116 research outputs found
Wide-area monitoring and control of future smart grids
Application of wide-area monitoring and control for future smart grids with substantial
wind penetration and advanced network control options through FACTS and HVDC
(both point-to-point and multi-terminal) is the subject matter of this thesis.
For wide-area monitoring, a novel technique is proposed to characterize the system dynamic
response in near real-time in terms of not only damping and frequency but also
mode-shape, the latter being critical for corrective control action. Real-time simulation
in Opal-RT is carried out to illustrate the effectiveness and practical feasibility of the proposed
approach. Potential problem with wide-area closed-loop continuous control using
FACTS devices due to continuously time-varying latency is addressed through the proposed
modification of the traditional phasor POD concept introduced by ABB. Adverse
impact of limited bandwidth availability due to networked communication is established
and a solution using an observer at the PMU location has been demonstrated.
Impact of wind penetration on the system dynamic performance has been analyzed along
with effectiveness of damping control through proper coordination of wind farms and
HVDC links. For multi-terminal HVDC (MTDC) grids the critical issue of autonomous
power sharing among the converter stations following a contingency (e.g. converter outage)
is addressed. Use of a power-voltage droop in the DC link voltage control loops
using remote voltage feedback is shown to yield proper distribution of power mismatch
according to the converter ratings while use of local voltages turns out to be unsatisfactory.
A novel scheme for adapting the droop coefficients to share the burden according
to the available headroom of each converter station is also studied.
The effectiveness of the proposed approaches is illustrated through detailed frequency
domain analysis and extensive time-domain simulation results on different test systems
Lightweight Monocular Depth Estimation Model by Joint End-to-End Filter pruning
Convolutional neural networks (CNNs) have emerged as the state-of-the-art in
multiple vision tasks including depth estimation. However, memory and computing
power requirements remain as challenges to be tackled in these models.
Monocular depth estimation has significant use in robotics and virtual reality
that requires deployment on low-end devices. Training a small model from
scratch results in a significant drop in accuracy and it does not benefit from
pre-trained large models. Motivated by the literature of model pruning, we
propose a lightweight monocular depth model obtained from a large trained
model. This is achieved by removing the least important features with a novel
joint end-to-end filter pruning. We propose to learn a binary mask for each
filter to decide whether to drop the filter or not. These masks are trained
jointly to exploit relations between filters at different layers as well as
redundancy within the same layer. We show that we can achieve around 5x
compression rate with small drop in accuracy on the KITTI driving dataset. We
also show that masking can improve accuracy over the baseline with fewer
parameters, even without enforcing compression loss
Small-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector Network
The detection performance of small objects in remote sensing images is not
satisfactory compared to large objects, especially in low-resolution and noisy
images. A generative adversarial network (GAN)-based model called enhanced
super-resolution GAN (ESRGAN) shows remarkable image enhancement performance,
but reconstructed images miss high-frequency edge information. Therefore,
object detection performance degrades for small objects on recovered noisy and
low-resolution remote sensing images. Inspired by the success of edge enhanced
GAN (EEGAN) and ESRGAN, we apply a new edge-enhanced super-resolution GAN
(EESRGAN) to improve the image quality of remote sensing images and use
different detector networks in an end-to-end manner where detector loss is
backpropagated into the EESRGAN to improve the detection performance. We
propose an architecture with three components: ESRGAN, Edge Enhancement Network
(EEN), and Detection network. We use residual-in-residual dense blocks (RRDB)
for both the ESRGAN and EEN, and for the detector network, we use the faster
region-based convolutional network (FRCNN) (two-stage detector) and single-shot
multi-box detector (SSD) (one stage detector). Extensive experiments on a
public (car overhead with context) and a self-assembled (oil and gas storage
tank) satellite dataset show superior performance of our method compared to the
standalone state-of-the-art object detectors.Comment: This paper contains 27 pages and accepted for publication in MDPI
remote sensing journal. GitHub Repository:
https://github.com/Jakaria08/EESRGAN (Implementation
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