A Stochastic Digital Implementation of a Neural Network Controller for Small Wind Turbine Systems

Abstract

This letter presents a reconfigurable hardware implementation of feed-forward neural networks using stochastic techniques. The design is based on the stochastic computation theory to approximate the nonlinear sigmoid activation functions with reduced digital logic resources. The large parallel neural network structure is then implemented on a reconfigurable field-programmable gate array (FPGA) device with high fault tolerance capability. The method is applied to a neural-network based wind-speed sensorless control of a small wind turbine system. The experimental results confirmed the validity of the developed stochastic FPGA implementation. The general design method can be extended to include other power electronics applications with different feed-forward neural network structures