symbolic dynamic models for highly varying power system loads

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4.5 Discussion
Load modeling: For load modeling applications, the proposed algorithm is
executed offline, which means that the historical data set, and thus the Symbolic
Dynamic dictionary is not updated once it is formed. From the point of view of
comparison, the RMS value of the current is the most important parameter. CSI and J
statistics are the measure of similarity between the two signals. In other words these two
indices are related to the waveshape of the actual and generated signal. In load modeling,
the waveshape of the predicted signal is of lesser importance. Load power depends
mainly on the RMS value of the current.
Power conditioning: Power conditioning is an online real time application of the
proposed algorithm. The historical data set and the Symbolic Dynamic dictionary are
updated by including the actual current value each time a new value is predicted. By
predicting the future value of current, it is possible to inject an out of phase current into
the system and thus attenuating unwanted components. Therefore the wave shape of the
forecasted signal is also as important as the RMS value. Being a real time process,
execution time for the algorithm is another important parameter. Execution time can be
minimized by reducing the number of cells and maximum word length for the dictionary.
Thus compromising on the accuracy of the results obtained, the execution time can be
improved. Table 4.11 shows the execution time for different combinations of word length
and number of cells.