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A mixed-integer linear programming approach for robust state estimation

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Journal of Modern Power Systems and Clean Energy 2014年第4期2卷 366-373页

作者： yanbo chen Jin MA State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power UniversityBeijing 102206China School of Electrical and Information Engineering University of SydneySydneyNSW 2006Australia

In this paper,a mixed integer linear programming(MILP)formulation for robust state estimation(RSE)is *** using the exactly linearized measurement equations instead of the original nonlinear ones,the existingmixed integer nonlinear programming formulation for RSE is converted to a MILP *** proposed approach not only guarantees to find the global optimum,but also does not have convergence *** results on a rudimentary 3-bus system and several IEEE standard test systems fully illustrate that the proposed methodology is effective with high efficiency.

关键词： State estimation Robustness Leverage point Mathematical programming Mixed integer linear programming(MILP)

Data-driven Power Flow Method Based on Exact Linear Regression Equations

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Journal of Modern Power Systems and Clean Energy 2022年第3期10卷 800-804页

作者： yanbo chen Chao Wu Junjian Qi State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Electrical&Electronic EngineeringNorth China Electric Power UniversityBeijing 102206China Department of Electrical and Computer Engineering Stevens Institute of TechnologyHobokenNJ 07030USA

Power flow(PF)is one of the most important calculations in power *** widely-used PF methods are the Newton-Raphson PF(NRPF)method and the fast-decoupled PF(FDPF)*** smart grids,power generations and loads become intermittent and much more uncertain,and the topology also changes more frequently,which may result in significant state shifts and further make NRPF or FDPF difficult to *** address this problem,we propose a data-driven PF(DDPF)method based on historical/simulated data that includes an offline learning stage and an online computing *** the offline learning stage,a learning model is constructed based on the proposed exact linear regression equations,and then the proposed learning model is solved by the ridge regression(RR)method to suppress the effect of data *** online computing stage,the nonlinear iterative calculation is not *** results demonstrate that the proposed DDPF method has no convergence problem and has much higher calculation efficiency than NRPF or FDPF while ensuring similar calculation accuracy.

关键词： Data driven exact linear regression equation Fast-decoupled power flow Newton-Raphson method

Optimal layout model of feeder automation equipment oriented to the type of fault detection and local action

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Protection and Control of Modern Power Systems 2023年第1期8卷 15-29页

作者： Ruizhi chen Xihong Li yanbo chen Bishan Power Supply Branch of Chongqing Electric Power Company Chong-qingBishanChina North China Electric Power University BeijingChina

In feeder automation transformation there are difficulties in equipment and location *** help with this,an optimal layout model of feeder automation equipment oriented to the type of fault detection and local action is *** analyzes the coordination relationship of the three most common types of automation equipment,i.e.,fault indicator,over-current trip switch and non-voltage trip switch in the fault handling process,and the explicit expres-sions of power outage time caused by a fault on different layouts of the above three types of equipment are *** constraints of power supply reliability and the goal of minimizing the sum of equipment-related capital invest-ment and power interruption cost,a mixed-integer quadratic programming model for optimal layout is established,in which the functional failure probability of equipment is linearized using the 3δprinciple in ***,the basic characteristics of the proposed model are illustrated by different scenarios on the IEEE RBTS-BUS6 *** can not only take into account fault location and fault isolation to enhance user power consumption perception,but also can guide precise investment to improve the operational quality and efficiency of a power company.

关键词： Feeder automation Equipment layout optimization Power outage time Explicit expression Mixed integer quadratic programming model Functional failure probability of equipment

Seismic effects on reinforcement load and lateral deformation of geosynthetic-reinforced soil walls

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Frontiers of Structural and Civil Engineering 2021年第4期15卷 1001-1015页

作者： Fei ZHANG Yuming ZHU yanbo chen Shangchuan YANG Key Laboratory of Ministry of Education for Geomechanics and Embankment Engineering Hohai UniversityNanjing210098China Jiangsu Province’s Geotechnical Research Center Nanjing210098China Southwest Jiaotong University Key Laboratory of High-speed Railway Engineering of the Ministry of EducationChengdu610031China

Current design methods for the internal stability of geosynthetic-reinforced soil(GRS)walls postulate seismic forces as inertial forces,leading to pseudo-static analyses based on active earth pressure theory,which yields unconservative reinforcement loads required for seismic *** seismic analyses are limited to the determination of maximum reinforcement *** study aimed to calculate the distribution of the reinforcement load and connection strength required for each layer of the seismic GRS *** the top-down procedure involves all of the possible failure surfaces for the seismic analyses of the GRS wall and then obtains the reinforcement load distribution for the limit *** distributions are used to determine the required connection strength and to approximately assess the facing lateral *** sufficient pullout resistance to be provided by each reinforcement,the maximum required tensile resistance is identical to the results based on the Mononobe-Okabe ***,short reinforcement results in greater tensile resistances in the mid and lower layers as evinced by compound failure frequently occurring in GRS walls during an *** studies involving backfill friction angle,reinforcement length,vertical seismic acceleration,and secondary reinforcement are conducted to investigate seismic impacts on the stability and lateral deformation of GRS walls.

关键词： geosynthetics reinforced soil retaining walls seismic performance

Data-driven Robust State Estimation Through Off-line Learning and On-line Matching

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Journal of Modern Power Systems and Clean Energy 2021年第4期9卷 897-909页

作者： yanbo chen Hao chen Yang Jiao Jin Ma Yuzhang Lin State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Electrical&Electronic EngineeringNorth China Electric Power UniversityBeijing 102206China School of Electrical and Information Engineering University of SydneySydney 2006Australia Department of Electrical and Computer Engineering University of MassachusettsLowellMA 01852USA

To overcome the shortcomings of model-driven state estimation methods, this paper proposes a data-driven robust state estimation (DDSE) method through off-line learning and on-line matching. At the off-line learning stage, a linear regression equation is presented by clustering historical data from supervisory control and data acquisition (SCADA), which provides a guarantee for solving the over-learning problem of the existing DDSE methods;then a novel robust state estimation method that can be transformed into quadratic programming (QP) models is proposed to obtain the mapping relationship between the measurements and the state variables (MRBMS). The proposed QP models can well solve the problem of collinearity in historical data. Furthermore, the off-line learning stage is greatly accelerated from three aspects including reducing historical categories, constructing tree retrieval structure for known topologies, and using sensitivity analysis when solving QP models. At the on-line matching stage, by quickly matching the current snapshot with the historical ones, the corresponding MRBMS can be obtained, and then the estimation values of the state variables can be obtained. Simulations demonstrate that the proposed DDSE method has obvious advantages in terms of suppressing over-learning problems, dealing with collinearity problems, robustness, and computation efficiency.

关键词： Robust state estimation historical snapshot off-line learning on-line matching collinearity

Dynamic State Estimation for Integrated Electricity-gas Systems Based on Kalman Filter

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CSEE Journal of Power and Energy Systems 2022年第1期8卷 293-303页

作者： yanbo chen Yuan Yao Yuzhang Lin Xiaonan Yang State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Electrical&Electronic EngineeringNorth China Electric Power University102206 BeijingChina Qinghai Key Laboratory of Efficient Utilization of Clean Energy Tus-Institute for Renewable EnergyQinghai UniversityXining 810016China School of Engineering Xining UniversityXining 810016China School of Electrical&Electronic Engineering North China Electric Power University102206 BeijingChina Department of Electrical and Computer Engineering University of MassachusettsLowellMA 01852USA State Key Laboratory of Power Grid Safety and Energy Conservation China Electric Power Research Institute100192 BeijingChina

In recent years, integrated electricity-gas systems(IEGSs) have attracted widespread attention. The unifiedscheduling and control of the IEGS depends on high-precisionoperating data. To this end, it is necessary to establish anappropriate state estimation (SE) model for IEGS to filter theraw measured data. Considering that power systems and naturalgas systems have different time scales and sampling periods, thispaper proposes a dynamic state estimation (DSE) method basedon a Kalman filter that can consider the dynamic characteristicsof natural gas pipelines. First, the standardized state transitionequations for the gas system are developed by applying the finitedifference method to the partial differential equations (PDEs) ofthe gas system;then the DSE model for IEGS is formulatedbased on a Kalman filter;also, the measurements from theelectricity system and the gas system with different samplingperiods are fused to ensure the observability of DSE by using theinterpolation method. The IEEE 39-bus electricity system and the18-nodes Belgium gas system are integrated as the test *** results verify the proposed method’s accuracy andcalculation efficiency.

关键词： Dynamic state estimation integrated electricitygas system Kalman filter two time-scale measurements transition state equation

Prediction of maximum upward displacement of shield tunnel linings during construction using particle swarm optimization-random forest algorithm

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2024年第1期25卷 1-17页

作者： Xiaowei YE Xiaolong ZHANG yanbo chen Yujun WEI Yang DING MOE Key Laboratory of Soft Soils and Geoenvironmental Engineering Zhejiang UniversityHangzhou310058China Department of Civil Engineering Zhejiang UniversityHangzhou310058China Zhejiang Engineering Research Center of Intelligent Urban Infrastructure Hangzhou City UniversityHangzhou310015China

During construction,the shield linings of tunnels often face the problem of local or overall upward movement after leaving the shield tail in soft soil areas or during some large diameter shield *** floating will increase the initial stress on the segments and bolts which is harmful to the service performance of the *** this study we used a random forest(RF)algorithm combined particle swarm optimization(PSO)and 5-fold cross-validation(5-fold CV)to predict the maximum upward displacement of tunnel linings induced by shield tunnel *** mechanism and factors causing upward movement of the tunnel lining are comprehensively *** input variables were selected according to results from analysis of influencing *** prediction performance of two models,PSO-RF and RF(default)were *** Gini value was obtained to represent the relative importance of the influencing factors to the upward displacement of *** PSO-RF model successfully predicted the maximum upward displacement of the tunnel linings with a low error(mean absolute error(MAE)=4.04 mm,root mean square error(RMSE)=5.67 mm)and high correlation(R^(2)=0.915).The thrust and depth of the tunnel were the most important factors in the prediction model influencing the upward displacement of the tunnel linings.

关键词： Random forest(RF) Particle swarm optimization(PSO) Upward displacement of lining Machine learning prediction Shieldtunneling construction

Improved Proximal Policy Optimization Algorithm for Sequential Security-constrained Optimal Power Flow Based on Expert Knowledge and Safety Layer

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Journal of Modern Power Systems and Clean Energy 2024年第3期12卷 742-753页

作者： yanbo chen Qintao Du Honghai Liu Liangcheng cheng Muhammad Shahzad Younis State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources and School of Electrical&Electronic Engineering North China Electric Power UniversityBeijing 102206China School of Engineering Xining UniversityXining 810008Chinaand Key Laboratory of Smart Operation of New Energy Power SystemMinistry of EducationQinghai UniversityXining 810016China China Electric Power Research Institute Nanjing 210003China National University of Sciences and Technology Islamabad44000Pakistan

In recent years,reinforcement learning(RL)has emerged as a solution for model-free dynamic programming problem that cannot be effectively solved by traditional optimization *** has gradually been applied in the fields such as economic dispatch of power systems due to its strong selflearning and self-optimizing ***,existing economic scheduling methods based on RL ignore security risks that the agent may bring during exploration,which poses a risk of issuing instructions that threaten the safe operation of power ***,we propose an improved proximal policy optimization algorithm for sequential security-constrained optimal power flow(SCOPF)based on expert knowledge and safety layer to determine active power dispatch strategy,voltage optimization scheme of the units,and charging/discharging dispatch of energy storage *** expert experience is introduced to improve the ability to enforce constraints such as power balance in training process while guiding agent to effectively improve the utilization rate of renewable ***,to avoid line overload,we add a safety layer at the end of the policy network by introducing transmission constraints to avoid dangerous actions and tackle sequential SCOPF *** results on an improved IEEE 118-bus system verify the effectiveness of the proposed algorithm.

关键词： Sequential security-constrained optimal power flow(SCOPF) expert experience safety layer renewable energy safe reinforcement learning

Preparation and mechanical properties of SiC/2024 composite by semisolid casting

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Rare Metals 2002年第3期21卷 203-206页

作者： MENGXianyun, DING Hua, chen yanbo, and WEN JinglinSchool of Materials and Metallurgy, Northeastern University, Shenyang 110004, China School of Materials and Metallurgy Northeastern University Shenyang 110004

A SiC/2024 composite was made by semisolid casting. The wetting between SiC and Al matrix is improved by treating SiC particles at a high temperature, coating K2ZrF6, and adding Mg to the Al melt. An effective way to remove the gas around SiC particles was also found. Microstructures were observed under optical microscope (OM) and scanning electron microscopy (SEM). The results show that SiC particles and Al matrix are well bonded and no gaps or cavities around the particles are observed. SiC particles distribute homogeneously in the Al matrix. The existence of SiC particles results in the increase of wear resistance and strength.

关键词： semisolid casting SiC/2024 composite wetting mechanical property