A Novel Data Driven Model for Voltage Stability Status Prediction and Instability Mitigation

Abstract

An intelligent power system is either a system that is smartly designed from zero to 100, or a system that was not smartly designed but currently uses all its facilities to be smartly operated in different sectors. This paper presents a novel data-driven model for real time voltage instability diagnosis and instability mitigating. The method combines deep recurrent neural techniques to forecast future voltage stability and mathematical morphology (MM) tools to pinpoint the specific on-load tap changers (OLTCs) contributing to instability and issuing blocking commands to prevent their operation and consequently instability. The approach for voltage stability assessment is centralized, using real-time data, while the method for voltage instability mitigation is localized, focusing on real-time voltage magnitude related to the secondary side of the load transformer. The network was trained and tested on the Nordic32 test system. Results show that the method accurately predicted the stability status just one second after a disturbance, and successfully mitigated all voltage instability events related to load restoration by blocking only the OLTCs that were effective in causing instability. This selective approach provides a significant selectivity index and improves the system resiliency index.