MATLAB Implementation for Power System Harmonic Analysis

Comprehensive analysis of power system harmonics, negative sequence components, voltage and frequency deviations serves as a crucial method for evaluating power quality. Implementing this analysis process using MATLAB enables efficient processing of complex signals in power systems, providing intuitive assessments of system operating conditions. ### Harmonic Analysis Harmonics in power systems are primarily caused by nonlinear loads, which may lead to equipment overheating or malfunction. In MATLAB implementation, Fourier Transform (FFT) algorithms can be applied to analyze voltage or current signals, extracting harmonic magnitudes and phases for each order. This enables calculation of Total Harmonic Distortion (THD) using functions like fft() and harmonic analysis toolboxes. The code typically involves signal preprocessing, windowing functions, and spectral analysis to identify dominant harmonic components. ### Negative Sequence Component Calculation Negative sequence components reflect the degree of imbalance in three-phase power systems, often resulting from asymmetric faults or unbalanced loads. Through symmetrical component transformation (decomposition into positive, negative, and zero sequences), MATLAB can compute the proportion of negative sequence voltage or current using matrix transformations and sequence component analysis functions. The implementation requires phase angle corrections and component separation algorithms to accurately assess system imbalance conditions. ### Voltage Deviation Analysis Voltage deviation refers to the difference between actual voltage and nominal voltage. MATLAB can perform RMS (Root Mean Square) calculations on sampled voltage signals using functions like rms() or custom algorithms, comparing results with standard values to determine if they exceed permissible ranges. The code implementation includes signal conditioning, moving average filters, and threshold-based alert mechanisms for continuous voltage monitoring. ### Frequency Deviation Detection Power system frequency stability is critical. MATLAB can calculate fundamental frequency through zero-crossing detection methods or spectral analysis techniques using functions such as findpeaks() or periodogram(). The computed frequency is then compared with standard frequencies (50Hz or 60Hz) to determine if it remains within acceptable limits. Implementation involves signal conditioning, anti-aliasing filters, and frequency tracking algorithms for real-time monitoring. ### Comprehensive Analysis Application By integrating the above analysis results, MATLAB can generate comprehensive power quality assessment reports containing key indicators such as harmonic distortion rates, negative sequence component ratios, voltage deviations, and frequency deviations. These data assist engineers in optimizing system operations or locating fault points through automated reporting functions and data visualization tools. Implementing this comprehensive analysis through MATLAB not only enhances calculation accuracy and efficiency but also leverages visualization tools (such as waveform plots, spectrum diagrams) to intuitively present analysis results, facilitating subsequent decision-making processes. The code typically incorporates data logging, graphical user interfaces (GUIs), and export functionalities for professional reporting.