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Power Quality Monitoring With Philip Keebler

⏱️ 17:57 🎤 Ellen Parson, Philip Keebler
AUDIO EPISODE
Power Quality Monitoring With Philip Keebler
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Chapters

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  • 0:00
    Introduction & Sponsor Message
    The host introduces the podcast, thanks the sponsor IEM, and sets the stage for the discussion on power quality monitoring with Philip Keebler.
  • 1:57
    Importance of Continuous Monitoring
    Philip Keebler explains how continuous power quality monitoring helps establish a baseline for voltage, current, and waveform quality, allowing early detection of deviations.
  • 7:05
    Key Data Points to Monitor
    Philip highlights specific data points like voltage and current distortion, and power factor, as crucial indicators for identifying potential power quality issues.
  • 8:35
    AI's Role in Monitoring
    The discussion covers the current state of AI in power quality monitoring, noting that while it has potential, it cannot yet replace human expertise for complex problem-solving.
  • 11:52
    Real-World Monitoring Examples
    Philip shares anecdotes about hidden power quality problems, such as transients, severe grounding issues, and harmonics, uncovered through monitoring, often presenting high-risk situations for customers.
  • 17:36
    Prioritizing Monitoring Features
    Advice is given on prioritizing key features in power quality monitoring systems, including sampling rate and adherence to international standards, to ensure actionable data.
  • 20:53
    Future of Power Quality Monitoring
    Philip speculates on future trends, predicting lower costs, improved performance, and the increased importance of cloud-based data management for effective analysis across multiple facilities.

Speakers

E
Ellen Parson
Host — editor-in-chief of EC&M
P
Philip Keebler
veteran electrical power engineer, subject matter expert and technical advisor with PBE engineers

Key Takeaways

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Implement continuous power quality monitoring to establish a baseline of normal electrical system behavior, enabling early detection of deviations.

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Regularly review data points such as voltage distortion, current distortion, and power factor, as significant changes can indicate impending problems.

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While AI is evolving, human expertise in power quality analysis remains irreplaceable for interpreting complex data and diagnosing root causes.

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Be proactive in identifying hidden culprits like transients, grounding problems, and harmonics through monitoring, as these can accumulate into high-risk scenarios.

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Prioritize monitoring systems with high sampling rates and adherence to international standards (IEC, IEEE) to ensure accurate and reliable data collection.

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Invest in effective data management solutions, particularly cloud-based systems, to efficiently store, analyze, and compare power quality data across facilities and over time.

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