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A Joint Estimation Method of SOC and SOH for Lithium-ion Battery Considering Cyber-Attacks Based on GA-BP

Computers, Materials & Continua 2024年第9期80卷 4497-4512页

作者： Tianqing Yuan Na Li Hao Sun Sen Tan Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of EducationNortheast Electric Power UniversityJilin132012China Department of Electrical Engineering Northeast Electric Power UniversityJilin132012China College of Information and Control Engineering Jilin Institute of Chemical TechnologyJilin132022China Center for Research on Microgrids Department of EnergyAalborg UniversityAalborg9220Denmark

To improve the estimation accuracy of state of charge(SOC)and state of health(SOH)for lithium-ion batteries,in this paper,a joint estimation method of SOC and SOH at charging cut-off voltage based on genetic algorithm(GA)combined with back propagation(BP)neural network is proposed,the research addresses the issue of data manipulation resulting ***,anomalous data stemming fromcyber-attacks are identified and eliminated using the isolated forest algorithm,followed by data ***,the incremental capacity(IC)curve is derived fromthe restored data using theKalman filtering algorithm,with the peak of the ICcurve(ICP)and its corresponding voltage serving as the health factor(HF).Thirdly,the GA-BP neural network is applied to map the relationship between HF,constant current charging time,and SOH,facilitating the estimation of SOH based on ***,SOC estimation at the charging cut-off voltage is calculated by inputting the SOH estimation value into the trained model to determine the constant current charging time,and by updating the maximum available *** show that the root mean squared error of the joint estimation results does not exceed 1%,which proves that the proposed method can estimate the SOC and SOH accurately and stably even in the presence of false data injection attacks.

关键词： Lithium-ion batteries state of charge state of health cyber-attacks genetic algorithm back propagation neural network

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Influence of Thermal Conductivity of Multi-scale Particles Epoxy Composites on High-Frequency electrical Insulation Property

Influence of Thermal Conductivity of Multi-scale Particles E...

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IEEE International Conference on Dielectrics (ICD)

作者： Yan-Hui Song Zhi-Min Dang Department of Electrical Engineering State Key Laboratory of Power System Operation and Control Tsinghua University Beijing China

ISBN: (数字)9798350308976

ISBN: (纸本)9798350308983

Epoxy, with excellent electrical insulation properties, processing performance, and low price, is commonly used for transformers (110kV and below) and power electronic transformers (high-frequency transformers) as the primary insulating material. Since the thermal conductivity of epoxy resins is only 0.2 W/(m·K), the electrical insulation properties decline significantly at high frequency. Preparing epoxy composites with high thermal conductivity and excellent electrical insulation at high frequencies is a great challenge for industrial production. It is considered that SiO 2 (9 eV), BN (5.9 eV), and Al 2 O 3 (7.2-8.8 eV) are thermally conductive particles with good electrical insulation properties. The materials filled with different micron-scale particles have higher thermal conductivity than the nanofilled materials. In this study, 5 μm SiO 2 , 25 μm BN, and 40 μm Al 2 O 3 were used as fillers, and the filler ratios were calculated according to the Dinger-Funk model, focusing on the effects of the variation of filler content on thermal conductivity and electrical insulation properties. The thermal conductivity of the epoxy composites improved with the increase of filler content up to 1.003 W/(m·K), while the AC breakdown field strength gradually decreased. However, the breakdown time at 20 kHz, 35 kV/mm bipolar square wave appeared to increase and then decrease with the increase of content. Therefore, the optimum content of fillers allows the preparation of efficient epoxy composites as insulating materials for power electronic transformers. This study provides ideas for designing thermally conductive composites operating at high frequency and high voltage fields.

关键词： Silicon compounds Insulation Electric breakdown High-voltage techniques Conductivity Thermal conductivity power electronics

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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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MCMC Wind power Sequence Modeling Method Considering Climbing Direction

MCMC Wind Power Sequence Modeling Method Considering Climbin...

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

作者： Hao, Ling Xu, Fei Chen, Lei Chen, Qun Min, Yong Gu, Yi Chang, Yiming State Key Laboratory of Power System Operation and Control Department of Electrical Engineering Tsinghua University Beijing100084 China Tsinghua University Key Laboratory for Thermal Science and Power Engineering of Ministry of Education Department of Engineering Mechanics Beijing100084 China State Grid Liaoning Electric Power Co. Ltd Liaoning 110055 China

ISBN: (纸本)9798350311358

Since the traditional Markov Chain Monte Carlo (MCMC) method has the problem that the wind power is stuck in a certain state and it is difficult to jump, this paper proposes an improved MCMC method considering the climbing direction, first dividing the state by the size of the output and the climbing direction, and then determining the duration of each state by changing the state, and then sampling and generating the corresponding number of output samples according to the duration of each state, and sorting these samples according to the climbing direction, and the sorted output is the output timing of the state. This method was used to generate wind power sequences from a wind farm in the Northeast China Sea, and the characteristics of a wind power sequence were compared and analyzed with the original wind power sequence, and the results were better than the wind power sequences generated by the traditional MCMC method. © 2023 IEEE.

关键词： Wind power

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Age-of-information-aware PI controller for load frequency control

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Protection and control of Modern power systems 2023年第3期8卷 32-46页

作者： Dan Jiao Changzheng Shao Bo Hu Kaigui Xie Chengrong Lin Zhuoxuan Ju State Key Laboratory of Power Transmission Equipment and System Security Chongqing UniversityChongqing 400044China Department of Electrical and Computer Engineering George Washington UniversityDC 20052 WashingtonUSA

Open communication system in modern power systems brings concern about information staleness which may cause power system frequency *** information staleness is often characterized by communication ***,communication delay is a packet-centered metric and cannot reflect the requirement of information freshness for load frequency control(LFC).This paper introduces the age of information(AoI),which is more compre-hensive and informative than the conventional communication delay modeling *** LFC controller and com-munication are integrated into the design for LFC performance *** AoI-aware LFC model is formulated first,and considering each allowable update period of the smart sensor,different AoI-aware PI controllers are then designed according to the exponential decay *** right AoI-aware controller and update period are selected according to the degree of frequency fluctuation of the power *** studies are carried out on one-area and two-area power *** results show the superior performance of the AoI-aware controllers in comparison to the delay-dependent controllers.

关键词： power system Load frequency control(LFC) Age of information(AoI) PI controller

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Efficient economic operation based on load dispatch of power systems using a leader white shark optimization algorithm

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Neural Computing and Applications 2024年第18期36卷 10613-10635页

作者： Hassan, Mohamed H. Kamel, Salah Selim, Ali Shaheen, Abdullah Yu, Juan El-Sehiemy, Ragab Ministry of Electricity and Renewable Energy Cairo Egypt Department of Electrical Engineering Faculty of Engineering Aswan University Aswan81542 Egypt Department of Electrical Power Engineering Faculty of Engineering Suez University Suez43533 Egypt State Key Laboratory of Power Transmission Equipment and System Security and New Technology College of Electrical Engineering Chongqing University Chongqing China Department of Electrical Engineering Faculty of Engineering Kafrelsheikh University Kafrelsheikh33516 Egypt

This article proposes the use of a leader white shark optimizer (LWSO) with the aim of improving the exploitation of the conventional white shark optimizer (WSO) and solving the economic operation-based load dispatch (ELD) problem. The ELD problem is a crucial aspect of power system operation, involving the allocation of power generation resources to meet the demand while minimizing operational costs. The proposed approach aims to enhance the performance and efficiency of the WSO by introducing a leadership mechanism within the optimization process, which aids in more effectively navigating the complex ELD solution space. The LWSO achieves increased exploitation by utilizing a leader-based mutation selection throughout each generation of white sharks. The efficacy of the proposed algorithm is tested on 13 engineer benchmarks non-convex optimization problems from CEC 2020 and compared with recent metaheuristic algorithms such as dung beetle optimizer (DBO), conventional WSO, fox optimizer (FOX), and moth-flame optimization (MFO) algorithms. The LWSO is also used to address the ELD problem in different case studies (6 units, 10 units, 11 units, and 40 units), with 20 separate runs using the proposed LWSO and other competitive algorithms being statistically assessed to demonstrate its effectiveness. The results show that the LWSO outperforms other metaheuristic algorithms, achieving the best solution for the benchmarks and the minimum fuel cost for the ELD problem. Additionally, statistical tests are conducted to validate the competitiveness of the LWSO algorithm. © The Author(s) 2024.

关键词： Electric load dispatching

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Dynamic state Estimation for DFIG with Unknown Inputs Based on Cubature Kalman Filter and Adaptive Interpolation

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Journal of Modern power systems and Clean Energy 2023年第4期11卷 1086-1099页

作者： Maolin Zhu Hao Liu Junbo Zhao Bendong Tan Tianshu Bi Samson Shenglong Yu State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power UniversityBeijingChina Department of Electrical and Computer Engineering University of ConnecticutStorrs06269USA School of Engineering Deakin University75 Pigdons RoadWaurn PondsVIC3216Australia

Dynamic state estimation(DSE)accurately tracks the dynamics of power systems and demonstrates the evolution of the system state in real *** paper proposes a DSE approach for a doubly-fed induction generator(DFIG)with unknown inputs based on adaptive interpolation and cubature Kalman filter(AICKF-UI).DFIGs adopt different control strategies in normal and fault conditions;thus,the existing DSE approaches based on the conventional control model of DFIG are not applicable in all ***,the DSE model of DFIGs is reformulated to consider the converter controller outputs as unknown inputs,which are estimated together with the DFIG dynamic states by an exponential smoothing model and augmented-state cubature Kalman ***,as the reporting rate of existing synchro-phasor data is not sufficiently high to capture the fast dynamics of DFIGs,a large estimation error may occur or the DSE approach may *** this end,in this paper,a local-truncation-error-guided adaptive interpolation approach is *** simulations conducted on a wind farm and the modified IEEE 39-bus test system show that the proposed AICKF-UI can(1)effectively address the divergence issues of existing cubature Kalman filters while being computationally more efficient;(2)accurately track the dynamic states and unknown inputs of the DFIG;and(3)deal with various types of system operating conditions such as time-varying wind and different system faults.

关键词： Adaptive interpolation cubature Kalman filter doubly-fed induction generator(DFIG) dynamic state estimation unknown input

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Study on partial discharge of transformer based on wide frequency equivalent circuit 8

Study on partial discharge of transformer based on wide freq...

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8th IEEE International Conference on Energy Internet, ICEI 2024

作者： Jiang, Changming Zhou, Yuanxiang Li, Ping Tang, Yunpeng He, Zhiyang Li, Huan Chen, Jianning Mu, Ka State Grid Corporation of China North China Branch Bejing China Tsinghua University State Key Laboratory of Power System Operation and Control Department of Electrical Engineering Bejing China State Grid Corporation of China Bejing China Electric Power Research Institute of State Grid Jibei Electric Power Company Limited Bejing China

ISBN: (纸本)9798331523558

When partial discharge occurs in the transformer, it will produce higher frequency pulse current, high frequency phenomenon will enhance the circuit capacitance effect, making the circuit bandwidth, in order to detect the local discharge signal so as to realize the transformer for equipment monitoring, the establishment of the transformer broadband equivalent circuit model, the propagation law of partial discharge pulse current signal in the transformer is investigated, and the effects of four denoising methods are compared under different evaluation indexes;finally, a typical transformer partial discharge condition is studied, and the validity of the transformer partial discharge research based on the wide-band equivalent circuit is verified. © 2024 IEEE.

关键词： Partial discharges

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Spatio-temporal Granularity Co-optimization Based Monthly Electricity Consumption Forecasting

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CSEE Journal of power and Energy systems 2023年第5期9卷 1980-1984页

作者： Kangping Li Yuqing Wang Ning Zhang Fei Wang Chunyi Huang the College of Smart Energy Shanghai Jiao Tong UniversityShanghai 200240China the Department of Electrical Engineering Tsinghua UniversityBeijing 100084China the Department of Electrical Engineering North China Electric Power UniversityBaoding 071003China the State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources(North China Electric Power University) Beijing 102206China the Hebei Key Laboratory of Distributed Energy Storage and Microgrid(North China Electric Power University) Baoding 071003China the Key Laboratory of Control of Power Transmission and Conversion Ministry of Education(Shanghai Jiao Tong University)Shanghai 200240China

Monthly electricity consumption forecasting(ECF)plays an important role in power system operation and electricity market *** popularity of smart meters enables collection of fine-grained load data,which provides an opportunity for improvement of monthly ECF *** this letter,a spatio-temporal granularity co-optimization-based monthly ECF framework is proposed,which aims to find an optimal combination of temporal granularity and spatial clusters to improve monthly ECF *** framework is formulated as a nested bi-layer optimization problem.A grid search method combined with a greedy clustering method is proposed to solve the optimization *** of the proposed method has been verified on a real smart meter dataset.

关键词： Electricity consumption forecasting Greedy clustering Grid searching Spatiotemporal

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Cooperative Prediction Approach for the Small-Signal Stability Margin of Renewable power system Based on Vertical Federated Learning 3

Cooperative Prediction Approach for the Small-Signal Stabili...

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3rd International Conference on power systems and electrical Technology, PSET 2024

作者： Lai, Xiang Chen, Zhihao Liu, Yun Dai, Tianchen Cai, Haiqing Gu, Haohan Chen, Wei South China University of Technology School of Electric Power Engineering Guangzhou China CSG Electric Power Research Institute State Key Laboratory of HVDC Guangzhou China Laboratory of Intelligent Operation and Control for New Energy Power System Guangdong Provincial Key Guangzhou China

ISBN: (纸本)9798350353204

Since sub-synchronous oscillations have been frequently witnessed in large-scale wind farms under weak grid conditions, it is significant to predict the critical stability situations in short term so that timely preventative actions can be taken to avoid potential small-signal stability (SS) risks. To this end, this paper proposes a SS prediction approach based on the grid strength metric of generalized short-circuit ratio (gSCR) under the mechanism of vertical federated learning. Specifically, the improved ridge regression prediction model is employed and its corresponding distributed training and the distributed predicting are derived by utilizing the separable characteristic of gradient vector calculation in the context of federated learning. On this basis, the original largescale predicting problem is decomposed into a large number of small-scale subproblems, and each subproblem is calculated locally by the corresponding wind farm. The feature data information of adjacent wind farms can be effectively used to improve the accuracy of the prediction of the stability margin while ensuring the data privacy and security of all participants. Finally, simulation results illustrate the effectiveness and efficiency of the proposed approach under various scenarios. © 2024 IEEE.

关键词： Federated learning

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