نتایج جستجو برای: p power convexification method

تعداد نتایج: 3129501  

2013
Masoud Farivar Steven H. Low

Abstract—We propose a branch flow model for the analysis and optimization of mesh as well as radial networks. The model leads to a new approach to solving optimal power flow (OPF) that consists of two relaxation steps. The first step eliminates the voltage and current angles and the second step approximates the resulting problem by a conic program that can be solved efficiently. For radial netw...

2012
Lingwen Gan Na Li Ufuk Topcu Steven Low

Power flow optimization is generally nonlinear and non-convex, and a second-order cone relaxation has been proposed recently for convexification. We prove several sufficient conditions under which the relaxation is exact. One of these conditions seems particularly realistic and suggests guidelines on integrating distributed generations.

2015
Carleton Coffrin Hassan L. Hijazi Pascal Van Hentenryck

Convexification is a fundamental technique in (mixed-integer) nonlinear optimization and many convex relaxations are parametrized by variable bounds, i.e., the tighter the bounds, the stronger the relaxations. This paper studies how bound tightening can improve convex relaxations for power network optimization. It adapts traditional constraintprogramming concepts (e.g., minimal network and boun...

پایان نامه :وزارت علوم، تحقیقات و فناوری - دانشگاه صنعتی خواجه نصیرالدین طوسی - دانشکده مهندسی برق و کامپیوتر 1391

power transformers are important equipments in power systems. thus there is a large number of researches devoted of power transformers. however, there is still a demand for future investigations, especially in the field of diagnosis of transformer failures. in order to fulfill the demand, the first part reports a study case in which four main types of failures on the active part are investigate...

پایان نامه :وزارت علوم، تحقیقات و فناوری - دانشگاه صنعت آب و برق (شهید عباسپور) - دانشکده مهندسی برق و کامپیوتر 1392

abstract according to increase in electricity consumption in one hand and power systemsreliability importance in another , fault location detection techniqueshave beenrecentlytaken to consideration. an algorithm based on collected data from both transmission line endsproposed in this thesis. in order to reducecapacitance effects of transmission line, distributed parametersof transmission line...

Journal: :Journal of Computational Physics 2023

The first numerical solution of the 3-D travel time tomography problem is presented. globally convergent convexification method applied.

Journal: :IEEE Transactions on Control of Network Systems 2021

In this article, a novel convexification approach for small-signal stability constraint optimal power flow has been presented that does not rely on eigenvalue analysis. The proposed methodology is based the sufficient condition stability, developed as bilinear matrix inequality (BMI), and uses network structure-preserving differential algebraic equation modeling of system. formulation semidefin...

‎By p-power (or partial p-power) transformation‎, ‎the Lagrangian function in nonconvex optimization problem becomes locally convex‎. ‎In this paper‎, ‎we present a neural network based on an NCP function for solving the nonconvex optimization problem‎. An important feature of this neural network is the one-to-one correspondence between its equilibria and KKT points of the nonconvex optimizatio...

2004
I. G. Akrotirianakis C. A. Meyer C. A. Floudas

In this paper, we propose a new method that produces new forms of tight convex underestimators for twice continuously differentiable nonconvex functions. The algorithm generalizes the main ideas used in aBB. The key idea is to determine a new convexification function that is able to handle the off-diagonal elements of the Hessian matrix of the original nonconvex function. The new convexificatio...

Journal: :IEEE Transactions on Power Systems 2021

This letter presents a novel optimization framework of formulating the three-phase optimal power flow that involves uncertainty. The proposed uncertainty-aware (UaO) is: 1) deterministic is less complex than existing frameworks involving uncertainty, and 2) convex such it admits polynomial-time algorithms mature distributed methods. To construct this UaO framework, methodology learning-aided mo...

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