Maximum frequency deviation assessment with clustering based on metric learning

Loss of massive generation caused by HVDC blocking or tripping of large power plants is a severe threat to the frequency security of receiving-end grids, especially those with a high penetration level of renewable generation. Due to the uncertainty of renewable generation, a large number of scenarios need to be checked for hour-ahead frequency security assessment, which is time-consuming if they are assessed with full time-domain simulation. To improve assessment efficiency, a maximum frequency deviation assessment method with clustering based on Metric Learning (ML) is proposed in this paper. The distance measure of samples is firstly adjusted by ML with Kernel Regression to make samples with similar frequency dynamics close to each other. The training process of ML is optimized with Mini-Batch Gradient Descent (MBGD) method. Then fuzzy k-means clustering is used to group the samples into multi clusters by maximizing the membership degree of each training sample with the distance measure adjusted by ML. Finally, corresponding to each cluster, a Support Vector Regression (SVR) model is established to build the relationship between maximum frequency deviation and steady-state power flow features, and Core Vector Regression (CVR) is used to accelerate the training process of SVR. The membership degree of the new scenario to assess with respect to each cluster is calculated according to the distance measure learned by ML to classify it into a specific cluster in the on-line assessment process. The corresponding SVR model is used to assess the maximum frequency deviation of the new scenario. The New England 39-bus system and a simplified provincial power system of China are adopted to verify the validity of the proposed assessment method.