Optimal Frequency Regulation in Multi-Microgrid Systems using Federated Learning

• Main Authors: Irudayaraj A.X.R., Wahab N.I.A., Veerasamy V., Premkumar M., Ramachandaramurthy V.K., Gooi H.B.
• Other Authors: 57216703079
• Format: Conference Paper
• Published: Institute of Electrical and Electronics Engineers Inc. 2024
• Subjects: and Load Frequency Control; Federated learning; Multi-microgrid system; Zeroing Neural Network; Controllers; Electric control equipment; Electric frequency control; Electric loads; Learning systems; Microgrids; Proportional control systems; Three term control systems; Two term control systems; And load frequency control; Control strategies; Load-frequency control; Microgrid systems; Multi micro-grids; Network-based; Neural-networks; Zeroing neural network; Neural networks

This paper proposes a Federated Learning-based Zeroing Neural Network (FL-ZNN) tuned optimal proportional-integral-derivative (PID) control strategy for frequency control of Multi-Microgrid (MMG) system. The proposed FL-ZNN technique employs a distributed learning approach that allows each neuron to train the network based on its own local data. The local models are then aggregated into a global model, which is used to update the neurons of the network to auto-tune the PID controller's parameters in each microgrid. The proposed FL-ZNN-based PID controller is able to provide robust and efficient frequency control in MMG under different operating conditions, including successive load variations and communication delay. Simulation results demonstrate the effectiveness and superiority of the proposed FL-ZNN-based control strategy over the ZNN PID, and conventional ZNN controller in terms of response time, overshoot, and settling time. Further, the proposed controller has been validated using Hardware-in-the-Loop (HIL) in OPAL-RT. � 2023 IEEE.