Reservoir-computing-based, biologically-inspired artificial neural network for modeling of a single machine infinite bus power system
Jing Jie Dai, Ganesh Kumar Venayagamoorthy, Ronald G. Harley, Steve M. Potter · 2012
Inspired by living neuron networks (LNNs) in the brain, artificial neural networks (ANNs) have been broadly used in various applications as a computational intelligence tool. However, due to many fundamental differences between ANNs and LNNs, despite the mature training mechanisms for ANNs, it is often challenging to use LNNs as a computational intelligence tool. To bridge the gap between ANNs and LNNs, a novel type of artificial neural network, i.e. biologically-inspired artificial neural network (BIANN) is proposed in this paper. The BIANN, which is based on spiking neuron models of LNNs, processes information in a more “brain-like” fashion than conventional ANNs. A reservoir-computing-based training approach is also proposed for BIANNs to serve as a novel modeling and control tool for practical applications. The feasibility of the proposed BIANN is illustrated for the prediction of a synchronous generator's speed and terminal voltage signals in a single machine infinite bus electric power system setup. The proposed BIANN model is able to provide an accurate prediction for online monitoring of a generator.