LPR-MLP: A Novel Health Prediction Model for Transmission Lines in Grid Sensor Networks

Yunliang Chen,Hao Liu,Shaoqian Chen,Nian Zhang,Honglei Jing,Geyong Min,Weitong Chen

doi:10.1155/2021/8867190

Yunliang Chen, Hao Liu + Show 5 more

Open Access

https://doi.org/10.1155/2021/8867190

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Journal: Complexity	Publication Date: Feb 9, 2021
Citations: 2	License type: CC BY 4.0

Affiliation: China University of Geosciences, University of Exeter

Abstract

The safety of the transmission lines maintains the stable and efficient operation of the smart grid. Therefore, it is very important and highly desirable to diagnose the health status of transmission lines by developing an efficient prediction model in the grid sensor network. However, the traditional methods have limitations caused by the characteristics of high dimensions, multimodality, nonlinearity, and heterogeneity of the data collected by sensors. In this paper, a novel model called LPR-MLP is proposed to predict the health status of the power grid sensor network. The LPR-MLP model consists of two parts: (1) local binary pattern (LBP), principal component analysis (PCA), and ReliefF are used to process image data and meteorological and mechanical data and (2) the multilayer perceptron (MLP) method is then applied to build the prediction model. The results obtained from extensive experiments on the real-world data collected from the online system of China Southern Power Grid demonstrate that this new LPR-MLP model can achieve higher prediction accuracy and precision of 86.31% and 85.3%, compared with four traditional methods.

Highlights

Smart grid has become a critical factor for the healthy development of smart cities and our social lives. e development of smart grids can be improved by modern technologies such as artificial intelligence and Internet-ofings for providing smarter and convenient services
By comparing with the existing random forest (RF) model, Naive Bayes (NB) model, support vector machine (SVM) model, and decision tree (DTree) model, the results show that the proposed hybrid LPR-multilayer perceptron (MLP) approach demonstrates the best performance in terms of accuracy, recall, and precision
We first introduce the generation of the experimental data source and report the performance of the proposed LPR-MLP model by comparing it with the RF model, NB model, SVM model, and DTree model

Summary

Introduction

Smart grid has become a critical factor for the healthy development of smart cities and our social lives. e development of smart grids can be improved by modern technologies such as artificial intelligence and Internet-ofings for providing smarter and convenient services. The icing of transmission lines in the power grid is one of the main causes of grid failures, more factors such as Complexity various meteorology, mechanics, and material properties should be considered in evaluating the health status of transmission lines. Ese data features lead to a low accuracy of the abovementioned models and a high cost of training these models All these algorithms only considered the single particular category of monitoring data but did not consider image data. Erefore, establishing a high-performance model for the health prediction of transmission lines has become a key problem to be addressed urgently. To cope with this challenge, this paper proposes a novel hybrid model called LPR-MLP to predict the health level of transmission lines.

Related Work and Preliminary

Hybrid Prediction Model

Experimental Results

Conclusions and Future Work