symbolic dynamic models for highly varying power system loads

42
After predicting the future data, predicted data set is shifted back to the original,
by subtracting the constant b. This is done in order to get the true value of RMS error,
when compared with the actual values.
In order to assess the performance of the Symbolic Dynamic algorithm, several
parameters are varied. These include: word length, number of historical data points,
number of predicted data points and number of cells used for transforming data. All these
statistics have been tabulated in Table 4.2 for different tests.
4.4 Test results
RT-D01: In this test, measurements for 18 cycles were used as historical data set
and 12 cycles of current were predicted using the load-modeling algorithm. Results
obtained are reported in Table 4.3. In the table, two types of error in the RMS value have
been shown: one is the deviation from the RMS historical data and the other is the
deviation from the actual RMS current. Three trials of the test have been reported in the
table. Deviation of RMS value from the historical data is less than 10% in all the cases
while the deviation from the actual data is greater than 10% in two out of three cases,
which indicates that the accuracy of the result is not very good. In Table 4.3 to Table 4.9,
the units of current (used in average and RMS values) are scaled values. Because the
scale factor varies from test to test, the average and RMS currents reported in Table 4.3