The Experts below are selected from a list of 4107 Experts worldwide ranked by ideXlab platform
Eliane S Souza - One of the best experts on this subject based on the ideXlab platform.
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Restoration of Electrical Distribution Systems Using a Relaxed Mathematical Model
Journal of Control Automation and Electrical Systems, 2018Co-Authors: Eliane S Souza, Ruben Romero, John F FrancoAbstract:This paper proposes a relaxed mathematical model to solve the Restoration Problem of radial and balanced electrical distribution systems. The mathematical model is a mixed-integer linear programming formulation that can be efficiently solved by commercial solvers. After a Restoration Problem is solved using this model, the quality and feasibility of the corresponding solution can be verified by using a conventional radial power flow. The performance of the proposed relaxed model is evaluated through exhaustive tests and the solutions found are compared with the ones provided by an exact mathematical formulation. The results obtained demonstrate the efficiency of the proposed approach.
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a new mathematical model for the Restoration Problem in balanced radial distribution systems
IEEE Transactions on Power Systems, 2016Co-Authors: Ruben Romero, John F Franco, Fabio Bertequini Leao, Marcos J Rider, Eliane S SouzaAbstract:This paper presents a comprehensive mathematical model to solve the Restoration Problem in balanced radial distribution systems. The Restoration Problem, originally modeled as mixed integer nonlinear programming, is transformed into a mixed integer second-order cone programming Problem, which can be solved efficiently using several commercial solvers based on the efficient optimization technique family branch and bound. The proposed mathematical model considers several objectives in a single objective function, using parameters to preserve the hierarchy of the different objectives: 1) maximizing the satisfaction of the demand, 2) minimizing the number of switch operations, 3) prioritizing the automatic switch operation rather than a manual one, and 4) prioritizing especial loads. General and specialized tests were carried out on a 53-node test system, and the results were compared with other previously proposed algorithms. Results show that the mathematical model is robust, efficient, flexible, and presents excellent performance in finding optimal solutions.
Naji Ghadernejad - One of the best experts on this subject based on the ideXlab platform.
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Service Restoration in distribution networks using combination of two heuristic methods considering load shedding
Journal of Modern Power Systems and Clean Energy, 2015Co-Authors: Meysam Gholami, Jamal Moshtagh, Naji GhadernejadAbstract:Two heuristic methods are proposed to find an effective and fast solution in modern power distribution networks. For solving the service Restoration Problem in distribution networks, switch selection indices based on an analytic approach and a practicable heuristic graph-based method are given. The formulation of the Problem includes four different objective functions: 1) maximizing the total load restored; 2) minimizing the number of switching operations; 3) maximizing the top priority restored load; 4) minimizing load shedding. A suitable evaluation of switch indices is used for all candidate tie switches (TSs) in the network to find the best solution and decrease the number of switching operations. A new graph-based approach is utilized for finding the best sectionalizes switch (SS) and minimizing the voltage drop. The accuracy and the validity of the approach are tested in two standard electrical distribution networks. The results of the approach are used for IEEE 69-bus and IEEE 119-bus test case.
Ruben Romero - One of the best experts on this subject based on the ideXlab platform.
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Restoration of Electrical Distribution Systems Using a Relaxed Mathematical Model
Journal of Control Automation and Electrical Systems, 2018Co-Authors: Eliane S Souza, Ruben Romero, John F FrancoAbstract:This paper proposes a relaxed mathematical model to solve the Restoration Problem of radial and balanced electrical distribution systems. The mathematical model is a mixed-integer linear programming formulation that can be efficiently solved by commercial solvers. After a Restoration Problem is solved using this model, the quality and feasibility of the corresponding solution can be verified by using a conventional radial power flow. The performance of the proposed relaxed model is evaluated through exhaustive tests and the solutions found are compared with the ones provided by an exact mathematical formulation. The results obtained demonstrate the efficiency of the proposed approach.
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a new mathematical model for the Restoration Problem in balanced radial distribution systems
IEEE Transactions on Power Systems, 2016Co-Authors: Ruben Romero, John F Franco, Fabio Bertequini Leao, Marcos J Rider, Eliane S SouzaAbstract:This paper presents a comprehensive mathematical model to solve the Restoration Problem in balanced radial distribution systems. The Restoration Problem, originally modeled as mixed integer nonlinear programming, is transformed into a mixed integer second-order cone programming Problem, which can be solved efficiently using several commercial solvers based on the efficient optimization technique family branch and bound. The proposed mathematical model considers several objectives in a single objective function, using parameters to preserve the hierarchy of the different objectives: 1) maximizing the satisfaction of the demand, 2) minimizing the number of switch operations, 3) prioritizing the automatic switch operation rather than a manual one, and 4) prioritizing especial loads. General and specialized tests were carried out on a 53-node test system, and the results were compared with other previously proposed algorithms. Results show that the mathematical model is robust, efficient, flexible, and presents excellent performance in finding optimal solutions.
Germana Landi - One of the best experts on this subject based on the ideXlab platform.
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A Lagrange method based L-curve for image Restoration
Journal of Physics: Conference Series, 2013Co-Authors: Germana LandiAbstract:The solution of image Restoration Problems usually requires the use of regularization strategies. The L-curve criterium is a popular heuristic tool for choosing good regularized solutions, when the data noise norm is not a priori known. In this work, we propose replacing the original image Restoration Problem with a noise-independent equality constrained one and solving it by an iterative Lagrange method. The sequence of the computed iterates defines a discrete L-shaped curve. By numerical results, we show that good regularized solutions correspond with the corner of this curve.
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A Truncated Lagrange Method for Total Variation-Based Image Restoration
Journal of Mathematical Imaging and Vision, 2007Co-Authors: Germana LandiAbstract:In the last years, Total Variation minimization has become a popular and valuable technique for the Restoration of noisy and blurred images. In this paper, we present a new technique for image Restoration based on Total Variation minimization and the discrepancy principle. The new approach replaces the original image Restoration Problem with an equality constrained minimization Problem. An inexact Newton method is applied to the first-order conditions of the constrained Problem. The stopping criterium is derived from the discrepancy principle. Numerical results of image denoising and image deblurring test Problems are presented to illustrate the effectiveness of the new approach.
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A fast truncated Lagrange method for large-scale image Restoration Problems
Applied Mathematics and Computation, 2007Co-Authors: Germana LandiAbstract:In this work, we present a new method for the Restoration of images degraded by noise and spatially invariant blur. In the proposed method, the original image Restoration Problem is replaced by an equality constrained minimization Problem. A quasi-Newton method is applied to the first-order optimality conditions of the constrained Problem. In each quasi-Newton iteration, the hessian of the Lagrangian is approximated by a circulant matrix and the Fast Fourier Transform is used to compute the quasi-Newton step. The quasi-Newton iteration is terminated according to the discrepancy principle. Results of numerical experiments are presented to illustrate the effectiveness and usefulness of the proposed method.
J B A London - One of the best experts on this subject based on the ideXlab platform.
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improved multi objective evolutionary algorithm in subpopulation tables with features from nsga ii for the service Restoration Problem
IEEE PowerTech Conference, 2019Co-Authors: Leandro Tolomeu Marques, Jose Paulo Ramos Fernandes, J B A LondonAbstract:Problems in electric power supply cause economic losses and affects people’s lives. To reduce these impacts, Distribution Systems (DSs) operators must have service Restoration plans, which must respect a series of constraints while reaching different objectives to properly restore the system. An improved version of the Multi-Objective Evolutionary Algorithm (MOEA) based on both the Non-Dominated Sorting Genetic Algorithm - II (NSGAII) and in the MOEA in Subpopulation Tables is proposed to get better Pareto fronts. In order to generate faster implemented and cheaper plans, the ability to prioritize switching operations in Remotely Controlled Switches (RCSs) is kept. Proposed MOEA was compared with four MOEAs from literature in a real and very large-scale DS in two different scenarios and outperformed them all in the tests conditions.
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combining subpopulation tables non dominated solutions and strength pareto of moeas to treat service Restoration Problem in large scale distribution systems
Conference of the Industrial Electronics Society, 2013Co-Authors: Danilo Sipoli Sanches, Alberto Carlos Botazzo Delbem, S C Mazucato, Marcelo Favoretto Castoldi, J B A LondonAbstract:The network reconfiguration for service Restoration (SR) in distribution systems is a combinatorial complex optimization Problem since it involves multiple non-linear constraints and objectives. For large networks, no exact algorithm has found adequate SR plans in real-time. On the other hand, methods combining Multi-objective Evolutionary Algorithms (MOEAs) with the Node-depth encoding (NDE) have shown to be able to efficiently generate adequate SR plans for large distribution systems (with thousands of buses and switches). This paper presents a new method that combining NDE with three MOEAs: (i) NSGA-II; (iii) SPEA 2; and (iii) a MOEA based on subpopulation tables. The idea is to obtain a method that cannot-only obtain adequate SR plans for large scale distribution systems, but can also find plans for small or large networks with similar quality. The proposed method, called MEA2N-STR, explores the space of the objectives solutions better than the other MOEAs with NDE, approximating better the Pareto-optimal front. This statement has been demonstrated by several simulations with DSs ranging from 632 to 1,277 switches.
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Integrating several subpopulation tables with node-depth encoding and strength Pareto for service Restoration in large-scale distribution systems
2013 IEEE Power & Energy Society General Meeting, 2013Co-Authors: Danilo Sipoli Sanches, M. M. Gois, J B A London, Alberto Carlos Botazzo DelbemAbstract:Network reconfiguration for service Restoration in distribution systems is a combinatorial complex optimization Problem that usually involves multiple non-linear constraints and objectives functions. For large scale distribution systems, no exact algorithm has found adequate Restoration plans in real-time. On the other hand, the combination of Multi-objective Evolutionary Algorithms (MOEAs) with the Node-Depth Encoding (NDE) has been able to efficiently generate adequate Restoration plans for relatively large distribution systems (with thousands of buses and switches). The method called MEAN-NDS results from the combination of NDE with a technique of MOEA based on subpopulation tables and the MOEA called NSGA-II. In order to obtain a more efficient MOEA to treat service Restoration Problem in large scale distribution systems, this paper proposes a new method, which results from the combination of MEAN-NDS with the MOEA called SPEA-2. The idea is to improve the capacity of MEAN-NDS to explore both the search and objective spaces. Simulations results with distribution systems ranging from 632 to 1,277 switches, have shown that the proposed method found the configurations of lower switching operations, and explores the space of the objective solutions better than the MEAN-NDS, approximating better the Pareto-optimal front.
