Support Vector Domain Description (SVDD) is inspired by the Support Vector Classifier. It obtains a sphere shaped decision boundary with minimal volume around a dataset. This data description can be used for novelty o...
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Support Vector Domain Description (SVDD) is inspired by the Support Vector Classifier. It obtains a sphere shaped decision boundary with minimal volume around a dataset. This data description can be used for novelty or outlier detection. Our approach is always to minimize the volume of the sphere describing the dataset, while at the same time maximize the separability between the spheres. To build such sphere we only need to solve a convexquadratic optimization problem that can be efficiently solved with the existing software packages, simulation results on seventeen benchmark datasets have successfully validated the effectiveness of the proposed method.
In this paper, we propose a new notion of 'exceptional family of elements' for convex optimization problems. By employing the notion of 'exceptional family of elements', we establish some existence res...
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In this paper, we propose a new notion of 'exceptional family of elements' for convex optimization problems. By employing the notion of 'exceptional family of elements', we establish some existence results for convex optimization problem in reflexive Banach spaces. We show that the nonexistence of an exceptional family of elements is a sufficient and necessary condition for the solvability of the optimization problem. Furthermore, we establish several equivalent conditions for the solvability of convex optimization problems. As applications, the notion of 'exceptional family of elements' for convex optimization problems is applied to the constrained optimization problem and convex quadratic programming problem and some existence results for solutions of these problems are obtained.
A neural network model is constructed to solve convexquadratic multi-objective programmingproblem (CQMPP). The CQMPP is first converted into an equivalent single-objective convex quadratic programming problem by the...
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A neural network model is constructed to solve convexquadratic multi-objective programmingproblem (CQMPP). The CQMPP is first converted into an equivalent single-objective convex quadratic programming problem by the mean of the weighted sum method, where the Pareto optimal solution (POS) are given by diversifying values of weights. Then, for given various values weights, multiple projection neural networks are employded to search for Pareto optimal solutions. Based on employing Lyapunov theory, the proposed neural network approach is established to be stable in the sense of Lyapunov and it is globally convergent to an exact optimal solution of the single-objective problem. The simulation results also show that the presented model is feasible and efficient.
The absorption and output characteristics of reactive power of the doubly-fed induction generator (DFIG) greatly influence the voltage stability of PCC (Point of Common Coupling) where the wind farms are integrated in...
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The absorption and output characteristics of reactive power of the doubly-fed induction generator (DFIG) greatly influence the voltage stability of PCC (Point of Common Coupling) where the wind farms are integrated into the bulk power grid. This study proposes a reactive power compensation strategy for coordinated voltage control (CVC) of PCC with large-scale wind farms to achieve the expected voltage quality of the power grid through a minimum amount of control actions in emergencies. To this end, the mechanism of reactive power and voltage control inside DFIG is first analyzed. Then, the concept of reactive power reserve (RPR) sensitivity concerning control actions is introduced and an index of voltage stability margin is proposed to evaluate and analyze the distance between the current operating point and the voltage collapse point by analyzing the relationship between reactive power reserve and voltage stability margin. In the event of an emergency, critical reactive power reserves are obtained to reduce the dimension and complexity of the control problem. The sensitivity of reactive power reserve and the control are formulated into a convex quadratic programming problem to optimize the control strategies for voltage stability. The proposed technology has been validated on the IEEE 39-bus system.
作者：
He, ChenyuanZhang, ZhouyuJiangsu Univ
Sch Automot & Traff Engn Zhenjiang Jiangsu Peoples R China Yango Univ
Fujian Key Lab Spatial Informat Percept & Intellig Fuzhou Peoples R China Jiangsu Univ
Sch Automot & Traff Engn Zhenjiang 212013 Jiangsu Peoples R China
Electric vehicle (EV) has emerged as a crucial component in addressing both energy and environmental problems, and has become an essential part of nowadays' intelligent transportation systems. However, the chargin...
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Electric vehicle (EV) has emerged as a crucial component in addressing both energy and environmental problems, and has become an essential part of nowadays' intelligent transportation systems. However, the charging demands of large amounts of EVs can put substantial pressure on power grid systems and cause potential grid congestion problems. In this article, we propose a hierarchical EV charging control strategy that considers the network-wide communication overheads, computational complexity, total energy cost, EV user preferences, and data privacy protection. The hierarchical charging structure contains two phases, that is, a centralized control for EV aggregators and a distributed control for EVs within an aggregator. We prove that the centralized control with the objective to minimize the total energy cost of the power system constitutes a convex quadratic programming problem. Then a unique global optimum for the energy consumption profiles of EV aggregators can be achieved. The distributed charging control for EVs within an aggregator is studied using a generalized Nash equilibrium problem (GNEP). We show that the solution of the GNEP can be obtained via a variational inequality. Then the Solodov and Svaiter hyperplane projection method is employed to iteratively approach the variational equilibrium while ensuring the protection of EV users' data privacy. Extensive simulation studies are conducted to verify the correctness and effectiveness of our proposed hierarchical charging control algorithm for EVs.
The paper presents a technique for solving the binary linear programming model in polynomial time. The general binary linear programmingproblem is transformed into a convex quadratic programming problem. The convex q...
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The paper presents a technique for solving the binary linear programming model in polynomial time. The general binary linear programmingproblem is transformed into a convex quadratic programming problem. The convex quadratic programming problem is then solved by interior point algorithms. This settles one of the open problems of whether P = NP or not. The worst case complexity of interior point algorithms for the convexquadraticproblem is polynomial. It can also be shown that every liner integer problem can be converted into binary linear problem.
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