Multi‐agent protection scheme for microgrid using deep learning - Abolfazl Najar; Hossein Kazemi Karegar; Saman Esmaeilbeigi (IET Renewable Power Generation, 2024)
Abstract Producing clean energy and feeding critical loads in islanding mode are the main reasons for interest in microgrids. Different operation topologies of microgrids make traditional protection schemes inefficient. This paper proposes a multi‐agent protection scheme in which each protection agent can detect different fault events and isolate faulty phases at a fast rate. A unique algorithm is utilized for determining fault location in microgrids and system operators are informed accordingly. Microgrids have various operation modes due to the stochastic behavior of distributed generators and different topologies. Here, a significant number of operating conditions of the studied microgrid are considered. These operation conditions are simulated in the DIgSILENT Power Factory, and different parameters are stored. Raw measured parameters need to be pre‐processed by a signal processing method in MATLAB. Discrete wavelet transform is chosen for this purpose. Deep learning is used as a machine learning technique due to the various operation modes of the microgrid. Deep neural networks are constructed using Python programming language. The proposed scheme ensures high accuracy in fault detection and fault location in the microgrid, as well as fault isolation in different operation conditions.