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An intelligent active power filter to mitigate Harmonics and Interharmonics
Citation
Eslami, A and Negnevitsky, M and Franklin, Evan and Lyden, S, An intelligent active power filter to mitigate Harmonics and Interharmonics, Proceedings 20th International Conference on Harmonics & Quality of Power (ICHQP) : Power Quality in the Energy Transition, 29 May 2022 - 01 June 2022, Naples, Italy, pp. 1-6. ISBN 9781665416399 (2022) [Refereed Conference Paper]
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DOI: doi:10.1109/ICHQP53011.2022.9808722
Abstract
Harmonics and waveform distortion are substantial power quality concerns for power systems with high penetration of renewable energy generation and non-linear loads. Harmonics and interharmonics should be mitigated for efficient and proper operation of power grids, with high harmonic injection from electric loads and devices. In this paper, an intelligent Active Power Filter (APF) based on Adaptive Linear Neuron (Adaline) is proposed which can provide accurate harmonic estimation and proper mitigation in real-time without any prior knowledge about harmonic/interharmonic orders. A novel formulation is derived, and adaptive learning with momentum is used for training. The proposed APF not only inherits the high adaptability of Adaline APFs but also compensates for their drawbacks by updating weights in the presence of unknown frequency orders. Additionally, an electric topology is proposed where one APF is able to mitigate harmonics of nonlinear loads connected to two adjacent Points of Common Coupling (PCCs). For a radial structure, harmonic currents from the grid are completely mitigated while for a ring structure, only harmonics from one side of the grid are mitigated. Highly varying and distorted load scenarios are studied. Comparison of the proposed APF with the state-of-the- art APFs in the literature proves the effectiveness and suitability of the proposed intelligent APF.
Item Details
| Item Type: | Refereed Conference Paper |
|---|---|
| Keywords: | Harmonics, Active Power Filter (APF), Adaline, Harmonic Mitigation, Intelligent Filtering |
| Research Division: | Engineering |
| Research Group: | Electrical engineering |
| Research Field: | Electrical energy generation (incl. renewables, excl. photovoltaics) |
| Objective Division: | Energy |
| Objective Group: | Renewable energy |
| Objective Field: | Renewable energy not elsewhere classified |
| UTAS Author: | Eslami, A (Mr Ahmadreza Eslami) |
| UTAS Author: | Negnevitsky, M (Professor Michael Negnevitsky) |
| UTAS Author: | Franklin, Evan (Associate Professor Evan Franklin) |
| UTAS Author: | Lyden, S (Dr Sarah Lyden) |
| ID Code: | 155400 |
| Year Published: | 2022 |
| Deposited By: | Engineering |
| Deposited On: | 2023-02-17 |
| Last Modified: | 2023-02-28 |
| Downloads: | 0 |
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