Continuous Power Consumption Research Articles

Solar PV tracking system using arithmetic optimization with dual axis and sensor

Presently, PV system is considered the best renewable energy source for electricity production. In order to maximize energy output in photovoltaic systems, a system for tracking the sun's position and adjusting panel positions was created. Despite the fact that several models for tracking solar radiation have been suggested to improve energy production, it faces challenges in continuous tracking and power consumption. This paper proposes a novel sensor-based solar tracking system with numerical optimization to increase photovoltaic systems' energy output. The initial model was for a two-axis tracking system based on sensors. Solar panel and sun positions are detected by this system using ultraviolet and microelectromechanical sun sensors. To improve tracking movements and photovoltaic energy production, we recommend using solar sensors to construct a novel two-axis solar tracking device. This technology benefits from increased solar radiation and solar energy harvesting capabilities. The main disadvantage of dual-axis active solar tracking systems is that the drive mechanism frequently uses up the output power of the solar panels. As a result, the net power gain of the solar panel is less than its maximum. Further, a control system was developed with proportional integral derivative (PID) controller and arithmetic optimization (AO) to adjust the panel position relative to suns movement. The AO technique tunes the PID controller gains until it reaches its desired level to enhance the production of power. With the use of sensors, the tracking system can use mathematical optimization algorithms and the sun's position to minimize power consumption while still maintaining optimal solar panel positioning. The developed model was designed in the MATLAB software and the outcomes are analyzed. The paper also presents an energy analysis of the system, which evaluates the energy input, output and overall system efficiency. We recommend using solar sensors to create a unique dual-axis solar tracking system. This technology benefits from increased solar radiation and solar energy harvesting capabilities.

Time Series Prediction Methodology and Ensemble Model Using Real-World Data

Time series data analysis and forecasting have recently received considerable attention, supporting new technology development trends for predicting load fluctuations or uncertainty conditions in many domains. In particular, when the load is small, such as a building, the effect of load fluctuation on the total load is relatively large compared to the power system, except for specific factors, and the amount is very difficult to quantify. Recently, accurate power consumption prediction has become an important issue in the Internet of Things (IoT) environment. In this paper, a traditional time series prediction method was applied and a new model and scientific approach were used for power prediction in IoT and big data environments. To this end, to obtain data used in real life, the power consumption of commercial refrigerators was continuously collected at 15 min intervals, and prediction results were obtained by applying time series prediction methods (e.g., RNN, LSTM, and GRU). At this time, the seasonality and periodicity of electricity use were also analyzed. In this paper, we propose a method to improve the performance of the model by classifying power consumption into three classes: weekday, Saturday, and Sunday. Finally, we propose a method for predicting power consumption using a new type of ensemble model combined with three time series methods. Experimental results confirmed the accuracy of RNN (i.e., 96.1%), LSTM (i.e., 96.9%), and GRU (i.e., 96.4%). In addition, it was confirmed that the ensemble model combining the three time series models showed 98.43% accuracy in predicting power consumption. Through these experiments and approaches, scientific achievements for time series data analysis through real data were accomplished, which provided an opportunity to once again identify the need for continuous real-time power consumption monitoring.

Design of Auto-Tuning Nonlinear PID Tracking Speed Control for Electric Vehicle with Uncertainty Consideration

This study presents a new auto-tuning nonlinear PID controller for a nonlinear electric vehicle (EV) model. The purpose of the proposed control was to achieve two aims. The first aim was to enhance the dynamic performance of the EV regarding internal and external disturbances. The second aim was to minimize the power consumption of the EV. To ensure that these aims were achieved, two famous controllers were implemented. The first was the PID controller based on the COVID-19 optimization. The second was the nonlinear PID (NPID) optimized controller, also using the COVID-19 optimization. Several driving cycles were executed to compare their dynamic performance and the power consumption. The results showed that the auto-tuning NPID had a smooth dynamic response, with a minimum rise and settling time compared to other control techniques (PID and NPID controllers). Moreover, it achieved low continuous power consumption throughout the driving cycles.

A Review of Silicon‐Based Integrated Optical Switches

AbstractRecently, silicon‐integrated optical circuits have attracted intensive interests, thanks to the compatibility with the complementary metal‐oxide‐semiconductor (CMOS) technology that enables mass production at low cost. The optical switch is an essential part of optical integrated circuits, with broad applications in optical communications and networks, optical computing, and sensing such as LiDAR. In general, the silicon‐integrated optical switch adopts thermo‐optic or carrier dispersion effect to realize reconfigurable signal routing. However, the use of thermo‐optic effect leads to high power consumption, and the carrier dispersion effect has the disadvantage of small refractive index change. In addition, both effects are non‐latching, and hence, continuous power consumption is required even when switching is not needed. For overcoming these drawbacks, phase‐change materials (PCMs) have been introduced into silicon‐integrated optical switches. In this paper, silicon‐integrated optical switches are classified according to the underlying structure and recent research is reviewed. Recent studies on silicon‐integrated optical switches incorporating PCMs are also reviewed. Furthermore, the pros and cons of different types of integrated optical switches with and without PCMs are compared and discussed.

Process Optimization of Biodiesel Production Using the Laplacian Harris Hawk Optimization (LHHO) Algorithm

Continuous power consumption from standard fuel resources is responsible for producing large-scale environmental greenhouse gases. Production of biodiesel fuels from the vegetable oils can be considered an alternative source. Effect of greenhouse gases can also be diminished. The production of biodiesel is done by a chemical process namely transesterification and usually maximized by using the Response Surface Methodology (RSM) tool. This paper presents a new approach to optimize the production of biodiesel by introducing a new variant of recently published metaheuristic Harris Hawk Optimization (HHO). The developed variant is based on the replacement of random numbers of normal distribution at the initialization phase by the random numbers generated from the Laplacian distribution. The proposed variant is named as the Laplacian Harris Hawk Optimization (LHHO) algorithm. The contribution of this paper is in twofold: firstly the performance of the proposed algorithm is verified over a well-known set of benchmark functions, and then, we applied the LHHO to maximize biodiesel production. Comparison of LHHO is carried out with five other recent metaheuristic algorithms. An optimization routine is formulated in the form of a single-objective function with a temperature, methanol to oil ratio, and catalyst concentration as the optimization variables. These parameters are optimized to maximize the production of biodiesel. The results obtained using the proposed LHHO show significant improvement as compared to other algorithms.

Detection of Potentially Compromised Computer Nodes and Clusters Connected on a Smart Grid, Using Power Consumption Data.

Monitoring what application or type of applications running on a computer or a cluster without violating the privacy of the users can be challenging, especially when we may not have operator access to these devices, or specialized software. Smart grids and Internet of things (IoT) devices can provide power consumption data of connected individual devices or groups. This research will attempt to provide insides on what applications are running based on the power consumption of the machines and clusters. It is therefore assumed that there is a correlation between electric power and what software application is running. Additionally, it is believed that it is possible to create power consumption profiles for various software applications and even normal and abnormal behavior (e.g., a virus). In order to achieve this, an experiment was organized for the purpose of collecting 48 h of continuous real power consumption data from two PCs that were part of a university computer lab. That included collecting data with a one-second sample period, during class as well as idle time from each machine and their cluster. During the second half of the recording period, one of the machines was infected with a custom-made virus, allowing comparison between power consumption data before and after infection. The data were analyzed using different approaches: descriptive analysis, F-Test of two samples of variance, two-way analysis of variance (ANOVA) and autoregressive integrated moving average (ARIMA). The results show that it is possible to detect what type of application is running and if an individual machine or its cluster are infected. Additionally, we can conclude if the lab is used or not, making this research an ideal management tool for administrators.

Efficient Online Classification and Tracking on Resource-constrained IoT Devices

Timely processing has been increasingly required on smart IoT devices, which leads to directly implementing information processing tasks on an IoT device for bandwidth savings and privacy assurance. Particularly, monitoring and tracking the observed signals in continuous form are common tasks for a variety of near real-time processing IoT devices, such as in smart homes, body-area, and environmental sensing applications. However, these systems are likely low-cost resource-constrained embedded systems, equipped with compact memory space, whereby the ability to store the full information state of continuous signals is limited. Hence, in this article,* we develop solutions of efficient timely processing embedded systems for online classification and tracking of continuous signals with compact memory space. Particularly, we focus on the application of smart plugs that are capable of timely classification of appliance types and tracking of appliance behavior in a standalone manner. We implemented a smart plug prototype using low-cost Arduino platform with small amount of memory space to demonstrate the following timely processing operations: (1) learning and classifying the patterns associated with the continuous power consumption signals and (2) tracking the occurrences of signal patterns using small local memory space. Furthermore, our system designs are also sufficiently generic for timely monitoring and tracking applications in other resource-constrained IoT devices.

Reversibly Programmable Photonics via Responsive Polyelectrolyte Multilayer Cladding

AbstractReversibly programmable photonic integrated circuits (PICs) that can facilitate multifunctionality have been long sought after to deliver user‐level design flexibility. Issues like complicated control, continuous power consumption, and high optical losses hinder their large‐scale adaptation. In this work, a novel approach toward programmable photonics using a responsive polyelectrolyte multilayer (PEM) cladding is presented. Reversible (de)swelling of PEMs by consecutive exposure to acidic and neutral pH solutions yields highly contrasting refractive index changes in the dry film. Utilizing this effect, an easily applied technique for programming photonic integrated devices with two different approaches, complete and area‐selective deposition, for several reversible cycles is demonstrated. These devices operate at two distinct states that are virtually lossless and nonvolatile. This proof‐of‐concept demonstration is suitable for various photonic integration platforms to facilitate reconfigurable photonic processors, static memories, and fine‐tuning of fabrication related limitations. Therefore, these results are the first step toward PEM‐assisted reversibly programmable multipurpose PICs for low‐cost mass production.

Highly accurate particulate matter detection assisted by an air heater based on a silver nanowire film

Recently, many studies have been carried out to solve the particulate matter pollution problem. However, the detection accuracy for particulate matter in the atmosphere remains unsatisfactory due to the influence of the air relative humidity. Herein, we report a Ag nanowire film air heater to enhance the detection accuracy through internal heating. From air temperature and air relative humidity relationship analysis, it has been found that the Ag nanowire film air heater can form the most suitable air relative humidity in the detection system, thereby enhancing the detection accuracy. Consequently, the Ag nanowire film air heater-assisted light scattering particulate matter detector has achieved tremendous enhancement in its detection accuracy, which is comparable with the data obtained by the beta gauge method. Film resistance plays a key role in internal air temperature distribution and the resultant air relative humidity at given voltages. To achieve the most suitable air relative humidity for continuous online monitoring, response time and power consumption should be balanced. Therefore, guidance for designing Ag nanowire films with proper resistance used in an optional-sized detector has been given for quick response, high accuracy and low power consumption. This work is of significance for providing insight for future studies in particulate matter detection and pollution remediation.

Nearly zero energy - construction site temporary office buildings

In the UAE temporary offices and buildings utilised during the construction phase of a project are not connected to the grid electricity. Thus, leaving on site generation as the only option to power these buildings. On-site generation for these temporary buildings in the UAE is, more often than not, through diesel generators. Diesel generators are not the most efficient nor environmentally friendly source of power generation and produce large amounts of carbon dioxide. These temporary buildings have a continuous power consumption throughout the construction period of a project and this can lead to significant amounts of carbon emissions to the atmosphere and detrimental environmental impacts. To alleviate the issue, we have identified a typical site office building in Dubai and analysed its consumption and how to offset or reduce the reliance on diesel generation thus increasing the efficiency and sustainability of the site office by trying to target a near zero energy building. Through dynamic energy modelling simulations of the existing site office we were able to meet the nearly zero energy requirements by implementing envelope improvements, mechanical system efficiencies and on-site renewable energy generation. A financial feasibility assessment was conducted on the improvements determined to achieve the nearly zero energy target. This ensured that a holistic approach was taken upon selection of the improvements.

Distributed Artificial Intelligence Based Cluster Head Power Allocation in Cognitive Radio Sensor Networks

This proposed work addresses the problem of dynamic and real-time power allocation to the cluster head (CH) in a cognitive radio sensor network (CRSN). The work is based on spectrum sensing outputs by these secondary users (SUs) for a nonergodic system. In general, a cooperative sensing network consists of multiple nodes communicating with each other about their respective spectrum sensing output, to perform the desired work. Therefore, the SU nodes of each cluster sense the spectrum all the time, which results in a continuous power consumption in CRSN. Now, the total amount of energy, which is used to allocate all the CHs at a particular place for spectrum sensing is being avoided and saved. This is performed, by the use of autocorrelation error for predicting the real-time behavior of primary users extended by the implementation of vector quantization to identify nearby active SU nodes and based on distributed artificial intelligence for power allocation. The simulation results and the mathematical derivation validate the proposed method.

Evaluation of 3D printed optofluidic smart glass prototypes.

Smart glass or smart windows are an innovative technology used for thermal management, energy efficiency, and privacy applications. Notable commercially available smart glass relies on an electric stimuli to modulate the glass from a transparent to a translucent mode of operation. However, the current market technologies, such as electrochromic, polymer dispersed liquid crystal, and suspended particle devices are expensive and suffer from solar absorption, poor transmittance modulation, and in some cases, continuous power consumption. The authors of this paper present a novel optofluidic smart glass prototype capable of modulating visible light transmittance from 8% to 85%.

Transmission ratio calibration for electro-mechanically actuated continuously variable transmission

Continuously variable transmission (CVT) with a metal pushing V-belt is widely used by car manufacturers for its advantages particularly in fuel economy. Nevertheless, this CVT currently applies a hydraulic-based actuation system to change and to maintain the desired CVT ratio which leads to reduction of CVT's efficiency due to engine's continuous power consumption. To address this issue, a prototype of electro-mechanically actuated CVT is introduced in this paper. Here, the prototype's working principle is explained and the process to evaluate its workability is described. This evaluation process is necessary to provide reference data for future development of the prototype especially in the areas of ratio control and belt's clamping force control. The evaluation's results suggest that the prototype has a ratio range from 2.93 to 0.67 with using only one DC motor to vary the ratio instead of typically two DC motors used by previous designs of electro-mechanically actuated CVT.

Nonvolatile All‐Optical 1 × 2 Switch for Chipscale Photonic Networks

Integrated chip‐level photonics has the potential to revolutionize future computer systems by eliminating the “von‐Neumann information bottleneck” and the power losses resulting from the use of electrical interconnects. Yet, the need for optical‐to‐electrical conversion has so far hindered the implementation of chip‐level all‐optical routing schemes, which remain operational without continuous power consumption. Here, a crucial component to successful implementation of such all‐photonic networks is demonstrated – an effective, practicable all‐optical nonvolatile switch. Current integrated all‐optical switches require constant bias power to operate, and lose their state when it is removed. By contrast, our switch is entirely nonvolatile, with the direction of light flow altered by switching the phase state of an embedded phase‐change cell using 1 ps optical pulses. High on/off switching contrast devices are achieved that are fully integrated and compatible with existing photonic circuits. It is shown that individual switching events occur with transition times below 200 ps and thus hold promise for ultrafast light routing on chip. The approach offers a reliable and simple route toward hybrid reconfigurable photonic devices without the need for electrical contacting.

Vacuum Packaged Low-Power Resonant MEMS Strain Sensor

This paper describes a technical approach toward the realization of a low-power temperature-compensated micromachined resonant strain sensor. The sensor design is based on two identical and orthogonally-oriented resonators where the differential frequency is utilized to provide an output proportional to the applied strain with temperature compensation achieved to first order. Interface circuits comprising of two front-end oscillators, a mixer, and low-pass filter are designed and fabricated in a standard 0.35- $\mu \text{m}$ CMOS process. The characterized devices demonstrate a scale factor of 2.8 Hz/ $\mu \varepsilon $ over a strain range of 1000 $\mu \varepsilon $ with excellent linearity over the measurement range. The compensated frequency drift due to temperature is reduced to 4% of the uncompensated value through this scheme. The total continuous power consumption of the strain sensor is 3 $\mu \text{W}$ from a 1.2 V supply. This low power implementation is essential to enable battery-powered or energy harvesting enabled monitoring applications. [2015-0328]

Rechargeable router placement based on efficiency and fairness in green wireless mesh networks

The wireless mesh networks are currently emerging as a promising solution for broadband access, while their deployment and operational costs are also ever increasing significantly due to the continuous electrical power consumption. The alternative is to deploy rechargeable mesh routers using renewable energy sources. In this paper, we study the rechargeable router placement problem for a green mesh network. The problem is formulated as an optimization with the objective of minimizing the number of deployed routers, while fulfilling QoS requirements on wireless coverage, traffic demand, energy efficiency and user fairness. Specifically, we introduce the network failure rate to evaluate the network performance and adopt the proportional fairness-based approach to do the cell association between users and routers. We first propose two cell association algorithms from two perspectives: the Nearest Cell Association Algorithm (NCA) for energy efficiency consideration and the Proportional Fairness Cell Association Algorithm (PFCA) to achieve a balance between the network performance and the user fairness. We then design two heuristic placement algorithms embedded with the proposed cell association methods to find approximate solutions for the rechargeable router placement problem. Simulation results verify that the proposed PFCA algorithm can guarantee the user fairness with a slight increase of deployment cost. Furthermore, compared with the optimal placement achieved by exhaustive search, ours can achieve good performance with greatly reduced computation complexity.

Design and Analysis Method for A DC Magnetic Contactor with a Permanent Magnet

The demand for a DC power distributed system is increasing as renewable energy sources and DC electrical load are proliferating. For the automation of a power system, a magnetic contactor for the DC power system is required. The conventional magnetic contactors are mostly equipped with a solenoid magnetic actuator. However, the conventional magnetic contactor has problems with continuous power consumption, and heat generation. To address these problems, a permanent magnet type DC magnetic contactor is proposed in this paper.

Solar energy harvesting for autonomous field devices

The energy required to power an autonomous field device can be drawn from its environment by means of energy harvesting. Outdoors, one abundant and ubiquitous source of energy is solar irradiation, which can be reliably converted into electricity by solar cells. In principle, this is also true indoors if artificial light sources produce sufficient optical irradiation. Different solar cell materials can play off their individual strengths in these different use cases. Since ambient optical irradiation usually is an intermittent phenomenon, a solar powered field device has to include an energy storage solution for bridging gaps that occur in energy harvesting. In this study, different solar cell materials and energy storage solutions are discussed and evaluated quantitatively according to data sheet information. According to calculations for an assumed continuous power consumption of an autonomous field device of 1 mW for a period of 10 years, a solar panel of 4 × 4 cm² in the outdoor case and 20 × 20 cm² in the indoor case can deliver the required energy. Along with an energy storage system consisting of a 100 F supercapacitor and a primary backup cell, solar energy harvesting for autonomous field devices seems technically feasible.

Bridge to recovery in two cases of dilated cardiomyopathy after long-term mechanical circulatory support.

Ventricular assist devices (VADs) have become an established therapeutic option for patients with end-stage heart failure. Achieving the potential for recovery of native heart function using VADs is an established form of treatment in a selected group of patients with HF. We report two cases of VAD patients with different types of pump used for mechanical circulatory support, a continuous flow pump (Heart-Ware®) and a pulsatile pump (POLVAD MEV®), which allow regeneration of the native heart. Patients were qualified as INTERMACS level 3-4 for elective implantation of an LVAD. Implantations were performed without complications. The postoperative course was uncomplicated. In the HeartWare patient the follow-up was complicated by episodes of epistaxis and recurrent GIB as well as driveline infection. The follow-up of the POLVAD MEV patient was uneventful. Recurrent GIB forced us to withdraw aspirin and warfarin therapy and maintain only clopidogrel in the HeartWare patient.. In mid-February 2013 the patient was admitted due to dysfunction of the centrifugal pump with a continuous low-flow alarm and increase power consumption. Under close monitoring of the patient a decision was made to stop the pump immediately and evaluate cardiac function. The serial echocardiography studies showed significant improvement in LVEF up to 45% and no significant valvular pathology. In February 2013 LVAD explant was performed by left thoracotomy without complications. At six-month follow-up the patient was in a good clinical condition, in NYHA class I/II, and on pharmacological treatment.

Reversible and Continuous Latching Using a Carbon Internanotube Interface

Mechanical multistability is greatly beneficial in microelectromechanical systems because it offers multiple stable positioning of movable microstructures without a continuous energy supply. Although mechanical latching components based on multistability have been widely used in microsystems, their latching positions are inherently discrete and the number of stable positions is quite limited because of the lithographical minimum feature size limit of microstructures. We report a novel use of aligned carbon nanotube (CNT) arrays as latching elements in a movable micromechanical device. This CNT-array-based latching mechanism allows stable latching at multiple latching positions, together with reversible and bidirectional latching capabilities. The latching element with integrated CNTs on the sidewalls of microstructures can be adopted for diverse microelectromechanical systems that need precise positioning of movable structures without the necessity of continuous power consumption.