46,551 research outputs found
Fault Location in Power Distribution Systems via Deep Graph Convolutional Networks
This paper develops a novel graph convolutional network (GCN) framework for
fault location in power distribution networks. The proposed approach integrates
multiple measurements at different buses while taking system topology into
account. The effectiveness of the GCN model is corroborated by the IEEE 123 bus
benchmark system. Simulation results show that the GCN model significantly
outperforms other widely-used machine learning schemes with very high fault
location accuracy. In addition, the proposed approach is robust to measurement
noise and data loss errors. Data visualization results of two competing neural
networks are presented to explore the mechanism of GCN's superior performance.
A data augmentation procedure is proposed to increase the robustness of the
model under various levels of noise and data loss errors. Further experiments
show that the model can adapt to topology changes of distribution networks and
perform well with a limited number of measured buses.Comment: Accepcted by IEEE Journal on Selected Areas in Communicatio
A Review of Fault Diagnosing Methods in Power Transmission Systems
Transient stability is important in power systems. Disturbances like faults need to be segregated to restore transient stability. A comprehensive review of fault diagnosing methods in the power transmission system is presented in this paper. Typically, voltage and current samples are deployed for analysis. Three tasks/topics; fault detection, classification, and location are presented separately to convey a more logical and comprehensive understanding of the concepts. Feature extractions, transformations with dimensionality reduction methods are discussed. Fault classification and location techniques largely use artificial intelligence (AI) and signal processing methods. After the discussion of overall methods and concepts, advancements and future aspects are discussed. Generalized strengths and weaknesses of different AI and machine learning-based algorithms are assessed. A comparison of different fault detection, classification, and location methods is also presented considering features, inputs, complexity, system used and results. This paper may serve as a guideline for the researchers to understand different methods and techniques in this field
"Going back to our roots": second generation biocomputing
Researchers in the field of biocomputing have, for many years, successfully
"harvested and exploited" the natural world for inspiration in developing
systems that are robust, adaptable and capable of generating novel and even
"creative" solutions to human-defined problems. However, in this position paper
we argue that the time has now come for a reassessment of how we exploit
biology to generate new computational systems. Previous solutions (the "first
generation" of biocomputing techniques), whilst reasonably effective, are crude
analogues of actual biological systems. We believe that a new, inherently
inter-disciplinary approach is needed for the development of the emerging
"second generation" of bio-inspired methods. This new modus operandi will
require much closer interaction between the engineering and life sciences
communities, as well as a bidirectional flow of concepts, applications and
expertise. We support our argument by examining, in this new light, three
existing areas of biocomputing (genetic programming, artificial immune systems
and evolvable hardware), as well as an emerging area (natural genetic
engineering) which may provide useful pointers as to the way forward.Comment: Submitted to the International Journal of Unconventional Computin
Classifiers With a Reject Option for Early Time-Series Classification
Early classification of time-series data in a dynamic environment is a
challenging problem of great importance in signal processing. This paper
proposes a classifier architecture with a reject option capable of online
decision making without the need to wait for the entire time series signal to
be present. The main idea is to classify an odor/gas signal with an acceptable
accuracy as early as possible. Instead of using posterior probability of a
classifier, the proposed method uses the "agreement" of an ensemble to decide
whether to accept or reject the candidate label. The introduced algorithm is
applied to the bio-chemistry problem of odor classification to build a novel
Electronic-Nose called Forefront-Nose. Experimental results on wind tunnel
test-bed facility confirms the robustness of the forefront-nose compared to the
standard classifiers from both earliness and recognition perspectives
Software that Learns from its Own Failures
All non-trivial software systems suffer from unanticipated production
failures. However, those systems are passive with respect to failures and do
not take advantage of them in order to improve their future behavior: they
simply wait for them to happen and trigger hard-coded failure recovery
strategies. Instead, I propose a new paradigm in which software systems learn
from their own failures. By using an advanced monitoring system they have a
constant awareness of their own state and health. They are designed in order to
automatically explore alternative recovery strategies inferred from past
successful and failed executions. Their recovery capabilities are assessed by
self-injection of controlled failures; this process produces knowledge in
prevision of future unanticipated failures
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