Renewable Energy Short Circuit Ratio Prediction Based on Spiking Neural Networks

The integration of renewable energy power significantly influences the voltage support strength of the power system. With the rapid increase in renewable energy generation capacity in recent years, the power system faces mounting pressure. The short circuit ratio (SCR), a key indicator of voltage support capability, plays a crucial role in the regulation of power systems. Therefore, we propose a renewable energy SCR Prediction model based on spiking neural networks (SNNs). Moreover, this model takes into consideration the correlation between wind power generation and SCR, enabling accurate multivariate prediction. The prediction results on real-world power system data from renewable energy wind farms in north-ern China demonstrate the effectiveness of our proposed SNNs prediction model. And the SNNs model can be deployed on edge devices for low-power and efficient online prediction.