Neural Network-Based Enhanced PI and Higher-Order Sliding-Mode Control for Harmonics Mitigation

Due to the varying load conditions in electrical networks, typical sliding mode control techniques are insufficient to control active power filters (APFs). Consequently, this study proposed a higher-order sliding mode and an enhanced PI technique to enhance the performance of the APF. This controller has been chosen because of its ability to respond to network parameter variations and interference of the network parameters. The results reveal that this approach gives outstanding results, with a rapid voltage stabilization time of 0.06 seconds after > 0.4 seconds. Furthermore, total harmonic distortion (THD) has been greatly reduced to 1.89%, meeting the IEEE 519 standard for reliability in electrical distribution networks.

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Published
2024-11-26
DOI
https://doi.org/10.1109/nigercon62786.2024.10926954
Primary Topic
Hydraulic and Pneumatic Systems
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article
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article

Neural Network-Based Enhanced PI and Higher-Order Sliding-Mode Control for Harmonics Mitigation

Omotayo Oshiga, Oghenewvogaga Oghorada, Bukola Babatunde Adetokun, Ibrahim Olawale Muritala et al.
Hydraulic and Pneumatic Systems
article

Neural Network-Based Enhanced PI and Higher-Order Sliding-Mode Control for Harmonics Mitigation

Omotayo Oshiga, Oghenewvogaga Oghorada, Bukola Babatunde Adetokun, Ibrahim Olawale Muritala, Zubairu Gatugel Usman
article en

Abstract

Due to the varying load conditions in electrical networks, typical sliding mode control techniques are insufficient to control active power filters (APFs). Consequently, this study proposed a higher-order sliding mode and an enhanced PI technique to enhance the performance of the APF. This controller has been chosen because of its ability to respond to network parameter variations and interference of the network parameters. The results reveal that this approach gives outstanding results, with a rapid voltage stabilization time of 0.06 seconds after > 0.4 seconds. Furthermore, total harmonic distortion (THD) has been greatly reduced to 1.89%, meeting the IEEE 519 standard for reliability in electrical distribution networks.

Ahmadu Bello University (NG), Nile University of Nigeria (NG)
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Openalex Percentile: Top 72%
Hydraulic and Pneumatic Systems
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Neural Network-Based Enhanced PI and Higher-Order Sliding-Mode Control for Harmonics Mitigation — Omotayo Oshiga, Oghenewvogaga Oghorada, et al. · (2024) | TGRS Research Map | TGRS