Sensing and Analyzing Partial Discharge Phenomenology in Electrical Asset Components Supplied by Distorted AC...

Area of Science:

• Electrical Engineering
• Materials Science
• Power Systems

Background:

• Power electronic devices in transportation and industry cause voltage waveform deviations, leading to insulation aging via partial discharges (PDs).
• PDs are a major cause of accelerated aging and premature failure in electrical insulation systems.
• While PDs under pulse width modulated (PWM) voltages are studied, less research addresses their harmfulness under AC distorted waveforms with harmonics and notches.

Purpose of the Study:

• To investigate PD sensing and phenomenology under AC distorted waveforms containing voltage harmonics and notches.
• To address the knowledge gap regarding the impact of distorted AC voltage on PD behavior and harmfulness.
• To adapt and evaluate AI algorithms for identifying PD typology and defect types under distorted AC conditions.

Main Methods:

• Analysis of PD patterns under AC distorted waveforms with voltage harmonics and notches.
• Comparison of PD patterns with those observed under sinusoidal AC voltage.
• Adaptation and testing of AI algorithms for PD typology identification on distorted AC waveforms.

Main Results:

• PD patterns significantly change under harmonic voltages and/or notches compared to sinusoidal AC.
• These pattern changes can affect the identification of PD typologies based on physical patterns.
• Effective identification of PD defect types and their harmfulness is achievable even with distorted AC waveforms using adapted AI algorithms.

Conclusions:

• Distorted AC waveforms, prevalent in industrial and transport applications, alter PD phenomenology.
• AI-based PD identification algorithms can be adapted to effectively analyze PD patterns under distorted AC conditions.
• Accurate identification of PD sources and their associated risks remains possible, enabling better insulation management.