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The normality of the distribution of the continuous variables was evaluated using histograms and the one-sample Kolmogorov-Smirnov test; distributions with p > 0.05 were accepted as normally distributed.
Four test distributions, the Normal/Gaussian, Lognomal, Weibull, and Uniform, were tested using a one-sample Kolmogorov-Simirnov test.
Relaxation of the rigid support assumption in favour of experimentally determined flexible support stiffnesses significantly nudges the predicted moment distributions toward the test distributions.
The algorithm employs multiple class-specific reference and test cumulative distribution functions, classifies noisy test features into their corresponding classes, and equalizes the features by using their corresponding class reference and test distributions.
Confidence values (P-values) for the χ test were calculated for each data set between random and test distributions.
By comparing several test distributions (or genomes) to a single reference, the relative amount of deviation or bias could be determined.
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The proposed method is applied to a modified IEEE 33-node test distribution system illustrated in Fig. 5.
Thus, for our test distribution network, uncontrolled charging at 50% EV penetration cannot be supported.
The proposed method is applied to IEEE 33-bus test distribution system [34].
Test distribution systems of different characteristics and size have been simulated during different time periods.
The performance of the proposed approach is assessed on a test distribution network.
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