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Advances and Operational Trade-offs in Lead-Free Piezoelectric Ceramics for Ultrasonic Detection of Partial Discharge in Power Cables

Xin Liu, Gelan Xu, Xishan Jiang, Qingming Meng, Mingquan Zhou, Lepeng Lin

Journal of Ceramic Science and Technology · Early View (advance online publication) (-1), pp. 1-12

DOI: 10.4416/JCST2026-00001

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Abstract

Partial Discharge (PD) in high-voltage cable insulation is a primary precursor to catastrophic failure, motivating non-intrusive online monitoring that remains robust in noisy substations. Ultrasonic sensing offers immunity to electromagnetic interference (EMI) and supports defect localization via Time of Flight (ToF), yet most deployed transducers rely on Lead Zirconate Titanate (PZT), whose high lead content is increasingly constrained by the Restriction of Hazardous Substances (RoHS) requirements. This review synthesizes material science, transducer engineering, and field evidence to assess lead-free alternatives for ultrasonic PD surveillance, focusing on Potassium Sodium Niobate (KNN), Bismuth Sodium Titanate (BNT), and Barium Calcium Zirconate Titanate (BCZT). A central theme is the unresolved debate over Morphotropic Phase Boundary- (MPB-) versus Polymorphic Phase Transition- (PPT-) enabled enhancement in KNN and its implications for temperature-dependent sensitivity drift under thermal excursions. We compare figures of merit including charge coefficient, coupling, mechanical quality, and thermal ceiling, and show why maximizing d33 alone is insufficient when BCZT offers high sensitivity but limited thermal headroom. At the device level, we analyze Bolted Langevin Transducer (BLT) redesign requirements, including resonance retuning into the 40 kHz to 300 kHz acoustic band and mitigation of stack impedance that elevates amplifier noise and capacitive loading sensitivity. Finally, we summarize conceptual signal chain strategies – from low-noise conditioning to wavelet denoising and machine-learning classifiers such as Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN) – to provide a system-level framework for compensating for the weaker raw voltages of lead-free sensors and improving maintenance-relevant diagnostics.

Keywords

Cross-linked polyethylene insulation, bolted Langevin transducer, polymorphic phase transition, wavelet transform denoising, time difference of arrival localization

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