Integrated advance assessment of power system transient voltage and transient angle stability based on two-stage ensemble spatio-temporal graph neural network.

• The TVS&TAS integrated assessment scheme based on TSEL-MT-STGNN model is proposed. The scheme can not only assess both TVS&TAS status through adaptive time window, but also achieve high accuracy by fusing spatio-temporal features. • A TVS&TAS integrated margin assessment method is proposed. The method gives intuitive safety margin results based on the safety boundary. • In the training process, a multi-graph parallel training scheme is proposed to speed up the training process. By introducing time constrained loss function (TCLF) and dynamic weight training method, the response time is greatly reduced on the basis of maintaining the existing accuracy. • Based on the ensemble learning framework, STGNN with different structures is integrated. By comparison, the proposed two-stage integration method has higher accuracy and stability in the face of PMU data loss and industrial noise. Transient angle stability (TAS) and transient voltage stability (TVS) are important bases for safe operation of power system. With the replacement of a high percentage of new energy, the voltage and power angle problems are closely coupled, so an integrated assessment method that takes into account the rapidity, accuracy and stability is urgently needed to deal with the system risks. In order to solve this problem, firstly, we propose a multi-task spatio-temporal graph neural network model based on two-stage ensemble learning (TSEL-MT-STGNN) for integrated assessment of TAS&TVS. In the first stage of this model, the topological spatial features of the system are extracted through multi-type graph neural networks, and the obtained knowledge is input to the gated recurrent unit (GRU) for further temporal feature extraction, so as to achieve spatio-temporal information fusion. In this stage, TAS and TVS were combined through the multi-task framework to achieve integrated assessment under the shared features. In the second stage, meta -learner multi-layer perceptron (MLP) is used to fuse the output results of multi-type spatio-temporal graph neural network. Secondly, an integrated assessment method is proposed based on the model, which enables end-to-end assessment, and gives the stability result and safety margin of the system in the current state without knowing the fault clearing time. Thirdly, in order to ensure the timeliness of the assessment method, a multi-graph parallel training scheme is proposed to accelerate the training process. By introducing time constrained loss function and dynamic weight training, the response time is greatly reduced on the basis of maintaining the existing accuracy, thus achieving advance assessment. Finally, the improved New England 39-bus system is taken as an example for verification and analysis. The results show that the proposed method can achieve high-precision advance assessment of TAS&TVS, and has strong robustness and anti-noise capability. © 2017 Elsevier Inc. All rights reserved. [ABSTRACT FROM AUTHOR]