An early warning method of transmission line galloping based on Adaboost algorithm
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Abstract:
Transmission line galloping is a worldwide problem which has not been fully understood, and it has seriously threatened the safe and stable operation of a transmission system. We investigated the factors of meteorological environment that influence galloping, and proposed an early warning method of transmission line galloping based on the Adaboost ensemble learning algorithm. In this method, the decision stump based on the Gini index is used as the weak classifier. The prediction result and its confidence are obtained by training and weighted summing of multiple weak classifiers, which are helpful information for the decision making of operators and dispatchers of power grids. The effectiveness of the proposed method is proved by the verification experiment with historical data.
