基于深度神经网络的低压台区线损异常识别方法

The study of coal⁃to⁃electricity district line loss anomaly identification method based on deep neural network

中文关键词: “煤改电”工程  深度神经网络  线损异常识别  异常点检测

英文关键词: coal to electricity  deep neural network  line loss anomaly identification  the anomaly detection

中文摘要:

      “煤改电”工程改变了电网的负荷特性，对线损造成了重大影响。为降低“煤改电”工程造成的负面影响，进而提高供电单位的效益，以实施“煤改电”工程后的低压台区为研究对象，提出了一种基于深度神经网络的线损异常识别方法。该方法将异常点检测、EM算法及深度神经网络进行结合，建立了线损异常识别模型，预判未实施“煤改电”台区的各项实施后指标是否可能导致线损异常，从而为“煤改电”工程提供指导性建议，以便采取相应措施进行有效降损。

英文摘要:

      The coal to electricity project changes the load characteristic of the power grid, and causes a significant impact on the line loss. In order to reduce the negative influence caused by coal-to-electricity project and improve the efficiency of the power supply unit, the low voltage district after the implementation of the coal-to-electricity project is studied, and then a method of line loss anomaly identification based on deep neural network is proposed.The proposed method establishes a model of line loss outlier identification combing the anomaly detection, EM algorithm and deep neural network. The model can predict whether the indicators of the low voltage district may cause the line loss be abnormal after implementing coal-to-electricity project, and then supply the guidance suggestions for the following coal-to-electricity project, so as to take corresponding measures to reduce the line loss.