配电网台区重过载风险预测方法

Heavy Overload Risk Prediction Method for Distribution Network Area

随着电力用户的陆续接入,区域电网负荷不断增加,在负荷高峰时部分配变处于重过载状态。配变长时间处于不正常状态容易导致设备故障,影响用户可靠性。重过载风险预测能够提前预测不正常状态,通过采取措施消除运行隐患,提供高质量的供电服务。通过对配电网台区运行负荷数据的分析,结合重过载内外部因素,设计预测算法,建立重过载预测模型,最后运用BP神经网络方法对模型进行验证,以达到重过载风险预测的目的。

With the continuous access of power users,the load of regional power grid is increasing and some distribution transformers are in heavy overload state at peak load.Distribution transformer in abnormal state for a long time is easy to cause equipment failure,affecting user reliability.Heavy overload risk prediction can predict abnormal state in advance and provide high quality power supply service by taking measures to eliminate hidden dangers.Based on the analysis of the operation load data of the distribution network area,combined with the internal and external factors of heavy overload of distribution transformers,the prediction algorithm is designed and the heavy overload prediction model is established.Finally,the BP neural network method is used to verify the model,so as to achieve the purpose of heavy overload risk prediction.