6533b860fe1ef96bd12c31b7

RESEARCH PRODUCT

Model Predictive Control for Shunt Active Filters With Fixed Switching Frequency

Luca TarisciottiAlberto GaetaMarco DeganoRoberto RabbeniPericle ZanchettaAndrea FormentiniMarcello Pucci

subject

Active filters; harmonic distortion; model predictive control (MPC); power filters; power quality; smart gridsEngineeringmodel predictive control (MPC)020209 energyRipple02 engineering and technologyIndustrial and Manufacturing EngineeringHarmonic analysisHarmonic spectrumControl theory0202 electrical engineering electronic engineering information engineeringElectronic engineeringsmart gridsWaveformElectrical and Electronic EngineeringActive filterModel Predictive ControlActive filtersTotal harmonic distortionbusiness.industry020208 electrical & electronic engineeringpower filtersAC powerpower qualityharmonic distortionModel predictive controlControl and Systems Engineeringbusiness

description

This paper presents a modification to the classical Model Predictive Control algorithm, named Modulated Model Predictive Control, and its application to active power filters. The proposed control is able to retain all the advantages of a Finite Control Set Model Predictive Control whilst improving the generated waveforms harmonic spectrum. In fact a modulation algorithm, based on the cost function ratio for different output vectors, is inherently included in the MPC. The cost function-based modulator is introduced and its effectiveness on reducing the current ripple is demonstrated. The presented solution provides an effective and straightforward single loop controller, maintaining an excellent dynamic performance despite the modulated output and it is self-synchronizing with the grid. This promising method is applied to the control of a Shunt Active Filter for harmonic content reduction through a reactive power compensation methodology. Significant results obtained by experimental testing are reported and commented, showing that MPC is a viable control solution for active filtering systems.

yearjournalcountryeditionlanguage
2016-09-07
10.1109/tia.2016.2606364https://dx.doi.org/10.1109/TIA.2016.2606364