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In this paper, the mechanical sensitivity of a vibration sensor is investigated by developing a mathematical model with the function of a relative movement modulus and measurement error. This model enables mechanical sensitivity to be improved by enhancing the performance of the vibration sensor. The purpose of the present work is to reduce measurement error by choosing the right damping rate that enables vibration sensor sensitivity to be optimized. The presented model is validated by computer simulation and experimental tests. The obtained results have shown that correct choice of damping rate and frequency range keeps the mechanical sensitivity constant.

The use of nonlinear loads has increased in power systems consequently the harmonic currents have also increased causing detrimental effects to the supply system and user equipment. The aim of the present paper is to identify harmonics in order to obtain a perfect compensation by active power filter (APF). A study of harmonic currents identification by two different methods is conducted in this work. The instantaneous power (PQ) theory method requires two low-pass filters for the extraction of direct power components from total power components. However the direct neural method based on ADALINE neural network method estimates total harmonic current as well as harmonic components separately. Moreover, the identification of each component separately enables the selective compensation of harmonics by the active filter if the objective is to minimize the cost. The method is easy to implement in real time compared to PQ method. In present paper two algorithms based on conventional PQ method and direct neural method were developed in order to identify the harmonic currents. These developed algorithms are confirmed by experimental tests by implementing these techniques in a dSPACE controller in order to show their effectiveness. The obtained results are compared, discussed and analyzed.

a b s t r a c tThe aim of the present work is to obtain a perfect compensation by extracting accurate harmonic currents. The objective is to avoid the consequences due to the presence of disturbances in the power system. A comparative study of harmonic currents extraction by simulation and implementation is carried out for two different techniques. The first technique is based on the instantaneous powers, taking advantage of the relationship between current and the power transformed from the supply source to the loads. The second is based on ADALINE neural network. The neural method can estimate the harmonic terms individually and online, therefore, the APF can realise a selective compensation. The developed architectures are validated by computer simulation and experimental tests. The algorithms are implemented in the dSPACE Board in order to show the effectiveness and capability of each technique. The results have demonstrated that the speed and the accuracy of the ADALINE can improve greatly the performances of active power filters.

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