Based on the comparison presented in Table 1, it can be concluded that
the integration of analytical method and traditional DT model enhances
the performance of both methods. Specifically, the proposed method has
achieved a 24.7% improvement in the accuracy of the traditional DT
method and a 20.1% improvement in the accuracy of the improved DT
method, as demonstrated by the MAE index. On the one hand, overvoltage
peak values calculated by theoretical analysis method are adopted as the
input features for the DT-based error correction part. On the other
hand, considering the advantages of DT model in mapping relationship
revealing, the error of the analytical method caused by model
simplification can be reduced by the data-driven error correction part.
The integration of model-driven method helps enhance the robustness of
the integrated method to insufficient training sample and the
inappropriate input feature selection.
Conclusion
This paper proposes an improved DT-based overvoltage level prediction
method integrating the model-driven scheme for hybrid AC/DC power grids.
A main factor that directly affects the performance of overvoltage
analysis method is the adaptability to operational scenarios. To address
the above key issue, an improved DT algorithm is proposed to predict the
overvoltage level considering the advantages in mapping relationship
revealing, and the prediction accuracy in high-risk scenarios is
enhanced by modifying the splitting rules in the DT training process. In
addition, a theoretical analysis method for evaluating the overvoltage
peak value of converter buses is proposed with an acceptable calculation
accuracy and the potential for online application, and the mathematical
relationship between the reactive power consumed by the rectifier and AC
voltage is derived. On this basis, an overvoltage analysis method
integrating the model-driven and data-driven techniques is proposed to
enhance the robustness to insufficient training sample and inappropriate
input feature selection, and the DT algorithm is adopted to reveal the
association pattern between theoretical analysis results and true
values, improving the interpretability of regression prediction results.
Simulations on a simplified hybrid AC/DC actual power grid have been
performed to verify the effectiveness of improved integrated method.
Ongoing research is focused on the extraction of input features for the
DT-based integrated method. As described in section 3, the input
features of DT model are comprised of the power system operation
information and voltage dynamic characteristic information. However, as
power grid scale expands and operation complexity increases, the
original input features may contain redundant information, which can
affect the model training time and evaluation accuracy. Consequently,
feature dimensionality reduction technology, such as feature selection
or transformation, should be adopted to obtain more expressive new
features, thereby improving the model training speed and the evaluation
accuracy.
Acknowledgments
This work was supported by the National Key R&D Program of China (No.
2021YFB2400800). The authors are with State Key Laboratory of Electrical
Insulation and Power Equipment, and school of Electrical Engineering,
Xi’an Jiaotong University, Xi’an 710049, Shaanxi Province, China.
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