Microstrip patch antenna with defected ground structure for biomedical application

In this article, we present a low-cost 2×2 metasurface-based rectenna array operating at the 2.45 GHz industrial, scientific, and medical (ISM) band for powering wearable medical sensors (WMS). The proposed antenna is a novel textile-based metasurface interlayer patch (meta-patch) antenna fabricated on a felt fabric substrate, replacing the conventional ground plane with a grounded metasurface consisting of a 3×3 unit cell array. A single excitation port is used for both the patch antenna and the metasurface to improve gain, efficiency, and bandwidth. The meta-patch antenna achieved a high gain of 7 dB, an efficiency of 77%, and an enhanced bandwidth of 120 MHz. The rectifier adopts a seven-stage Cockcroft-Walton voltage multiplier (CWVM) topology fabricated on a rigid substrate. To evaluate the performance of the meta-patch rectenna array and the effect of adding more elements, we tested the proposed meta-patch rectenna in single-element, 2×1 array, and 2×2 array configurations under continuous RF power. The single-element, 2×1 rectenna array, and 2×2 rectenna array achieved power conversion efficiencies (PCE) of 52%, 53%, and 56%, with DC output powers of 414 μW, 429 μW, and 450 μW, respectively, at an input power of -1 dBm. The results demonstrate the potential of the proposed rectenna arrays for efficiently energizing low-power WMS.

This paper comprehensively reviews advanced signal processing methods for partial discharge (PD) analysis, covering traditional time-frequency techniques, wavelet transform, Hilbert–Huang transform, and artificial intelligence-based methods. This paper critically examines the principles, advantages, limitations, and applicable scenarios of each method. A key contribution of this review is the systematic comparison of these methods, highlighting their evolution and complementary roles in processing non-stationary and noisy PD signals. However, a significant gap in current research remains the lack of standardized, explainable, and embeddable AI solutions for real-time, fine-grained PD classification. Future trends point to hybrid approaches and edge AI systems that combine physical insights with lightweight deep learning models to improve diagnostic accuracy and deployability.

In compliance with next-generation high-voltage substations, ultra-high frequency (UHF) planar sensors are becoming more popular than ever for their online features in partial discharge (PD) diagnostics. However, PD signals mixing with different noises still exists as a major challenge for UHF sensors. Especially in open substations, telecommunication interferences become predominant noise sources to an external UHF sensor placed outside of the equipment chamber. Despite band-stop filters are additionally connected to UHF sensors for de-noising, the inherent noise cancellation mechanism is not yet found in any UHF sensor. With the best of our knowledge and survey, we are the first to introduce here a planar sensor that inherently eliminates telecommunication interferences during PD detection for open substations even without using any post-processing filters. To design our sensor, we have applied techniques of truncated partial ground plane for wideband characteristics and modified radiating patch for enhanced gain performance. A novel intermingled coupling resonator is designed to remove the GSM-900 band from within the sensor bandwidth. We have tested our prototype both for the free-space propagation and PD detection performances. The prototype has a compact physical size of 16×8 cm ² and an operational bandwidth within 792-1414 MHz. For a surface discharge source, the prototype successfully detects the PD signals at the earliest stage. Our prototype minimizes the ambient noise-floor to 2.5 mV and improves the signal-to-noise ratio by more than 27.9 dB which is relatively higher than conventional UHF sensors. The proposed sensor also has potentials of classifying PD sources.

This paper comprehensively reviews advanced signal processing methods for partial discharge (PD) analysis, covering traditional time-frequency techniques, wavelet transform, Hilbert-Huang transform, and artificial intelligence-based methods. This paper critically examines the principles, advantages, limitations, and applicable scenarios of each method. A key contribution of this review is the systematic comparison of these methods, highlighting their evolution and complementary roles in processing non-stationary and noisy PD signals. However, a significant gap in current research remains the lack of standardized, explainable, and embeddable AI solutions for real-time, fine-grained PD classification. Future trends point to hybrid approaches and edge AI systems that combine physical insights with lightweight deep learning models to improve diagnostic accuracy and deployability.