Week-Ahead Phase-Separated Overload Warning Method for Distribution Transformers Considering the Spatiotemporal Characteristics of Three-Phase Loads and the Dynamic Safety Loading Region
He Huajin, Ren Zhouyang, Feng Jianbing, Zhang Haifeng
State Key Laboratory of Power Transmission Equipment Technology Chongqing University Chongqing 400044 China
Abstract：With the large-scale integration of new energy sources and diverse load profiles, three-phase imbalance conditions in distribution networks have become increasingly frequent, leading to significant issues of phase-overloaded distribution transformers. Existing pre-warning methods for transformer overloads fail to adequately reveal the spatiotemporal characteristics of three-phase loads, do not meet the accuracy requirements under light load and heavy overload conditions, and overlook the impact of three-phase imbalance on transformer load capacity. This often results in false alarms and missed warnings. To address these challenges, this paper proposes a transformer phase-overload pre-warning method for the week ahead, considering the spatiotemporal characteristics of three-phase loads and the dynamic safety loading region of the transformer. Firstly, the spatiotemporal characteristics of three-phase loads on transformers are explored, and a feature enhancement expression method based on dynamic feature combination selection and spatial representation techniques is established. Secondly, a load forecasting model for transformer phases one week ahead is developed using SwinLSTM-D, which fully exploits the spatiotemporal characteristics of three-phase loads. To meet the high-precision prediction requirements under light load and heavy overload conditions for the pre-warning task, a dual weighted hybrid (DWH) loss function is constructed to optimize the model’s attention distribution pattern. Subsequently, the Dynamic Safety Loading Region of the transformer, which accounts for hotspot temperature rise constraints, is defined to accurately assess the transformer's limit load capacity. Finally, a pre-warning strategy based on the Dynamic Safety Loading Region is proposed to effectively pre-warn against transformer overload issues. Case studies using measured data from a distribution transformer operated by a power supply bureau in southern China validate the proposed method. The results indicate that: (1) The proposed forecasting method exhibits significant accuracy advantages, with annual RMSE errors being 81.25% and 80.95% of the baseline model, and MAPE errors being 80.14% and 72.58% of the baseline model, respectively. Additionally, under light load and heavy overload conditions, the method demonstrates optimal adaptability, with RMSE errors at 58.68% and 78.99%, and MAPE errors at 59.42% and 69.74% of the baseline model, respectively. (2) The proposed pre-warning method achieves precise overload warnings for transformers under three-phase imbalance conditions, outperforming traditional methods. Simulation analysis leads to the following conclusions: (1) Effectively capturing the spatiotemporal characteristics of three-phase transformer loads significantly enhances prediction accuracy. (2) By increasing the model’s attention distribution on samples under light load and heavy overload conditions, the model’s accuracy performance in these scenarios is ensured. (3) By characterizing the dynamic safety loading region of the transformer, the limit load space that meets hotspot temperature rise constraints within the safe operating timeframe is accurately defined, enabling precise pre-warning of transformer overloads.
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2025, Vol. 40 
