Adaptive PI secondary control for smart autonomous microgrid systems.

In this paper, the authors present a neural-network-based distributed secondary control to regulate the output voltage and frequency of a smart autonomous microgrid system. Generally, the secondary controller is implemented in a centralized manner using a fixed-gain proportional-plus-integral controller which may perform well under certain operating conditions only. Also the failure of centralized controller implies no secondary control action for the entire system. The control technique proposed in this paper is a distributed one and makes use of neural network (NN) concept to improve the performance of system. A well-trained NN supplies the controller with suitable gains according to each operating point. Before training the NN, evolutionary optimization technique, differential evolution, is employed to obtain the optimal gains of controller at each operating load condition which forms the training set for NN. Simulation results show that the proposed controller damps the oscillations caused by load changes, restores the output voltage and frequency of the system to their nominal values, and maintains proper load sharing property of the baseline controller. The performance of the controller is also compared with fixed-gain controller. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]