RLC Equivalent Circuit Research Articles

Performance improvement of THz MIMO antenna with graphene and prediction bandwidth through machine learning analysis for 6G application

This article provides the findings of a study that integrated simulation, an RLC equivalent circuit, and machine learning (ML) techniques to improve wireless indoor communications clusters with future 6 G applications. The antenna being presented is constructed on a polyimide substrate. It exhibits an isolation of 27 dB and has a bandwidth of 4.331 THz, ranging from 0.631 THz to 4.962 THz. Along with its small size (95.52 × 227.24) µm2, it boasts an impressive maximum gain of 13.3 dB and an efficiency rating of 95 %. The ECC value drops below 0.0002 when the DG goes over 9.99. An advanced design system (ADS) creates a model like the proposed MIMO antenna to compare the return loss caused by CST (Computer Simulation Technology). Subsequently, following extensive data sampling with CST MWS (Microwave Studio) simulation, we employed supervised regression ML techniques. Gaussian process regression demonstrates exceptional accuracy, reaching almost 99 %, as evidenced by the high R-square and var scores. Additionally, it achieves the lowest error, less than one, while predicting bandwidth. The proposed antenna demonstrates strong potential as a formidable contender for 6 G THz band applications, as evidenced by the outcomes of the CST simulations and the prognostications derived from the machine learning techniques.

An ultra-fast MMC-HVDC fault location algorithm based on transient voltage features and regression neural network

An ultra-fast fault location algorithm based on the single-ended transient voltage features and regression neural network (RNN) is proposed, which utilizes 2.5 ms postfualt data window and is suitable for modular multilevel converter-based high-voltage DC (MMC-HVDC) grids equipped with quick-action protections and hybrid DC circuit breakers (HDCCBs). Firstly, the analyses based on the lumped RLC equivalent circuit demonstrate that the delay time, the first negative peak time and its value all have exact relationships with the fault location. Nevertheless, considering the actual parameters and topology of the MMC-HVDC grid, three features can only be approximately extracted. Thus, RNN is utilized to estimate fault locations. 2.02 × 104 distinct fault cases validate the algorithm’s high accuracy across all fault locations and transition resistances up to 1005 Ω. It can well tolerate reasonable deviations of line parameter and current limiting reactor value, as well as 40-dB white noise. Besides, it also has remarkable adaptability, and can be suitable for different systems.

Design and Comparative Characterization of Water Level IDE Sensors at Resonant Frequencies

Background: This paper reports the design and characterization of three Interdigital electrode (IDE) water-level sensors at resonant frequencies. The geometries of the proposed IDE sensors are comb type, circular type, and Archimedean spiral type. These IDE sensors have been fabricated by the printed circuit board technology. The sensor’s performance has been evaluated on both tap and distilled water. Methods: The multiple resonant frequencies are investigated for the frequency span of 40 Hz to 110 MHz using a 4294A impedance analyzer. The peak of the projected admittance graph appeared at the first resonant frequency. This first resonant frequency is chosen here for the characterization of the proposed sensors. Results: The study asses that the variations in resonant frequency are caused by both the sensor's geometry and the water under test. The resonant frequency subtly states that sensors can be presented as lumped element equivalent series RLC circuits. In this work, an attempt has been made to show that the change in capacitance plays a pivotal role in estimating the resonant frequency. It is found that the sensor’s sensitivity decreases with water elevation and always be negative. Conclusion: The circular and Archimedean spiral sensors have comparable sensitivity performance, while the comb IDE sensor is found to be the most sensitive. The IDE sensor features the highest sensitivity at 1 cm of water elevation. The circular and spiral IDE sensor more closely follows the reference resonant frequency fr α 1/(√C) when compared with the comb IDE sensor.

Broadband high gain performance MIMO antenna array for 5 G mm-wave applications-based gain prediction using machine learning approach

This paper presents the findings about implementing a machine learning (ML) technique to optimize the performance of 5 G mm wave applications utilizing multiple-input multiple-output (MIMO) antennas operating at the 28 GHz frequency band. This article examines various methodologies, including simulation, measurement, and the utilization of an RLC-equivalent circuit model, to evaluate the appropriateness of an antenna for its intended applications. In addition to its compact dimensions, the proposed design exhibits a maximum gain of 10.34 dBi, superior isolation exceeding 26 dB, and a broad bandwidth of 16.56 % Centered at 28 GHz and spanning from 25.905 to 30.544 GHz. Another supervised regression machine learning technique is utilized to predict the antenna's gain accurately. Machine learning (ML) models can be assessed by several measures, such as the variance score, R square, mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE), and Mean Absolute Percentage Error (MAPE). Among the six machine learning models considered, it is seen that the Gaussian Process Regression (GPR) model exhibits the lowest error and achieves the highest level of accuracy in forecasting gain. The antenna under consideration has promising qualities for its intended use in high-band 5 G applications. This is evidenced by the modelling findings obtained from Computer Simulation Technology (CST) and Advanced Design System (ADS)and the measured and projected results derived using machine learning methodologies.

A wide-band high gain beamsteerable variable patch reflectarray antenna for THz applications

This article proposed a compact size, high gain, wideband reflectarray antenna for THz and 6 G applications. To enhance the aperture efficiency and gain, a single layer L-shape delay line-based unit cell with a size of 0.45λ0 is adopted. The λ0 is the free space wavelength at 1.35 THz. The SiO2 substrate is sandwiched between L- shape delay line and the ground plane of gold. The unit cell, equivalent parallel RLC circuit modelled with gap coupled capacitance is also presented which is validated through the Ansys-High Frequency Structure Simulator (HFSS) and Advanced Design System (ADS). The dimension of the RA is 3 mm having 610 elements. It is excited by the pyramidal horn antenna at an optimum distance (F/D =1.21). The optimum ratio (F/D) is calculated with the help of the cosq model at −10 dB edge taper. The operating bandwidth is obtained 46.15 % from 1 to 1.6 THz. The reflection phase and magnitude are 500° and 3.93 dB at 1.35 THz. It is adjusted by varying the length of the delay line. The maximum aperture efficiency and realized gain are 35 % and 28 dBi respectively. The Side lobe level is −15.5 dB and the Co-Cross polarization level is below −30 dB.

Maze-enclosed quad symmetric kite shaped SRR based metamaterial absorber for doppler navigation aids and earth exploration satellite remote sensing services

This paper represents a wide incident angle-polarization insensitive, stable in results, and compact-sized metamaterial absorber that can be used for Doppler navigation aids and remote sensing applications in Earth Exploration Satellite Services. The unit cell dimension was subwavelength-sized of 0.112λ X 0.112λ, with a good effective medium ratio (8.9), and achieved polarization insensitivity at a maximum incident angle up to 180°, making it more effective for proposed applications. The absorber resonated at 3.855 GHz, 5.13 GHz, 9.855 GHz, 13.335 GHz, and 16.02 GHz with single negative metamaterial properties due to negative permeability and absorptions of 99.7 %, 99.2 %, 98.6 %, 98.1 %, and 97.9 %, respectively. An RLC equivalent circuit on ADS was analyzed to validate the simulated results using the dipoles created by the surface currents. The absorber is cost-effective as no lumped elements or multilayer substrate materials were used, making fabricating less time-consuming and simple. The simulated results were validated by measuring data from a vector network analyzer after fabricating it. The proposed metamaterial absorber is suitable for use in Doppler navigation and Earth Exploration Satellite Services, and this research can further be extended to beamforming and sensing applications in the future.

Study of the interdigital electrode sensor at resonance frequency during water transition

This paper uses the co-planar interdigital electrode (IDE) sensor to measure water level. The researchers generally characterize the interdigital electrode sensor as a fringe field capacitor sensor developed on the printed circuit board and utilize the capacitor sensor's properties for liquid-level measurement. The interdigital electrode sensors illustrate more than one resonance at the higher frequencies, and in this study, the first resonance frequency fr -has been utilized for the water level measurement. Three water types are examined here: distilled, tap, and river. The study assesses that with the transition of water, the permittivity between the electrodes is changed and, it leads to a change in capacitance hence, the change in resonance frequency was observed. The proposed sensor can be represented by the lumped element equivalent series RLC circuit. The developed IDE sensor has good repeatability, small variability, and small hysteresis error. The maximum standard error for distilled, tap, and river water are 0.02833, 0.02503, and 0.02618, respectively, and the hysteresis error is less than 1.903% of full-scale output variation. The maximum error for the fr estimation is about ±2 Hz.

Adjustable graphene disk‐based THz absorber for biomedical sensing: Theoretical description

AbstractAs a basic building block, the THz wave absorber has intense imaging, sensing, and nondestructive testing applications. There are several methods for tuning THz absorbers, including electricity modulation, light modulation, mechanical tuning, using phase change materials, liquid crystal, flexible materials, MEMS technology, and thermally tuning vanadium dioxide. The choice of tuning method depends on the specific application and the desired performance characteristics of the THz absorber. In this work, we report a theoretical description of mechanically tunable THz absorber based on overlapping periodic arrays of graphene nano‐disks. The basis of this work is based on the movement of a dielectric surface covered on both sides with graphene disks. This surface moves on a fixed plane while the distance between these two surfaces is free space. Also, the fixed surface consists of a relatively thick layer of gold at the bottom, dielectric on it, and graphene disk patterns on the dielectric. Now, by moving the movable surface in the horizontal direction, it is possible to adjust the amount of absorption in different frequencies of the terahertz (THz) band. Additionally, an equivalent RLC circuit model is developed and theoretical results match with simulated data. The proposed mechanically tunable THz absorber can be exploited in various emerging applications such as sensing due to its capability of covering all of the THz gap and beyond with multiple absorption peaks.

Machine Learning-Based Approach for bandwidth and frequency Prediction for N77 band 5G Antenna

Yagi antennas are useful for wireless communications because of the directional gain they provide, allowing the antenna to concentrate the signal in either the transmission or reception direction. It is built on a substrate made of FR-4, this antenna has a return loss of −46.85 dB at 3.6 GHz and a bandwidth of 3.3–4.2 GHz within a −10 dB range, making it ideal for use in the n77 bands. Not only is it small, with a size of 0.642λ 0 × 0.583λ 0, but it also has a maximum gain of 7.95 dB and a maximum directivity of 8.58 dB. This study investigates several approaches to estimating the performance of an antenna. These approaches include simulation with a variety of software tools, including as CST, HFSS, and Altair Feko; curve fitting technology; and the RLC equivalent circuit model. After that, simulation with CST MWS is used to collect a large amount of data samples, and then supervised regression machine learning (ML) methods are used to determine the resonance frequency and bandwidth of the antenna. When it comes to predicting bandwidth and frequency, Random Forest Regression demonstrates an exceptional level of performance, particularly when comparing with the results produced by curve-fitting tools, neural networks, and regression machine learning models. When all of these considerations are taken into account, it is clear that the antenna is an outstanding option for the n77 band of a 5G communication system.

An equivalent RLC circuit loss mechanism introduced by Fe2O3 nanoneedle arrays towards high-performance electromagnetic wave absorption materials

The introduction of an equivalent RLC circuit loss mechanism realizes strong EMW absorption in a low-filling ratio and even lower thicknesses.

Magnetic field tunable piezoelectric resonator for MEMS applications

In this report, the Ni0.50Mn0.35In0.15/Pb0.96La0.04Zr0.52Ti0.48O3 (Ni-Mn-In/PLZT) bulk acoustic wave (BAW) resonator has been fabricated using the sputtering deposition technique. The magnetoelectric effect in the Ni-Mn-In/PLZT resonator has been investigated. The increase in magnetoelectric coupling coefficient (MECC) with the increment in the frequency of applied HAC is observed. The potential of the Ni-Mn-In/PLZT BAW resonator as a magnetic sensor is tested by measuring the shift in the resonant frequency of the reflective (S11 parameter) response in the presence of the externally applied magnetic field. The Ni-Mn-In/PLZT resonator exhibits a frequency shift of 730 MHz in the 1200 Oe magnetic field, among the largest magnetic field-induced frequency shifts in literature. The rarely reported complete dependence of acoustic velocity, coupling coefficients, magnetic sensitivity, and frequency peak shift on the strength of the external magnetic field is investigated. The acoustic velocity increases from 1237 m/s to 1394 m/s, and the electromechanical coupling coefficient increases from 0.0199 % to 0.0206 % after applying the 1200 Oe magnetic field. The reflective responses have been fitted using the Modified-Butterworth Van Dyke (MBVD) model in terms of hardly reported RLC equivalent circuits. The experimental results and extracted parameters from MBVD modeling exhibit high accuracy. This interaction between multiferroic orders and elastic resonance peaks opens a new window for highly sensitive future magnetic field sensors, wireless communication devices, and magnetic field tunable filters.

Quasi-Yagi antenna design for LTE applications and prediction of gain and directivity using machine learning approaches

In recent years, improvements in wireless communication have led to the development of microstrip or patch antennas. The article discusses using simulation, measurement, an RLC equivalent circuit model, and machine learning to assess antenna performance. The antenna's dimensions are 1.01 λ0×0.612λ0 with respect to the lowest operating frequency, the maximum achieved gain is 6.76 dB, the maximum directivity is 8.21 dBi, and the maximum efficiency is 83.05%. The prototype's measured return loss is compared to CST and ADS simulations. The prediction of gain and directivity of the antenna is determined using a different supervised regression machine learning (ML) method. The performance of ML models is measured by the variance score, R square, mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE), and mean squared logarithmic error (MSLE), etc. With errors of less than unity and an accuracy of roughly 98%, Ridge regression gain prediction outperforms the other seven ML models. Gaussian process regression is the best method for predicting directivity. Finally, modeling results from CST and ADS, as well as measured and anticipated results from machine learning, reveal that the suggested antenna is a good candidate for LTE.

Machine learning-based technique for resonance and directivity prediction of UMTS LTE band quasi Yagi antenna

In this study, we have presented our findings on the deployment of a machine learning (ML) technique to enhance the performance of LTE applications employing quasi-Yagi-Uda antennas at 2100 MHz UMTS band. A number of techniques, including simulation, measurement, and a model of an RLC-equivalent circuit, are discussed in this article as ways to assess an antenna's suitability for the intended applications. The CST simulation gives the suggested antenna a reflection coefficient of -38.40 dB at 2.1 GHz and a bandwidth of 357 MHz (1.95 GHz-2.31 GHz) at a -10 dB level. With a dimension of 0.535λ0×0.714λ0, it is not only compact but also features a maximum gain of 6.9 dB, a maximum directivity of 7.67, VSWR of 1.001 at center frequency and a maximum efficiency of 89.9%. The antenna is made of a low-cost substrate, FR4. The RLC circuit, sometimes referred to as the lumped element model, exhibits characteristics that are sufficiently similar to those of the proposed Yagi antenna. We use yet another supervised regression machine learning (ML) technique to create an exact forecast of the antenna's frequency and directivity. The performance of machine learning (ML) models can be evaluated using a variety of metrics, including the variance score, R square, mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE), and mean squared logarithmic error (MSLE). Out of the seven ML models, the linear regression (LR) model has the lowest error and maximum accuracy when predicting directivity, whereas the ridge regression (RR) model performs the best when predicting frequency. The proposed antenna is a strong candidate for the intended UMTS LTE applications, as shown by the modeling results from CST and ADS, as well as the measured and forecasted outcomes from machine learning techniques.

Machine learning-based technique for gain and resonance prediction of mid band 5G Yagi antenna

In this study, we present our findings from investigating the use of a machine learning (ML) technique to improve the performance of Quasi-Yagi–Uda antennas operating in the n78 band for 5G applications. This research study investigates several techniques, such as simulation, measurement, and an RLC equivalent circuit model, to evaluate the performance of an antenna. In this investigation, the CST modelling tools are used to develop a high-gain, low-return-loss Yagi–Uda antenna for the 5G communication system. When considering the antenna’s operating frequency, its dimensions are {0.642}lambda _0times {0.583}lambda _0. The antenna has an operating frequency of 3.5 GHz, a return loss of -43.45 dB, a bandwidth of 520 MHz, a maximum gain of 6.57 dB, and an efficiency of almost 97%. The impedance analysis tools in CST Studio’s simulation and circuit design tools in Agilent ADS software are used to derive the antenna’s equivalent circuit (RLC). We use supervised regression ML method to create an accurate prediction of the frequency and gain of the antenna. Machine learning models can be evaluated using a variety of measures, including variance score, R square, mean square error, mean absolute error, root mean square error, and mean squared logarithmic error. Among the nine ML models, the prediction result of Linear Regression is superior to other ML models for resonant frequency prediction, and Gaussian Process Regression shows an extraordinary performance for gain prediction. R-square and var score represents the accuracy of the prediction, which is close to 99% for both frequency and gain prediction. Considering these factors, the antenna can be deemed an excellent choice for the n78 band of a 5G communication system.

Short-circuit Fault Detection in Low-voltage DC Microgrids Based on Improved Current Change Rate

Along with the explosion of new energy sources, DC microgrid has obvious advantages as well as better development paths, but reliable fault detection is still one of the key issues to be solved for DC microgrid. The fault current characteristics are analyzed by a fault RLC equivalent circuit, and the characteristics at different fault location points are further analyzed on the independent DC microgrid model composed of a PV generation unit, an energy storage unit, and a load unit. A fault detection method based on the improved current change rate is proposed by combining the current abrupt change direction. A DC microgrid model with a bus voltage of 400 V is built and simulated in Matlab/Simulink. The simulation results show that the proposed method can detect and isolate bus and unit short-circuit faults in less than 2 ms, ensuring the proper operation of the whole system, which has a certain reference significance for fault protection of low-voltage DC microgrids at independent operation sites.

Interconversion between dual-peak electromagnetically induced transparency and dual-peak electromagnetically induced absorption utilizing metasurface

Utilizing metasurface (MS), the transition between dual-peak electromagnetically induced transparency (EIT) and dual-peak electromagnetically induced absorption (EIA) in four states has been proposed in the terahertz (THz) band with the incidence of a circularly polarized (CP) wave. Additionally, the phase-change material vanadium dioxide (VO2) and photosensitive material silicon (Si) have been adopted. The four state transitions can be achieved by adjusting the pump light and the temperature which is not affected by incident CP waves. With the incidence of the pump light, Si can transform from the insulating state to the metallic state. Similarly, when the temperature exceeds 68 °C, VO2 enters the metallic state. Two different dual-peak EIT (State 1 and State 2) accompanied by slow light effect can be observed by modulating the state of VO2 without the action of Si. However, once Si is in metallic state, two different states of dual-peak EIA (State 3 and State 4) are achieved by changing the state of VO2. Notably, the MS has the property of polarization insensitivity. The theoretical oscillator model and equivalent RLC-circuit model verify the feasibility of dual-peak EIT and EIA.

Strong and Broadband Pure Optical Activity in 3D Printed THz Chiral Metamaterials

AbstractOptical activity (polarization rotation of light) is one of the most desired features of chiral media, as it is important for many polarization‐related applications. However, in the THz region, chiral media with strong optical activity are not available in nature. Here, a chiral metamaterial (CMM) structure composed of pairs of vertical U‐shape resonators of “twisted” arms is studied, and it is revealed that it demonstrates large pure optical activity (i.e., optical activity associated with negligible transmitted wave ellipticity) in the low THz regime. The experimental data show polarization rotation up to 25° for an unmatched bandwidth of 1 THz (relative bandwidth 80%), from a 130 µm‐thickness structure, while theoretical optimizations show that the rotation can reach 45°. The enhanced chiral response of the structure is analyzed through an equivalent RLC circuit model, which also provides simple optimization rules for the enhancement of its chiral response. The proposed chiral design allows for easy fabrication via direct laser writing (DLW) and electroless metal plating, making the associated structures suitable candidates for polarization control applications.

Broadband electromagnetically induced transparency to broadband electromagnetically induced absorption conversion with silicon based on metastructure

Electromagnetically induced transparency-like (EIT-like) and electromagnetically induced absorption-like (EIA-like) can be easily generated separately in metastructure (MS), but it is rare to achieve both states simultaneously in a single device, let alone broadband EIT-like and EIA-like. Notably, the responses of the left-handed circularly polarized waves and the right-handed circularly polarized waves to the MS are the same, demonstrating the generation of polarization-insensitive EIT-like and EIA-like. The photosensitive silicon (Si) is chosen to control the state of the device, realizing the mutual conversion between EIT-like and EIA-like. With the incidence of circularly polarized (CP) waves, the broadband EIT-like can be observed between 0.505 THz and 1.403 THz, where the maximum group delay and group index can arrive at 1.57 ps and 7.26, respectively. Besides, the transition from broadband EIT-like to broadband EIA-like can be successfully achieved in the same frequency band when Si is excited by the pump light, where Si-based ladder structure (SBLS) is adapted to enhance absorption efficiency to realize broadband EIA-like. The relative bandwidths of EIT-like and EIA-like have reached 26.5% and 15.2%, respectively. The theoretical RLC (resistance, inductance, and capacitance) equivalent circuit model demonstrates the feasibility of broadband EIT-like and EIA-like implementation.

DC fault detection in hybrid MTDC systems using transient average of DC reactor voltage

The existence of various types of converters makes the fault characteristics of the hybrid multi-terminal DC (MTDC) system more complicated than the voltage source converter-based (VSC-based) MTDC system. A reduced line commutated converter (LCC) model for DC fault analysis is first proposed in this paper, where the LCC-based rectifier with control effects is regarded as an equivalent RLC circuit. By neglecting the resistive and capacitive components in the high-frequency domain, the original network analysis is significantly simplified to derive concise time-domain expressions of the initial DC fault current. On top of this, a single-terminal DC fault detection scheme using the transient average of DC reactor voltage is designed for the hybrid MTDC system. The thresholds for detecting faults on the LCC-connected and VSC-connected lines are calculated based on the derived fault current expressions, respectively. Simulation studies based on PSCAD/EMTDC have validated the proposed fault analysis model is accurate within the initial period after fault, while the proposed fault detection scheme for hybrid MTDC systems is fast, sensitive, and tolerant to high fault resistances.

Wireless strain sensing using carbon nanotube composite film

Carbon nanotubes in polymer matrix form a complex network which consists of resistors, inductors, and capacitors. The present research focuses on wireless strain sensing using multi-walled carbon nanotube/epoxy resin films. Following a specific process, the nanocomposite strain gauges with different multi-walled carbon nanotube concentrations were fabricated. The test on these prototypes shows that the multi-walled carbon nanotube/epoxy resin films can respond to wireless electromagnetic excitation by means of generating induced voltage. Experimental measurement also proves that mechanical strain affects the resonant frequencies of the multi-walled carbon nanotube/epoxy resin strain gauges. Therefore, the nanocomposite films can be used to detect mechanical strain wirelessly by calculating the shift of the resonant frequency. Aiming to reveal the working mechanism of the wireless sensing, this study hypothesizes that the nanocomposite strain gauges can be modelled by an equivalent RLC circuit. The subsequent theoretical analysis, and the comparison between the analytical predictions and the experimental results both indicate that the hypothesis is reasonable, since the RLC model can successfully explain the phenomenon of the strain-induced shift of the resonant frequency. The current work provides a promising method to make tag-type wireless strain sensors.