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This paper presents a single-phase shunt active power filter for current harmonic compensation based on neural filtering. The shunt active filter, realized by a current controlled inverter, has been used to compensate a nonlinear current load by receiving its reference from a neural adaptive notch filter. This is a recursive notch filter for the fundamental grid frequency (50 Hz) and is based on the use of a linear adaptive neuron (ADALINE). In this way the inverter creates a current equal in amplitude and opposite in sign to the load harmonic current, thus creating an almost sinusoidal grid current. The methodology has been applied in numerical simulations and experimentally on a properly devised test setup. With this regard, a reduced scale electrical grid has been built and used for assessing this methodology.

This paper summarizes the research activity of the authors in the field of sensorless control of induction machine drives based on new linear neural techniques. In particular it describes and compares three speed observers: the MCA EXIN + MRAS Observer, the MCA EXIN + Reduced Order Observer and the TLS Full-order Luenberger Adaptive Observer. Common to all of three observers is the on-line estimation of the speed by a new linear neural technique, which solves in a recursive way a Total Least-Squares problem: one of them employs the TLS EXIN neuron and the other two the MCA EXIN + neuron, which is an improvement of the former. The speed observers have been verified in numerical simulations and experimentally on a test setup and have been also compared experimentally with one another.