Design and Simulation of Active Power Filter Based on Feed Forward Neural Network for Harmonic Detection and Elimination

Uddin, Mohammad Naim; Amin, Md. Tawfiq

doi:10.1109/iccece48148.2020.9223026

Cited by 1 publication

(1 citation statement)

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“…The method mentioned above for detecting harmonic currents cannot work with good performance under an electrical system with a DC quantity occurs. A neural network (NN) has a learning and training property that can quickly and accurately estimate the harmonic current components [26][27][28][29][30][31][32][33][34][35][36]. The NN can be designed to counteract the drawback of the traditional detection methods with good performance.…”

Section: Introductionmentioning

confidence: 99%

Harmonic Detection for Shunt Active Power Filter Using ADALINE Neural Network

Janpong

Areerak

2021

Energies

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This paper presents an efficient harmonic detection for real-time generation of the reference current fed to a shunt active power filter using the ADALINE neural network. This proposed method is a single layer with 101 nodes generating the coefficients referred to as weights of the reference current model. It effectively overcomes the drawback of the current technology, which is instantaneous power theory (PQ). The proposed method was implemented on the TMS320F28335 DSP board and tested against MATLAB with Simulink as a hardware-in-loop (HIL) structure. This method gives a good performance by producing a precise reference current in a short period with uncomplicated calculation. It also efficiently can eliminate individual harmonic current. The achieved percentage of total harmonic distortion (%THD) in the current is reduced following the IEEE standard, while the power factor can be maintained to unity.

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