Event Detection-Free Framework for Transient Stability Prediction via Parallel CNN-LSTMs

Although the transient stability collapse (TSC) rarely occurs in daily power system operations, it has a serious ripple effect on the system reliability. Thus, preventing and mitigating the ripple effects from an event or disturbance are important issues for stable operation. In this regard, phasor measurement unit (PMU) facilities enable the acquisition of synchrophasor information and fast transient stability assessments (TSAs) before a stability collapse occurs. In addition, the application of a data-driven method to PMU data further enables transient stability prediction (TSP) stage from the near real-time event analysis. However, streaming environments require additional considerations, as always-on prediction must be conducted even before the event occurrences are confirmed, while recognizing events in real-time remains challenges. Consequently, this study proposes a framework that combines a parallel convolutional neural network (CNN) and long short-term memory (LSTM) structures to automatically perform an event-based TSP. The proposed method uses single event signals to deliver TSP results as streaming data across all the times, eliminating the false alarm rate (FAR) without requiring the event detection. Finally, the significance of the proposed detection-free TSP (DFTSP) method is verified through case studies, addressing anticipated challenges in real-world applications.