Real-Time Harmonic Optimization in Multilevel Inverter Using Artificial Neural Network (ANN)

Ismail Adeyemi Adeyemo, Olabode Moses Ola and Demilade Olujide Babajide

European Journal of Engineering and Technology Research, 2022, vol. 7, issue 5, 12-17

Abstract: Multilevel inverters (MLIs) are increasingly used in real time applications. Among several pulse width modulation (PMW) techniques currently deployed for the control of MLIs, selective harmonic elimination PWM (SHEPWM) technique arguably gives the best performance due to its direct harmonic mitigation capability. However, real time application of SHEPWM technique is presently infeasible due to the heavy computational cost involved in solving the transcendental nonlinear equations known as selective harmonic elimination (SHE) equations, which characterize the harmonics that are selected for elimination or mitigation. This paper presents a twostage approach to the online generation of switching angles that mitigate selected lower-order harmonics in multilevel inverters. The first stage involves an offline solution of SHE equations using ant colony optimisation (ACO). In the second stage, ACO computed results are used to train an artificial neural network (ANN) predictive model. The results obtained from the simulation of the proposed method in MATLAB/SIMULINK environment show that the method is highly efficient and accurate.

Keywords: ACO; ANN; Cascaded H-Bridge; Harmonics; Multilevel Inverter. (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
https://eu-opensci.org/index.php/ejeng/article/view/62720 Abstract page (text/html)
https://eu-opensci.org/index.php/ejeng/article/download/62720/12851 Full text (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:epw:ejeng0:v:7:y:2022:i:5:id:62720

DOI: 10.24018/ejeng.2022.7.5.2720

Access Statistics for this article

More articles in European Journal of Engineering and Technology Research from European Open Science
Bibliographic data for series maintained by Support ().