A comparative experimental study of neural and conventional controllers for an active power filter

This paper will consider the benefits of neural controllers over model-based current regulators to supervise the current generation of a shunt active power filter. The task consists in generating appropriate compensation currents with a system composed of a voltage source inverter and a low-pass filter. These currents cancel the harmonic terms introduced by nonlinear loads in a power distribution grid. The performances of conventional controllers such as a PI and resonant current regulators are confronted to neural controllers. If conventional regulators present some advantages in terms of engineering specifications, their tuning remains difficult and their design relies on a rough linearization of the system. Their performances are acceptable without perturbations. However, fast changes of nonlinear loads lead to different operating points of the system. Furthermore, the inverter´s nonlinearities and low-pass filter parameters have to be considered for generating precise currents. Two neural approaches have therefore been proposed, one which estimates the input-output relationship of the system, and one which relies on a state-space representation of the system. These approaches combine learning capabilities with a priori knowledge of the system. The benefits of the neural approaches are discussed and illustrated by simulations and by experimental tests with real-time implementations on a digital signal processing board.