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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

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