<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The construction of an intelligent power grid system has promoted innovation and development in the power grid industry, but the distribution network covers a wide range and has more access data, and the network attack risk is the focus of attention. In this regard, the stochastic forest model is introduced to build the distribution network risk detection model based on the software-defined network. The first is to study the power grid system based on a software-defined network, realize the analysis and extraction of the characteristics of abnormal power grid engineering data, and realize the diagnosis of risk data through a random forest model. At the same time, considering the long modeling time of the random forest model and the low classification accuracy in unbalanced samples, the feature selection model is introduced to optimize the random forest model, and the sampling weight function is set to improve the insufficient sampling accuracy of random forest model in small samples. The model classification effect is evaluated during the model performance test. The proposed model is the most accurate of the three data sets. In the three data classification tests of Satimage, Senbased, and Cleveland, the accuracy of MICRO Average is respectively 0.886, 0.986 and 0.856. At the same time, the proposed model is superior to other models in terms of training time and variance stability test. When the data set is 18000, the model accuracy is 0.968, which is better than the other two models. The research content has important reference value for maintaining communication security and improving system stability in distribution networks.</p>
</abstract>

The construction of an intelligent power grid system has promoted innovation and development in the power grid industry, but the distribution network covers a wide range and has more access data, and the network attack risk is the focus of attention. In this regard, the stochastic forest model is introduced to build the distribution network risk detection model based on the software-defined network. The first is to study the power grid system based on a software-defined network, realize the analysis and extraction of the characteristics of abnormal power grid engineering data, and realize the diagnosis of risk data through a random forest model. At the same time, considering the long modeling time of the random forest model and the low classification accuracy in unbalanced samples, the feature selection model is introduced to optimize the random forest model, and the sampling weight function is set to improve the insufficient sampling accuracy of random forest model in small samples. The model classification effect is evaluated during the model performance test. The proposed model is the most accurate of the three data sets. In the three data classification tests of Satimage, Senbased, and Cleveland, the accuracy of MICRO Average is respectively 0.886, 0.986 and 0.856. At the same time, the proposed model is superior to other models in terms of training time and variance stability test. When the data set is 18000, the model accuracy is 0.968, which is better than the other two models. The research content has important reference value for maintaining communication security and improving system stability in distribution networks.

ReliefF-RF model application in communication security risk detection of distribution network

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

The construction of an intelligent power grid system has promoted innovation and development in the power grid industry, but the distribution network covers a...

ReliefF-RF model application in communication security risk detection of distribution network

Pubblicato online: 25 nov 2023

Pagine: -

Ricevuto: 08 gen 2023

Accettato: 05 giu 2023

DOI: https://doi.org/10.2478/amns.2023.2.01245

Parole chiave
SDN technology, Random forest model, Communication, Data characteristics, Risk detection

© 2023 Jing Zhang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

ReliefF-RF model application in communication security risk detection of distribution network

Pubblicato online: 25 nov 2023

Pagine: -

Ricevuto: 08 gen 2023

Accettato: 05 giu 2023

DOI: https://doi.org/10.2478/amns.2023.2.01245

Parole chiaveSDN technology, Random forest model, Communication, Data characteristics, Risk detection

© 2023 Jing Zhang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
SDN technology, Random forest model, Communication, Data characteristics, Risk detection