14,722 research outputs found
Transmission Network Reduction Method using Nonlinear Optimization
This paper presents a new method to determine the susceptances of a reduced
transmission network representation by using nonlinear optimization. We use
Power Transfer Distribution Factors (PTDFs) to convert the original grid into a
reduced version, from which we determine the susceptances. From our case
studies we find that considering a reduced injection-independent evaluated PTDF
matrix is the best approximation and is by far better than an
injection-dependent evaluated PTDF matrix over a given set of
arbitrarily-chosen power injection scenarios. We also compare our nonlinear
approach with existing methods from literature in terms of the approximation
error and computation time. On average, we find that our approach reduces the
mean error of the power flow deviations between the original power system and
its reduced version, while achieving higher but reasonable computation times
Automating Vehicles by Deep Reinforcement Learning using Task Separation with Hill Climbing
Within the context of autonomous driving a model-based reinforcement learning
algorithm is proposed for the design of neural network-parameterized
controllers. Classical model-based control methods, which include sampling- and
lattice-based algorithms and model predictive control, suffer from the
trade-off between model complexity and computational burden required for the
online solution of expensive optimization or search problems at every short
sampling time. To circumvent this trade-off, a 2-step procedure is motivated:
first learning of a controller during offline training based on an arbitrarily
complicated mathematical system model, before online fast feedforward
evaluation of the trained controller. The contribution of this paper is the
proposition of a simple gradient-free and model-based algorithm for deep
reinforcement learning using task separation with hill climbing (TSHC). In
particular, (i) simultaneous training on separate deterministic tasks with the
purpose of encoding many motion primitives in a neural network, and (ii) the
employment of maximally sparse rewards in combination with virtual velocity
constraints (VVCs) in setpoint proximity are advocated.Comment: 10 pages, 6 figures, 1 tabl
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