8,428 research outputs found
Forced Oscillation Source Location via Multivariate Time Series Classification
Precisely locating low-frequency oscillation sources is the prerequisite of
suppressing sustained oscillation, which is an essential guarantee for the
secure and stable operation of power grids. Using synchrophasor measurements, a
machine learning method is proposed to locate the source of forced oscillation
in power systems. Rotor angle and active power of each power plant are utilized
to construct multivariate time series (MTS). Applying Mahalanobis distance
metric and dynamic time warping, the distance between MTS with different phases
or lengths can be appropriately measured. The obtained distance metric,
representing characteristics during the transient phase of forced oscillation
under different disturbance sources, is used for offline classifier training
and online matching to locate the disturbance source. Simulation results using
the four-machine two-area system and IEEE 39-bus system indicate that the
proposed location method can identify the power system forced oscillation
source online with high accuracy.Comment: 5 pages, 3 figures. Accepted by 2018 IEEE/PES Transmission and
Distribution Conferenc
Maximum Entropy Based Lexical Reordering Model for Hierarchical Phrase-based Machine Translation
A Bootstrapping Method for Finer-Grained Opinion Mining Using Graph Model
PACLIC 23 / City University of Hong Kong / 3-5 December 200
Identification of a Novel PIP2 Interaction Site and its Allosteric Regulation by the RCK1 Site Associated with Ca2+ Coordination in Slo1 Channels
We consider the Schr\"odinger operator on a combinatorial graph consisting of
a finite graph and a finite number of discrete half-lines, all jointed
together, and compute an asymptotic expansion of its resolvent around the
threshold . Precise expressions are obtained for the first few coefficients
of the expansion in terms of the generalized eigenfunctions. This result
justifies the classification of threshold types solely by growth properties of
the generalized eigenfunctions. By choosing an appropriate free operator a
priori possessing no zero eigenvalue or zero resonance we can simplify the
expansion procedure as much as that on the single discrete half-line.Comment: 55 pages, minor revisions, final versio
- …