Resnet-based power system frequency security assessment considering frequency spatiotemporal distribution characteristics

Abstract

Under the background of the accelerated transformation of the modern power system, renewable energy will gradually replace the role of traditional energy in the power system. Because of the randomness, uncertainty, and low inertia of renewable energy, the power system frequency security assessment is becoming a serious issue under the influence of large-scale renewable energy integrations. To achieve the rapid assessment of power system frequency security, a resnet-based frequency security assessment method considering frequency spatiotemporal distribution characteristics is proposed. In this paper, to provide more comprehensive frequency response information, the spatiotemporal characteristics of frequency are introduced and integrated into the input feature set. At the same time, the Resnet network and multi-task-learning framework are applied to construct a frequency security assessment model. Based on the experimental results on the modified IEEE39 bus system, the assessment accuracy is higher than 99%, which shows the excellent assessment performance of the proposed model.

Keywords

• Deep learning
• Frequency security
• Power system
• Multi-task-learning