M-File for Calculating Harmonic Content in Current Signals

In power system analysis, harmonic content in current signals serves as a critical indicator for assessing power quality. The MATLAB-implemented harmonic analysis algorithm enables rapid and accurate detection of various harmonic components in current waveforms. The core methodology employs Fast Fourier Transform (FFT) to convert time-domain current signals into frequency domain. The implementation involves: 1. Preprocessing sampled signals by removing DC components and applying window functions (e.g., Hanning window) to minimize spectral leakage 2. Performing FFT analysis to obtain frequency spectrum 3. Identifying fundamental frequency and calculating harmonic magnitudes as percentages relative to the fundamental component Key MATLAB features utilized include: - Matrix operations for efficient bulk data processing - Signal Processing Toolbox functions for FFT implementation and windowing - Automated harmonic detection through peak identification in frequency spectrum This approach benefits from MATLAB's computational strengths, particularly in handling large datasets efficiently. For power engineers, this automated harmonic analysis tool significantly improves diagnostic capabilities for grid distortion issues. Practical implementation considerations include: - Ensuring sampling rates comply with Nyquist criterion - Optimizing FFT points to balance frequency resolution and computational load - Validating results through comparison with theoretical harmonic models