Research on abnormal diagnosis model of electric power measurement based on small sample learning

Abstract

For a long time, abnormal metering of electricity meters has caused huge economic losses to power grid companies. Abnormal diagnosis of power metering is an important means to ensure the normal operation of electricity meters and power automation operation and maintenance systems and is a hot topic of research for power workers. This article proposes a known measurement anomaly diagnosis model based on small sample learning to address the problem of insufficient labeled samples in power measurement anomaly diagnosis. The embedded network maps samples from the original sample space to the embedded space adjusts the embedded network structure, and improves the loss function. The experimental results show that the improved classification network has a higher recognition accuracy for known anomalies than the original network and other small sample learning models.