A User - Friendly Signal-Processing App For Harmonics

Yıl 2024, Cilt: 6 Sayı: 2, 153 - 164, 29.10.2024

Sıtkı Akkaya

https://doi.org/10.46387/bjesr.1470226

Öz

Harmonics, especially the 3^rd,5^th, and 7^th components, are significant disturbances in power systems, occurring simultaneously and at varying time intervals. Measurements of these components depend on factors such as fundamental frequency deviation, event duration, amplitude values, and noise levels. Accurate detection and measurement are crucial for effective harmonic mitigation and preventive strategies. This study introduces a harmonic signal analysis application utilizing methods like FFT, STFT, CWT, EMD, and HHT, with individual user interfaces for in-phase and time-variant harmonic signals. The application provides results for amplitude, frequency, event time intervals, and noise effects simultaneously. Test results at a 60 dB signal-to-noise ratio (SNR) reveal that FFT achieves precise frequency and amplitude results due to its high resolution, whereas other methods offering time-frequency domain results exhibit lower resolution. EMD, in particular, demonstrates high errors in frequency and amplitude responses, reducing four frequency components to three. HHT, utilizing EMD results, yields higher accuracy with minimal errors compared to other methods. This application, combined with test results, facilitates signal synthesis and comparative analysis in time and time-frequency domains.

Anahtar Kelimeler

Harmonics, IEEE 1459, app, fundamental frequency, noise, IEC 61000-4-30

Harmonikler İçin Kullanıcı Dostu Bir Sinyal İşleme Uygulaması

Öz

Harmonikler, özellikle 3., 5. ve 7. bileşenler, güç sistemlerinde aynı anda ve değişen zaman aralıklarında meydana gelen önemli bozulmalardır. Bu bileşenlerin ölçümleri temel frekans sapması, olay süresi, genlik değerleri ve gürültü seviyeleri gibi faktörlere bağlıdır. Etkin harmonik azaltma ve önleyici stratejiler için doğru tespit ve ölçüm çok önemlidir. Bu çalışmada, FFT, STFT, CWT, EMD ve HHT gibi yöntemleri kullanan, faz içi ve zaman değişkenli harmonik sinyaller için ayrı kullanıcı arayüzlerine sahip bir harmonik sinyal analizi uygulaması tanıtılmaktadır. Uygulama aynı anda genlik, frekans, olay zaman aralıkları ve gürültü efektleri için sonuçlar sağlar. 60 dB sinyal-gürültü oranındaki (SNR) test sonuçları, FFT'nin yüksek çözünürlüğü nedeniyle hassas frekans ve genlik sonuçları elde ettiğini, oysa zaman-frekans alanı sonuçları sunan diğer yöntemlerin daha düşük çözünürlük sergilediğini ortaya koyuyor. Özellikle EMD, frekans ve genlik yanıtlarında yüksek hatalar göstererek dört frekans bileşenini üçe indiriyor. EMD sonuçlarını kullanan HHT, diğer yöntemlere kıyasla minimum hatayla daha yüksek doğruluk sağlar. Test sonuçlarıyla birleştirilen bu uygulama, zaman ve zaman-frekans alanlarında sinyal sentezini ve karşılaştırmalı analizi kolaylaştırır.

Anahtar Kelimeler

Harmonik, IEEE 1459, uygulama, temel frekans, gürültü, IEC 61000-4-30.

Kaynakça

• “General guide on harmonics and interharmonics measurements and measuring instruments for power supply networks and attached devices used for the measurements”, IEC Standard 61000-4-7, 2009.
• “Testing and measurement techniques - Power quality measurement methods”, IEC Standard 61000-4-30, 2015.
• “Definitions for the Measurement of Electric Power Quantities Under Sinusoidal, Nonsinusoidal, Balanced, or Unbalanced Conditions”, IEEE 1459-Standard, 2010.
• “Recommended Practice for Monitoring Electric Power Quality”, IEEE 1159, 2009.
• S. Akkaya and Ö. Salor, “Flicker Detection Algorithm Based on the Whole Voltage Frequency Spectrum for New Generation Lamps – Enhanced VPD Flickermeter Model and Flicker Curve”, Electric Power Components and Systems, vol. 49, no. 6-7, pp. 637–651, 2021.
• S. Akkaya and Ö. Salor, “New flickermeter sensitive to high-frequency interharmonics and robust to fundamental frequency deviations of the power system”, IET Science, Measurement and Technology, vol. 13, no. 6, pp. 783-793, 2019.
• S. Akkaya and Ö. Salor, “A new flicker detection method for new generation lamps both robust to fundamental frequency deviation and based on the whole voltage frequency spectrum”, Electronics (Switzerland), vol. 7, no. 6, pp. 1-24, 2018.
• S. Akkaya and Ö.S. Durna, “Enhanced spectral decomposition method for light flicker evaluation of incandescent lamps caused by electric arc furnaces,” Journal of the Faculty of Engineering and Architecture of Gazi University, vol. 18, no. 2, pp. 987–1005, 2018.
• S. Akkaya, “A Review of the Experimental Studies on Analysis of Power Quality Disturbances”, In Pioneer And Contemporary Studies In Engineering, Chapter. 24, pp. 454-478, 2023.
• S. Akkaya, “An Overview of the Empirical Investigations into the Classification of Power Quality Disturbances”, In Pioneer And Contemporary Studies In Engineering, Chapter. 22, pp. 410-431, 2023.
• S. Akkaya, “A Conspectus of PQD Analysis”, In 5th ICAENS 2023, pp. 325-329, Konya, Türkiye, 2023.
• S. Akkaya, “Empirical Investigations: Power Quality Disturbance Classification”, In 5th ICAENS 2023, pp. 320-324, Konya, Türkiye, 2023.
• S. Akkaya, E. Yüksek, and H.M. Akgün, “A New Comparative Approach Based on Features of Subcomponents and Machine Learning Algorithms to Detect and Classify Power Quality Disturbances”, Electric Power Components and Systems, vol. 52, no. 8, pp. 1269-1292, 2024.
• M., and S. G.J. Frigo, “FFTW: An Adaptive Software Architecture For The FFT,” In Proceedings of the International Conference on Acoustics, Speech, and Signal Processing, vol. 3, pp. 1381– 1384, 1998.
• B. Sharpe, “Invertibility of overlap-add processing- STFT-accessed Dec 2023”, Accessed: Dec. 02, 2023. [Online]. Available: https://gauss256.github.io/blog/cola.html
• J.M. Lilly, “Element analysis: A wavelet-based method for analysing time localized events in noisy time series,” In Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 473, no. 2200, Dec. 2017.
• J. Bedi and D. Toshniwal, “Empirical Mode Decomposition Based Deep Learning for Electricity Demand Forecasting,” IEEE Access, vol. 6, pp. 49144–49156, 2018.
• Norden E., Huang and S.S. Shen, “Hilbert-Huang transform and its applications”, In Interdisciplinary Mathematical Sciences, World Scientific, vol. 2, 2014.
• Y. Guo et al., “A Hilbert-Huang Transform-Based Traffic Estimation Algorithm To Power Line Communications,” In Proceedings - IEEE International Conference on Industrial Internet Cloud, pp. 132–137, 2019.