Indirect adaptive control of an active filter using Echo State Networks

An active filter is a power electronic device used in a power system to decrease “harmonic current pollution” caused by nonlinear loads. The Echo State Network (ESN) has been widely used as an effective system identifier with much faster training speed than the other Recurrent Neural Networks (RNNs). However, only a few attempts have been made to use an ESN as a system controller. As the first attempt to use an ESN in indirect neurocontrol, this paper proposes an indirect adaptive neurocontrol scheme using two ESNs to control an active filter in a multiple-reference frame. As the first step in the proposed neurocontrol scheme, an online system identifier using an ESN is implemented in PSCAD to identify the load harmonics. Then another ESN is trained online as the controller. The performances of the indirect adaptive control scheme using ESNs show that the ESN is capable of providing accurate control for the active filter, even when the load condition changes nonlinearly.