Search Results (124)

This paper presents a fully passive and wireless glucose concentration sensor that integrates a capacitively coupled complementary split-ring resonator (CSRR) with an H-slot UHF RFID antenna. The CSRR serves as the primary sensing element, where changes in glucose concentration alter the effective permittivity of the surrounding solution, thereby modifying the resonator capacitance and shifting its resonance behavior. Through near-field capacitive coupling, these dielectric variations affect the antenna input impedance and backscatter response, enabling wireless sensing by modulating the maximum read range. The proposed sensor operates within the 902–928 MHz UHF RFID band and is interrogated using commercial RFID readers, eliminating the need for specialized laboratory equipment such as vector network analyzers. Full-wave electromagnetic simulations and experimental measurements validate the sensor performance, demonstrating a variation in the read range from 6.23 m to 4.67 m as glucose concentration increases from 50 to 200 mg/dL. Moreover, the sensor exhibits excellent linearity, with a high coefficient of determination ($R^2=0.986$) based on the curve-fitted data. These results underscore the feasibility of the proposed sensor as a low-cost and fully portable platform for concentration monitoring, with potential applications in liquid characterization and chemical sensing. Full article

A novel partial discharge (PD) source localization method was proposed based on the traditional time difference in arrival (TDOA) method. Specifically, the non-line-of-sight (NLOS) propagation phenomenon of the ultra-high-frequency (UHF) signal was considered, and the NLOS propagation error was approximately replaced by a constant, thereby limiting the effect of NLOS propagation. Moreover, the strategy of utilizing more than four sensors was adopted to reduce the possible effect of overcorrection on NLOS propagation. In this paper, the derivation and implementation process of the proposed method is introduced from the perspectives of mathematical model and geometrical model, and its localization results were compared with the traditional TDOA method through an experimental study. The results showed that the speed of error increase of the traditional method presented faster, and the increment of sensor number helped to improve the localization accuracy, but the reduction in localization error becomes insignificant when the sensors exceed six. Finally, the experimental verifications were conducted based on a 35 kV testing transformer with six sensor installations. The experiments found that the proposed localization method had a better calculated accuracy and stability; the obtained minimum calculated error was 10.88 cm, the calculated accuracy can be improved by 82.04% and 78.94%, respectively, with six sensors than four and five sensors arrangement. Full article

Partial discharge (PD) in insulators will not only lead to the gradual degradation of insulation performance but even cause power system failure in serious cases. Because there is strong noise interference in the field, it is difficult to accurately locate the position of the PD source. Therefore, this paper proposes a three-dimensional spatial localization method of the PD source with a four-element ultra-high-frequency (UHF) array based on improved wavelet packet dynamic threshold denoising and the $G_{xx}-\beta$ generalized cross-correlation algorithm. Firstly, considering the field noise interference, the PD signal is decomposed into sub-signals with different frequency bands by the wavelet packet, and the corresponding wavelet packet coefficients are extracted. By using the improved threshold function to process the wavelet packet coefficients, the PD signal with low distortion rate and high signal-to-noise ratio (SNR) is reconstructed. Secondly, in order to solve the problem that the amplitude of the first wave of the PD signal is small and the SNR is low, an improved weighting function, $G_{xx}-\beta$, is proposed, which is based on the self-power spectral density of the signal and is adjusted by introducing an exponential factor to improve the accuracy of the first wave arrival time and time difference calculation. Finally, the influence of different sensor array shapes and PD source positions on the localization results is analyzed, and a reasonable arrangement scheme is found. In order to verify the performance of the proposed method, simulation and experimental analysis are carried out. The results show that the improved wavelet packet denoising algorithm can effectively realize the separation of PD signal and noise and improve the SNR of the localization signal with low distortion rate. The improved $G_{xx}-\beta$ weighting function significantly improves the estimation accuracy of the time difference between UHF sensors. With the sensor array designed in this paper, the relative localization error is 3.46%, and the absolute error is within 6 cm, which meets the requirements of engineering applications. Full article

This paper presents a Virtualized Computational Radio Frequency Identification (VCRFID) solution that utilizes far-field UHF RF for sensing, computing, and self-powering at the edge. A standard UHF RFID system is asymmetric as it consists of a relatively large, complex “reader”, which acts as an RF transmitter and controller for a number of small simple battery-less “tags”, which work in passive mode as they communicate and harvest RF energy from the reader. Previously proposed Computational RFID (CRFID) solutions enhance the standard RFID tags with microcontrollers and sensors in order to gain enhanced functionality, but they end up requiring a relatively high level of power, and thus ultimately reduced range, which limits their use for many Internet-of-Things (IoT) application scenarios. Our VCRFID solution instead keeps the functionality of the tags minimalistic by only providing a sensor interface to be able to capture desired environmental data (temperature, humidity, vibration, etc.), and then transmit it to the RFID reader, which then performs all the computational load usually carried out by a microcontroller on the tag in prior work. This virtualization of functions enables the design of a circuit without a microcontroller, providing greater flexibility and allowing for wireless reconfiguration of tag functions over RF for a 97% reduction in energy consumption compared to prior energy-harvesting RFID tags with microcontrollers. The target application is Industry 4.0 where our VCRFID solution enables battery-less fine-grain monitoring of vibration and temperature data for pumps and motors for predictive maintenance scenarios. Full article

The growing complexity of electrical infrastructure has elevated partial discharge (PD) detection to a crucial methodology for ensuring power system safety. Current PD pattern recognition approaches encounter persistent challenges in low-data scenarios, particularly regarding classification accuracy and model generalizability. This study develops a Firefly Algorithm with a Black Hole Mechanism-ResNet18 (FBH-ResNet18) framework that synergistically integrates the Firefly Algorithm with the Black Hole Mechanism (FBH algorithm) optimization with residual neural networks for PD signal classification using phase-resolved partial discharge (PRPD) mappings. A dedicated experimental platform first acquires PD signals through UHF sensors, which are subsequently converted into two-dimensional PRPD representations. The FBH algorithm systematically optimizes four key hyperparameters within the ResNet18 architecture during network training. The Black Hole Mechanism and improved population dynamics enhance optimization efficiency, resulting in more accurate hyperparameter tuning and improved model performance. Comparative evaluations demonstrate the enhanced performance of this parameter-optimized model against alternative configurations. Experimental results indicate that the improved ResNet18 achieves fast convergence and strong generalization on small-sample datasets, significantly enhancing recognition accuracy. During the first 80 generations of training, the classification accuracy reaches 89.11%, and in the final iteration, the model’s recognition accuracy increases to 92.55%, outperforming other models with accuracies generally below 90%. Additionally, the model shows excellent performance on the test set, with a loss function value of 0.250785, significantly lower than that of other models, indicating superior performance on small sample datasets. This research provides an effective solution for power cable fault diagnosis, offering high practical value. Full article

This paper presents a fully integrated bandpass filter (BPF) with high tunability based on a novel differential active inductor (DAI), designed for sensor interface circuits operating in the ultra-high frequency (UHF) band. The design of the proposed DAI is based on a symmetrical configuration, utilizing a differential amplifier for the feedforward transconductance and a common-source (CS) transistor for the feedback transconductance. By integrating a cascode scheme with a feedback resistor, the quality factor of the active inductor is significantly improved, leading to enhanced mid-band gain for the bandpass filter. To facilitate independent tuning of the BPF‘s center frequency and mid-band gain, an active resistor adjustment and bias voltage control are employed, providing precise control over the filter’s operational parameters. Post-layout simulations and process corner results are conducted with 0.13 µm CMOS technology at 1.2 V supply voltage. The proposed second order BPF achieves a broad tuning range of 280 MHz to 2.426 GHz, with a passband gain between 8.9 dB and 16.54 dB. The design demonstrates a maximum noise figure of 16.54 dB at 280 MHz, an input-referred 1 dB compression point of −3.78 dBm, and a third-order input intercept point (IIP3) of −0.897 dBm. Additionally, the BPF occupies an active area of only 68.2×30 µm², including impedance-matching part, and consumes a DC power of 14–20 mW. The compact size and low power consumption of the design make it highly suitable for integration into modern wireless sensor interfaces where performance and area efficiency are critical. Full article

High-voltage direct-current (HVdc) transmission lines are gaining more attention as an integral part of modern power system networks. Monitoring the dc current is important for metering and the development of dynamic line rating control schemes. However, this has been a challenging task, and there is a need for wireless sensing methods with high accuracy and a dynamic range. Conventional methods require direct contact with the high-voltage conductors and utilize bulky and complex equipment. In this paper, an ultra-high-frequency (UHF) radio frequency identification (RFID)-based sensor is introduced for the monitoring of the dc current of an HVdc transmission line. The sensor is composed of a passive RFID tag with a custom-designed antenna, integrated with a Hall effect magnetic field device and an RF power harvesting unit. The dc current is measured by monitoring the dc magnetic field around the conductor using the Hall effect device. The internal memory of the RFID tag is encoded with the magnetic field data. The entire RFID sensor can be wirelessly powered and interrogated using a conventional RFID reader. The advantage of this approach is that the sensor does not require batteries and does not need additional maintenance during its lifetime. This is an important feature in a high-voltage environment where any maintenance requires either an outage or special equipment. In this paper, the detailed design of the RFID sensor is presented, including the antenna design and measurements for both the RFID tag and the RF harvesting section, the microcontroller interfacing design and testing, the magnetic field sensor calibration, and the RF power harvesting section. The UHF RFID-based magnetic field sensor was fabricated and tested using a laboratory experimental setup. In the experiment, a 40 mm-diameter-aluminum conductor, typically used in 500 kV HVdc transmission lines carrying a dc current of up to 1200 A, was used to conduct dc current tests for the fabricated sensor. The sensor was placed near the conductor such that the Hall effect device was close to the surface of the conductor, and readings were acquired by the RFID reader. The sensitivity of the entire RFID sensor was 30 mV/mT, with linear behavior over a magnetic flux density range from 0 mT to $4.5$ mT. Full article

This paper presents a passive displacement sensor based on the inductive coupling between a printed UHF RFID tag and a metallic strip. The sensor operates by exploiting variations in mutual inductive coupling, which modulate the tag impedance and transmission coefficient, thereby altering the backscattered signal strength and the maximum read range of the RFID tag. The performance of the sensor is validated through simulations and experiments, which demonstrate a sensitivity characterized by an approximately 9 dB reduction in the received signal strength indicator (RSSI) and a 2.3 m decrease in the read range within the first 12 mm of displacement. Furthermore, its potential for wearable applications is showcased through respiratory monitoring, where RSSI variations of approximately 5 dB are observed between the inspiration and expiration phases when positioned on the abdominal region of a volunteer. Thus, the proposed displacement sensing approach offers a cost-effective and battery-free solution for wearable applications with remote monitoring capabilities. Full article

UHF partial discharge sensors are key equipment for substation monitoring, but they are subject to complex multi-physical field stresses in substation applications, which leads to a significantly higher failure rate among UHF partial discharge sensors used in substations compared to other applications. Effective fault diagnosis is of great significance for improving the safety of substations. In this paper, we propose an improved model based on ViT (Vision Transformer), which effectively identifies the local features of the data by designing a sliding window mechanism, and has a good feature extraction capability for the feature library formed by UHF partial discharge sensors. The experimental results show that the diagnostic accuracy of the improved model, based on the ViT model, can reach 97.6%, which can effectively improve classification accuracy and shorten training times compared with the ViT model. Full article

Leaf moisture content (LMC) directly affects the life activities of plants and becomes a key factor to evaluate the growth status of plants. To explore a low-cost, real-time, rapid, and accurate method for LMC detection, this paper employs Ultra-High-Frequency Radio-Frequency Identification (UHF RFID) sensor technology. By reading the tag information attached to the back of leaves, the parameters of the RSSI, phase, and reading distance of the tags are collected. In this paper, we propose an enhanced Multi-Feature Fusion algorithm based on Hyperdimensional Computing (HDC) called MFFHDC. In our proposed method, the real-valued features are encoded into hypervectors and then combined with Multi-Linear Discriminant Analysis (MLDA) for the feature fusion of different features. Finally, a retraining method based on Cosine Annealing with Warm Restarts (CAWR) is proposed to improve the model and further enhance its accuracy. Tests conducted in the experimental forest show that the proposed mechanism can effectively predict the LMC. The model’s Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Coefficient of Determination (R²) reached 0.0195, 0.0255, and 0.9131, respectively. Additionally, comparisons with other methods demonstrate that the presented system performs excellently in most aspects. As a lightweight model, this study shows great practical application value, particularly for the limited data volume and low hardware costs. Full article

Modern power grids are undergoing a significant transformation with the massive integration of renewable, decentralized, and electronically interfaced energy sources, alongside new digital and wireless communication technologies. This transition necessitates the widespread adoption of robust online diagnostic and monitoring tools. Sensors, known for their intuitive and smart capabilities, play a crucial role in efficient condition monitoring, aiding in the prediction of power outages and facilitating the digital twinning of power equipment. This review comprehensively analyzes various sensor technologies used for monitoring power transformers, focusing on the critical need for reliable and efficient fault detection. The study explores the application of fiber Bragg grating (FBG) sensors, optical fiber sensors, wireless sensing networks, chemical sensors, ultra-high-frequency (UHF) sensors, and piezoelectric sensors in detecting parameters such as partial discharges, core condition, temperature, and dissolved gases. Through an extensive literature review, the sensitivity, accuracy, and practical implementation challenges of these sensor technologies are evaluated. Significant advances in real-time monitoring capabilities and improved diagnostic precision are highlighted in the review. It also identifies key challenges such as environmental susceptibility and the long-term stability of sensors. By synthesizing the current research and methodologies, this paper provides valuable insights into the integration and optimization of sensor technologies for enhancing transformer condition monitoring and reliability in modern power systems. Full article

This article presents two monopole antennas with an endfire radiation pattern in the UHF band that can be installed on dry walls or metallic cabinets as a part of wireless sensor nodes, making them a suitable choice for smart home applications, such as the wireless remote control of house appliances. Two different antennas are proposed to cover the RFID bands of North America (902–928 MHz) and worldwide (860–960 MHz). The antennas have wide horizontal radiation patterns that provide great reading coverage in their communication with a base station placed at a certain distance from the antennas. The structures have two ground planes, one in-plane and the other vertical. The vertical ground helps the antenna to have a directive radiation and also makes it easily installed on walls. The antenna feeding line lies over the vertical ground substrate. The maximum dimensions of the narrow-band antenna are L × W = 0.3$\lambda \times$ 0.14$\lambda$, and those for the wide-band antenna are L × W = 0.39$\lambda \times$ 0.14$\lambda$. The measured results show that the bandwidth of the proposed antennas for the North America and worldwide RFID bands are from 902 MHz to 939 MHz and 822 MHz to 961 MHz, with maximum gains of 4.2 dBi and 4.9 dBi, respectively. Full article

This article presents an innovative combination of textile electrical circuits with advanced capabilities of electronic RFID sensors, indicating the revolutionary nature of the development of textronics, which is used in various areas of life, from fashion to medicine. A review of the literature relating to the construction of textronic RFID identifiers and capacitive textronic sensors is performed. Various approaches to measuring capacity using RFID tags are discussed. This article focuses on presenting the concept of a capacitive sensor with an RFID interface, consisting of a microelectronic part and a textile part. The textile part is based on the WL4007 material, where antennas and capacitive sensors are embroidered using SPARKFUN DEV 11791 conductive thread. The antenna is a half-wave dipole designed to operate at a frequency of 860 MHZ. The microelectronic part is sewn to the textile part and consists of a microcontroller, an RFID-integrated circuit and a coupling loop, placed on the PCB. The embroidered antenna is coupled with a loop on the microelectronic module. This article focuses on presenting various designs of textronic electrodes, enabling various types of measurements. Article presents capacitance measurements of individual sensor electrodes, made using a measuring bridge and a built RFID tag. The sensors’ capacity measurement results are shown. Full article

This manuscript presents the use of three novel technologies for the implementation of wireless green battery-less sensors that can be used in agriculture. The three technologies, namely, additive manufacturing, energy harvesting, and wireless power transfer from airborne transmitters carried from UAVs, are considered for smart agriculture applications, and their combined use is demonstrated in a case study experiment. Additive manufacturing is exploited for the implementation of both RFID-based sensors and passive sensors based on humidity-sensitive materials. A number of energy-harvesting systems at UHF and ISM frequencies are presented, which are in the position to power platforms of wireless sensors, including humidity and temperature IC sensors used as agriculture sensors. Finally, in order to provide wireless energy to the soil-based sensors with energy harvesting features, wireless power transfer (WPT) from UAV carried transmitters is utilized. The use of these technologies can facilitate the extensive use and exploitation of battery-less wireless sensors, which are environmentally friendly and, thus, “green”. Additionally, it can potentially drive precision agriculture in the next era through the implementation of a vast network of wireless green sensors which can collect and communicate data to airborne readers so as to support, the Artificial Intelligence and Machine Learning-based decision-making with data. Full article

In answer to the demand for high sensitivity and miniaturization of ultra-high frequency (UHF) sensors for partial discharge (PD) detection in power equipment, this paper proposes research on miniaturized UHF-sensing technology for PD detection in power equipment based on symmetric cut theory. The symmetric cut theory is applied for the first time to the miniaturization of PD UHF sensors for power equipment. A planar monopole UHF sensor with a size of only 70 mm × 70 mm × 1.6 mm is developed using an exponential asymptotic feed line approach, which is a 50% size reduction. The frequency–response characteristics of the sensor are simulated, optimized and tested; the results show that the standing wave ratio of the sensor developed in this paper is less than 2 in the frequency band from 427 MHz to 1.54 GHz, and less than 5 in the frequency band from 300 MHz to 1.95 GHz; in the 300 MHz~1.5 GHz band; the maximum and average gains of the sensor E-plane are 4.76 dB and 1.02 dB, respectively. Finally, the PD simulation experiment platform for power equipment is built to test the sensor’s sensing performance; the results show that the sensor can effectively detect the PD signals; the sensing sensitivity is improved by about 95% relative to an elliptical monopole UHF sensor. Full article