Adaptive filtering-based current reconstruction in non-contact magnetic sensor array measurement system

• Autorzy: Chen Yafeng , Huang Qi

Identyfikatory

DOI

10.24425/mms.2019.130567

• Języki publikacji: EN

Abstrakty

EN

The non-contact current measurement method with magnetic sensors has become a subject of research. Unfortunately, magnetic sensors fail to distinguish the interested magnetic field from nearby interference and suffer from gauss white noise due to the intrinsic noise of the sensor and external disturbance. In this paper, a novel adaptive filtering-based current reconstruction method with a magnetic sensor array is proposed. Interference-rejection methods based on two classic algorithms, the least-mean-square (LMS) and recursive-least-square (RLS) algorithms, are compared when used in the parallel structure and regular triangle structure of three-phase system. Consequently, the measurement range of RLS-based algorithm is wider than that of LMS-based algorithm. The results of carried out simulations and experiments show that RLS-based algorithms can measure currents with an error of around 1%. Additionally, the RLS-based algorithm can filter the gauss white noise whose magnitude is within 10% of the linear magnetic field range of the sensor.

Słowa kluczowe

EN

current measurement; adaptive filtering; sensor array; gauss white noise

• Wydawca: Komitet Metrologii i Aparatury Naukowej PAN
• Czasopismo: Metrology and Measurement Systems
• Rocznik: 2019
• Tom: Vol. 26, nr 4
• Strony: 697--711
• Opis fizyczny: Bibliogr. 31 poz., fot., rys., tab., wzory

Twórcy

autor

Chen Yafeng

• University of Electronic Science and Technology of China, The Sichuan Provincial Key Lab of Power System Wide-Area Measurement and Control, Chengdu 611731, Sichuan, China

autor

Huang Qi

• University of Electronic Science and Technology of China, The Sichuan Provincial Key Lab of Power System Wide-Area Measurement and Control, Chengdu 611731, Sichuan, China

Bibliografia

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Uwagi

EN

This project is supported by Sichuan Youth Science and Technology Innovation Team Fundunder Grant 2017TD0009.