A novel compact multiple-input multiple-output (MIMO) antenna is proposed with wideband gain enhancement. It consists of two identical antenna elements spacing about half wavelength. Firstly, a 1-to-2 Y-shaped power divider with wideband anti-phase outputs is designed to feed two modified Vivaldi radiators. The currents on the two radiating surfaces keep the same direction in a wide band to increase radiation without being cancelled. Secondly, the square-ring unit cell is analyzed to form metasurface, placed in the front of the radiator. It can guide the forward electromagnetic radiation while reducing the backward radiation. Thirdly, a U-shaped slot is etched between the two modified Vivaldi radiators. It is approximated as an effective electrically small dipole radiator. Such arrangement further effectively enhances the radiation performance in the direction of the main lobe and destructively interferes with the side lobe radiation. These features make the antenna far-field radiation pattern re-shaped. Thereby, the gain is improved in a wide bandwidth. Meanwhile, the MIMO antenna diversity performance is analyzed with low mutual coupling. Wideband gain enhancement contributes to improving the detection capability of the frequency modulated continuous wave (FMCW) radar sensor. As a proof of concept, the proposed MIMO antenna prototype is fabricated. The measured impedance bandwidth ranges from 11.5 to 21.3 GHz (59.76% FBW) with isolation of ≥ 24.92 dB. The gain is up to 10.6 dBi, the radiation efficiency is 88.01-90.02%, and the envelope correlation coefficient (ECC) is ≤ 0.00122. Integrated with the transceiver chip fabricated on the 65-nm CMOS process, the proposed MIMO antenna is applied in the FMCW radar sensor. The test system is built up, and the experiments on different breathing conditions are conducted for different human subjects. The proposed MIMO antenna could reduce measurement error, thereby improving measurement accuracy for human respiratory monitoring.

This paper presents a high-precision bandgap voltage reference (BGR) with high-order temperature compensation. The compensation signal is generated by using both strong-inversion MOSFETs and Bipolar Junction transistors (BJTs), which cancels the high-order nonlinear term $T\ln (T)$ in the BJT base-emitter voltage (V _BE ), and thus a low temperature coefficient (TC) over a wide temperature range is achieved. The proposed BGR circuit is fabricated in a 0.18- $\mu \text{m}$ CMOS process with an active area of $0.256 m{m^{2}}$ and a max power consumption of 1.35 mW. A minimum TC of 0.706 ${\mathrm{ppm}}/{}^ \circ C$ from $- 25\,\,{}^ \circ C$ to 125 ${}^ \circ C$ is achieved after an 8-bit resistance trimming. The line sensitivity is 0.0146%/V operating from 3.2 V to 3.7 V. The BGR achieves a power supply rejection (PSR) of −63.4 dB and a noise spectrum density of $0.92 ~\mu \text{V}/\sqrt {Hz} $ at 10 Hz.

Developing an accurate partial discharge (PD) monitoring system for switchgear has attracted significant attention in recent times. Detecting and distinguishing PDs with a portable PD detector is challenging due to the inherent noise interference and the similarity among different PD signals in field conditions. In this study, we propose an innovative approach that combines knowledge distillation (KD) and multitask learning (MTL) to efficiently transfer valuable insights acquired by an advanced network to a more lightweight student network. Notably, a novel energy-adaptive sampling strategy is applied to frequency feature to represent acquired pulse waveforms efficiently. Furthermore, a novel spatial information distillation module is also proposed to enhance the knowledge transfer, thereby enhancing PD recognition efficiency and accuracy. Signals captured in substations undergo clustering through a $k$ -means model, effectively separating PDs from noise. Using the phase resolved PD (PRPD) patterns generated from clustered signals, we train our PD classification network. With the integration of KD and MTL modules, our PD classification model outperforms the baseline model in terms of both speed and accuracy. A comprehensive assessment of each proposed module was conducted through ablation studies in our experiments. Furthermore, our constructed PD recognition dataset was used to conduct a comparative analysis against other methodologies, showcasing the superior performance of our approach.

Ultra-high frequency (UHF) partial discharge (PD) detection is essential for routine maintenance of gas-insulated switchgear (GIS). Realizing low-noise detection with cost-effectiveness and ease of installation is promising but challenging. This paper presents a novel broadband noise-shaping network (BNSN) integrated with a printed monopole antenna (PMA) sensor to realize sufficient noise rejection and good impedance matching. Moreover, by analyzing the equivalent noise circuit and codesigning a high-speed preamplifier circuit with the proposed sensor, the detection sensitivity and anti-interference of the proposed sensor module are improved. The effectiveness and superiority of the proposed sensor are first verified in accordance with IEC 62478, followed by validations using a PD simulator and a real 24-kV GIS. The experimental results demonstrate that the proposed sensor module has the capability to identify different PD types even in noisy environments.

The current collector is a key component of most light rail transit (LRT) trains. It draws electric power from the catenary or the power rail for each moving train. Due to wear, tear, and harsh operating environments, the current collector will malfunction, which can lead to unexpected power interruptions for the train and directly affect service reliability. This article presents an inductive coupling method for current collector health monitoring of LRT trains powered by the three-phase ac power rail. The monitoring system developed using the proposed method can be easily fitted on a train so that it doubles up as a train-borne health monitoring system (TBHMS) during revenue hours. The TBHMS monitors the electrical contact quality (ECQ) between the current collector shoe (CCS) and the power rail in a noncontact and safe manner. By doing so, it eliminates the need of inspection during engineering hours, which is both labor-intensive and time-consuming. The current collector of such trains comprises a current collector assembly that holds the CCSs. Two performance indexes, the contact quality index and the contact loss index, are defined to quantify the health conditions of CCS and CCA, respectively.

Developing intelligent methods for partial discharge (PD) diagnosis, capable of handling various types of insulation defects in switchgear, has garnered significant attention in recent years. Certain PD signals exhibit similar characteristics, often leading to their confusion with noisy signals during data acquisition. To mitigate noise interference and enhance the precision of PD recognition, this article introduces a novel framework for separating PD signals from noise and acquiring discriminative features for identifying different types of PDs. Specifically, the proposed approach incorporates an adaptive frequency sampling strategy to extract effective and efficient features for the separation of PD signals and noise, followed by the clustering of the captured signals. Phase Resolved PD (PRPD) patterns are then generated for each clustered signal group, forming the PRPD pattern database. In order to identify the informative region within the PRPD patterns, we introduce spatial correlation attention and discriminative feature learning modules. These modules aim to reduce intraclass variance and increase interclass differences in the PRPD patterns. To evaluate the effectiveness of the proposed method in separating PD signals from noise and recognizing different PD patterns, we constructed a PD recognition dataset that encompasses noise as well as three types of PDs: 1) corona, 2) internal, and 3) surface. By conducting experiments and comparing the results with state-of-the-art methods, we demonstrate the performance of our method in achieving accurate PD recognition with a notable improvement of 1.9% on the constructed PD dataset.