基于多重生成对抗网络的智能开关设备状态感知与诊断研究

随着电力物联网数据驱动技术的不断发展,传感器采集的设备量测数据规模爆发式增长,海量异构的多源监测数据给智能开关设备的实时状态感知和诊断带来了新的挑战.针对上述问题,提出一种基于多重生成对抗网络和DS证据理论的开关设备状态感知方法.首先基于DS证据理论构造融合视频、温度、压力、姿态传感器等多源数据的基本信任分配,获取表征...

Research on state perception and diagnosis of intelligent switches based ontriple generative adversarial networks

With the continuous development of data driving technology in the power Internet of Things, the scale of device measurement data collected by sensors has grown enormously. Massive and heterogeneous multi-source monitoring data has brought new challenges to real-time state perception and diagnosis of intelligent switches. To tackle these challenges, a switches state perception method based on a Triple Generative Adversarial Network (TGAN) and DS evidence theory is proposed. First, based on DS evidence theory, basic belief assignment that combines multi-source data such as video, temperature, pressure, and attitude sensors are constructed to obtain characteristic information that shows the state of switches. Based on this characteristic information and state classification, a TGAN that includes sample generation, data classification, and feature recognition is established. Comparison, association, clustering and other algorithms are combined with stochastic gradient descent to update layer parameters. Finally, the state perception and diagnosis of switches are achieved. Switches of a certain regional power grid are taken as an example. Analytical results show that the method can accurately perceive the real-time status of switches and raise an alarm for abnormal information.This work is supported by the National Natural Science Foundation of China (No. 51877072) and the Science and Technology Project of the Headquarters of State Grid Corporation of China (No. 5216A0180002).