A Parameter-Oriented FFT Signal Processing App

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.