학술논문

ANN Driven FOSMC Based Adaptive Droop Control for Enhanced DC Microgrid Resilience
Document Type
Periodical
Source
IEEE Transactions on Industry Applications IEEE Trans. on Ind. Applicat. Industry Applications, IEEE Transactions on. 60(2):2053-2064 Apr, 2024
Subject
Power, Energy and Industry Applications
Signal Processing and Analysis
Fields, Waves and Electromagnetics
Components, Circuits, Devices and Systems
Microgrids
Voltage control
Resilience
Real-time systems
Impedance
Artificial neural networks
Adaptive systems
DC-DC converter
artificial neural networks
DC microgrid
sliding mode control
Language
ISSN
0093-9994
1939-9367
Abstract
Parallel operation of power converters in islanded DC microgrids exhibits significant trade-off in voltage regulation and current sharing with conventional droop control. The converters exhibit inaccuracies in proportionate sharing of current when subject to heavy and transient loading while sharing a common bus. Moreover, the inaccuracies further persist due to unmodeled dynamics, parametric uncertainties, disturbance in the system and communication reliability. Therefore, the resilient parallel operation of power converters in DC microgrids requires a robust and fast control strategy that can mitigate the effect of disturbances and maintain regulated bus voltage with proportional current sharing amongst the power converters. Consequently, this work proposes a novel ANN driven droop control for a DC microgrid to enhance the transient response and mitigate disturbance in finite time. Two controllers based on adaptive droop strategy are proposed; the primary controller is a generalized Hebb's learning law-based PI integrated controller that can adjust the gains in real time for finite-time disturbance compensation in the networks and the secondary control regulates the bus voltage using fractional order sliding mode control. The effectiveness of the proposed method is evaluated by simulation and experiment and compared with the conventional and distributed droop control methods, proving its robust and adaptive performance for resilient DC microgrid applications.