In this paper, an efficient and reliable neural active power filter (APF) to estimate and compensate for harmonic distortions from an AC line is proposed. The proposed filter is completely based on Adaline neural networks which are organized in different independent blocks. We introduce a neural method based on Adalines for the online extraction of the voltage components to recover a balanced and equilibrated voltage system, and three different methods for harmonic filtering. These three methods efficiently separate the fundamental harmonic from the distortion harmonics of the measured currents. According to either the Instantaneous Power Theory or to the Fourier series analysis of the currents, each of these methods are based on a specific decomposition. The original decomposition of the currents or of the powers then allows defining the architecture and the inputs of Adaline neural networks. Different learning schemes are then used to control the inverter to inject elaborated reference currents in the power system. Results obtained by simulation and their real-time validation in experiments are presented to compare the compensation methods. By their learning capabilities, artificial neural networks are able to take into account time-varying parameters, and thus appreciably improve the performance of traditional compensating methods. The effectiveness of the algorithms is demonstrated in their application to harmonics compensation in power systems.Index Terms-Active power filter (APF), adaptive control, artificial neural networks (ANNs), harmonics, selective compensation, three-phase electric system.

Abstract-This paper presents an original method, based on artificial neural networks, to reduce the torque ripple in a permanent-magnet non-sinusoidal synchronous motor. Solutions for calculating optimal currents are deduced from geometrical considerations and without a calculation step which is generally based on the Lagrange optimization. These optimal currents are obtained from two hyperplanes. The study takes into account the presence of harmonics in the back-EMF and the cogging torque. New control schemes are thus proposed to derive the optimal stator currents giving exactly the desired electromagnetic torque (or speed) and minimizing the ohmic losses. Either the torque or the speed control scheme, both integrate two neural blocks, one dedicated for optimal currents calculation and the other to ensure the generation of these currents via a voltage source inverter. Simulation and experimental results from a laboratory prototype are shown to confirm the validity of the proposed neural approach.

No abstract

A new method for feedback control of asynchronous\ud electrical machines is introduced, with application\ud example the problem of the traction system of electric trains.\ud The control method consists of a repetitive solution of an\ud H-infinity control problem for the asynchronous motor, that\ud makes use of a locally linearized model of the motor and\ud takes place at each iteration of the control algorithm. The\ud asynchronous motor’s model is locally linearized round its\ud current operating point through the computation of the associated\ud Jacobian matrices. Using the linearized model of the\ud electrical machine an H-infinity feedback control lawis computed.\ud The known robustness features of H-infinity control\ud enable to compensate for the errors of the approximative\ud linearization, as well as to eliminate the effects of external\ud perturbations. The efficiency of the proposed control scheme\ud is shown analytically and is confirmed through simulation\ud experiments

No abstract