Low Power Utilization Research Articles

State-of-the-art power factor correction in EV charger

With the vigorous development of the automobile industry, the exploitation of oil resources has gradually deepened, which has led to the gradual depletion of oil resources. In this situation, electricity has come to replace oil resources in the automobile industry. However, in the charging process of electric vehicles, the use of the efficiency of electric energy is particularly important. If the efficiency is low, then a lot of power is lost in the power transmission line or alternating magnetic field conversion. And an important factor affecting efficiency is the power factor. The traditional AC-DC converter with a full bridge has low power utilization and efficiency, existing studies combine full-wave bridge with other circuits with different functions to form power factor correction (PFC) topologies. And these fundamental topologies continue to develop, derived from the bridgeless, interleaved PFC topologies, etc. This paper reviews the fundamental full-wave bridge PFC topologies and some derived bridgeless PFC topologies and interleaved parallel PFC topologies, including showing their circuit diagrams, explaining the operation principle of these circuits and comparing the advantages and disadvantages of each PFC topology.

Design of highly stable, high speed and low power 10T SRAM cell in 18-nm FinFET technology

Many scientists are working to develop a static random-access memory (SRAM) cell that used little power and has good stability and speed. This work introduces a fin field effect transistor developed SRAM cell with 10 transistors (10T FinFET SRAM). A cross connected standard inverter and schmitt-trigger inverter is used in the proposed 10T FinFET SRAM cell. We introduce the schmitt trigger based SRAM cell with single-ended read decoupled and feedback-cutting approaches to enhance the static noise margin (SNM) and access time of the SRAM cell. The proposed cell’s power utilization is decreased with the help of stacked N-FinFETs. For determining the relative performance of the proposed 10T FinFET SRAM cell design in terms of fundamental design metrics, it has also been compared with some of the current SRAM cells, including 6T, SBL9T SRAM, 10T SRAM, and DS10T SRAM. The simulation results at 0.6V demonstrate that the suggested design achieves low power utilization when Reading, writing and hold modes of operation in comparison to the aforementioned bit cells. It maintains a high SNM during all operations. The suggested cell is the one with fastest read access. The simulation is carried out with cadence tool using FinFET 18 nm technology.

Potential model and simulation analysis of dual material gate vertical TFET with impact of interface trap charges

This paper presents modeling and simulation of the dual material gate vertical tunnel FET (VTFET-DMG) by including the interface trap charges. The interface trap charges are considered at the high-k and silicon substrate interface near the source and the channel region. The surface potential model is developed here by utilizing the 1-D and 2-D Poisson equations solved with different boundary conditions after dividing the whole device into eight different regions. The parabolic approximation is assumed to find the numerical solution of the Poisson equations. The same device is also designed and simulated in the Silvaco TCAD simulator with the inclusion of the impact of the interface trap charges and the outcomes of the simulations are compared with the modeled results. The variation of the surface potential with the increase in positive interface charges and negative interface charges is plotted and analyzed. It is seen that the surface potential enhances with the increase in positive interface charges and it reduces with the increase in the negative interface trap charges. We use the dual material gate approach in proposed structure to show an overall increase in the performance of the device. Incorporating the high-K gate oxide in the presence of interface traps improves the device's reliability for low power utilization.

Digital Control and Readout of MEMS Gyroscope Using Second-Order Sliding Mode Control

Microelectromechanical system (MEMS) is a kind of MEMS that has been utilized to monitor the angular speed of moving objects on several occasions and has attracted the interest of several organizations across the globe. This type of sensor has the benefits of excellent integration, cheap cost, and low power utilization. This study begins by describing the research and development of silicon MEMS gyroscopes since the 1980s, including the work of several universities, such as Draper Laboratory and UC Berkeley, as well as various design ideas, control systems, and architectures. Matching resonance frequencies of the driving and sense mechanical resonators may significantly improve the efficiency of MEMS gyroscopes. In a closed-loop readout circuit, however, exact control of the resonance frequency is difficult. Because of their inexpensive production costs in large numbers, MEMS gyroscopes are gaining appeal. Because of substantial nonstationary noise processes in the output of MEMS gyros, such as 1/ <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${f}$ </tex-math></inline-formula> noise, this creates difficulty for navigation system designing. In the implementation, relative error in ramp and step function is determined through the proposed methodology. Furthermore, the second-order sliding mode control (SoSMC) is used in the sliding mode of the gyroscope to minimize the chattering effect.

A Memory Efficient FPGA Implementation of Offset-Free Explicit Model Predictive Controller

In the explicit model predictive control (EMPC), memory increases exponentially with the number of states, inputs, constraints, and prediction horizons; this often limits its applicability to large systems. In this article, we present a novel memory reduction technique for the lightweight EMPC using a novel posit number format implemented on a field-programmable gate array (FPGA), aiming to reduce the memory footprints and power utilization of the EMPC. We developed a fully automatic framework for the design of fast embedded EMPC on FPGAs using posit arithmetic and logical unit (ALU). The proposed technique is based on encoding all data (i.e., the critical regions and the feedback laws) as posit numbers, which can be viewed as a more memory-efficient alternative to the IEEE 754 floating-point standard. The performance and efficiency of the developed posit-based offset-free EMPC are demonstrated on the anesthesia control problem. We show the results of hardware-in-the-loop co-simulation with the detailed analysis of the resource utilization, power utilization, clock achieved, and the memory footprints comparison between IEEE 754 floating-point and posit formats. By doing so, we illustrate that the total memory footprints can be reduced by 50%–75% with achieving low power utilization as compared to floating-point numbers without sacrificing the control performances. The proposed technique can be applied on top of other existing complexity reduction techniques used in EMPC as well as for the online optimization methods.

Analyzing greenhouse gas emissions from municipal wastewater treatment plants using pollutants parameter normalizing method：a case study of Beijing

Wastewater treatment plants (WWTPs) contribute increasing amounts of greenhouse gases (GHGs), due to rapid development and increasingly stringent wastewater discharge limits in China. In this study, GHG emissions from 38 WWTPs in Beijing were estimated using the pollutants parameter normalizing method (PPNM) and the effects of various factors were analyzed. The result showed that, the total GHG emissions of Beijing WWTPs in 2017 were 1 045 661.5 t CO2-eq (tons of CO2-equivalent), the direct and indirect emissions were 186 366.0 and 859 295.5 t CO2-eq, respectively. The average emission intensity was 0.603 kg CO2-eq/m3. Direct and indirect GHG emissions were influenced by various factors, such as treatment process, influent parameters and treatment scale. The anaerobic-anoxic-oxic (AAO) process demonstrated low emission intensities (0.092 kg CO2/m3), while biofilm processes (MBBR) obtained relatively high emission intensities (0.277 kg CO2/m3). Direct GHG emission has a positive correlation with the amount of pollutant removed and low influent pollutant concentrations (chemical oxygen demand [COD]<300 mg/L and total nitrogen [TN] <20 mg/L) can lead to high indirect GHG emission intensities. The low power utilization efficiency of small-scale WWTPs (<1 × 107 t/a; 6.60 kWh/kg COD; 30.54 kWh/kg TN) contributed to additional GHG emissions. Compared with the GHG emissions in Shanghai in 2016, higher influent pollutant concentrations and stricter discharge limits contributed to higher GHG emission intensity in Beijing for both direct (0.108 kg CO2/m3) and indirect (0.496 kg CO2/m3) emissions. The comparison results between cities indicated that the PPNM method achieved a more accurate account of GHG emissions, and the analysis results provided support for carbon emission reduction.

Evolution of Novel Process for Smart Inverter for High Penetration of Rooftop Solar Energy in Power Utility With Secondary Distribution Network – A Review

With the critical change of Rooftop Solar Photovoltaic Energy System (RSPES) between the two different Renewable Energy Systems, the real problems, impacts and a few working qualities of the housetop sunlight based PVs with Low Power Utility Network Distribution System (DS) are currently to be examined and explored throught out the worldwide. The most vital target of review looks into about the load flow in LV Networks and searches a way for implementing high penetration PV network. The study papers discuss the implementation of the Smart Inverter-coordinated secondary distribution system with the proximity of distribution generation (DG) units. This paper reviews based on the methods which are utilized to tackle the issues and improvement of performance in the rooftop solar photovoltaic energy system in the period between 2014 to 2017.

Implementation of 12 bit R-2R DAC using cadence(90nm)

An R-2R DAC utilizes less special esteem which is in contrast with to the binary weighed- input DAC. Continuous sampling and measuring of an analog signal occurs over time. This project depends on R-2R Ladder for investing Low power utilization, no dynamic chip and Low DNL. The DAC is executed in a virtuoso device on 90 Nanometer CMOS Technology. The two stages in our projects seems to be Operational Amplifier and R-2R Ladder Digital to analog converters enable transmission of analog signals over digital signal processing chips [1].Two stages are involved in the OP-Amp, the first one being a differential amplifier. We use this Differential Amplifier to obtain high gain and second stage namely Common Source Amplifier. It increases Gain that leaded from the first stage and increases it Output swing. In our project,R-2R DAC is implemented using CADENCE 90nm tool.

Distinct ρ-based model of silicon N-channel double gate MOSFET

&lt;span lang="EN-US"&gt;Growing endless demand for digital processing technology, to perform high speed computations with low power utilization and minimum propagation delay, the metal-oxide-semiconductor (MOS) technology is implemented in the areas of very large scale integrated (VLSI) circuit technology. But MOS technology is facing the challenges in linear scaling the transistors with different channel modelling for the present day microelectronic regime. Linear scaling of MOSFET is restricted through short-channel-effects (SCEs). Use of silicon N-channel double gate MOSFETs (DG MOSFETs) in present day microelectronic regime features the short channel effect of MOSFET through a reasaonable forward transfer admittance with the characteristics of varying input capacitance values ratio. In this research paper, a distinct ρ-based model is designed to simulate SCEs through the designed silicon N-channel double gate MOSFETs with the varying front and back gate doping level and surface regions to estimate the varying junction capacitances can limit the intrusion detection systems (IDS) usage in VLSI applications. Analytical model for channel length and simulated model for total internal device capacitance through distinct ρ-based model are presented. The proposed distinct ρ-based model is suitable for silicon nanowire transistors and the effectiveness of the proposed model is validated through comparative results.&lt;/span&gt;

Random forest based power sustainability and cost optimization in smart grid

Abstract Presently power control and management play a vigorous role in information technology and power management. Instead of non-renewable power manufacturing, renewable power manufacturing is preferred by every organization for controlling resource consumption, price reduction and efficient power management. Smart grid efficiently satisfies these requirements with the integration of machine learning algorithms. Machine learning algorithms are used in a smart grid for power requirement prediction, power distribution, failure identification etc. The proposed Random Forest-based smart grid system classifies the power grid into different zones like high and low power utilization. The power zones are divided into number of sub-zones and map to random forest branches. The sub-zone and branch mapping process used to identify the quantity of power utilized and the non-utilized in a zone. The non-utilized power quantity and location of power availabilities are identified and distributed the required quantity of power to the requester in a minimal response time and price. The priority power scheduling algorithm collect request from consumer and send the request to producer based on priority. The producer analysed the requester existing power utilization quantity and availability of power for scheduling the power distribution to the requester based on priority. The proposed Random Forest based sustainability and price optimization technique in smart grid experimental results are compared to existing machine learning techniques like SVM, KNN and NB. The proposed random forest-based identification technique identifies the exact location of the power availability, which takes minimal processing time and quick responses to the requestor. Additionally, the smart meter based smart grid technique identifies the faults in short time duration than the conventional energy management technique is also proven in the experimental results.

Advanced metering infrastructure and energy storage for location and mitigation of power quality disturbances in the utility grid with high penetration of renewables

Microgrids with renewable power are becoming a widespread alternative for decarbonizing the electrical sector in light of climate change and global warming. However, such widespread penetration of renewables degrades some parameters of power quality along the low voltage utility grid. This research conducts an experiment with an advanced metering infrastructure of a power utility grid with hundreds of thousands of smart grid devices. The experiment identifies the location of the power quality disturbances. The results of the measurements have shown that the power quality parameters are mainly degraded along the downstream section of the low voltage power utility grid, despite the fulfillment of the regulatory quality requirements in the upstream high voltage substation. Thus, the novelty of the approach presented in the paper consists in the use of relatively low capacity energy storage units to locally mitigate the power quality disturbances, keeping them as close as possible to their source in the low voltage grid, instead of using large energy storage units connected to the medium voltage grid. Moreover, the research evaluated the parameters of power capacity, charge–discharge rate, weight, size, and the Levelized Cost Of Energy (LCOE) implemented with five types of energy storage technology. Lithium-Ion Capacitor (LIC) presents the lowest LCOE. However, service type and location parameters also play an essential role in selecting other different types of energy storage technology.This research shows that locating small energy storage units close to the source of power quality disturbance is cost-effective and offers excellent potential for widespread implementation in the low voltage distribution grid.

Solar Powered Water Quality Monitoring System Using Zig-bee

This work presents a solar power based water quality monitoring wireless sensor network (WSN) technology, to screen water quality over various destinations as an ongoing application, an astounding framework engineering comprised by disseminated sensor nodes and a base station is proposed. The model and base station are associated utilizing WSN innovation like Zig-Bee. Modeling and implementation of a proposed model using one node powered by solar cell and WSN technology is the challenging work. Data collection by different sensing element at the node side like pH, turbidity conductivity, saltiness and temperature is sent by means of WSN to the base station. Information gathered from the distant site can be shown in visual organization too as it very well may be examined utilizing diverse reproduction apparatuses at base station. This original framework has benefits like no fossil fuel byproduct, low power utilization, more adaptable to send data at far off site, etc.

Determination and Application of Maximum Efficiency Curve of Crawler Electric Tractor Motors

With the excessive use of fossil fuels such as oil, the energy crisis and environmental pollution have become important problems related to people’s livelihoods. In agriculture, as a new type of green agricultural machinery, the electric tractor has the advantages of high energy utilization and no exhaust emission. The motor is the core component of an electric tractor’s drive system. Its characteristics and control directly affect a tractor’s operating efficiency, operating quality, and energy consumption. A motor drive control scheme based on a characteristic curve at the maximum efficiency of an electric motor was adopted to address the problems of low motor power utilization and short continuous operation time on a full charge in electric tractors. By leveraging methods to obtain characteristic curves at the lowest fuel consumption for gasoline engines, we determined the characteristic curve at the maximum efficiency for a motor in a crawler-type electric tractor. Plowing is the most basic form of tractor operation, and it represents the agricultural work that accounts for most of a tractor’s use. A field test was conducted on the drive control in plowing operation based on the curve, and the energy consumed to plow each m2, continuous plowing operation time on a full charge, and operating efficiency were tested. The test results showed plowing power consumption per m2 of about 8.40 × 10−3 kWh and work efficiency of 707.07 m2/h. Compared with the traditional tractor, the cost of plowing 1 m2 by the crawler-type electric tractor was reduced by 20.3%–32.5%. Because the control improves operating efficiency, battery energy consumption is reduced and continuous operation time is extended; hence, the control achieves its purpose.

Cost-Effective and Low Power IoT-Based Paper Supply Monitoring System: An Application Modeling Approach

IoT designers face the dual complexity of obtaining good application-level performance and user satisfaction under constraints on computing and power resources. We introduce a new IoT device for paper roll supply management in bathrooms and kitchens, both for homes and businesses, that is extremely cost effective and battery power-efficient. The device can be installed on practically any paper roll dispenser and makes use of existing Wi-Fi infrastructure. Despite Wi-Fi’s reputation as “unsupportive for power saving,” we introduce and experimentally validate a methodology for using Wi-Fi networks with low power utilization, resulting in a system that provides very good management of paper supplies while only requiring battery charging once every 3–4 months. The new device has the potential to provide more households and businesses with real-time, data-driven automated supply chains.

EvAn: Neuromorphic Event-Based Sparse Anomaly Detection.

Event-based cameras are bio-inspired novel sensors that asynchronously record changes in illumination in the form of events. This principle results in significant advantages over conventional cameras, such as low power utilization, high dynamic range, and no motion blur. Moreover, by design, such cameras encode only the relative motion between the scene and the sensor and not the static background to yield a very sparse data structure. In this paper, we leverage these advantages of an event camera toward a critical vision application—video anomaly detection. We propose an anomaly detection solution in the event domain with a conditional Generative Adversarial Network (cGAN) made up of sparse submanifold convolution layers. Video analytics tasks such as anomaly detection depend on the motion history at each pixel. To enable this, we also put forward a generic unsupervised deep learning solution to learn a novel memory surface known as Deep Learning (DL) memory surface. DL memory surface encodes the temporal information readily available from these sensors while retaining the sparsity of event data. Since there is no existing dataset for anomaly detection in the event domain, we also provide an anomaly detection event dataset with a set of anomalies. We empirically validate our anomaly detection architecture, composed of sparse convolutional layers, on this proposed and online dataset. Careful analysis of the anomaly detection network reveals that the presented method results in a massive reduction in computational complexity with good performance compared to previous state-of-the-art conventional frame-based anomaly detection networks.

LoRa based Wireless Weather Station

In this Project we develop a prototype of “LoRa Based Wireless Weather Station”. Where LoRa is a wireless network technology supporting the internet of things (IoT) system. This technology is an elective to other wireless network modules that have already been well known such as GSM modules, Wi-Fi Modules and Bluetooth Modules. The utilize of the LoRa network serves to increase the range of wireless cells that can reach distances of up to 8 kilometres whereas still having low power utilization. Weather parameters that we are measured in this project are Temperature, Pressure, Dew point, Altitude, Rainfall, Humidity and Light Intensity. In this prototype we use Arduino UNO, Barometric Sensor, Rain Sensor, Light Intensity Sensor and LoRa modules. And Thingspeak Web application for sharing data to the users. This system has the potential to be executed in urban and rural zones with different sorts of sensors associated to it. This project can be overseen to form a Low Power-Wide Area Network as a more extensive sensor network.

Single-Phase Buck–Boost Inverter With Pulse Energy Modulation

As a popular low-cost solution for achieving both buck–boost and dc–ac conversions in distributed generation systems, the flyback-type buck–boost inverters operating under discontinuous conduction mode (DCM) have linear modulation under low power but bad dc voltage utilization under high power, whereas the buck–boost inverters under continuous conduction mode (CCM) have better dc voltage utilization under high power but more low-order harmonics under low power. In order to combine the advantages of DCM and CCM, a pulse energy modulation (PEM) technique is proposed and studied in this article, enabling the single-phase buck–boost inverter to operate under both DCM and CCM and switch between them seamlessly. The operation principle of the single-phase buck–boost inverter is introduced, and the PEM technique is specifically analyzed and modeled. The small-signal modeling analyses and results of PEM on the buck–boost inverter are compared with the commonly used sinusoidal pulsewidth modulation (SPWM), showing that PEM provides better performance and stability to the investigated single-phase buck–boost inverter.

Lifetime Estimation of Grid-Connected Battery Storage and Power Electronics Inverter Providing Primary Frequency Regulation

Battery Energy Storage Systems (BESSs) are a new asset for Primary Frequency Regulation (PFR), an ancillary service for improving the grid stability. The system operators determine the implementation and remuneration of PFR. However, assessing the revenue stream is not enough to define the business case, as also the components' lifetime has to be estimated. Previous studies of lifetime estimation for BESSs performing PFR considered only the electrochemical storage, disregarding the power electronics (PE). Nonetheless, researchers have shown the importance of estimating PE wear due to the operation when applied in renewable energy generation and microgrids. This paper presents a lifetime analysis of BESSs providing PFR considering IGBT modules, electrolytic capacitors and electrochemical storage degradation. The lifetime information is used to estimate BESS's Net-Present-Value (NPV), evaluating the benefits of deploying PE-based BESS in the European grid. A comparison between different countries, Germany, the Netherlands, and the U.K., is performed, considering the PFR implementation and remuneration differences. The analysis shows that the BESS management strategy can extend its lifetime and that the component that exhibits the shortest lifetime is the electrochemical storage. The PE components are subject to low wear due to the low power utilization and, therefore, small thermal swings while performing PFR. In conclusion, the provision of PFR by means of BESS has been found to be profitable in all three countries. However, in the Netherlands, the potential NPV has been estimated to be 47% and 76% higher than in Germany and the U.K., respectively.

Low Power Self disabling Comparator for Asynchronous ADC

A low power single channel ADC with high sampling rate is designed in this work. The low power is accomplished by utilizing a self-disabling continuous time comparator those outcomes in less delay cells. The proposed ADC executed and simulated in 180nm CMOS technology. The possibility of 1.8 V supply in this technology provides improvement in step size and the improved delay cells yields increased sampling rate. The decision of asynchronous ADC is because of the way that it eliminates complicated correction methods to acquire better execution, as in flash, pipelined and SAR ADC. Asynchronous digital slope ADC, accomplishes power lower than the synchronous ADC. It does not need the complex alignment and can accomplish low power utilization.. So to improve speed of sampling, improvisation of delay cell and shortening of delay time is done. In this paper an improvised design of the StrongARM comparator having faster performance, smaller area, and higher power efficiency is designed.

Evaluation and Prediction of Wind Power Utilization Efficiency Based on Super-SBM and LSTM Models: A Case Study of 30 Provinces in China

Although China’s wind industry has made great progress in recent years, the wind abandonment phenomenon caused by the unbalanced development of regional wind power is still prominent. It is particularly important for the scientific development of wind power to accurately measure the utilization efficiency of wind power and understand its regional differences in China. This study establishes the improved super-efficiency slack-based measure (Super-SBM) model and long short-term memory (LSTM) network models, systematically and comprehensively measures and predicts the wind power utilization efficiency of 30 regions in China from 2013 to 2020, and explores regional differences in wind power utilization efficiency. Our results show the following: (1) China’s overall wind power utilization efficiency is relatively low but has been on a steady upward trend since 2013. (2) Regional differences are obvious, showing that the spatial distribution pattern of wind power utilization efficiency is greatest in Northeast China, followed by North China, East China, South China, Northwest China, and Central China. The “Three-North” region with abundant wind energy resources has relatively high wind power utilization efficiency and exhibits a good development trend. East China, South China, and Central China, where wind energy resources are relatively poor, have low wind power utilization efficiency, and their development trends are not stable and are more prone to change. (3) The utilization efficiency of wind power in coastal areas is generally better than that in inland areas. There are also differences among the thirty Chinese regions studied. Inner Mongolia and Shandong have achieved real efficiency in wind power utilization efficiency, with optimal allocation of input and output, and a good development trend. The other 28 regions have varying degrees of inefficiency, and there is still room for improvement.