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2024 IEEE Conference on Technologies for Sustainability (sustech)

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Distributed generation of electric power, Distributed resources (Electric utilities) -- Research


Maintaining grid frequency at its nominal value is crucial for power system stability and supply-demand balance. Swift and accurate detection of frequency events is vital for providing primary frequency response support. Frequency event detection algorithms often rely on Phasor Measurement Unit data, which may contain noise. Implementing a denoising preprocessing step enhances detection precision and accuracy. In previous works, a frequency event detection algorithm based on wavelet transform was developed, which uses discrete wavelet transform (DWT) for denoising purposes. In this paper, several denoising techniques are considered as potential replacements for the current DWT method. This research investigates and compares three denoising techniques: Fast Fourier Transform, Butterworth, and Simple Moving Average, as alternatives to DWT. Unlike other studies using metrics like Signal-to-Noise Ratio, Root Mean Square Error, and Percentage Root Mean Square Error, this research analyzes the impact of each denoising method on event detection performance and visually assesses pre- and post-denoised frequency data. The results highlight the effectiveness of the new denoising methods, suggesting them as optimal replacements for the DWT technique in terms of detection performance evaluation.


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