Quaternion Neural Network in Application of MPPT Controller

Proposed article deals with implementation of quaternion neural network in application of maximum power point tracking controller (MPPT) for photovoltaic panel. The research compared ordinary 2-layer feedforward neural network with quaternion neural network. The quaternionic model confirmed promising computational potential in terms of final mean square error, 84% reduction of trainable parameters and 57% reduction of multiplication operations. Reduced computational complexity of quaternion neural network-based MPPT controller makes it possible to increase the switching frequency of DC-DC converters, and therefore to use smaller and cheaper electrical components.