基于物联网与神经网络的大型场馆智能保电巡检系统

Intelligent Power Maintenance Inspection System for Large Venues Based on Internet of Things and Neural Network

针对目前大型场馆电力设施复杂化,巡检管理分散性较大,缺乏统一平台管控电力设备运行情况、巡检业务、资源整合等问题,提出了一种基于神经网络电力负荷预测的非侵入式智能保电巡检系统。在已投入使用的场馆中,设计了后装保电巡检系统的整体架构,该架构由电力参数与温度监测系统、无线通信系统、智能云端巡检服务平台构成。采用了开口式互感器和无源无线温度传感器,实现非侵入式作业与测量。采用本地通信管理机和云端平台同时数据展示,实现云端实时监测与预警。采用LSTM长短期记忆神经网络模型对短期电力负荷进行预测,实现巡检保电措施的提前落实。

In view of the complexity of power facilities in large venues, the large dispersion of inspection management, the lack of a unified platform to control the operation of power equipment, inspection business, resource integration and other issues, a non-invasive intelligent power maintenance inspection system based on neural network power load forecasting is proposed.Considering the venues that have been put into use, the overall architecture of the post installation power inspection system is designed, which consists of power parameter and temperature detection system, wireless communication system and intelligent Cloud inspection service platform.The open transformer and passive wireless temperature sensor are used to realize non-invasive operation and measurement.Local communication management machine and cloud platform are used to display data at the same time to realize real-time detection and early warning of cloud.LSTM short-term and long-term memory neural network model is used to predict the short-term power load, so as to implement the patrol inspection and power protection measures in advance.