This paper reports a compact fiber optical electric field (E-field) sensor aiming for the precise detection of transient E-field distributions. Here, a reflective polarization-reciprocal optical path is proposed, which inherently mitigates the temperature-induced birefringence interference of the electro-optical crystal without the need for additional optical elements, thereby facilitating a reduced-size sensing probe. Furthermore, an adaptive particle swarm optimization (A-PSO) algorithm has been utilized for the first time to optimize the insulation structure of the optical E-field sensor, which significantly suppresses field distortion within the sensing region by 50%. This method addresses the technical gap in optical E-field sensor structure optimization, providing an effective means to improve its spatial resolution. The experimental results demonstrate that the proposed sensor exhibits a broad response frequency range from 50 Hz to 15 MHz, with a sensitivity of 0.675 V/kV cm−1. The proposed sensor successfully monitors the spatial distribution of electric fields in the lightning interception region of a lightning rod, thereby validating its effectiveness.

This paper presents a trapezoidal toothed log-periodic ultra-high frequency antenna for partial discharge (PD) detection in high-voltage power equipment. The proposed antenna features a novel trapezoidal toothed structure and a systematic parametric optimization process, achieving an impedance bandwidth of 0.5–3 GHz (VSWR < 5) and a maximum gain of 6.34 dBi at 1.75 GHz. Experimental validation in a controlled sulfur hexafluoride (SF6) gas environment demonstrates the antenna’s ability to detect weak PD signals with a high signal-to-noise ratio of up to 20 dB and generate clear phase-resolved partial discharge (PRPD) patterns. The obtained PRPD patterns aligning well with theoretical expectations and commercial detection systems, highlighting the antenna’s potential for real-time PD monitoring and fault diagnosis in high-voltage power systems.

This paper presents an ultrasonic intelligent diagnostic method for internal defects in ultra-high voltage basin insulators using an improved YOLOv8 model. This approach addresses the limitations of manual interpretation in traditional ultrasonic inspections by enabling automatic analysis and intelligent defect identification. The proposed method incorporates a deeper network architecture with optimized loss functions and adaptive feature extraction mechanisms, significantly enhancing adaptability in complex scenarios. Compared to YOLOv5 and YOLOv8n, the improved model achieves a 7% and 3% increase in F1 score, respectively, along with a 13.6% and 10.3% improvement in mAP@0.5. Experimental validation on standard defect samples and a 550 kV three-phase common box basin insulator demonstrates the model’s capability to detect 1 mm-scale bubbles and cracks with less than 2% localization error, over 80% diagnostic confidence, and 93.5% consistency with industrial Digital Radiography inspections, providing a robust and intelligent solution for defect detection in critical power system components.

With the rapid development of high-voltage DC (HVDC) power systems, accurate measurement of surface electrostatic potential on insulating components has become critical for electric field assessment and insulation reliability. This paper proposes an electrostatic potential sensor based on cantilever micro-vibration modulation, which employs piezoelectric actuators to drive high-frequency micro-vibration of cantilever-type shielding electrodes, converting the static electrostatic potential into an alternating induced charge signal. An electrostatic induction model is established to describe the sensing principle, and the influence of structural and operating parameters on sensitivity is analyzed. Multi-physics coupled simulations are conducted to optimize the cantilever geometry and modulation frequency, aiming to enhance modulation efficiency while maintaining a compact sensor structure. To validate the effectiveness of the proposed sensor, an electrostatic potential measurement platform for insulating components is constructed, obtaining response curves of the sensor at different potentials and establishing a compensation model for the working distance correction coefficient. The experimental results demonstrate that the sensor achieves a maximum measurement error of 0.92% and a linearity of 0.47% within the 1–10 kV range. Surface potential distribution measurements of a post insulator under DC voltage agreed well with simulation results, demonstrating the effectiveness and applicability of the proposed sensor for HVDC insulation monitoring.