학술논문

Real Time Selective Harmonic Control—PWM Based on Artificial Neural Networks
Document Type
Periodical
Source
IEEE Transactions on Power Electronics IEEE Trans. Power Electron. Power Electronics, IEEE Transactions on. 39(1):768-783 Jan, 2024
Subject
Power, Energy and Industry Applications
Aerospace
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Fields, Waves and Electromagnetics
General Topics for Engineers
Nuclear Engineering
Signal Processing and Analysis
Transportation
Harmonic analysis
Power harmonic filters
Switches
Active filters
Modulation
Table lookup
Artificial neural networks
Active power filter (APF)
artificial neural network (ANN)
metaheuristic algorithm
numerical algorithm
real-time (RT)
selective harmonic control (SHC-PWM)
Language
ISSN
0885-8993
1941-0107
Abstract
Selective harmonic elimination-pulse width modulation (SHE-PWM) is a widely used low switching frequency modulation technique for medium-voltage high-power converters. This approach is able to adjust the converter fundamental component while eliminating low-order harmonics. However, some applications such as active power filters (APFs) require regulating simultaneously, both the fundamental and low-order harmonics in amplitude and phase. This article presents a novel selective harmonic control-PWM (SHC-PWM) modulator, valid for APFs, based on artificial neural networks (ANNs) and sequential quadratic programming (SQP). A new offline search methodology, based on a hybrid metaheuristic-numerical algorithm, is defined to calculate the solution space when both the fundamental and a low-order harmonic are controlled in phase and amplitude. The solutions obtained are used to train the ANNs offline. Afterwards, the ANN + SQP calculation method is used to solve the SHC-PWM problem in real-time (RT). Experimental results are provided for a three-level converter to verify the effectiveness of the proposed RT control method.