Transient Stability Assessment of Large-scale AC/DC Hybrid Power Grid Based on Separation Feature and Deep Belief Networks

Transient stability greatly challenges the safe and stable operation of the power system. Therefore, it is extremely necessary to assessment transient stability quickly and accurately. This paper proposes an intelligent model for transient stability assessment (TSA) based on the separation feature and deep belief networks (DBN) for large-scale AC/DC hybrid power grid. With this model, a training method which is suitable for large-scale AC/DC hybrid power grids was proposed. According to the correlation degrees between different electrical characteristics and power system transient stability, the input features are separated into operation mode and disturbance mode, and then, using DBN to extract the features of the two types of input data respectively. In order to adapt the large-scale AC/DC hybrid power grid, different pre-training sample sets are applied for different DBN feature extraction networks to improve the generalization ability and reduce the complexity of model training. Test results with the proposed model on the 265-bus AC/DC hybrid power grid designed by the China Electric Power Research Institute indicated that the new model is easy to train, and it can effectively reduce the influence of imbalanced samples, with good generalization ability.