A method and system for regulating class imbalance of sample set of transient voltage stability of power network

Abstract

The invention discloses a method and a system for regulating the unbalance of a sample set type of transient voltage stability of a power network, belonging to the field of power system stability analysis and evaluation. The method of the invention takes the history sample set collected from the power network history record as the main component of the training sample set, generates the predictionsample by using the time domain simulation of the anticipated fault set under the dispatching operation plan, and adopts the Euclidean distance comparison among different types of samples to test thequalification of the prediction sample. Combining the predicted samples and the historical samples, the training sample set for mining and learning is constructed, which can alleviate and adjust theimbalance of the historical sample set. Decision tree algorithm is used to classify and learn the training sample set, and the decision tree model is obtained. The decision tree model is used as the transient voltage stability assessment model in the real-time monitoring process.

Description

technical field [0001] The invention belongs to the field of power system stability analysis and evaluation, and in particular relates to a method and system for adjusting category imbalance of a power grid transient voltage stability sample set. Background technique [0002] With the rapid development of technologies such as data mining and machine learning, many power grids have adopted data mining methods for online monitoring and evaluation of power grid transient voltage stability. The traditional grid transient voltage stability monitoring and evaluation mainly obtains data mining training samples through offline time-domain simulation of the grid, but due to the inherent errors of grid modeling and time-domain simulation, training samples generated through time-domain simulation may bring The problem of insufficient reliability. In order to improve the reliability of the training sample data source, the historical samples of the power grid under various historical fa...

AI Technical Summary

Problems solved by technology

However, the power grid can maintain transient voltage stability in most cases in actual operation, and there are relatively few cases of instability, which will cause a serious imbalance in the categories of historical sample sets collected from historical records

If we do not do anything to deal with the problem of category imbalance, it will lead to too little attention to the unstable samples in the mining training process, resulting in too low recall of the unstable samples in the final grid transient voltage stability evaluation model

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