Static Voltage Stability Margin Prediction of Island Microgrid Based on Tri-Training-Lasso-BP Network

In this paper, neural network, semi-supervised training, integrated learning, and other techniques are applied to the prediction and analysis of static voltage stability margin of island microgrid power systems and an online prediction method based on the Tri-Training-Lasso-BP network is proposed. T...

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Main Authors: Tang, Yingqi, Tang, Kunjie, Zhu, Chengzhi, Dong, Shufeng, Lin, Liheng, Wu, Jincheng
Format: Conference Proceeding
Language:English
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ensemble learning
island microgrid
static voltage stability margin
Tri-Training-Lasso-BP network
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Tang, Kunjie
Zhu, Chengzhi
Dong, Shufeng
Lin, Liheng
Wu, Jincheng
description In this paper, neural network, semi-supervised training, integrated learning, and other techniques are applied to the prediction and analysis of static voltage stability margin of island microgrid power systems and an online prediction method based on the Tri-Training-Lasso-BP network is proposed. The network consists of Tri-Training, the least absolute shrinkage and select operator (Lasso) algorithm and the backpropagation (BP) neural network. The analysis results on an 115-node example show that the proposed method can reduce the requirement of the data volume of the training set, take advantage of the massive data collected during the daily operation of the power system, improve the prediction accuracy of the network and reduce manual intervention. Finally, this paper uses statistical methods to make a comprehensive and objective description of the performance of the method.
doi_str_mv 10.1109/PESGM41954.2020.9282010
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subjects ensemble learning
island microgrid
static voltage stability margin
Tri-Training-Lasso-BP network
title Static Voltage Stability Margin Prediction of Island Microgrid Based on Tri-Training-Lasso-BP Network
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