Enhancing the stability of active harmonic filter using artificial neural network‐based current control scheme. Issue 19 (18th February 2021)

• Record Type: Journal Article
• Authors: Vaidya, Tushar; Chatterjee, Kishore
• Is Part Of: IET power electronics. Volume 13:Issue 19(2020)
• Page Start: 4601
• Page End: 4609
• Publication Date: 2021-02-18
• Subjects: power factor correction -- neurocontrollers -- electric current control -- active filters -- PI control -- reactive power -- compensation -- power harmonic filters -- adaptive control -- stability -- control system synthesis; resonant frequency -- PFC capacitors -- reactive power requirement -- operating conditions -- adaptive control mechanism -- SAHF -- network conditions -- artificial neural network‐based current control scheme -- shunt active harmonic filter -- adjustable power factor correction capacitors -- cost effective solution -- load compensation -- harmonic components -- stability enhancement -- controller design -- apparent power 20.0 kVA; Power electronics -- Periodicals; 621.31705
• DOI: 10.1049/iet-pel.2020.0364
• Languages: English
• ISSNs: 1755-4535

Abstract

Abstract : A combination of shunt active harmonic filter (SAHF), and adjustable power factor correction (PFC) capacitors is generally employed in industrial applications to achieve cost effective solution for the load compensation. It has been experienced that SAHFs operating in conjunction with PFC capacitors may lose their stability while compensating certain harmonic components which are near the resonant frequency of the network. This resonant frequency is not known beforehand while designing the controller of the SAHF, and further it keeps on varying with time as the value of PFC capacitors vary based on the reactive power requirement of the load. Therefore, the conventional controllers having predetermined values for gains fail to stabilise the operation of the SAHF under varying operating conditions of the network. This necessitates the involvement of an adaptive control mechanism within the SAHF with which the SAHF exhibits stable operation even under varying network conditions. In order to accomplish this requirement an artificial neural network‐based control scheme is proposed in this study. The proposed scheme is verified by conducting thorough simulation studies. The viability of the proposed scheme is also confirmed by carrying out detailed experimental studies on a laboratory prototype.