A Method for Power Flow Estimation in Power Systems with Incomplete Data

Machine learning-based power flow estimation is a critical technique for modeling the operational state of power systems. However, traditional power flow estimation methods face limitations under conditions of low-quality measurements. The challenge of accurately performing power flow estimation wit...

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Bibliographic Details
Published in:Asia Conference on Power and Electrical Engineering (Online) pp. 2268 - 2272
Main Authors: He, Xuzhong, Yin, Yushan, Wang, Yuhong, Zheng, Zongsheng, Zhou, Xu
Format: Conference Proceeding
Language:English
Published: IEEE 15.04.2025
Subjects:
Accuracy
Autoencoders
Convolutional autoen-coder
Data models
Estimation
Feature extraction
Incomplete Data
Load flow
Power flow estimation
Power system reliability
Power system stability
Robustness
Stability analysis
ISSN:2996-2951
Online Access:Get full text
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Summary:Machine learning-based power flow estimation is a critical technique for modeling the operational state of power systems. However, traditional power flow estimation methods face limitations under conditions of low-quality measurements. The challenge of accurately performing power flow estimation with missing data has become a key area of research. This paper proposes a convolutional autoencoder model to enhance power flow estimation in power systems. The convolutional layers effectively capture local dependencies in the power flow data, while the autoencoder extracts the intrinsic features of the flow, which are then used to repair missing data. Validation on a large dataset demonstrates the model's ability to restore power flow data across varying missing data rates, showing excellent accuracy and stability.
ISSN:2996-2951
DOI:10.1109/ACPEE64358.2025.11040401