79,315 research outputs found
Proposed shunt rounding technique for large-scale security constrained loss minimization
The official published version can be obtained from the link below - Copyright @ 2010 IEEE.Optimal reactive power flow applications often model large numbers of discrete shunt devices as continuous variables, which are rounded to their nearest discrete value at the final iteration. This can degrade optimality. This paper presents novel methods based on probabilistic and adaptive threshold approaches that can extend existing security constrained optimal reactive power flow methods to effectively solve large-scale network problems involving discrete shunt devices. Loss reduction solutions from the proposed techniques were compared to solutions from the mixed integer nonlinear mathematical programming algorithm (MINLP) using modified IEEE standard networks up to 118 buses. The proposed techniques were also applied to practical large-scale network models of Great Britain. The results show that the proposed techniques can achieve improved loss minimization solutions when compared to the standard rounding method.This work was supported in part by the National Grid and in part by the EPSRC. Paper no. TPWRS-00653-2009
Reinforcement Learning: A Survey
This paper surveys the field of reinforcement learning from a
computer-science perspective. It is written to be accessible to researchers
familiar with machine learning. Both the historical basis of the field and a
broad selection of current work are summarized. Reinforcement learning is the
problem faced by an agent that learns behavior through trial-and-error
interactions with a dynamic environment. The work described here has a
resemblance to work in psychology, but differs considerably in the details and
in the use of the word ``reinforcement.'' The paper discusses central issues of
reinforcement learning, including trading off exploration and exploitation,
establishing the foundations of the field via Markov decision theory, learning
from delayed reinforcement, constructing empirical models to accelerate
learning, making use of generalization and hierarchy, and coping with hidden
state. It concludes with a survey of some implemented systems and an assessment
of the practical utility of current methods for reinforcement learning.Comment: See http://www.jair.org/ for any accompanying file
Optimal control of nonlinear partially-unknown systems with unsymmetrical input constraints and its applications to the optimal UAV circumnavigation problem
Aimed at solving the optimal control problem for nonlinear systems with
unsymmetrical input constraints, we present an online adaptive approach for
partially unknown control systems/dynamics. The designed algorithm converges
online to the optimal control solution without the knowledge of the internal
system dynamics. The optimality of the obtained control policy and the
stability for the closed-loop dynamic optimality are proved theoretically. The
proposed method greatly relaxes the assumption on the form of the internal
dynamics and input constraints in previous works. Besides, the control design
framework proposed in this paper offers a new approach to solve the optimal
circumnavigation problem involving a moving target for a fixed-wing unmanned
aerial vehicle (UAV). The control performance of our method is compared with
that of the existing circumnavigation control law in a numerical simulation and
the simulation results validate the effectiveness of our algorithm
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