An Intelligent Transient Stability Assessment Framework With Continual Learning Ability

Data driven method based on big data and deep model is an effective tool to solve the problem of online power system transient stability assessment. Nevertheless, when the system suffers from some large disturbances such as the changes of topology, there is no proper way for a model to supplement the original knowledge base but to retrain it from scratch. Meanwhile, with the continuous emergence of new scenarios, data management has become a new dilemma reflected in the increase of data accumulation and retraining time. To address these issues, a novel TSA framework with continual learning ability is proposed. First, the improved convolutional neural network-based orthogonal weight modification algorithm is selected as a transient stability predictor. Then, large disturbance is regarded as a new recognition task which can be continuously learned by the predictor. Through the proposed updating process, the original knowledge base can be updated and supplemented only by the data corresponding to new scenario. The test results on two power systems show that the proposed scheme can realize the tradeoff between the integrity of knowledge base and the update speed without losing too much accuracy in a limited capacity.