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Unlocking Deeper Power Quality Insights with Python-Based Waveform Analysis
This white paper demonstrates how advanced waveform analysis can be achieved by exporting raw power quality (PQ) data from PMI's ProVision software and leveraging Python with scientific libraries like NumPy and Matplotlib. It details the process of extracting, processing, and visualizing waveform data, including performing FFT and calculating Total Harmonic Distortion (THD), using provided scripts and libraries compatible with multiple operating systems. The approach enables more granular and customized PQ investigations beyond standard software capabilities.
Key topics include:
- General Setup and Necessary Files
- Analyzing the Script
- Selecting a Cycle and Calculating the FFT
- Total Harmonic Distortion: Calculating the THD vs the Fundamental
- Graphing the Results
- Generated Graph
Why utilities should care:
Enhanced waveform analysis using Python empowers engineers to identify subtle power quality issues, customize analytics, and visualize harmonics and distortion, leading to improved diagnostics, system reliability, and more informed decision-making for grid operations.
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