Fig.10 Mean absolute error rate and actual values of
testing samples ranging from 1.30 p.u. to 1.36 p.u.
Fig.11 Mean absolute error rate and actual values of
testing samples ranging from 1.38 p.u. to 1.44 p.u.
In addition, the 2-D nomogram of improved DT model is depicted in Fig.
12, where the splitting rules of each internal node during the DT
generation process is indicated. In addition, the predicted value,
sample size, and node purity of each terminal node can be obtained,
achieving the visualization of the decision-making process.
Fig.12 2-D nomogram of improved DT model
Prediction effect of integrated method
(1) Theoretical analysis results
The simulation parameters of test system are as follows:U dN=±400kV; R d=6.04Ω;U N=800kV; Q CN=4000MVar;I dmin=0.5kA; the rated DC current is 5kA; the
rated active power is 1000MW. Considering a typical operation scenario,Q ac=105MVar; the output of synchronous generators
and renewable energy generation system is 5000MW and 1500MW,
respectively. When the S C is 32000MVA, assuming
that the single CF occurs in Qingyu DC, the transient overvoltage can be
calculated as 1.328 according to . The simulation results are depicted
in Fig. 13, which validates the effectiveness of the theoretical
analysis method.
Fig.13 Voltage response curves of Qingnan bus
(2) Prediction effect of integrated method
Based on the overvoltage theoretical analysis method in section 3.1.1,
the theoretical overvoltage peak values of 4480 samples generated
offline are calculated and adopted as partial input features of the
training samples. Along with the key electrical inputs and output labels
obtained by PSASP in section 3.1.2, the sample set is formed to train
the DT model, revealing the association pattern between theoretical
analysis values and true values. Based on the well-trained integrated DT
model, the regression prediction results are compared with traditional
DT model. Results of the overvoltage peak value at Qingnan 750kV bus
under different prediction models are shown in Fig. 14. It can be
concluded that the integrated model proposed in this paper has a better
regression prediction effect on the testing data set, and the predicted
values are closer to the true values.
Fig.14 Prediction results of data-driven method and
integrated method
Furthermore, performances of the traditional DT model, improved DT model
and integrated method are compared in Table 3.
Table 3 Prediction effect under different evaluation indices