Applications of Digital Signal Processing Techniques in Power Quality Detection and Improvement

DOI

10.2991/978-94-6463-821-9_39How to use a DOI?

Keywords

Digital Signal Processing (DSP); Smart Grids; Fault Diagnosis; Deep Learning

Abstract

The integration of digital signal processing (DSP) techniques into smart grids has greatly improved power quality detection, fault diagnosis, and system stability. This paper examines the use of DSP methods such as digital low-pass filters, adaptive filters, Kalman filtering, wavelet transform, sparse signal processing, and deep learning in addressing key power system challenges. Digital low-pass filters enhance zero-crossing detection accuracy by compensating for time delays, while adaptive filters, like the Least Mean Squares (LMS) algorithm, improve noise reduction and harmonic suppression in fault diagnosis. Kalman filtering methods, including the Distributed Quaternion Kalman Filter (DQKF), offer precise frequency estimation under unbalanced grid conditions. Wavelet transform techniques, such as Discrete Wavelet Transform (DWT) and Multi-Resolution Analysis (MRA), effectively detect and mitigate voltage sags and swells. Sparse signal processing methods, like Compressed Sensing (CS), enable efficient data compression and recovery, reducing transmission and storage demands. Deep learning models, including MLPs and LSTMs, provide high accuracy in load forecasting and renewable energy generation prediction, addressing energy supply variability. Results show that DSP techniques, especially when integrated with deep learning, outperform traditional methods in accuracy, adaptability, and computational efficiency, offering robust solutions for smart grid challenges.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Menglei Zhu
PY  - 2025
DA  - 2025/08/31
TI  - Applications of Digital Signal Processing Techniques in Power Quality Detection and Improvement
BT  - Proceedings of the 2025 2nd International Conference on Mechanics, Electronics Engineering and Automation (ICMEEA 2025)
PB  - Atlantis Press
SP  - 377
EP  - 385
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-821-9_39
DO  - 10.2991/978-94-6463-821-9_39
ID  - Zhu2025
ER  -