低压配电网中因断零与缺相故障对电网公司造成的安全隐患和经济损失一直是电网公司迫切解决的难题,随着智能化检测设备在电网中普及,可利用智能电表采集的低压侧负载电压和各序电流数据开展故障检测。首先,建立基于Transformer神经网络(TNN)和双向长短期记忆(Bi-LSTM)的混合模型TNN-BL;其次,通过选择合适的损失函数和正则化函数完善模型以进一步提高模型检测性能;最后,采用南网数据集对模型性能进行试验验证。试验结果表明,该方法拥有更有效的特征提取能力,相比于其他故障检测方法具有更高的检测准确度和更强的鲁棒性。

The potential safety risks and economic losses caused by open-neutral and open-phase faults in low-voltage distribution networks have been longstanding challenges for power grid companies. With the popularization of intelligent detection equipment in power grids, fault detection can now be performed using voltage and sequence current data collected by smart meters on the low-voltage side. This paper first established a hybrid model, TNN-BL, based on transformer neural network (TNN) and bi-directional long short-term memory (Bi-LSTM). Secondly, by selecting appropriate loss functions and regularization functions, the model was refined to further improve its detection performance. Finally, the model performance was validated using a dataset from the China Southern Power Grid. Experimental results showed that the proposed method had a more effective feature extraction capability, higher detection accuracy and stronger robustness compared to other fault detection methods.

海南电网科技项目(070400KK52220001)