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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was recorded with the same sampling frequency as of real arc furnace current. The real<br />
current and synthetic voltage was discretized in the same manner. Using the algorithm,<br />
future current value was predicted in the discretized <strong>for</strong>m. Assuming the voltage to be<br />
constant, average <strong>power</strong> was obtained <strong>for</strong> predicted current and actual current. For each<br />
phase, comparison of the predicted and actual average <strong>power</strong> <strong>for</strong> 10 different trials has<br />
been reported in Table 4.7.<br />
Table 4.7 Error in the average <strong>power</strong> <strong>for</strong> test RT-D05<br />
Trial<br />
Absolute % error in average <strong>power</strong> of the predicted EAF current<br />
Phase A Phase B Phase C<br />
1 8.13 14.71 8.73<br />
2 6.37 13.01 0.45<br />
3 0.19 6.24 10.18<br />
4 0.19 6.24 10.99<br />
5 3.98 14.71 7.22<br />
6 10.09 13.01 5.87<br />
7 3.45 14.71 11.12<br />
8 9.09 6.24 10.35<br />
9 4.37 6.24 8.73<br />
10 0.66 13.01 0.45<br />
Expected error (%) 4.65 10.81 7.41<br />
RT-E01: As mentioned in the previous chapter, E-category tests belong to the<br />
algorithm applicable <strong>for</strong> <strong>power</strong> conditioning. In this particular test, 100 data points of<br />
current have been taken as the historical data set and 500 points of future current have<br />
been generated. The numbers of cells are variable in this test and are 29, 61, and 62
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