A low cost digital implementation of feed-forward neural networks applied to a variable-speed wind turbine system
Da Zhang, Hui Li · 2006
This paper presents a low cost hardware implementation of feed-forward neural networks using VHDL techniques. The design is based on the stochastic theory to achieve the nonlinear sigmoid function with reduced digital logic resources. The large parallel neural network structure is therefore implemented on a low cost 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-ANN-FPGA implementation. The general implementation method can be extended to other power electronics applications with different feed-forward ANN structures.