The free-surface wave interaction with a pontoon-type very large floating structure(VLFS) is analyzed by utilizing a modal expansion method. The modal expansion method consists of separating the hydrodynamic analysis ...The free-surface wave interaction with a pontoon-type very large floating structure(VLFS) is analyzed by utilizing a modal expansion method. The modal expansion method consists of separating the hydrodynamic analysis and the dynamic response analysis of the structure. In the dynamic response analysis of the structure,the deflection of the structure with various edge conditions is decomposed into vibration modes that can be arbitrarily chosen. Free-free beam model, pinned-free beam model and fixed-free beam model are three different types of edge conditions considered in this study. For each of these beam models, the detailed mathematical formulations for calculating the corresponding eigenvalues and eigenmodes have been given, and the mathematical formulations corresponding to the beam models of pinned-free beam and fixed-free beam are novel. For the hydrodynamic analysis of the structure, the boundary value problem(BVP) equations in terms of plate modes have been established, and the BVP equations corresponding to the beam models of pinned-free beam and fixedfree beam are also novel. When these BVP equations are solved numerically, the structure deflections and the wave reflection and transmission coefficients can be obtained. These calculation results point out some findings valuable for engineering design.展开更多
On the basis of a potential theory and Euler-Bernoulli beam theory, an analytical solution for oblique wave scattering by a semi-infinite elastic plate with finite draft floating on a step topography is developed usin...On the basis of a potential theory and Euler-Bernoulli beam theory, an analytical solution for oblique wave scattering by a semi-infinite elastic plate with finite draft floating on a step topography is developed using matched eigenfunction expansions. Different from previous studies, the effects of a wave incident angle, a plate draft, three different plate edge conditions （free, simply supported and built-in） and a sea-bottom topography are all taken into account. Moreover, the plate edge conditions are directly incorporated into linear algebraic equations for determining unknown expansion coefficients in velocity potentials, which leads to a simple and efficient solving procedure. Numerical results show that the convergence of the present solution is good, and an energy conservation relation is well satisfied. Also, the present predictions are in good agreement with known results for special cases. The effects of the wave incident angle, the plate draft, the plate edge conditions and the sea-bottom topography on various hydrodynamic quantities are analyzed. Some useful results are presented for engineering designs.展开更多
State estimation plays a vital role in the stable operation of modern power systems,but it is vulnerable to cyber attacks.False data injection attacks(FDIA),one of the most common cyber attacks,can tamper with measure...State estimation plays a vital role in the stable operation of modern power systems,but it is vulnerable to cyber attacks.False data injection attacks(FDIA),one of the most common cyber attacks,can tamper with measure-ment data and bypass the bad data detection(BDD)mechanism,leading to incorrect results of power system state estimation(PSSE).This paper presents a detection framework of FDIA for PSSE based on graph edge-conditioned convolutional networks(GECCN),which use topology information,node features and edge features.Through deep graph architecture,the correlation of sample data is effectively mined to establish the mapping relationship between the estimated values of measurements and the actual states of power systems.In addition,the edge-conditioned convolution operation allows processing data sets with different graph structures.Case studies are undertaken on the IEEE 14-bus system under different attack intensities and degrees to evaluate the performance of GECCN.Simulation results show that GECCN has better detection performance than convolutional neural networks,deep neural net-works and support vector machine.Moreover,the satisfactory detection performance obtained with the data sets of the IEEE 14-bus,30-bus and 118-bus systems verifies the effective scalability of GECCN.展开更多
基金the Research Project from the Chinese State Key Laboratory of Ocean Engineering of Shanghai Jiao Tong University(No.GKZD010038)
文摘The free-surface wave interaction with a pontoon-type very large floating structure(VLFS) is analyzed by utilizing a modal expansion method. The modal expansion method consists of separating the hydrodynamic analysis and the dynamic response analysis of the structure. In the dynamic response analysis of the structure,the deflection of the structure with various edge conditions is decomposed into vibration modes that can be arbitrarily chosen. Free-free beam model, pinned-free beam model and fixed-free beam model are three different types of edge conditions considered in this study. For each of these beam models, the detailed mathematical formulations for calculating the corresponding eigenvalues and eigenmodes have been given, and the mathematical formulations corresponding to the beam models of pinned-free beam and fixed-free beam are novel. For the hydrodynamic analysis of the structure, the boundary value problem(BVP) equations in terms of plate modes have been established, and the BVP equations corresponding to the beam models of pinned-free beam and fixedfree beam are also novel. When these BVP equations are solved numerically, the structure deflections and the wave reflection and transmission coefficients can be obtained. These calculation results point out some findings valuable for engineering design.
基金The National Natural Science Foundation of China under contract Nos 51490675,51322903 and 51279224
文摘On the basis of a potential theory and Euler-Bernoulli beam theory, an analytical solution for oblique wave scattering by a semi-infinite elastic plate with finite draft floating on a step topography is developed using matched eigenfunction expansions. Different from previous studies, the effects of a wave incident angle, a plate draft, three different plate edge conditions （free, simply supported and built-in） and a sea-bottom topography are all taken into account. Moreover, the plate edge conditions are directly incorporated into linear algebraic equations for determining unknown expansion coefficients in velocity potentials, which leads to a simple and efficient solving procedure. Numerical results show that the convergence of the present solution is good, and an energy conservation relation is well satisfied. Also, the present predictions are in good agreement with known results for special cases. The effects of the wave incident angle, the plate draft, the plate edge conditions and the sea-bottom topography on various hydrodynamic quantities are analyzed. Some useful results are presented for engineering designs.
基金supported in part by the Key-Area Research and Development Program of Guangdong Province under Grant 2020B010166004in part by the Guangdong Basic and Applied Basic Research Foundation under Grant 2020A1515111100+1 种基金in part by the National Natural Science Foundation of China under Grant 52207106in part by the Open Fund of State Key Laboratory of Operation and Control of Renewable Energy&Storage Systems(China Electric Power Research Institute)under Grant KJ80-21-001.
文摘State estimation plays a vital role in the stable operation of modern power systems,but it is vulnerable to cyber attacks.False data injection attacks(FDIA),one of the most common cyber attacks,can tamper with measure-ment data and bypass the bad data detection(BDD)mechanism,leading to incorrect results of power system state estimation(PSSE).This paper presents a detection framework of FDIA for PSSE based on graph edge-conditioned convolutional networks(GECCN),which use topology information,node features and edge features.Through deep graph architecture,the correlation of sample data is effectively mined to establish the mapping relationship between the estimated values of measurements and the actual states of power systems.In addition,the edge-conditioned convolution operation allows processing data sets with different graph structures.Case studies are undertaken on the IEEE 14-bus system under different attack intensities and degrees to evaluate the performance of GECCN.Simulation results show that GECCN has better detection performance than convolutional neural networks,deep neural net-works and support vector machine.Moreover,the satisfactory detection performance obtained with the data sets of the IEEE 14-bus,30-bus and 118-bus systems verifies the effective scalability of GECCN.
