Data-driven strategy for three-phase unbalance governance in distribution transformer districts
DOI:
https://doi.org/10.52152/4128Keywords:
Three-phase unbalance, Date-Driven, Distribution Transformer Districts, Phase-identification, Multi-time-scale predictionAbstract
Addressing the critical issue of three-phase unbalance in power systems that affect stable operation, this paper proposes a data-driven strategy for managing three-phase unbalance. By utilizing smart meter data, a voltage correlation data-driven model is employed to achieve phase identification, and a multi-time-scale prediction method is adopted to enhance accuracy. Furthermore, real-time monitoring of load phase changes is conducted through online gradient detection, and a gradient-based load phase adjustment model is constructed. Additionally, an unbalance compensation algorithm is introduced to dynamically adjust the load distribution based on real-time data. In practical application in a low-voltage area, the proposed method significantly reduced the degree of three-phase unbalance, validating its effectiveness and practicality, and providing a new solution to improve the stability and efficiency of power systems.
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Copyright (c) 2025 Cheng Gong, Hao Ma, Yifei Li, Ying Zhang, Bin Zhao (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
