A Data-Driven Hybrid Methodology Using Randomized Low-Rank DMD Approximation and Flat-Top FIR Filter for Voltage Fluctuations Monitoring in Grid-Connected Distributed Generation Systems
Article
Mohan, Neethu, Kumar, S. Sachin, Soman, K. P., Sujadevi, V. G., Poornachandran, Prabaharan and Acharya, Rajendra. 2023. "A Data-Driven Hybrid Methodology Using Randomized Low-Rank DMD Approximation and Flat-Top FIR Filter for Voltage Fluctuations Monitoring in Grid-Connected Distributed Generation Systems
." IEEE Access. 11, pp. 39228-39242. https://doi.org/10.1109/ACCESS.2023.3267125
| Article Title | A Data-Driven Hybrid Methodology Using Randomized Low-Rank DMD Approximation and Flat-Top FIR Filter for Voltage Fluctuations Monitoring in Grid-Connected Distributed Generation Systems |
|---|---|
| ERA Journal ID | 210567 |
| Article Category | Article |
| Authors | Mohan, Neethu, Kumar, S. Sachin, Soman, K. P., Sujadevi, V. G., Poornachandran, Prabaharan and Acharya, Rajendra |
| Journal Title | IEEE Access |
| Journal Citation | 11, pp. 39228-39242 |
| Number of Pages | 15 |
| Year | 2023 |
| Publisher | IEEE (Institute of Electrical and Electronics Engineers) |
| Place of Publication | United States |
| ISSN | 2169-3536 |
| Digital Object Identifier (DOI) | https://doi.org/10.1109/ACCESS.2023.3267125 |
| Web Address (URL) | https://ieeexplore.ieee.org/document/10102450 |
| Abstract | The voltage fluctuations are caused by variations in renewable energy source outputs, increased usage of nonlinear loads, high reactive power consumption of loads, etc. This results in damage to electric components and enormous economic losses. It is necessary to measure the parameters of voltage fluctuations and flicker levels to achieve a secure supply of power. However, higher-order harmonics and noises in voltage waveform make such assessment a burdensome task. The traditional techniques fail to provide exact monitoring due to the uncharacteristic variations in the input voltage signal. Nowadays, data-driven methods have become a prominent choice for non-stationary, nonlinear signal analysis as it efficiently tackles atypical changes by its capability to identify spatio-temporal information from given data. The present paper proposes a data-driven hybrid methodology for monitoring voltage fluctuations using the low-rank approximation of dynamic mode decomposition (DMD) and flat-top finite impulse response (FIR) filter. The low-rank approximation of DMD enables to identify the spatio-temporal coherent structures from the data by mapping the data into a lower-dimensional space. Adaptive flat-top FIR filter tracks the fundamental dynamic phasor by identifying the instantaneous frequency, magnitude and phase variations. The performance of the proposed hybrid methodology is assessed using different test cases, and is compared with various existing approaches. The promising results suggest that the proposed methodology can be used for accurate parameter identification and monitoring voltage fluctuations in smart grid scenarios. It can also be used as an accurate tool in distribution grids to detect higher-order harmonics. |
| Keywords | Dynamic mode decomposition; flat-top FIR filter; power quality; Hankelization; voltage fluctuations |
| ANZSRC Field of Research 2020 | 400306. Computational physiology |
| Byline Affiliations | Amrita Vishwa Vidyapeetham, India |
| School of Mathematics, Physics and Computing | |
| Singapore University of Social Sciences (SUSS), Singapore | |
| Kumamoto University, Japan |
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