基于LSTM⁃BP组合模型的配电台区低电压预测

A low voltage prediction based on LSTM-BP combined model for distribution station area

台区低电压预测是实现配电网低电压问题及时治理的关键。现有低电压预测方法大多依赖于对电网拓扑参数和用户信息的采集,存在所需数据繁杂、实时性较差、预测误差大的缺点。为此,提出一种基于LSTM-BP组合模型的台区低电压预测方法。首先,通过分析台区低电压的形成机理,得出影响节点电压的主导因素为用户功率值。基于此,利用长短期记忆网络(LSTM)实现配电台区各节点用户负荷曲线的短期预测。接着,利用BP神经网络的自学习能力,建立节点用户功率和节点电压间的非线性映射关系。通过有效组合上述2个神经网络模型,实现以历史负荷数据精准快速预测台区所有节点用户的未来电压情况。最后,以某台区为研究对象,对比实际电压和本文预测所得电压数据,结果表明,在1 V误差范围内,预测准确率为99.98%,与传统电压预测方法相比,本文所提方法在线实施过程无需台区拓扑结构、线路参数、用户电压等信息,即可实时实现台区低电压的简单、快速、精确预测。

The voltage prediction of the station area is the key to realize the timely management of the low voltage problem in distribution network.Most of the existing voltage prediction methods rely on the collection of power grid topology parameters and electricity information,which have the disadvantages of complicated required data,poor real-time performance,and large prediction errors.Therefore,this paper proposes a voltage prediction method based on the combined LSTM-BP model.Firstly,by analyzing the low-voltage formation mechanism in the station area,it is found that the dominant factor affecting the bus voltage is the users’power consumption.Then the LSTM neural network is used to realize the short-term forecasting of load curves in the distribution station area,and the self-learning ability of BP neural network is used to establish the mapping relationship between users’power and users’voltage.By effectively combining the above two neural network models,the historical load data can be used to accurately and quickly predict the future low voltage situation in the station area.Finally,taking a station area as the research object and comparing the actual voltage data with the voltage data predicted in this paper,the results show that within the 1V error range,the prediction accuracy is 99.8%.Compared with the traditional voltage prediction method,during online implementation,the method proposed in this paper can realize real-time and accurate low voltage prediction without the need of information such as station topology,line parameters,users’voltage,etc.