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A nonlinear adaptive filter for online signal analysis in power systems: applications
Abstract
This paper presents various applications of a nonlinear adaptive notch filter which operates based on the concept of an enhanced phase-locked loop (PLL). Applications of the filter for online signal analysis for power systems protection, control and power quality enhancement are presented. The proposed scheme can be applied for signal analysis both under stationary and nonstationary conditions. Based on digital time-domain simulations, applications of the filter for (a) sinusoidal waveform peak detection, (b) harmonic identification/detection, (c) detection/extraction of individual components of a signal, (d) instantaneous reactive current extraction, (e) disturbance detection, (f) noise reduction in zero-crossings detection, and (g) amplitude (phase) demodulation for flicker estimation, are presented.
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Cited by
Self-Synchronized Synchronverters: Inverters Without a Dedicated Synchronization Unit
Zhong, Q.-C., Nguyen, P.-L., Ma, Z., & Sheng, W. (2014) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2013.2258684 · Cited by 1,026
Metadata
- Authors
- Karimi-Ghartemani, M., & Iravani, M. R.
- Venue
- IEEE Transactions on Power Delivery
- Published
- 2002-04
- Volume
- 17
- Issue
- 2
- Pages
- 617-622
- ISSN
- 0885-8977 (ISSN-L)
- OpenAlex
- W4237048645
- Cited by
- 301
- References
- 0
- FWCI
- 4.91
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: A nonlinear adaptive filter for online signal analysis in power systems: applications. https://app.cochat.ai/papers/10.1109/61.997949
- The paper
- Karimi-Ghartemani, M., & Iravani, M. R. (2002). A nonlinear adaptive filter for online signal analysis in power systems: applications. IEEE Transactions on Power Delivery. https://doi.org/10.1109/61.997949
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
