A Cyber-Physical Architecture for Microgrids based on Deep learning and LORA Technology

no code implementations • 14 Dec 2023 • Mojtaba Mohammadi, Abdollah KavousiFard, Mortza Dabbaghjamanesh, Mostafa Shaaban, Hatem. H. Zeineldin, Ehab Fahmy El-Saadany

Additionally, since the cyber layer of smart grids is highly vulnerable to cyber-attacks, t1his paper proposes a deep-learning-based cyber-attack detection model (CADM) based on bidirectional long short-term memory (BLSTM) and sequential hypothesis testing (SHT) to detect false data injection attacks (FDIA) on the smart meters within AMI.

Cyber Attack Detection energy management +1

Deep Learning-Based Cyber-Attack Detection Model for Smart Grids

no code implementations • 14 Dec 2023 • Mojtaba Mohammadi, Arshia Aflaki, Abdollah KavousiFard, Mohsen Gitizadeh

In this paper, a novel artificial intelligence-based cyber-attack detection model for smart grids is developed to stop data integrity cyber-attacks (DIAs) on the received load data by supervisory control and data acquisition (SCADA).

Cyber Attack Detection Load Forecasting +1

AI-based Optimal scheduling of Renewable AC Microgrids with bidirectional LSTM-Based Wind Power Forecasting

no code implementations • 8 Jul 2022 • Hossein Mohammadi, Shiva Jokar, Mojtaba Mohammadi, Abdollah KavousiFard, Morteza Dabbaghjamanesh

In terms of the operation of microgrids, optimal scheduling is a vital issue that must be taken into account.

Scheduling