Using modified prediction interval-based machine learning model to mitigate data attack in microgrid

Zhangfan Ye

doi:10.1016/j.ijepes.2021.106847

Using modified prediction interval-based machine learning model to mitigate data attack in microgrid

2021-07-01

11

PRE

AI

DOI：10.1016/j.ijepes.2021.106847

Original

Original request for help

Save

Abstract

En 中文

Recently, microgrids (MGs) have been attracted more attention due to their technique and economic advantages. However, along with these advantages, because of the cyber and physical structure of MGs, they are more prone to cyber and physical attacks. To this end, in this paper, a new machine learning framework is developed to detect and mitigate the fake data. More specifically, a new machine learning technique has been developed, which is mainly based on the long-short term memory (LSTM); however, modified with recurrent neural network (RNN) and prediction intervals (PIs). Finally, an evolutionary algorithm has been used to address the nonlinearity and complexity associated with the problem. The proposed framework is tested on real MG data. Results show the efficiency and merit of the proposed techniques, compare to the conventional techniques.

Keywords:

MG operation

Cyber resilience

Prediction interval

Data integrity

M-GWO

LSTM

LUBE

Using modified prediction interval-based machine learning model to mitigate data attack in microgrid

Abstract

AI Summary

Journal

Organization