Accurate Voltage Research Articles

A 0.9‐V, 747‐nW Capacitively Biased Diode‐Based Single BJT Branch Bandgap Circuit

ABSTRACTIn this paper, a bandgap reference (BGR) circuit that combines the capacitively biased diode (CBD) structure and the proportional‐to‐absolute‐temperature (PTAT) voltage‐embedded amplifier has been proposed. In order to enhance compatibility with digital domain supply voltage and achieve low power consumption, the supply voltage of the circuit is set at , benefiting from the fact that both the CBD structure and the PTAT‐embedded amplifier can operate at sub‐1 V supply voltages. The circuit generates a complementary‐to‐absolute‐temperature (CTAT) voltage through the CBD structure. PTAT voltage is achieved by a PTAT‐embedded amplifier in a unity‐gain feedback configuration. The calibration of temperature coefficient (TC) is realized by applying a time‐domain trimming method through an on‐chip RC delay circuit. The reference clock frequency is a typical 32‐kHz crystal oscillator clock frequency, enabling high versatility of the circuit. Implemented in 0.13‐m CMOS, measurement results show that the proposed BGR achieves an average temperature coefficient of 28 ppm/°C over −40°C to 125°C, voltage accuracy of 0.25%, power supply rejection (PSR) of , with an active area of , and a power consumption of .

Event-triggered boost converter model predictive control with Kalman filter

Event-triggered control has been gaining popularity as a method to reduce the computational burden of model predictive control (MPC). Existing literature reports its successful use in power converter applications. In our survey, event-triggered model predictive control (ET-MPC) is used to improve the computational performance of an enumeration-based MPC controlled boost converter. ET-MPC solves an optimal control problem (OCP) to generate an optimal actuating value only when an event is triggered as opposed to solving the OCP at every time step, and hence reduce the computational load. In addition, a Kalman Filter-based estimator is added to the control system to ensure accurate voltage tracking even in the presence of model mismatch, which commonly occurs during load transients. The novelty of this work lies in the selection of the actuating control signal, where the control actions are selected from the optimal switching sequence as opposed to upholding the last value of the optimal actuating value as reported in prior literature. Extensive simulation evaluations are conducted to compare the performance of conventional time-triggered MPC and the proposed event-triggered MPC, where the event-trigger threshold is used as a tuning parameter to balance computation and control performance.

A novel approach using iterative g-function and chaotic cooperation search for accurate voltage calculation of double and triple diode solar cell models

The solar cells can be accurately represented via double and triple-diode models (DDM and TDM, respectively). In this paper, a novel iterative procedure based on applying the g-function is proposed for double and triple solar cell voltage calculation for the first time. The proposed iterative procedure is performed without any approximations, while its application overcomes numerical limitations when applying literature-accepted approaches based on the Lambert W function. Also, one notable advantage of the proposed procedure is its effectiveness with arbitrary starting point values. Second, in this paper, an original equation for calculating solar cells’ root mean square error (RMSE) voltage (RMSEU) in the DDM and TDM, which aids in parameter estimation, is proposed. Third, the paper introduces a new metaheuristic algorithm called the Chaotic Cooperation Search Algorithm (ChCSA) for estimating solar cell parameters. The proposed approach (iterative procedure for voltage-current representation as well as a novel algorithm) is tested on two well-known solar cells in the literature and on measured current-voltage characteristics for different weather conditions. Applying the proposed algorithm and the novel iterative procedure for DDM and TDM voltage calculations demonstrated the accuracy and efficiency of the proposed approach for solar cell parameter estimation. In order to achieve the appropriate accuracy, it was shown that only a few iterations of the proposed iterative procedure are needed for the majority of measurement points in both observed cells. Furthermore, the application of the proposed algorithm outperforms existing algorithms in solar cell parameter estimation. Moreover, with the well-known Photowatt-PWP 201 solar module, the application of the proposed algorithm enables the estimation of the parameters of the solar cell so that the RMSEU value is reduced by about 50 % compared to the results obtained for the parameters determined by numerous approaches from the literature. Therefore, the presented research offers a novel and original double and triple solar cell modeling perspective.

Enhancing Grid-Connected Photovoltaic Systems' Power Quality through a Dynamic Voltage Restorer Equipped with an Innovative Sliding Mode and PR Control System.

This study focuses on enhancing power quality in on-grid photovoltaic (PV) schemes through an innovative dynamic voltage restorer (DVR) that integrates two control strategies with a multi-level inverter. The DVR, a power electronic compensator, corrects voltage disturbances such as sag and swell by adjusting the voltage at the point of common coupling (PCC). It utilizes sliding mode control (SMC) for normal operations and proportional resonance (PR) during voltage disruptions, ensuring accurate voltage correction. The system's multi-level inverter offers a distinct advantage over traditional high-frequency inverters, which typically suffer from significant switching losses in high-power applications. The multi-level inverter effectively detects and mitigates voltage issues while minimizing harmonic distortion. The study evaluates the DVR's performance under various conditions, including voltage swell, disruption, mild sag, and severe sag. Simulation results using the Simulink tool demonstrate that the proposed DVR significantly reduces voltage disturbances and enhances power quality in the PV panel's output, the PCC, and the injected voltage. The findings suggest that this dual-control DVR designe, with its efficient use of a multi-level inverter, is a promising solution for improving power quality and stability in on-grid PV arrangements.

Low Pass Filter Based Lithium‐ion Battery Equivalent Circuit Model With High Accuracy

AbstractThe equivalent circuit model (ECM) of lithium batteries provides a simplified way to describe their output behaviors. In this paper, a low pass filter‐based ECM of lithium battery is proposed with high accuracy. A voltage source is employed to represent the capability of the lithium battery to store energy chemically, a RC branch paralleled with the voltage source represents the charge transfer process. This RC branch acts as a low pass filter, and the capacitance buffers the charges during charging or discharging process, and this explains the dynamic voltage recovery process. However, with the traditional RC branches‐based ECM, the dynamic voltage is realized by the resistance in parallel with the capacitor, which discharges the capacitor and the RC branch is series‐connected with the voltage source. The proposed ECM is compared with the 1‐RC Thevenin model, which has the same number of model parameters with the proposed model. The parameters are obtained by the hybrid pulse power characterization (HPPC) test. The output voltage accuracy of the 1‐RC Thevenin model and the proposed model with LFP battery and NCM battery is verified with different test conditions. The experimental results show that the proposed ECM has higher accuracy, this validates the correctness of the proposed model.

Distribution grid monitoring based on feature propagation using smart plugs

Smart home power hardware makes it possible to collect a large number of measurements from the distribution grid with low latency. However, the measurements are imprecise, and not every node is instrumented. Therefore, the measured data must be corrected and augmented with pseudo-measurements to obtain an accurate and complete picture of the distribution grid. Hence, we present and evaluate a novel method for distribution grid monitoring. This method uses smart plugs as measuring devices and a feature propagation algorithm to generate pseudo-measurements for each grid node. The feature propagation algorithm exploits the homophily of buses in the distribution grid and diffuses known voltage values throughout the grid. This novel approach to deriving pseudo-measurement values is evaluated using a simulation of SimBench benchmark grids and the IEEE 37 bus system. In comparison to the established GINN algorithm, the presented approach generates more accurate voltage pseudo-measurements with less computational effort. This enables frequent updates of the distribution grid monitoring with low latency whenever a measurement occurs.

Deep learning-based system for measuring weak electrical signals in plants

Abstract Due to the characteristics of plant electrical signals being weak, low-frequency, and susceptible to interference, this study proposes a hardware solution involving silver chloride medical adhesive electrodes and the design of conditioning circuits to amplify the plant electrical signals and reduce noise. On the software side, deep learning algorithms are proposed to extract the voltage values from a self-built plant electrical signal acquisition system. Experiments were conducted on two aloe vera plants grown in different environments. Voltage signals were synchronously collected by using a high-precision digital multimeter with anti-interference capabilities. The measured signals from the system were used as input signals for a 1D-CNN, and the synchronized high-precision digital multimeter measurements served as network labels. The 1D-CNN network was then trained by using deep learning algorithms to fit the voltage values from the acquisition system to those of the high-precision digital multimeter. This approach effectively reduces noise and extracts accurate voltage values in the self-built measurement system. By combining hardware and software, the precision of the measurements is improved, providing a new method for measuring plant electrical signals.

Accuracy of endocardial abnormal electrogram characterization using a 17-AHA-segmental late boundary and voltage-dependent sensitivity annotation for ventricular tachycardia ablation

Abstract Background Substrate-based catheter ablation of ventricular tachycardia (VT) has become the mainstay therapy for patients with scar-related VT. Correct identification and timing of abnormal near-field EGMs and discerning it from far-field EGMs, is key for procedural success. The novel CARTO™ Late Activation Mapping (LAM) module provides fast and accurate annotation of near-field EGMs including late abnormal ventricular activation. For the algorithm to succeed, a late boundary (LB) annotation line should be positioned immediately behind the normal near-field EGM, however, since the timing of normal local near-field activation varies greatly throughout the endocardium, there is currently no consensus for the optimal LB positioning. We hypothesized that 17-AHA segmental annotation of the endocardial LB followed by scar-dependent sensitivity settings, will improve the accuracy of the near-field EGM characterization. Purpose To evaluate the accuracy of endocardial abnormal EGM characterization using a 17-AHA-segmental late boundary and voltage-dependent sensitivity annotation as compared to conventional mid-QRS LB positioning. Methods Five high-resolution substrate voltage maps were obtained from 5 patients (80% male, 68±7 years old, 80% ischemic cardiomyopathy) that underwent a catheter-based ablation for scar-related VT. A personalized CT-derived anatomical reconstruction of the 17-segment AHA model was co-registered offline with the electroanatomic voltage map, using ADAS3D software. Per segment, individual LB timings were set at the highest sensitivity. A gold standard annotation map was created by two independent experts after manual scrutiny of individual EGMs. Next, the expert map was compared to automatic maps created from segmental LB and mid-QRS LB positions at normal, high, highest or mixed (normal/high or normal/highest) sensitivities, focusing on EGM voltage and activation accuracy. Results A mean of 2041±1028 points per map were analyzed. Correct correlation of EGM annotation was found on average in 91% (activation) and 81% (voltage) for all segmental LB positioned maps (segLB), and in 83% (activation) and 77% (voltage) for all mid-QRS LB positioned maps (mid-QRSLB). The highest correlation with expert map for both activation and voltage annotation was found in the mixed sensitivity normal/highest map with segmental LB positioning (activation 98%, voltage 88%). The correlation on activation was significantly superior in the segLB map when compared to the normal sensitivity maps (p=0.0008 for mid-QRSLB and p=0.02 for segLB). For correlation on voltage, the segLBmap with mixed sensitivity was significantly superior compared to three mid-QRSLB maps at normal (p=0.04), high and highest (p=0.01) sensitivity. Conclusion Individualized, 17-AHA-segmental late boundary and voltage-dependent sensitivity annotation provides a possible accurate and clinically-relevant framework for substrate assessment. Prospective validation is needed.

Model Predictive Current Control for Six-Phase PMSM with Steady-State Performance Improvement

The application of finite control set model predictive control (FCS-MPC) in six-phase permanent magnet synchronous motors (PMSMs) often faces a trade-off between computational burden and accurate voltage vector selection, as well as challenges related to harmonic components and torque generation. This paper introduces an improved model predictive current control (MPCC) method to address these problems. Firstly, 12 virtual voltage vectors are synthesized to improve torque output performance while suppressing harmonic currents. Then, to generate symmetrical switching signals and reduce switching loss, the largest basic vector used to synthesize the virtual vector is replaced by two medium vectors. Secondly, to solve the problem of the increased computational burden caused by the increase in discrete virtual vectors, a two-step vector selection method is proposed. In this method, each part is divided into several parts according to N, and the traditional cost function is also replaced by two-step functions. Different control performances can be achieved according to different values of N. Experimental results show that the proposed control scheme not only achieves stable current quality but also significantly improves steady-state performance throughout the entire speed range.

Kilohertz volumetric imaging of in-vivo dynamics using squeezed light field microscopy.

Volumetric functional imaging of transient cellular signaling and motion dynamics poses a significant challenge to current microscopy techniques, primarily due to limitations in hardware bandwidth and the restricted photon budget within short exposure times. In response to this challenge, we present squeezed light field microscopy (SLIM), a computational imaging method that enables rapid detection of high-resolution three-dimensional (3D) light signals using only a single, low-format camera sensor area. SLIM pushes the boundaries of 3D optical microscopy, achieving over one thousand volumes per second across a large field of view of 550 μm in diameter and 300 μm in depth with a spatial resolution of 3.6 μm laterally and 6 μm axially. Using SLIM, we demonstrated blood cell velocimetry across the embryonic zebrafish brain and in a free-moving tail exhibiting high-frequency swinging motion. The millisecond temporal resolution also enables accurate voltage imaging of neural membrane potentials in the leech ganglion, and in the hippocampus of behaving mice. These results collectively establish SLIM as a versatile and robust imaging tool for high-speed microscopy applications.

Cell-to-cell capacity inconsistency evaluation considering temperature effect of battery pack with cloud data

Due to the initial and dynamic differences of battery cells, cell-to-cell capacity inconsistency exists in a battery pack. Considering the difference between the laboratory data and the operation data, this paper studies the battery pack capacity inconsistency of an electric vehicle based on cloud data. Firstly, the characteristic of different charge modes is analyzed, and the charge segment suitable for Incremental Capacity (IC) calculation is screened. Secondly, the Probability Density Frequency (PDF) method is introduced to obtain a relatively smooth IC curve considering voltage accuracy and sampling frequency. Meanwhile, the accuracy of the IC curve calculated by the low-precision cloud data is verified by high-precision laboratory test data. Based on the characteristic peak height of IC curve, an in-depth analysis of the correlation between capacity inconsistency and temperature difference is carried out. Results show that capacity consistency is affected by temperature difference. Fortunately, the capacity inconsistency affected by the temperature difference is recoverable. Subsequently, the normal distribution of battery capacity is detected, and the results show that the distribution of battery cell capacity is also subjected to the temperature. Finally, a battery cell capacity classification method considering temperature effect is proposed to realize low-capacity battery detection. The proposed method can achieve the detection of low capacity state batteries.

Transforming Hydroponics with IoT for Sustainable Water Management

General Background: Urbanization is reducing agricultural land, necessitating innovative cultivation techniques like hydroponics to thrive in limited spaces. Specific Background: The study investigates the use of IoT technology in hydroponic systems, specifically focusing on remote monitoring and control of water pumps for efficient resource management. Knowledge Gap: Limited research has explored the use of IoT in optimizing water pump operations in hydroponic systems, focusing on energy management and remote accessibility. Aims: The research aims to create an IoT-based automated water pump control system for hydroponics, enabling remote monitoring and evaluating its energy efficiency and operational effectiveness. Results: The experimental setup, including an ESP32 microcontroller, ADS1115 module, solar charger controller, and submersible pump, demonstrated optimal performance with a time-based control strategy, achieving 96.7% current and 97% voltage accuracy. Novelty: The study showcases a successful model for integrating IoT in hydroponic systems, thereby enhancing sustainability and operational efficiency. Implications: The study underscores the potential of IoT technologies in enhancing water management in hydroponics, thereby providing a feasible solution to contemporary agricultural challenges in space-constrained environments. Highlights: IoT Integration: Remote control enhances hydroponic system monitoring and efficiency. Energy Optimization: Effective solar charging improves automated water pump performance. Real-time Monitoring: Enables accessible tracking from significant distances via smartphones. Keywords: Hydroponics, IoT, Water Management, ESP32, Energy Efficiency

Low-Temperature Drift Bandgap Reference Based on Exponential Compensation Design

Abstract In analogue and mixed-signal systems, a stable and accurate reference voltage source is essential to ensure system performance. In this paper, a high-precision bandgap reference (BGR) with a high power supply ripple rejection ratio (PSRR) and lowtemperature drift is proposed, which mainly consists of a core circuit, an exponential temperature compensation circuit, a PSRR enhancement circuit and a start-up circuit. The PSRR is improved by using a PSRR enhancement circuit and a low-pass filter. An exponential compensation circuit is used to reduce the temperature drift coefficient of the bandgap reference. Based on a 0.18 μm BCD process for design and simulation, the output reference voltage is 1.203 V with a temperature drift coefficient of 2.635 ppm/°C over the temperature range of −40°C to 125°C. The PSRR is −144.4 dB at a frequency of 10 Hz, and −43.8 dB at a frequency of 1 MHz, and the layout area is 316 μm × 156 μm.

Adaptive reactive power control for voltage rise mitigation on distribution network with high photovoltaic penetration

This research addresses the challenge of voltage rise on low voltage distribution networks with high photovoltaic penetration. The proliferation of distributed generators, particularly small-scale PV systems, has raised concerns about voltage stability and power quality in these networks. Existing reactive power control techniques, such as fixed power factor and voltage-based methods (Q(V)), have limitations in effectively mitigating voltage rise while considering load variations and network sensitivity. To overcome these limitations, an adaptive reactive power control technique is proposed in this research. The technique combines both PV active power injection and network voltage considerations in real-time to dynamically adjust reactive power output. Unlike traditional methods, which directly link reactive power reference to PV active power or voltage, the adaptive technique calculates the change in reactive power reference (ΔQ) based on both factors. This dynamic approach enables more responsive and accurate voltage regulation. The effectiveness of the adaptive technique is demonstrated through MATLAB simulations on a representative low voltage distribution network. The results show that the adaptive technique outperforms existing methods, providing better voltage regulation and reduced losses. The technique's adaptability to different scenarios and variations is also highlighted. However, it is noted that the adaptive technique may have a slightly slower response time compared to existing methods due to its dynamic nature.

Unsupervised online poor contact detection in the secondary circuit of voltage transformers

Voltage transformers (VTs) are fundamental and crucial instrument devices for accurate voltage measurement. Generally, measurement errors can be caused by both the VT and the secondary circuit. Most research focused on the VT but ignored the secondary circuit between it and the measurement device. Poor contact in the secondary circuit is more challenging to capture than those caused by faulty VTs. In this study, we present an unsupervised online poor contact detection method. It first exploits a time series similarity matrix derived from the measured voltage data. The physical constraints deriving from the electrical topology are utilized to eliminate the non-fault fluctuation. To ensure the versatility of the proposed method, an adaptive threshold determination strategy is designed based on knee point location. Numerical experiments compared with some state-of-the-art methods have validated the effectiveness of the proposed method. The versatility of the proposed method is further validated in three real-world cases.

Direct operational data-driven workflow for dynamic voltage prediction of commercial alkaline water electrolyzers based on artificial neural network (ANN)

The thermal inertia of alkaline water electrolysis (AWE) system under dynamic operation reduces efficiency and poses security risks. To mitigate this, predictive model control (MPC) emerges as a potential solution, aiming to precisely regulate temperature through temperature trend prediction, which relies on accurate voltage predictions. However, the current MPC implementation for AWE system utilizes empirical model for voltage prediction, requiring extensive experimental data for calibration, making it time-consuming and costly for large-scale water electrolysis projects. Data-driven models are a promising alternative for voltage prediction, but research is limited by the lack of long-term commercial AWE operational data and the ambiguity in developing effective data-driven models. In this study, a 10-kW level AWE test system underwent 300 h of simulated solar-driven dynamic operation to assess the resilience of AWE system to dynamic conditions. Subsequently, a robust data-driven workflow using artificial neural networks (ANNs) was developed for precise dynamic voltage prediction based on obtained operational data. The data-driven workflow incorporates automated processes for voltage prediction, including data importing, data cleaning, ANN architecture optimization, model training, and ultimately voltage prediction, avoiding the need for manual parameters fitting. The model’s performance was evaluated and compared with empirical model across four dynamic operation types (cold-start, ramp, stepwise, and random response), achieving an impressive accuracy over 98 %. Furthermore, the ANN model also exhibits application potential in U-I curves prediction (accuracy > 98 %), influential features assessment, operational optimization and MPCs. This ANN-based workflow serves as a versatile framework for precise voltage prediction under dynamic operations and provides an analytical platform for operational data analysis, system optimization, and advanced control system development.

Harmonic voltage measurement based on capacitive equipment dielectric equivalent model and responding current

Abstract With the increasing proportion of new energy and the power electronic equipment in the power grid, accurate measurement of harmonic voltage has become increasingly important for power quality monitoring. In order to solve the problem of high-precision measurement of harmonic voltage in the power grid, this manuscript proposes a high-precision harmonic voltage measurement method based on the dielectric equivalent model (DEM) of capacitive equipment and its responding current. Based on DEM, a voltage-current transfer function of the capacitive device is established, and harmonic voltage is reconstructed with the responding current. Considering the dielectric relaxation characteristics of capacitive device other than a pure capacitor model, this manuscript analyzes the fitting performance of different equivalent capacitance models and improves the traditional pure capacitance model to a more suitable DEM for harmonic voltage reconstruction. The DEM parameters of capacitive devices are obtained through the frequency domain spectroscopy (FDS) and intelligent parameter identification algorithms, which improved the measurement accuracy of harmonic voltage and reduced computational complexity. The harmonic voltage testing platform is established to test the simulated high-voltage harmonics and the harmonic voltage of the actual grid voltage. The results show that the proposed harmonic voltage measurement method can meet the high-precision reconstruction of harmonic voltage in the frequency range of 50~2500Hz, and the system testing error with sensors is less than 2%. The testing accuracy is higher than traditional voltage transformers and testing systems based on pure capacitance models.

Fast and Accurate Neutral-Point Voltage Control Under Balanced and Unbalanced Neutral-Point Voltage for Coupled Ten-Switch Three-Phase Three-Level Inverter

Fast and Accurate Neutral-Point Voltage Control Under Balanced and Unbalanced Neutral-Point Voltage for Coupled Ten-Switch Three-Phase Three-Level Inverter

AM-HNN based real-time voltage estimation method of distribution network using joint PMU and AMI data

A large amount of distributed energy access increases the risk of voltage fluctuation and off-limit voltage. Accurate voltage estimation is more and more important to ensure the safety of distribution networks. With the introduction of phasor measurement unit (PMU), multi-source measuring data with different time scales and measuring precision exist in distribution networks. How to fully integrate multi-source measurement data to achieve fast and accurate state estimation of distribution networks is an important prerequisite for efficient operation decision making. In this paper, a distribution network voltage state estimation method based on AM-HNN model is proposed. The attentional mechanism (AM) is introduced to mine the influence of PMU measurement data on voltage state estimation, and the feature weight is modified adaptively. The highway neural network (HNN) was used to fit the mapping relationship between the injected power of grid nodes and the voltage of key nodes. Taking the distribution network data of different scenes as an example, the superiority of the proposed optimal measurement model is verified by comparing with various voltage estimation methods.

Distributed power conditioning unit of large-scale space solar power station

In this paper, a multi-bus distributed Power Conditioning Unit (PCU) is proposed for the Space Solar Power Station with large scale photovoltaic (PV) array and power levels reaching MW level. In this unit, there are multiple independent PV arrays. In each PV array, there are multiple independent PV subarrays. In this paper, a V-P droop control method with adaptive droop coefficient is proposed, which modifies the droop intercept based on the bus voltage deviation and the power per unit value of the PV array. This method ensures the accuracy of bus voltage and achieves proportional distribution of power between PV arrays based on the proposed topology structure in this paper. When the load changes or the output power of the PV array fluctuates, this method can ensure that power is distributed proportionally. The principle and control method of the proposed droop control method is analyzed in this paper. The effectiveness of the method is verified through MATLAB/Simulink simulation and experiment. Simulation and experimental results show that the proposed method can achieve power distributed proportionally when load changes and PV output power fluctuates, reduce bus voltage error caused by line impedance and differences in rated power of different PV arrays, and improve the performance of PV power generation system applied to space.