symbolic dynamic models for highly varying power system loads
symbolic dynamic models for highly varying power system loads
symbolic dynamic models for highly varying power system loads
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G n<br />
H 0<br />
H 1<br />
IM<br />
IO<br />
I pred<br />
IREQ<br />
J<br />
KS test<br />
M D<br />
M F<br />
N<br />
NLDM<br />
N w<br />
PRS<br />
PSCAD<br />
Q(s)<br />
q * α<br />
rand<br />
RT<br />
S/N<br />
Empirical distribution function <strong>for</strong> the population having n<br />
number of elements<br />
Null hypothesis: F(t)=G(t) <strong>for</strong> all t<br />
Hypothesis: F(t)≠G(t) <strong>for</strong> at least one t<br />
Induction motor<br />
Input output<br />
Predicted value of current<br />
Hydro Quebec Institute of Research<br />
Two-sided, two-sample Kolmogorov Smirnov statistics<br />
Kolmogorov-Smirnov test<br />
Mini dictionary, derived from the <strong>symbolic</strong> <strong>dynamic</strong><br />
dictionary D<br />
Fractional occurrence associated with mini dictionary<br />
Number of cells used to descretize a signal<br />
Nonlinear <strong>dynamic</strong> <strong>models</strong><br />
Maximum word length in a dictionary<br />
Pseudo random sequences<br />
Power <strong>system</strong> computer aided design.<br />
Significance function in KS test<br />
Parameter in the KS significance function<br />
Matlab function <strong>for</strong> generating a random number<br />
Real test, per<strong>for</strong>med on actual EAF data<br />
Signal to noise ratio