Hybrid analytical and data-driven modeling based instance-transfer method for power system online transient stability assessment

Data-driven methods are widely concerned and show good effects in online transient stability assessment. However, tedious and time-consuming sample collection process is always neglected. In power systems, continuous changes in operation mode require repetitive sample collection process, underlining importance of collection efficiency. To achieve high sample collection efficiency after operation mode change, a new instance-transfer method based on compressing and matching strategy is proposed, by which useful previous samples are inherited directly for new sample base construction. Further, a hybrid model is put forward to guarantee rationality in sample similarity comparison and selection, where high-importance features of analytical models are introduced to data-driven methods. Meanwhile, data-driven method is also utilized to realize fast error correction of analytical models in the hybrid model, helping achieve fast and accurate post-disturbance transient stability assessment. As a paradigm, scheme for online critical clearing time (CCT) estimation is concerned, where integrated extended equal area criterion (IEEAC) and extreme learning machine (ELM) are used as analytical model and data-driven error correction model part respectively. Results validate effectiveness of the proposed method.