The Experts below are selected from a list of 31641 Experts worldwide ranked by ideXlab platform
G R M Da Costa - One of the best experts on this subject based on the ideXlab platform.
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Optimal reactive power flow via the modified barrier Lagrangian Function approach
Electric Power Systems Research, 2012Co-Authors: V.a. De Sousa, Edmea Cassia Baptista, G R M Da CostaAbstract:Abstract A new approach called the Modified Barrier Lagrangian Function (MBLF) to solve the Optimal Reactive Power Flow problem is presented. In this approach, the inequality constraints are treated by the Modified Barrier Function (MBF) method, which has a finite convergence property; i.e. the optimal solution in the MBF method can actually be in the bound of the feasible set. Hence, the inequality constraints can be precisely equal to zero. Another property of the MBF method is that the barrier parameter does not need to be driven to zero to attain the solution. Therefore, the conditioning of the involved Hessian matrix is greatly enhanced. In order to show this, a comparative analysis of the numeric conditioning of the Hessian matrix of the MBLF approach, by the decomposition in singular values, is carried out. The feasibility of the proposed approach is also demonstrated with comparative tests to Interior Point Method (IPM) using various IEEE test systems and two networks derived from Brazilian generation/transmission system. The results show that the MBLF method is computationally more attractive than the IPM in terms of speed, number of iterations and numerical conditioning.
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MODIFIED BARRIER Lagrangian Function METHOD APPIED TO THE MA XIMUM LOADING PROBLEM
2010Co-Authors: V.a. De Sousa, Edmarcio Antonio Belati, G R M Da CostaAbstract:In this p aper the Modified Barrier Lagrangian Function (MBLF) method, i.e., a variant of the Interior Point method, is applied for the determination of the buses with the smallest loading margin and the smallest magnitude voltage in power systems, respectively. For the formulation of the problem, the power flow equations are in a parameterized form, and the inequality constraints are the voltage limits in the buses and the reactive generation limits in the buses with reactive control. The results obtained with the static optimization technique MBLF used in this study are confronted with the results obtained with the Primal-dual Logarithmic Barrier method (PDLB). Test results for the IEEE 57 and IEEE 118-bus test systems are presented to demonstrate the robustness and the effectiveness of the proposed algorithm. Resumo─ Neste artigo o metodo da Funcao Lagrangiana Barreira Modificada (FLBM), uma variante do metodo dos Pontos Interior es, e aplicado na determinacao das barras com menor margem de carregamento e menor magnitude de tensao em sistemas de potencia, respectivamente. Na formulacao do problema, as equacoes de fluxo de potencia estao na forma parametrizada, e as restricoes de desigualdade sao os limites de tensao nas barras e os limites de geracao de reativos nas barras de controle de reativo. Os resultados obtidos com a tecnica de otimizacao estatica MBLF (FLBM) usado neste trabalho sao confrontados com os resultados obtidos com o metodo Primal-Dual Barreira Logaritmica. Resultados dos testes para os sistemas IEEE 57 e IEEE 118 barras sao apresentados para demonstrar a robustez e efetividade do metodo proposto.
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primal dual logarithmic barrier and augmented Lagrangian Function to the loss minimization in power systems
Electric Power Components and Systems, 2006Co-Authors: Edmea Cassia Baptista, Edmarcio Antonio Belati, Vanusa Alves De Sousa, G R M Da CostaAbstract:This article presents a new approach to minimize the losses in electrical power systems. This approach considers the application of the primal-dual logarithmic barrier method to voltage magnitude and tap-changing transformer variables, and the other inequality constraints are treated by augmented Lagrangian method. The Lagrangian Function aggregates all the constraints. The first-order necessary conditions are reached by Newton's method, and by updating the dual variables and penalty factors. Test results are presented to show the good performance of this approach.
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logarithmic barrier augmented Lagrangian Function to the optimal power flow problem
International Journal of Electrical Power & Energy Systems, 2005Co-Authors: Edmea Cassia Baptista, Edmarcio Antonio Belati, G R M Da CostaAbstract:This paper presents a new approach to solve the Optimal Power Flow problem. This approach considers the application of logarithmic barrier method to voltage magnitude and tap-changing transformer variables and the other constraints are treated by augmented Lagrangian method. Numerical test results are presented, showing the effective performance of this algorithm.
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Optimal-power-flow solution by Newton's method applied to an augmented Lagrangian Function
IEE Proceedings - Generation Transmission and Distribution, 1995Co-Authors: Amilton Cesar Dos Santos, G R M Da CostaAbstract:The paper describes a new approach to the optimal-power-flow problem based on Newton's method which it operates with an augmented Lagrangian Function associated with the original problem. The Function aggregates all the equality and inequality constraints. The first-order necessary conditions for optimality are reached by Newton's method, and by updating the dual variables and the penalty terms associated with the inequality constraints. The proposed approach does not have to identify the set of binding constraints and can be utilised for an infeasible starting point. The sparsity of the Hessian matrix of the augmented Lagrangian is completely exploited in the computational implementation. Tests results are presented to show the good performance of this approach.
Giovanni Pistone - One of the best experts on this subject based on the ideXlab platform.
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Lagrangian Function on the Finite State Space Statistical Bundle.
Entropy (Basel Switzerland), 2018Co-Authors: Giovanni PistoneAbstract:The statistical bundle is the set of couples ( Q , W ) of a probability density Q and a random variable W such that.
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Lagrangian Function on the Finite State Space Statistical Bundle
2018Co-Authors: Giovanni PistoneAbstract:The statistical bundle is the set of couples ( Q , W ) of a probability density Q and a random variable W such that EQ [W] = 0. On a finite state space, we assume Q to be a probability density with respect to the uniform probability and give an affine atlas of charts such that the resulting manifold is a model for Information Geometry. Velocity and accelleration of a one-dimensional statistical model are computed in this set up. The Euler-Lagrange equations are derived from the Lagrange action integral. An example of Lagrangian using minus the entropy as potential energy is briefly discussed.
Edmea Cassia Baptista - One of the best experts on this subject based on the ideXlab platform.
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Optimal reactive power flow via the modified barrier Lagrangian Function approach
Electric Power Systems Research, 2012Co-Authors: V.a. De Sousa, Edmea Cassia Baptista, G R M Da CostaAbstract:Abstract A new approach called the Modified Barrier Lagrangian Function (MBLF) to solve the Optimal Reactive Power Flow problem is presented. In this approach, the inequality constraints are treated by the Modified Barrier Function (MBF) method, which has a finite convergence property; i.e. the optimal solution in the MBF method can actually be in the bound of the feasible set. Hence, the inequality constraints can be precisely equal to zero. Another property of the MBF method is that the barrier parameter does not need to be driven to zero to attain the solution. Therefore, the conditioning of the involved Hessian matrix is greatly enhanced. In order to show this, a comparative analysis of the numeric conditioning of the Hessian matrix of the MBLF approach, by the decomposition in singular values, is carried out. The feasibility of the proposed approach is also demonstrated with comparative tests to Interior Point Method (IPM) using various IEEE test systems and two networks derived from Brazilian generation/transmission system. The results show that the MBLF method is computationally more attractive than the IPM in terms of speed, number of iterations and numerical conditioning.
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primal dual logarithmic barrier and augmented Lagrangian Function to the loss minimization in power systems
Electric Power Components and Systems, 2006Co-Authors: Edmea Cassia Baptista, Edmarcio Antonio Belati, Vanusa Alves De Sousa, G R M Da CostaAbstract:This article presents a new approach to minimize the losses in electrical power systems. This approach considers the application of the primal-dual logarithmic barrier method to voltage magnitude and tap-changing transformer variables, and the other inequality constraints are treated by augmented Lagrangian method. The Lagrangian Function aggregates all the constraints. The first-order necessary conditions are reached by Newton's method, and by updating the dual variables and penalty factors. Test results are presented to show the good performance of this approach.
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logarithmic barrier augmented Lagrangian Function to the optimal power flow problem
International Journal of Electrical Power & Energy Systems, 2005Co-Authors: Edmea Cassia Baptista, Edmarcio Antonio Belati, G R M Da CostaAbstract:This paper presents a new approach to solve the Optimal Power Flow problem. This approach considers the application of logarithmic barrier method to voltage magnitude and tap-changing transformer variables and the other constraints are treated by augmented Lagrangian method. Numerical test results are presented, showing the effective performance of this algorithm.
Jun Wang - One of the best experts on this subject based on the ideXlab platform.
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convergence of a recurrent neural network for nonconvex optimization based on an augmented Lagrangian Function
International Symposium on Neural Networks, 2007Co-Authors: Jun WangAbstract:In the paper, a recurrent neural network based on an augmented Lagrangian Function is proposed for seeking local minima of nonconvex optimization problems with inequality constraints. First, each equilibrium point of the neural network corresponds to a Karush-Kuhn-Tucker (KKT) point of the problem. Second, by appropriately choosing a control parameter, the neural network is asymptotically stable at those local minima satisfying some mild conditions. The latter property of the neural network is ensured by the convexification capability of the augmented Lagrangian Function. The proposed scheme is inspired by many existing neural networks in the literature and can be regarded as an extension or improved version of them. A simulation example is discussed to illustrate the results.
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ISNN (3) - Convergence of a Recurrent Neural Network for Nonconvex Optimization Based on an Augmented Lagrangian Function
Advances in Neural Networks – ISNN 2007, 1Co-Authors: Jun WangAbstract:In the paper, a recurrent neural network based on an augmented Lagrangian Function is proposed for seeking local minima of nonconvex optimization problems with inequality constraints. First, each equilibrium point of the neural network corresponds to a Karush-Kuhn-Tucker (KKT) point of the problem. Second, by appropriately choosing a control parameter, the neural network is asymptotically stable at those local minima satisfying some mild conditions. The latter property of the neural network is ensured by the convexification capability of the augmented Lagrangian Function. The proposed scheme is inspired by many existing neural networks in the literature and can be regarded as an extension or improved version of them. A simulation example is discussed to illustrate the results.
Xiaoqi Yang - One of the best experts on this subject based on the ideXlab platform.
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Duality and Penalization in Optimization via an Augmented Lagrangian Function with Applications
Journal of Optimization Theory and Applications, 2008Co-Authors: Y. Y. Zhou, Xiaoqi YangAbstract:This paper aims to establish duality and exact penalization results for the primal problem of minimizing an extended real-valued Function in a reflexive Banach space in terms of a valley-at-0 augmented Lagrangian Function. It is shown that every weak limit point of a sequence of optimal solutions generated by the valley-at-0 augmented Lagrangian problems is a solution of the original problem. A zero duality gap property and an exact penalization representation between the primal problem and the valley-at-0 augmented Lagrangian dual problem are obtained. These results are then applied to an inequality and equality constrained optimization problem in infinite-dimensional spaces and variational problems in Sobolev spaces, respectively.
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augmented Lagrangian Function non quadratic growth condition and exact penalization
Operations Research Letters, 2006Co-Authors: Y. Y. Zhou, Xiaoqi YangAbstract:In this paper, under the assumption that the perturbation Function satisfies a growth condition, necessary and sufficient conditions for an exact penalty representation and a zero duality gap property between the primal problem and its augmented Lagrangian dual problem are established.
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duality and exact penalization via a generalized augmented Lagrangian Function
2005Co-Authors: X X Huang, Xiaoqi YangAbstract:In this paper, we introduce generalized augmented Lagrangian by relaxing the convexity assumption on the usual augmenting Function. Applications are given to establish strong duality and exact penalty representation for the problem of minizing an extended real valued Function. More specifically, a strong duality result based on the generalized augmented Lagrangian is established, and a necessary and sufficient condition for the exact penalty representation in the framework of generalized augmented Lagrangian is obtained.
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Approximate Optimal Solutions and Nonlinear Lagrangian Functions
Journal of Global Optimization, 2001Co-Authors: X X Huang, Xiaoqi YangAbstract:There is an increasing interest in the study of optimality conditions of approximate solutions for nonlinear optimization problems. In this paper, relationships between approximate optimal values and approximate roots of a nonlinear Function are explored via a nonlinear Lagrangian Function. Almost approximate optimal solutions are investigated by means of nonlinear Lagrangian Functions.
