FFT-Based Power Harmonic Analysis Using MATLAB

In power systems, harmonic analysis serves as a critical method for evaluating power quality. The Fast Fourier Transform (FFT) algorithm enables effective detection and quantification of harmonic components in grid signals. Implementing this process with MATLAB proves both intuitive and efficient, leveraging built-in functions like fft() for spectral decomposition and thd() for distortion measurements.

Power harmonic analysis typically involves several key steps: First, acquiring voltage or current signals from the power grid, which may contain fundamental frequency components and multiple harmonic orders. After obtaining raw signals, FFT converts them from the time domain to the frequency domain, revealing clear amplitude and phase information for each frequency component through MATLAB's abs() and angle() functions applied to FFT results.

MATLAB's built-in FFT function significantly streamlines this process. Critical considerations include setting the sampling frequency (Fs), which determines the maximum detectable harmonic order based on the Nyquist theorem. Additionally, selecting appropriate window functions (e.g., Hanning window via hann()) reduces spectral leakage and improves analysis accuracy. A typical application involves calculating Total Harmonic Distortion (THD) using MATLAB's thd() function, a vital power quality metric derived from harmonic magnitudes relative to the fundamental component.

For power engineers, this analytical approach not only identifies harmonic sources but also evaluates their impact on electrical equipment. By adjusting parameters such as sampling rate and window length, the method adapts to various voltage levels and load conditions, with MATLAB scripts enabling automated harmonic monitoring through loop structures and conditional statements.