目前,低压配电台区线损问题日益突出,而传统监测方法效率低下.提出一种基于近场通信(Near Field Communication,NFC)的低压配电台区线损变化趋势监测方法,通过构建NFC网络实现高频率、高精度的数据采集,利用长短期记忆(Long Short-Term Memory,LSTM)深度学习模型进行线损趋势预测.实验结果表明,与传统方法相比,该方法在预测精度、实时性以及适应性方面均有显著优势,尤其在短期预测中表现突出,为精细化线损管理提供了新的技术支持.
Line Loss Change Trend Monitoring of Low-Voltage Distribution Station Area Based on NFC
At present,the problem of line loss in low-voltage distribution station area is increasingly prominent,while the efficiency of traditional monitoring methods is low.In this paper,a monitoring method of line loss trend in low-voltage distribution station area based on Near Field Communication(NFC)is proposed.By constructing NFC network,high-frequency and high-precision data acquisition is realized,and the line loss trend is predicted by using Long Short-Term Memory(LSTM)deep learning model.The experimental results show that compared with traditional methods,this method has obvious advantages in prediction accuracy,real-time performance and adaptability,especially in short-term prediction,which provides new technical support for refined line loss management.
Near Field Communication(NFC)low-voltage power distributionline loss monitoring
NETL
NSTL
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