A Parameter-Oriented FFT Signal Processing App

Year 2024, Volume: 13 Issue: 4, 923 - 938, 31.12.2024

Sıtkı Akkaya

https://doi.org/10.17798/bitlisfen.1471912

Abstract

In power systems, noise, harmonics, and interharmonics arise in electrical signals due to varying sources and loads, affecting signal purity. Continuous monitoring and accurate analysis of electrical signals are mandatory. The Fast Fourier Transform (FFT) continuously analyzes electrical signals using sliding windows per the IEC-61000-4-7 standard. Parameters from this analysis are compared with threshold values specified in the IEEE-1159 standard. However, variable conditions and factors like sampling frequency, measurement window, main frequency, additional component frequencies, and Signal-to-Noise Ratio (SNR) cause measurement errors. These challenges complicate accurate measurement, leading to errors in preventive measures and control procedures. Understanding the effects of these parameters and improving methods is crucial. The Visible Thinking pedagogical framework is effective in this achievement. This study highlights the importance of parameter selection for FFT and investigates FFT responses to different parameters with synthetical and experimental signal examples. It also presents measurement errors due to signal changes, and a basic interface design shows these errors. Small changes, like a 1/2000 shift in sampling frequency, a 0.5 Hz shift in fundamental frequency, or a 1/1000 difference in the measurement window, cause significant errors. These findings underscore the need for careful parameter selection for accurate computation and signal monitoring, showing the need for FFT method improvements to adapt to changing conditions.

Keywords

Power Quality, FFT, main frequency, sampling frequency, measurement window, visible thinking

Ethical Statement

The study complies with research and publication ethics.

Thanks

The author would like to thank the Sivas University of Science and Technology, Department of Electrical Electronics Engineering, Smart Grids Laboratory.

Parametre odaklı bir FFT Sisnyal İşleme Uygulaması

Abstract

Güç sistemlerindeki elektrik sinyallerinde, değişken kaynaklar ve yükler nedeniyle gürültü, harmonikler veya ara harmonikler gibi çeşitli bileşenler ortaya çıkar ve bu durum elektrik sinyalinin saflığında değişikliklere neden olur. Dolayısıyla, elektrik sinyallerinin sürekli izlenmesi ve doğru analizi zorunlu hale gelmektedir. Bu alanda yaygın yöntemlerden biri olan Hızlı Fourier Dönüşümü (HFD) kullanılarak elektrik sinyalleri, IEC-61000-4-7 standardının öngördüğü aralıklarla kayan pencereler aracılığıyla sürekli analize tabi tutulur. Bu analizden çeşitli parametreler türetilir ve IEEE-1159 standardında belirtilen eşik değerleriyle karşılaştırılır. Ancak değişken saha koşullarının yanı sıra örnekleme frekansı, ölçüm penceresi, ana frekans, ek bileşenlerin frekansı ve Sinyal-Gürültü Oranı (SNR) gibi faktörlerden dolayı çeşitli ölçüm hataları ortaya çıkar. Bu zorluklar, değişken koşullarda zaten zor olan doğru ölçüm görevini daha da karmaşık hale getirir ve önleyici tedbirler ve kontrol prosedürlerinde hata yapılmasına yol açabilir. Bu nedenle, bu parametrelerin etkilerinin anlaşılması ve buna göre yöntemlerin iyileştirilmesi çok önemlidir. Bu çalışmada FFT için parametre seçiminin önemi vurgulanmış ve FFT cevapları farklı parametreler açısından incelenmiştir. Ayrıca, sinyaldeki değişikliklere göre ölçüm hataları ortaya konmuş ve bu hataları gösteren temel bir arayüz tasarımı sunulmuştur. Örnekleme frekansında 1/2000'lik bir değişiklik, ana frekansta 0,5 Hz'lik bir kayma veya ölçüm penceresindeki 1/1000’lik bir fark gibi küçük değişiklikler önemli hatalara neden olmutur. Bu bulgular, hassas hesaplama ve sinyal izleme çalışmaları için mantıklı parametre seçiminin gerekliliğinin altını çizmekte ve değişken koşullara uyum sağlamak için FFT yönteminde iyileştirmeler yapılması gerektiğini göstermektedir. Bu uygulama güç sistemi sinyallerine yönelik tasarlanmış olsa da uygulanabilirliği biyomedikal, ses ve diğer elektrik sinyali çalışmaları için de kullanılabilir.

Keywords

Güç kalitesi, IEEE-1159, HFD, örnekleme frekansı, ana frekans

References

• [1] “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, 2008.
• [2] “Testing and measurement techniques - Power quality measurement methods”, IEC Standard 61000-4-30, 2003.
• [3] "IEEE Standard Definitions for the Measurement of Electric Power Quantities Under Sinusoidal, Nonsinusoidal, Balanced, or Unbalanced Conditions," IEEE Std 1459-2010, pp.1-50, 19 March 2010, doi: 10.1109/IEEESTD.2010.5439063
• [4] "IEEE Recommended Practice for Monitoring Electric Power Quality, "IEEE Std 1159-2019, pp.1-98, 13 Aug. 2019, doi: 10.1109/IEEESTD.2019.8796486
• [5] 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. 0, no. 0, pp. 1–15, 2022, doi: 10.1080/15325008.2021.2011487.
• [6] 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, 2019, doi: 10.1049/iet-smt.2018.5338.
• [7] 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, 2018, doi: 10.3390/electronics7060099.
• [8] 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. 2018, no. 18–2, pp. 987–1005, 2018, doi: 10.17341/gazimmfd.460497.
• [9] S. Akkaya, “A Review of the Experimental Studies on Analysis of Power Quality Disturbances,” in Pioneer and Contemporary Studies in Engineering, 2023, pp. 453–477.
• [10] S. Akkaya, “An Overview of the Empirical Investigations into the Classification of Power Quality Disturbances,” in Pioneer and Contemporary Studies in Engineering, 2023, pp. 409–430.
• [11] S. Akkaya, “A Conspectus of PQD Analysis,” in ICAENS, 2023, pp. 325–329. [Online]. Available: https://as-proceeding.com/index.php/icaens/article/view/1015/950 . [Accessed: Dec. 11, 2004].
• [12] S. Akkaya, “Empirical Investigations: Power Quality Disturbance Classification,” in ICAENS, 2023, pp. 320–324. [Online]. Available: https://as-proceeding.com/index.php/icaens/article/view/1014/949 . [Accessed: Dec. 11, 2004].
• [13] 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, 2023, doi: 10.1080/15325008.2023.2260375.
• [14] K. H. Cheong and J. M. Koh, “Integrated virtual laboratory in engineering mathematics education: Fourier theory,” IEEE Access, vol. 6, pp. 58231–58243, 2018, doi: 10.1109/ACCESS.2018.2873815.
• [15] Y. Y. Zhuang, Y. H. Lin, M. Liyanawatta, A. H. Saputro, Y. D. Utami, and J. H. Wang, “An interactive programming learning environment supporting paper computing and immediate evaluation for making thinking visible and traceable,” Interactive Learning Environments, 2023, doi: 10.1080/10494820.2023.2212709.
• [16] B. Pambayun, J. V. D. Wirjawan, H. Herwinarso, A. Wijaya, B. Untung, and E. Pratidhina, “Designing Mobile Learning App to Help High School Students to Learn Simple Harmonic Motion,” International Journal on Social and Education Sciences, vol. 1, no. 1, 2019.
• [17] D. Malandrino, D. Pirozzi, and R. Zaccagnino, “Learning the harmonic analysis: is visualization an effective approach?,” Multimed Tools Appl, vol. 78, no. 23, pp. 32967–32998, Dec. 2019, doi: 10.1007/s11042-019-07879-5.
• [18] D. Buongiorno and M. Michelini, “Experimental use of mobile apps in physics education,” AAPP Atti della Accademia Peloritana dei Pericolanti, Classe di Scienze Fisiche, Matematiche e Naturali, vol. 99, 2021, doi: 10.1478/AAPP.99S1A22.
• [19] F. Vatansever and N. A. Yalcin, “e-Signals&Systems: A web-based educational tool for signals and systems,” Computer Applications in Engineering Education, vol. 25, no. 4, pp. 625–641, Jul. 2017, doi: 10.1002/cae.21826.
• [20] C. S. Wang, “Teaching Fourier series with tone experiments based on smartphone applications,” Computer Applications in Engineering Education, vol. 31, no. 5, pp. 1358–1371, Sep. 2023, doi: 10.1002/cae.22644.
• [21] P. C. P. S. Andreas Spanias, “A New Signal Processing Course for Digital Culture,” in Frontiers in Education 2015 : launching a new vision in engineering education, IEEE, 2015.
• [22] A. Marquez, J. I. Leon, L. G. Franquelo, and S. Vazquez, “Educational Hardware/Software Interface for Power Electronic Applications,” in 6th IEEE International Conference on e-Learning in Industrial Electronics, IEEE, 2012.
• [23] M. J. C. S. Reis, S. Soares, S. Cardeal, R. Morais, E. Peres, and P. J. S. G. Ferreira, “FouSE: An android tool to help in the teaching of fourier series expansions in undergraduate education,” in CSEDU 2013 - Proceedings of the 5th International Conference on Computer Supported Education, 2013, pp. 166–171. doi: 10.5220/0004401101660171.
• [24] B. Erişti, Ö. Yıldırım, H. Erişti, and Y. Demir, “An FPGA-based System for Real-time Monitoring of Voltage Harmonics,” in 19th IMEKO TC 4 Symposium and 17th IWADC Workshop Advances in Instrumentation and Sensors Interoperability, 2013, pp. 677–682.
• [25] Z. Khan, M. K. Karim, and M. M. Ashraf, “Design Of A Prototype Model For Harmonics Estimation Of Real-Time Current/Voltage Waveforms By Using MATLAB App Designer At Laboratory Level,” in MDSRC 2021, 2021, pp. 1–9.