International Journal of Science, Engineering and TechnologyAuthors: Lesuanu Dumkhana, Blue-Jack, Kinba-Queen Ishmael
Abstract: The increasing demand for reliable electrical power systems has intensified the need for accurate and non-invasive insulation condition monitoring techniques. Partial discharge (PD) remains a major indicator of insulation deterioration in electrical insulators and joints; however, conventional detection approaches are often limited by poor sensitivity, electromagnetic interference, and difficulty in identifying early-stage defects. This study investigates the performance of high-frequency acoustic and electromagnetic sensors for partial discharge detection on electrical insulation systems. A mathematical modelling framework was developed using partial discharge apparent charge, electromagnetic pulse propagation, acoustic pressure wave generation, sensor sensitivity, signal-to-noise ratio, frequency spectrum energy, and a combined sensor performance index model. A dataset containing ten insulation conditions, ranging from healthy insulation to advanced ageing, was developed using applied voltage levels of 8.5–18.5 kV, defect sizes of 0–5 mm, apparent charge values of 5–820 pC, and PD repetition rates of 2–240 pulses/s. The acoustic and electromagnetic responses were evaluated based on signal amplitude, frequency characteristics, noise immunity, and detection accuracy. The results demonstrated that acoustic sensor amplitude increased from 12.4 mV to 315.6 mV, while electromagnetic amplitude increased from 18.6 mV to 468.3 mV as insulation degradation progressed. The dominant frequency response increased from 25 MHz to 120 MHz, indicating higher-frequency energy concentration during severe defect conditions. Furthermore, the signal-to-noise ratio reduced from 32.5 dB to 23.8 dB due to increased interference associated with insulation deterioration, while detection accuracy remained consistently high between 98.7% and 96.2%. The proposed sensor performance index framework produced values ranging from 0.77 to 0.95, providing a comprehensive evaluation method for selecting suitable sensing technologies. The study supports the adoption of high-frequency acoustic and electromagnetic monitoring techniques within modern asset management policies, emphasizing early fault identification, predictive maintenance, reduced unplanned outages, and improved reliability of high-voltage electrical infrastructure.
DOI: https://doi.org/10.5281/zenodo.23727166
