Protection of Power System during Cyber-Attack using Artificial Neural Network
DOI:
https://doi.org/10.18034/ei.v7i2.478Keywords:
Load Frequency Control (LFC), Automatic Voltage Regulator (AVR), cyber-attack, cyber-security, Artificial Neural Network (ANN), Genetic Algorithm (GA), Proportional-Integral-Derivative (PID), supervisory control and data acquisition (SCADA)Abstract
Impacts of frequency and voltage disturbance on an isolated power system caused by cyber-attack have been discussed, and a neural network-based protective approach has been proposed in this research work. Adaptive PID controllers for both load frequency control and automatic voltage regulator have been implemented using an artificial neural network-oriented by genetic algorithm. The parameters of the PID controller have been tuned offline by using a genetic algorithm over a wide range of system parameter variations. These data have been used to train the neural network. Three input switch has been implemented to control governor speed regulation and amplifier gain. For load frequency control neural network tuned PID controller mitigate frequency disturbance 48% faster than manually tuned PID and for the automatic voltage regulator, neural network tuned PID controller mitigate voltage disturbance 70% faster than manually tuned PID during cyber-attack.
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