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A shunt active filter is a power electronic device used in a power system to decrease ldquoharmonic current pollutionrdquo 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 a shunt 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 the Innovative Integration M67 card consisting of the TMS320C6701 processor to identify the load harmonics in a typical electric ship power system. The shunt active filter and the ship power system are simulated using a Real-Time Digital Simulator (RTDS) system. The required computational effort and the system identification accuracy of an ESN with different dynamic reservoir size are discussed, which can provide useful information for similar applications in the future. The testing results in the real-time implementation show that the ESN is capable of providing fast and accurate system identification for the indirect neurocontrol of a shunt active filter.