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In machine learning the random subspace method, also called attribute bagging or feature bagging, is an ensemble learning method that attempts to reduce the correlation between estimators in an ensemble by training them on random samples of features instead of the entire feature set.
Method and device for identifying galloping mode of power transmission line based on magnetic field induction
PendingCN122108337ASubsonic/sonic/ultrasonic wave measurementUsing electrical meansRandom subspace methodComputational physics
The application relates to the field of non-contact monitoring of overhead transmission lines, and particularly provides a method and device for identifying the galloping mode of a transmission line based on magnetic field induction. The method comprises the following steps: obtaining the initial magnetic induction intensity of a magnetic dipole in each direction in a space coordinate system when a target overhead conductor gallops; wherein the initial magnetic induction intensity is measured by two magnetic induction sensors above the target overhead conductor; inversely calculating the conductor movement by using a nonlinear optimization method according to the magnetic induction intensity in each direction; wherein the conductor movement comprises the vertical displacement and the torsion angle of the target overhead conductor; and identifying the target mode of the target overhead conductor galloping by using a random subspace method based on the vertical displacement and the torsion angle. The technical scheme provided by the application can improve the accuracy of the galloping mode identification of the transmission line to a certain extent.
Owner:STATE GRID ZHEJIANG ELECTRIC POWER CO LTD QUZHOU POWER SUPPLY CO