A Novel Intelligent Intrusion Detection and Prevention Framework for Shore-Ship Hybrid AC/DC Microgrids Under Power Quality Disturbances

Abstract

This paper presents an adaptive deep neural network (DNN) approach for intrusion detection and prevention in shipboard AC/DC microgrids, focusing on shore-to-ship power connections and power quality (PQ) challenges. The method addresses false data injection attacks (FDIAs) that disrupt secondary control, leading to voltage and current regulation failures. By integrating Fast Fourier Transform (FFT) for frequency-domain feature extractions such as voltage sag/swell, harmonics, and transient distortions with DNN classification, the model achieves high accuracy in detecting cyberattacks under PQ disturbances. Optimized using the Adam optimizer and ReLU-sigmoid activation, the framework enhances detection accuracy while reducing false positives. The obtained results demonstrate superior performance of FFT-DNN over state-of-the-art methods, achieving 97.7% accuracy across attack scenarios. The approach effectively classifies between cyber-intrusions and power quality disturbances, offering a scalable cybersecurity solution for maritime systems. This study advances secure and resilient shore-ship DC microgrids by addressing cybersecurity vulnerabilities and power quality challenges in shore-power connections.