A fast evaluation method of power limit of transmission section based on hybrid supervised learning

Abstract

The quick， accurate and instantaneous evaluation of power limit of transmission section is of great significance to safe， stable and economic operation of power grid. The paper proposes an adaptive method based on a hybrid supervised learning algorithm. Through numerous simulations and analysis of historic operation modes， characteristic extraction， training， and regular updating of AI model， the correct matching relationship between grid operation status and power limit is established for fast， accurate and instantaneous power limit evaluation of the transmission section. The proposed method employs multiple supervised learning forecast algorithms， including deep neural networks， support vector regression， gradient boosting decision tree， and random forest. The hybrid models for power limit forecasting can make best of the advantages of the algorithms. The effectiveness of the method is verified on a 500-bus transmission model with real operation characteristics.

Keywords

• ai
• transmission section
• machine learning
• power limit forecast
• grid operation condition