS attack, a Bernoulli distribution is used for DoS modeling. Utilizing frequency domain analysis methods, coprime factorization, all-pass decomposition, and Youla parameterization, an explicit expression for the optimal tracking performance (OTP) of the system under the two-degree-of-freedom (TDOF) controller is derived. The analysis indicates that non-minimum phase (NMP) zeros, unstable poles (UPs), DoS attack, and network constraints affect the OTP of NCSs. Finally, the theoretical results are validated through simulations of MIMO NCS and vehicle control system. © 2024 IEEE.

关键词： White noise

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Energy Storage Sizing for IES Considering Carbon Trading and Demand Response 6

Energy Storage Sizing for IES Considering Carbon Trading and...

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6th International Conference on Power and Energy Applications, ICPEA 2023

作者： Zhang, Lipeng Cao, Kuan Li, Yuwen Gao, Jie Shandong University Key Laboratory of Power System Intelligent Dispatch and Control Ministry of Education Jinan China State Grid Weihai Power Supply Company Development Planning Department Weihai China

ISBN: (纸本)9798350359053

An optimal energy storage sizing method for integrated energy system (IES) considering carbon trading and demand response is presented in this paper. Firstly, to effectively limit carbon emissions, a reward-penalty laddered carbon trading model is designed to convert carbon emissions into system costs or revenues. Secondly, the integrated demand response (IDR) model for electrical-cold-heat loads is established based on time-of-use price to enhance the operational flexibility and economy. Then, based on these two sub-models, an optimal energy storage sizing model is built to minimize the total life cycle cost including investment, operation and carbon trading cost. Finally, the simulation results indicate that a successful balance between low-carbon and economy can be achieved by the proposed method. © 2023 IEEE.

关键词： Energy storage

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Frequency Domain Stability Analysis and Simulation of New Energy Power Generation systems

Frequency Domain Stability Analysis and Simulation of New En...

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Power and Electrical Engineering (ACPEE), 5th Asia Conference on

作者： Haobo Wang Hongtao Wang Peiqi Xin Zhen Song Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong University Jinan China

ISBN: (数字)9798350309638

ISBN: (纸本)9798350309645

Stability analysis of grid-connected inverters is advanced through impedance analysis in this study, which leads to the development of a comprehensive mathematical model and simulation. control parameters, notably the voltage inner-loop control parameter $K_{v}$, the proportional gain $K_{p}$ of the phase-locked loop (PLL), and the integral gain $K_{i}$, were meticulously adjusted to assess their effects on subsynchronous oscillations. It was determined that while the proportional gain $K_{p}$ and integral gain $K_{i}$ have a marginal impact on sub-synchronous oscillations, the voltage inner-loop control parameter $K_{i}$ significantly influences these oscillations. The insights gained from this analysis contribute to a deeper understanding of, and potential solutions for, synchronous oscillation issues. The ability to mitigate such oscillations facilitates the integration of a higher proportion of renewable energy sources, thereby enhancing the stability and reliability of the electrical power system. This enhancement is of critical importance in the pursuit of sustainable energy transitions and the advancement of clean energy sources.

关键词： Power system stability Stability analysis Mathematical models Inverters Power system reliability Reliability Voltage control

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Optimal Dispatch for Large-Scale Renewable Energy Bases Without Conventional Power Support

Optimal Dispatch for Large-Scale Renewable Energy Bases With...

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IEEE Region 10 International Conference TENCON

作者： Kunpeng Zhang Tianhao Liu Yutian Liu Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong University Jinan China

ISBN: (数字)9798350350821

ISBN: (纸本)9798350350838

In large-scale renewable energy base and HVDC systems, the absence of conventional power support makes commutation failures a significant threat to system security and stability. An optimal dispatch method based on Kolmogorov-Arnold Networks (KAN) is proposed, which considers voltage and frequency dynamic security constraints. First, a time-domain simulation model of the system is developed to generate training data for KAN. The KAN is then designed to estimate the transient overvoltage at the point of common coupling in the renewable energy base after the occurrence of commutation failure. Frequency fluctuation is calculated by frequency nadir prediction model. Thirdly, an optimal dispatch model is formulated to maximize the delivered power of HVDC, which considers both operational and dynamic security constraints. Simulation results demonstrate that the proposed optimal dispatch model enhances system security with an acceptable reduction in delivered power.

关键词： Renewable energy sources Simulation HVDC transmission Training data Predictive models Stability analysis Security Voltage control Transient analysis Time-domain analysis

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A Novel Thermal Analysis Method for Tubular PM Linear Motors Based on Transfer Learning

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IEEE Transactions on Transportation Electrification 2025年第3期11卷 7379-7388页

作者： Wu, Tao Dai, Peipei Xue, Gang Guo, Youguang Lei, Gang Zhu, Jianguo Wang, Yifei China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China University of Technology Sydney School of Electrical and Data Engineering UltimoNSW2007 Australia The University of Sydney School of Electrical and Computer Engineering SydneyNSW2006 Australia

Thermal modeling and analysis are critical for permanent magnet linear synchronous motors (PMLSMs), particularly in multiphysical analysis and motor design optimization. This article proposes a new method for thermal steady state modeling and analysis based on transfer learning (TL), which combines the thermal mechanism model with the data-driven model to achieve high-precision temperature analysis with small number of finite element analysis (FEA) samples. First, an equivalent thermal circuit (ETC) model is established for PMLSMs based on the structural parameters and heat transfer principles. Second, a hybrid model based on TL is developed: the model is pretrained by a large sample set generated from the ETC in the source field (mechanism analytical model), and then, the trained model is transferred to the target field (FEA data-driven model). The parameters of the neural network in the target field are optimized by a few FEA sample sets with higher precision to improve the prediction accuracy of the target model. Experimental results demonstrate that the proposed method significantly boosts the performance of the deep neural network (DNN) model, particularly when training sets are small. The root mean square error (RMSE) of the DNN model decreases by 0.901 in the 5:5 (training/testing) dataset and 1.726 in the 2:8 dataset;and the corresponding R^{2} and maximal absolute error of motor performances are significantly decreased. The smaller the proportion of the training set, the better performance of the hybrid modeling will achieve. Furthermore, a temperature rise experiment conducted on a prototype motor validates the efficacy and precision of the proposed hybrid modeling methodology. © 2015 IEEE.

关键词： Permanent magnets

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Voltage and Frequency control Coordination Strategy of Wind-PV-ESS system for Black-Start

Voltage and Frequency Control Coordination Strategy of Wind-...

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IEEE International Conference on Power and Energy Applications (ICPEA)

作者： Haoda Lei Kuan Cao Runjia Sun Yutian Liu Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong University Jinan China

ISBN: (数字)9798350356113

ISBN: (纸本)9798350356120

In blackout scenarios of new power systems, wind farms (WF) and photovoltaic power stations (PVS) configured with scaled energy storage system (ESS) can be used as sources to support the black-start of thermal power units. Aiming at the disturbance of system voltage and frequency during black-start, a voltage and frequency control coordination strategy of Wind-PV-ESS system for black-start is proposed. The voltage control strategy includes normal control mode and correction control mode. When the bus voltage keeps within the limit, the former coordinately distributes the reactive power of each WT and PVA to maximize the dynamic reactive power reserve. After the bus voltage is disturbed beyond the limit, the latter makes full use of the fast dynamic reactive power response characteristics of SVG and ESS to provide voltage support. In the frequency control strategy. the reference power generated by the PI controller is allocated according to the proportion of the reserve power of each wind turbine (WT) or photovoltaic array (PVA) to the total reserve to eliminate the frequency deviation. Simulation results show the effectiveness of the proposed strategy.

关键词： Photovoltaic systems Reactive power PI control Simulation Power system dynamics Wind farms Wind turbines Voltage control Frequency control Radio spectrum management

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A Distributionally Robust Optimal Allocation Method of Energy Storage for Resilience Enhancement of Distribution Network

A Distributionally Robust Optimal Allocation Method of Energ...

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2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023

作者： Cao, Kuan Liu, Yutian Wang, Chunyi Shandong University Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Jinan China State Grid Shandong Electric Power Company Development Planning Department Jinan China

ISBN: (纸本)9781665471640

To solve the problem of active power imbalance in distribution network with high penetrations of photovoltaic (PV) under extreme weather, the paper proposes a distributionally robust optimal allocation method of energy storage considering extreme scenarios. Firstly, considering that the effect of energy storage on enhancing the resilience of distribution network is strongly related to its location, resilience indexes based on node evaluation are established to select the installation sites of energy storage. Secondly, in view of the correlation and randomness of PV output and load, the typical scenarios with the empirical probability distribution are generated based on Frank Copula joint probability distribution function. Then, Kullback-Leibler divergence is utilized to characterize the probability distribution ambiguity set of the typical scenarios. Based on the ambiguity set, a two-stage distributionally robust optimization model for energy storage capacity is established to minimize the cost of energy storage investment and distribution network operation. For further improving the resilience, an operational resilience index as a chance constraint considering extreme scenarios is embedded into the model. Finally, the case study shows that the proposed method can effectively improve PV accommodation capacity of distribution network and reduce load loss in extreme scenarios. © 2023 IEEE.

关键词： copula function distributionally robust optimization energy storage allocation Kullback-Leibler divergence resilience

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Energy Storage Configuration Method for Distribution Network Resilience Enhancement Using Two-Stage Game Optimization 8

Energy Storage Configuration Method for Distribution Network...

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8th International Conference on Power and Renewable Energy, ICPRE 2023

作者： Liu, Shuai Li, Yuwen Zhou, Heng Cao, Kuan Liu, Yutian State Grid Weihai Power Supply Company Development Planning Department Weihai China Shandong University Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Jinan China

ISBN: (纸本)9798350328813

In order to cope with the impact of extreme weather on distribution network, a two-stage multi-objective game optimization-based method of energy storage configuration for distribution network resilience enhancement is proposed in this paper. First, typical scenarios are generated based on N-k contingency as well as distributed photovoltaic (DPV) output characteristics, and evaluation indexes of resilience and economy are established. Then a two-stage multi-objective optimal configuration model is proposed, considering the operating state and resilience demand of distribution network before and after the power supply transfer. Finally, the model is solved by multi-objective particle swarm (MOPSO) and Nash game. Simulation results on the modified IEEE33-node model demonstrate that the proposed method can achieve good equilibrium solution between resilience and economy. © 2023 IEEE.

关键词： MOPSO Nash game power supply transfer resilience two-stage optimal configuration

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Detection of Termite Nests in Dams Using Ground Penetrating Radar Based on Deep Learning 20

Detection of Termite Nests in Dams Using Ground Penetrating ...

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20th International Conference on Ground Penetrating Radar, GPR, 2024

作者： Peng, Jiao Xue, Wei Wang, Wenkai Song, Junlei Zhou, Feng Huang, Pengchun Zhang, Zhixuan Zhang, Feng Zhang, Xu Chen, Longjia Sun, Li School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China Dam Safety Monitoring and Termite Control Center of Hubei Provincial Department of Water Resources China Key Laboratory of Termite Control Ministry of Water Resources China School of Mechanical Engineering and Electronic Information China University of Geosciences Wuhan China

Termites have always been one of the major hidden dangers to the operational safety of dams. Traditional detection methods such as manual inspection and termite baiting have limitations in terms of efficiency and accuracy. To solve the problems, paper proposes a termite nest detection method based on ground penetrating radar (GPR) and deep learning. First, the simulated GPR data are generated using gprMax software, and the real data are collected using a GPR at several dams. Subsequently, the YOLOv8 deep neural network is employed for termite nest recognition in GPR images. The experimental results show that the proposed model can achieve the mean average precision (mAP) of 0.96 and a detection speed of 53.76 frames per second (FPS). © Published under licence by IOP Publishing Ltd.

关键词： Deep neural networks

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Short-Term Wind Power Forecasting Based on VMD-QPSO-LSTM

Short-Term Wind Power Forecasting Based on VMD-QPSO-LSTM

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Power Electronics, Computer Applications (ICPECA), IEEE International Conference on

作者： Peiqi Xin Hongtao Wang Haobo Wang Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong University Jinan China

The unpredictable and highly variable nature of wind power generation demands advanced predictive models to enhance the operational stability of power systems under non-stationary wind conditions. This study presents an innovative approach for short-term wind power prediction, ingeniously integrating Variational Mode Decomposition (VMD), Quantum Particle Swarm Optimization (QPSO), and Long Short-Term Memory (LSTM) network into a cohesive forecasting methodology. Initially, VMD is applied to deconstruct wind power data into distinct sub-modes, thus reducing data complexity and facilitating more accurate analysis. The LSTM model is then fine-tuned using QPSO to refine its hyperparameters, thereby optimizing forecast performance. Each sub-mode is forecasted through a specialized LSTM model, and the collective forecasts are compounded to form the final prediction. Comparative analyses with conventional LSTM, PSO-enhanced LSTM, and QPSO-LSTM models reveal that our proposed VMD-QPSO-LSTM approach yields significantly higher accuracy in forecasting, highlighting its potential to efficiently navigate the uncertainties associated with wind power generation.

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