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

A technology of static voltage stabilization and prediction method, applied in prediction, neural learning method, biological neural network model, etc., can solve the problems of prediction accuracy impact, reduce network generalization ability, etc. The effect of reducing requirements

Pending Publication Date: 2020-01-10
STATE GRID ZHEJIANG ELECTRIC POWER
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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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  • Quiescent voltage stability margin prediction method based on Tri-Training-Lasso-BP network
  • Quiescent voltage stability margin prediction method based on Tri-Training-Lasso-BP network
  • Quiescent voltage stability margin prediction method based on Tri-Training-Lasso-BP network

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[0036] In order to facilitate the understanding and implementation of this invention by those of ordinary skill in the art, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the implementation examples described here are only used to illustrate and explain the present invention, and are not intended to limit this invention.

[0037] The present invention is an online static voltage stability margin prediction method based on the Tri-Training-Lasso-BP network, which puts pseudo-labels on the massive state estimation unlabeled data in the smart grid measurement system, participates in the update of network parameters, and compensates for only using Offline simulation data training network may bring defects such as insufficient sample coverage, improve the generalization performance of the network, reduce the requirements for the number of training set samples and manual interve...

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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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Application Information

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IPC IPC(8): G06Q10/04G06Q50/06G06F30/27G06N3/04G06N3/08
CPCG06Q10/04G06Q50/06G06N3/08G06N3/044
Inventor 唐滢淇董树锋朱承治
Owner STATE GRID ZHEJIANG ELECTRIC POWER
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