4 research outputs found
A Bayesian Approach to Forced Oscillation Source Location Given Uncertain Generator Parameters
Since forced oscillations are exogenous to dynamic power system models, the
models by themselves cannot predict when or where a forced oscillation will
occur. Locating the sources of these oscillations, therefore, is a challenging
problem which requires analytical methods capable of using real time power
system data to trace an observed oscillation back to its source. The difficulty
of this problem is exacerbated by the fact that the parameters associated with
a given power system model can range from slightly uncertain to entirely
unknown. In this paper, a Bayesian framework, via a two-stage Maximum A
Posteriori optimization routine, is employed in order to locate the most
probable source of a forced oscillation given an uncertain prior model. The
approach leverages an equivalent circuit representation of the system in the
frequency domain and employs a numerical procedure which makes the problem
suitable for real time application. The derived framework lends itself to
successful performance in the presence of PMU measurement noise, high generator
parameter uncertainty, and multiple forced oscillations occurring
simultaneously. The approach is tested on a 4-bus system with a single forced
oscillation source and on the WECC 179-bus system with multiple oscillation
sources.Comment: 9 pages; submitted to IEEE Transactions on Power System
Learning Feature Sparse Principal Components
This paper presents new algorithms to solve the feature-sparsity constrained
PCA problem (FSPCA), which performs feature selection and PCA simultaneously.
Existing optimization methods for FSPCA require data distribution assumptions
and are lack of global convergence guarantee. Though the general FSPCA problem
is NP-hard, we show that, for a low-rank covariance, FSPCA can be solved
globally (Algorithm 1). Then, we propose another strategy (Algorithm 2) to
solve FSPCA for the general covariance by iteratively building a carefully
designed proxy. We prove theoretical guarantees on approximation and
convergence for the new algorithms. Experimental results show the promising
performance of the new algorithms compared with the state-of-the-arts on both
synthetic and real-world datasets
Cost-Sensitive Feature Selection by Optimizing F-Measures
Feature selection is beneficial for improving the performance of general
machine learning tasks by extracting an informative subset from the
high-dimensional features. Conventional feature selection methods usually
ignore the class imbalance problem, thus the selected features will be biased
towards the majority class. Considering that F-measure is a more reasonable
performance measure than accuracy for imbalanced data, this paper presents an
effective feature selection algorithm that explores the class imbalance issue
by optimizing F-measures. Since F-measure optimization can be decomposed into a
series of cost-sensitive classification problems, we investigate the
cost-sensitive feature selection by generating and assigning different costs to
each class with rigorous theory guidance. After solving a series of
cost-sensitive feature selection problems, features corresponding to the best
F-measure will be selected. In this way, the selected features will fully
represent the properties of all classes. Experimental results on popular
benchmarks and challenging real-world data sets demonstrate the significance of
cost-sensitive feature selection for the imbalanced data setting and validate
the effectiveness of the proposed method
Zero-Shot Feature Selection via Transferring Supervised Knowledge
Feature selection, an effective technique for dimensionality reduction, plays
an important role in many machine learning systems. Supervised knowledge can
significantly improve the performance. However, faced with the rapid growth of
newly emerging concepts, existing supervised methods might easily suffer from
the scarcity and validity of labeled data for training. In this paper, the
authors study the problem of zero-shot feature selection (i.e., building a
feature selection model that generalizes well to "unseen" concepts with limited
training data of "seen" concepts). Specifically, they adopt class-semantic
descriptions (i.e., attributes) as supervision for feature selection, so as to
utilize the supervised knowledge transferred from the seen concepts. For more
reliable discriminative features, they further propose the
center-characteristic loss which encourages the selected features to capture
the central characteristics of seen concepts. Extensive experiments conducted
on various real-world datasets demonstrate the effectiveness of the method.Comment: Published in IJDWM2