Instability Prediction in Smart Cyber-physical Grids Using Feedforward Neural Networks

Abstract

Due to the use of huge number of sensors and the increasing use of communication networks, cyber-physical systems (CPS) are becoming vulnerable to cyber-attacks. The ever-increasing complexity of CPS bring up the need for data-driven machine learning applications to fill in the need of model creation to describe the system behavior. In this paper, a novel stability condition predictor based on cascaded feedforward neural network is proposed. The proposed method aims to identify anomaly due to cyber or physical disturbances as an early sign of instability. The proposed neural network utilizes cascaded connections in order to increase accuracy of the prediction. The conjugate gradient backpropagation and Polak-Ribière formula are utilized for training process. This method also can predict the critical generators to mitigate the effect of the cascading failure and consequent blackout in the system. Simulations results on the IEEE 39-bus system indicate the superiority of the proposed method in terms of accuracy, speed, and robustness.

Intelligent Smart Grid Stability Predictive Model for Cyber-Physical Energy Systems

“…The SG data set utilized in this work is open-source data gathered from UC Irvine (UCI) ML source. Jafari et al [14] proposed a new stability state forecaster based cascaded FFNN method. The presented strategy focuses on identifying anomalies as a result of physical or cyber disruptions as an earlier indication of instability.…”

Online Fault Prediction Based on Collaborative Filtering in Smart Grid

“…However, the characteristic of coupled networks is neglected in these works. Jafari et al [9] have realized the interdependence and have used feedforward neural networks to predict instability in smart cyber-physical grids; Malbasa et al [10] have utilized active machine learning for voltage stability prediction to ensure the reliability of the grid system. The optimal operation of the energy storage system, which uses the prediction interval to reduce the peak value of the distribution network, has been proposed to manage the supply-demand balance between power generation and consumption rationally [11].…”