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Unlocking Interharmonic Insights in Power Quality Analysis with Python
This white paper explores the process of interharmonic analysis in electrical systems using Python, highlighting the limitations of traditional harmonic analysis and demonstrating how the ml_canvass library enables precise detection and quantification of interharmonic components. Through practical code examples and comparison with IEEE 519 standards, the paper reveals that interharmonic magnitudes can be significantly stronger than traditional harmonics, offering deeper insights into power quality issues such as light flicker.
Key topics include:
- Analyzing a Waveform Capture for Interharmonics
- Code Dissection
Why utilities should care:
Interharmonic analysis reveals hidden power quality issues, such as light flicker, that traditional harmonic methods can miss. Leveraging Python and advanced libraries enables more accurate diagnostics, supporting proactive mitigation and improved system reliability.
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