Quiescent voltage stability margin prediction method based on Tri-Training-Lasso-BP network

Abstract

The invention discloses a quiescent voltage stability margin prediction method based on a Tri-Training-Lasso-BP network. The quiescent voltage stability margin prediction method applies technologies such as a neural network, semi-supervised training and ensemble learning to prediction of the quiescent voltage stability margin of the power system, proposes an online prediction method based on the Tri-Training-Lasso-BP network, and is formed by a three-body training method (Tri-Training), a least absolute shrinkage and select operator Lasso method, and a BP (back propagation) neural network. Themethod is characterized in that the method is composed of a Lasso method and a BP (back propagation) neural network. The quiescent voltage stability margin prediction method can reduce the requirements on the data quantity and quality of the training set, gives play to the advantages of mass data collected in the daily operation process of a power system, improves the generalization capability and prediction precision of a network, reduces the manual intervention, and irons out the problems that a conventional method is difficult to achieve the online real-time prediction of the voltage stability margin, needs a large number of training samples, and is liable to cause the over-fitting.

Description

technical field [0001] The invention relates to the field of static voltage stability margin prediction of electric power system, in particular to a static voltage stability margin prediction method based on Tri-Training-Lasso-BP network. Background technique [0002] Static voltage stability is the basis and important evaluation index for safe and stable operation of power system. In the static voltage stability analysis of power system, the voltage stability margin index can provide intuitive information. The traditional voltage stability margin assessment methods are all based on the power flow equation, the calculation speed is slow, and it is difficult to meet the requirements of online real-time prediction. With the rapid changes of the grid structure of the power system, the traditional off-line method of evaluating the voltage stability margin of the power system is difficult to meet the requirements of speed and accuracy. [0003] The method of using offline simula...

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Problems solved by technology

In addition, such a method is prone to overfitting, which will reduce the generalization ability of the network and affect the prediction accuracy.

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