A hierarchical self-adaptive method for post-disturbance transient stability assessment of power systems using an integrated CNN-based ensemble classifier

Data-driven approaches using synchronous phasor measurements are playing an important role in transient stability assessment (TSA). For post-disturbance TSA, there is not a definite conclusion about how long the response time should be. Furthermore, previous studies seldom considered the confidence level of prediction results and specific stability degree. Since transient stability can develop very fast and cause tremendous economic losses, there is an urgent need for faster response speed, credible accurate prediction results, and specific stability degree. This paper proposed a hierarchical self-adaptive method using an integrated convolutional neural network (CNN)-based ensemble classifier to solve these problems. Firstly, a set of classifiers are sequentially organized at different response times to construct different layers of the proposed method. Secondly, the confidence integrated decision-making rules are defined. Those predicted as credible stable/unstable cases are sent into the stable/unstable regression model which is built at the corresponding decision time. The simulation results show that the proposed method can not only balance the accuracy and rapidity of the transient stability prediction, but also predict the stability degree with very low prediction errors, allowing more time and an instructive guide for emergency controls.Published versio

Reactive Power Optimization of a Distribution System Based on Scene Matching and Deep Belief Network

With a large number of distributed generators (DGs) and electrical vehicles (EVs) integrated into the power distribution system, the complexity of distribution system operation is increased, which arises to higher requirements for online reactive power optimization. This paper proposes two methods for online reactive power optimization, a scene-matching method based on Random Matrix (RM) features and a deep learning method based on Deep Belief Network (DBN). Firstly, utilizing the operation and ambient Big Data (BD) of the distribution system, we construct the high-dimension Random Matrices and extract 57 state features for the subsequent scene-matching and DBN training. Secondly, the feature-based scene-matching method is proposed. Furtherly, to effectively deal with the uncertainty of DGs and to avoid the performance deterioration of the scene-matching method under a new unknown scene, the DBN-based model is constructed and trained, with the former features as the inputs and the conventional reactive power control solutions as the outputs. This DBN model can learn the nonlinear complicated relationship between the system features and the reactive power control solutions. Finally, the comprehensive case studies have been conducted on the modified IEEE-37 nodes active distribution system, and the performances of the proposed two methods are compared with the conventional method. The results show that the DBN-based method possesses the better performance than the others, and it can reduce the network losses and node voltage deviations obviously, even under the new unknown and unmatched scenes. It does not depend on the distribution system model and parameters anymore and can provide online decision-making more quickly. The discussions of the two methods under different DG penetrations and the historical data volume were given, verifying the adaptability, robustness and generalization ability of the DBN-based method