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Advances in Electrical, Electronics and Computing Technology (EECT), International Conference on

作者： Chang Xu Mingqiang Wang Zihan Wang Yizhe Zhang Jialiang Wang Fengbin Sun Key Laboratory of Power System Intelligent Dispatch and Control Ministry of Education Shandong University Jinan China

ISBN: (数字)9798331541545

ISBN: (纸本)9798331541552

The widespread integration of renewable energy presents significant challenges for power system scheduling. Traditional power system scheduling is mainly based on the concept of “coordinating on a multi-time-level and gradually refining at each level,” using a sequential scheduling framework. However, with the growing integration of renewable energy, the uncertainty in power systems has significantly increased, leading to severe issues such as conflicts in the coordination between different modules. This paper first introduces the scheduling framework of the traditional sequential scheduling model. Then, addressing the problems of module coordination and insufficient flexibility, an integrated multi-time-scale scheduling model is proposed. This model combines day-ahead and intraday scheduling models, optimizing decision variables including unit start/shut states, operation points, and reserves simultaneously to maximize the collaborative potential between different scheduling modules. After linearization, the proposed model is converted into a mixed-integer linear programming (MILP) model, which can be solved directly using commercial optimization solvers. Finally, case analyses demonstrate the effectiveness and superiority of the proposed integrated model compared to the traditional sequential scheduling model.

关键词： Renewable energy sources Analytical models Uncertainty Fluctuations Processor scheduling Refining Regulation power systems power system reliability Load modeling

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Optimal Scheduling Considering Multiple Reserves Based on Active power Curtailment Operation of Wind power

Optimal Scheduling Considering Multiple Reserves Based on Ac...

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Advances in Electrical, Electronics and Computing Technology (EECT), International Conference on

作者： Zihan Wang Mingqiang Wang Chang Xu Jialiang Wang Yizhe Zhang Caishuai Liang Key Laboratory of Power System Intelligent Dispatch and Control Ministry of Education Shandong University Jinan China

ISBN: (数字)9798331541545

ISBN: (纸本)9798331541552

Under the current general trend of the power industry's transformation towards clean energy, wind power generation, as an important form of renewable energy utilization, is steadily increasing its proportion in the power system. However, the inherent volatility and uncertainty of wind power bring numerous challenges to the stable operation of the power system. Firstly, based on the traditional unit commitment model, this paper comprehensively considers the capacity reserve and ramping reserve of the power system, and constructs a unit commitment model that considers the supply and demand constraints of both types of reserves simultaneously. Then, on the basis of this model, an in-depth and comprehensive discussion is carried out, focusing on the key operation strategy of the active power curtailment operation of wind power. This not only helps to improve the power system's ability to accommodate wind power, but also enables a better response to the challenges of volatility and uncertainty brought by wind power to the power system, thus ensuring the safe, stable and economic operation of the power system. Finally, the model is transformed into a mixed-integer linear programming model that can be directly called by the commercial solver CPLEX, and the effectiveness of the model is verified by a modified IEEE 26-node distribution network.

关键词： Uncertainty Fluctuations Costs Supply and demand power supplies Wind power generation power system stability Stability analysis power system reliability Wind turbines

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Robust Consensus Based Secondary control for Microgrids Considering Communication Delay and Noises: A Kalman Filter Based Approach

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IEEE Transactions on Smart Grid 2025年

作者： Zhao, Qi Zhang, Wen Zhang, Tingting Chen, Jian Ye, Hua Shandong University Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education School of Electrical Engineering Jinan250061 China Shandong University Shandong Research Institute of Industrial Technology Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education School of Electrical Engineering Jinan250061 China

To achieve the frequency and voltage regulation as well as active and reactive power sharing, the consensus-based secondary controls of microgrids rely on communication network to exchange signals between neighbor inverters, which may be affected by the communication delay and noises. Unlike the existing methods that focus solely on communication delay, this paper proposes a Kalman filter-based robust consensus control to address the communication delay and noises comprehensively. Based on the discrete differential equation of the signal transmitted via communication network, the multi-step prediction model of the delayed signal is firstly built by considering the prediction errors. The state space model is then constructed by taking the predicted signals at each step as the state variables while the delayed and noise-involved signal and its differential value are seen as the measurements. A Kalman filter is further designed to comprehensively handle the communication delay, noises and prediction errors to estimate the true signal generated from neighbor inverters. With the estimated signals of all neighbor inverters, the consensus control is performed to coordinate the grid-following and grid-forming inverters within the microgrid. The effectiveness of the proposed method is demonstrated with simulations and experiments performed on the modified IEEE-33 bus-based microgrid. © 2010-2012 IEEE.

关键词： Kalman filters

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Deep Learning and Projection Neural Network With Finite-Time Convergence for Energy Management of Multi-Energy system

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IEEE Transactions on Smart Grid 2025年第3期16卷 2156-2168页

作者： Liu, Xueying He, Xing Li, Chaojie Huang, Tingwen Southwest University Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Chongqing400715 China North China Electric Power University State Key Laboratory of Alternate Electrical Power System With Renewable Energy Sources Beijing102206 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

In this paper, an approach based on projection neural network (PNN), sliding mode control technique, and deep learning is proposed to solve the energy management problem of multi-energy systems (MES) containing dynamic parameters. First, the sliding mode technique is introduced in the PNN to design a finite-time PNN (FTPNN). The stability and finite-time convergence of the proposed FTPNN are proved by the Lyapunov method and the setting time bound is given. Then, the deep FTPNN (DFTPNN) is designed by combining deep learning with the proposed FTPNN. The dynamic parameters in the MES that change over time are used as input variables for the DFTPNN, allowing the trained DFTPNN to respond immediately to changes in the dynamic parameters and to predict the solutions of the FTPNN with different parameters directly. Simulation experiments show that FTPNN has faster convergence compared to PNN. DFTPNN significantly reduces the computation time compared to FTPNN. DFTPNN provides predicted solutions to FTPNN. Since DFTPNN can respond immediately to changes in dynamic parameters and directly provide energy management strategies under different parameters, it can adapt to changing environments and promote the economic and stable operation of MES. © 2010-2012 IEEE.

关键词： Cogeneration Heuristic algorithms Convergence Deep learning Prediction algorithms Optimization Heating systems Energy management Approximation algorithms power system stability

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Turbulent drag reduction by sector-shaped counter-flow dielectric barrier discharge plasma actuator

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Chinese Physics B 2025年第2期34卷 362-370页

作者： Borui Zheng Shaojie Qi Minghao Yu Jianbo Zhang Linwu Wang Dongliang Bian School of Automation and Information Engineering Xi’an University of TechnologyXi’an 710048China School of Mechanical and Precision Instrument Engineering Xi’an University of TechnologyXi’an 710048China Science and Technology on Plasma Dynamics Laboratory Air Force Engineering UniversityXi’an 710038China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an 710048China Shaanxi University Key Laboratory of Photonic Power Devices and Discharge Regulation Xi’an 710048China

The primary objective in aircraft transportation is to minimize turbulent drag, thereby conserving energy and reducing emissions. We propose a sector-shaped counter-flow dielectric barrier discharge plasma actuator, which leverages jet synthesis for drag reduction. A drag control experiment was conducted in a low-speed wind tunnel with a controlled flow velocity of 9.6 m/s(Re = 1.445 × 10^(4)). This study investigated the effects of varying pulse frequencies and actuation voltages on the turbulent boundary layer. Using a hot-wire measurement system, we analyzed the pulsating and time-averaged velocity distributions within the boundary layer to evaluate the streamwise turbulent drag reduction. The results show that the local TDR decreases as the pulse frequency increases, reaching a maximum reduction of approximately 20.97% at a pulse frequency of 50 Hz. In addition, as the actuation voltage increases, the friction coefficient decreases, increasing the drag reduction rate. The maximum drag reduction of approximately 33.34% is achieved at an actuation voltage of 10 kV.

关键词： plasma flow control turbulent boundary layer turbulent drag reduction

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Prediction of Distributed Photovoltaic Accommodation Capability Based on Ridge Regression 9th

Prediction of Distributed Photovoltaic Accommodation Capabil...

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9th Purple Mountain Form on Smart Grid Protection and control, PMF 2024

作者： Ji, Xingquan Zhang, Bowen Zhang, Yumin Sun, Pengkai Zhang, Xiaofeng Liu, Wei dong Shandong University of Science and Technology Qingdao266590 China Key Laboratory of Power System Intelligent Dispatch and Control Shandong University Jinan250061 China Information and Communication Company State Grid Shandong Electric Power Company Jinan250013 China Qingdao Yihe Electric Group Co. Ltd. Qingdao266510 China

ISBN: (纸本)9789819616701

A prediction method of distributed photovoltaic accommodation capability is proposed based on ridge regression. First, the factors influencing the distributed photovoltaic accommodation capacity are analyzed, and the concept of the contribution degree of distributed photovoltaic accommodation is introduced. The gray relation analysis method is used to evaluate the correlation between different factors and distributed photovoltaic accommodation, and a model of factors affecting distributed photovoltaic accommodation is constructed. Then, a prediction model of distributed photovoltaic accommodation capability is established based on ridge regression. The factors with high photovoltaic accommodation contribution degrees are incorporated in the prediction model. The mapping relationship between the driving factors of distributed photovoltaic accommodation and the prediction model is derived, then the development strategies for future photovoltaic accommodation capacity are formulated. Finally, simulation is performed on the SPSSPRO platform, and the results show that the proposed method can predict the distributed photovoltaic accommodation capability accurately and provide strategic suggestions for improving future photovoltaic accommodation capacity. © State Grid Electric power 2025.

关键词： Photovoltaics

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Cascading Failure Screening Based on Gradient Boosting Decision Tree for HVDC Sending-End systems with High Wind power Penetration

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IEEE Transactions on power systems 2025年

作者： Liu, Tianhao Yan, Jiongcheng Liu, Yutian Chung, Chi Yung Shandong University Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Jinan China The Hong Kong Polytechnic University Department of Electrical and Electronic Engineering Kowloon Hong Kong

In LCC-HVDC sending-end AC systems, cascading failures combined with the dynamic response of wind turbines (WTs) can lead to HVDC commutation failures. The resulting transient voltage disturbances cause WT tripping in sending-end systems. Cascading failures that involve the interaction between WTs and HVDC significantly limit the wind power transmitted by HVDC systems. This paper proposes an online cascading failure screening method based on gradient boosting decision tree (GBDT) for HVDC sending-end systems with large-scale WTs. First, a confidence level-based WT tripping model is proposed for cascading failure risk assessment considering a typical cascading failure propagation pattern. Then, Monte Carlo tree search is improved using a contrastive pruning technique to generate evenly distributed samples of cascading failures offline. Dynamic insecure scenarios are quickly identified using an improved support vector machine. Finally, GBDT is utilized to screen for cascading failures online by predicting subsequent high-risk failures using operating features. A dynamic weighting technique is proposed for GBDT to improve the fault prediction accuracy. Simulation results of a modified New England test system and the Ningxia provincial power grid in western China demonstrate that the proposed method can quickly screen for cascading failures considering the dynamic interaction between WTs and HVDC. © 1969-2012 IEEE.

关键词： Risk assessment

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Small Signal Stability-Guaranteed Moving Target Defense Against Load Redistribution Attack On IoT-based Smart Grid

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IEEE Internet of Things Journal 2025年第11期12卷 17413-17429页

作者： Wang, Bingdong Zhang, Zhenyong Wang, Mufeng Liu, Mengxiang Ye, Rui Deng, Ruilong Zhang, Xin Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China China Industrial Control Systems Cyber Emergency Response Team Beijing100040 China University of Sheffield Department of Automatic Control and System Engineering Sheffield United Kingdom Guizhou Power Grid Co. Ltd. Intelligent Operation Center China Zhejiang University State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Hangzhou310027 China Huzhou Institute of Industrial Control Technology Huzhou313000 China

Moving target defense (MTD) is a promising approach to defend against load redistribution attacks on the internet-of-things (IoT)-based smart grid networks by probing the distorted state estimates with the distributed flexible AC transmission system. However, existing studies mainly focus on optimizing the performance of MTD and ignore the safety effect of it on the system’s operation. In this paper, we fill this gap by deeply analyzing the effect of MTD on the small signal stability and aim to alleviate the negative impact and guarantee its defending performance simultaneously. Firstly, the stability is formally described using the eigenvalue sensitivity. The relationship between the MTD-induced perturbation (MTDper) and the stability criteria is derived. Secondly, a new indicator is proposed to measure the effectiveness of MTDper. Thirdly, a constrained optimization problem is formulated to compute the bound of MTDper for guaranteeing the small signal stability. In addition, a surprising finding is that the stability margin can be improved and enhanced by optimizing the value of MTDper without losing the MTD’s effectiveness. Finally, we evaluate the performance of MTDper and its impact on the small signal stability with extensive simulations on the IEEE 30-bus, 39-bus, and 68-bus test power systems. © 2014 IEEE.

关键词： Constrained optimization

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Multi-Objective Coordination and Stability control of High Proportion Wind power system Based on K-Domain Tuning Damping 19th

Multi-Objective Coordination and Stability Control of High P...

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19th Annual Conference of China Electrotechnical Society, ACCES 2024

作者： Zhang, Qingtao Gao, Diju Zhang, Dan Cai, Haiqing Gu, Haohan Chen, Wei Wu, Wencong Chen, Zhihao Institute of Logistics Engineering Shanghai Maritime University Shanghai China Institute of Logistic Science and Engineering Shanghai Maritime University Shanghai China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guangzhou China State Key Laboratory of HVDC Electric Power Research Institute China Southern Power Grid Guangzhou China National Energy Power Grid Technology R&D Centre Guangzhou China CSG Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid Guangzhou China

ISBN: (纸本)9789819608966

With the further development of "double high" characteristics of power system, the traditional stability problem dominated by synchronous generator has gradually evolved into a new stability problem dominated by power electronic equipment control. In addition, different types of failure problems can occur in wind power systems. In order to achieve multi-objective coordinated and stable operation of high proportion wind power system, a damping control method with parameters tuning is proposed, which includes the design of filtering link and proportional amplification link. Among them, the key parameters of the proportional link are adjusted. Finally, the feasibility of the proposed control method is verified by simulation tests. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Electric power system stability

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Enhancing Network Abnormal Detection With NMF-SECNN: Leveraging Deep Feature Learning for High-Precision Traffic Analysis

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IEEE Transactions on Network Science and Engineering 2025年第3期12卷 2069-2080页

作者： Yuan, Yazhou Yu, Ning Zheng, Zhuolin Yang, Yong Ma, Kai Liu, Zhixin Chen, Cailian Zhang, Jianmin Yanshan University Institute of Electrical Engineering Qinhuangdao066004 China Yanshan University Key Laboratory of Intelligent Control and Neural Information Processing of the Ministry of Education of China Qinhuangdao066004 China State Grid Handan Electric Power Supply Company Hebei 056000 China Shanghai Jiao Tong University Department of Automation Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China Baosteel Research Institute Automation Department Shanghai200240 China

Detection of abnormalities in industrial network traffic plays a crucial role in maintaining network system security. However, current abnormal detection models suffer from low precision, and extracting deep-level feature information from industrial network traffic is difficult. This leads to the loss of partial feature information during the detection process, thereby affecting detection efficiency. To address this issue, this paper proposes an abnormal traffic detection framework for industrial networks. By employing a Non-negative Matrix Factorization (NMF)-based method for extracting abnormal traffic features and optimizing the NMF decomposition process through constructing label consistency constraints, we facilitate effective feature extraction. Additionally, the Squeeze-and-Excitation attention mechanism is introduced into a Convolutional Neural Network (CNN) to construct a classifier that enhances detection precision without increasing complexity, enabling efficient identification of complex network traffic patterns. This results in the NMF-Squeeze-and-Excitation-CNN (NMF-SECNN) model, which combines effective feature extraction capability with a lightweight structural design, achieving superior detection performance in industrial network environments. The proposed method achieves a detection accuracy of 99.4%, representing a 5.6% improvement over baseline methods, and a recall rate of 98.2%, showcasing the model's capability to identify abnormalities across diverse scenarios. Various classification metrics confirm the model's robustness and effectiveness, demonstrating its significant advantages over traditional methods. © 2013 IEEE.

关键词： Non-negative matrix factorization

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