Application of recurrent neural network for active filter

Active filters remove harmonic current by pouring in a compensation current which is equal to the quantity of harmonic current with opposite sign. They require a high performance harmonic analyzer. Recurrent neural networks (RNN) have the ability of conversion without affecting phase change. They also learn how to convert load current to fundamental current by themselves. These abilities enable RNN to be applied to the harmonic current analyzer of active filters. We suggest such a use for RNN and investigate their ability to eliminate harmonics. We show that RNN can eliminate harmonic current without being influenced by the composition rate and phase of the harmonic current and that they can work as a high performance harmonic analyzer.