Power Consumption Monitoring Research Articles

Improved modal identification using wireless continuous dynamic monitoring systems without real time synchronization

In the context of vibration based Structural Health Monitoring using automated operational modal analysis, data synchronization methods permit a reduction in the cost of wireless monitoring systems, since the use of extra hardware for perfect data synchronization can be avoided. Furthermore, the relaxation of data synchronization requirements permits a more flexible topology (higher distance between sensors, no need of external antennas for data synchronization using GPS, lower power consumption), which is particularly advantageous in the dynamic monitoring of large structures. In this paper, continuous dynamic monitoring without real-time synchronization is studied. Firstly, using numerical simulations, the effects of synchronicity faults on operational modal analysis is deeply analysed, showing that time delays have a great impact on modal identification. Secondly, a new data synchronization method based on mode shapes identified with operational modal analysis methods is proposed to improve the accuracy of modal identification. This does not need the assumption of proportional damping. Therefore, it overcomes this limitation that up to the knowledge of the authors is present in all other available methods. The proposed processing strategy is validated in the processing of monitoring data continuously collected at a long span arch bridge (Infante D. Henrique Bridge) with two wireless acceleration nodes. The full-scale application shows high time synchronization accuracy and proves that the proposed synchronization method is adequate for continuous dynamic monitoring, providing reliable modal parameters estimates with any assumptions about the damping, permitting a reduction in the monitoring costs, easier installation and power consumption reductions (very important to increase the autonomy of wireless nodes). In short, hardware costs are replaced by a smarter processing.

Irrigation remote control system based on LoRa intelligence

In order to realize automatic remote monitoring and intelligent management of intelligent irrigation system, aiming at the existing problems of water-saving irrigation system, an intelligent irrigation system based on wireless remote control was designed by using LoRa wireless communication technology to realize low power consumption and low loss monitoring technology. This paper introduces the way and system architecture of ad hoc network in detail, and the establishment of irrigation mathematical model and irrigation algorithm in different periods of cotton field, which not only improves the utilization rate of water resources, but also realizes the intelligence of irrigation system, and promotes the transformation from traditional agriculture to intelligent agriculture.

Crowd Monitoring and Localization Using Deep Convolutional Neural Network: A Review

Crowd management and monitoring is crucial for maintaining public safety and is an important research topic. Developing a robust crowd monitoring system (CMS) is a challenging task as it involves addressing many key issues such as density variation, irregular distribution of objects, occlusions, pose estimation, etc. Crowd gathering at various places like hospitals, parks, stadiums, airports, cultural and religious points are usually monitored by Close Circuit Television (CCTV) cameras. The drawbacks of CCTV cameras are: limited area coverage, installation problems, movability, high power consumption and constant monitoring by the operators. Therefore, many researchers have turned towards computer vision and machine learning that have overcome these issues by minimizing the need of human involvement. This review is aimed to categorize, analyze as well as provide the latest development and performance evolution in crowd monitoring using different machine learning techniques and methods that are published in journals and conferences over the past five years.

Volcano multiparameter monitoring system based on Internet of Things (IoT)

ABSTRACT Indonesia is an archipelagic country that lies within the Circum-Pacific belt or the Ring of Fire. Currently, the country hosts 127 active volcanoes, of which only 69 are properly monitorable for the activity around the clock. In light of volcanic disaster risk reduction and early warning system, volcano monitoring becomes indispensable. Since volcanoes have steep terrestrial contour and vast area, plenty of monitoring points are thus necessary. The higher the number of the monitoring points, the more costly it will be to procure the instruments required for the volcano monitoring. Therefore, it requires monitoring devices that are effective and efficient. This paper presents an application monitoring for active volcano using Wireless Sensor Network (WSN) and Internet of Things (IoT). Our main goals were to create low power consumption monitoring systems that are easy to maintain, easy to deploy, energy-saving, flexible and integrated. The wireless communication system on this device uses the IEEE 802.15.4 standard. Finally, this paper demonstrates the applicability of the proposed system for the detection of volcanic multi parameters such as Carbon Monoxide (CO), Hydrogen Sulphide (H2S), Crater Temperature, pH and Seismic.

Internet of Things Based Power Monitoring and Theft Detection

Today, our world is facing a challenging environment everywhere, energy crisis being the most important. The only solution to this is having an applicable system to govern the power usage. Our motivation is to achieve independence, sustainability, reliability, security and quality, in terms of power, keeping aside our huge savings in the longer run. This project aims to deliver solutions to these problems by means of IoT as the means of communication and also attacking numerous other matters that smart systems can contract to avoid redundant losses to the energy suppliers and also contribute towards developing a sustainable environment. In this paper, a sincere attempt has been taken to list out the essentials of “Smart Energy Grid” and to overcome the listed issues and providing an accurate billing to allow the user to analyze their energy consumption without the interception of others. This grid can be implemented in advanced metering that provides an easy way of detecting the theft and monitoring power consumption.

Development of a Thin Film Gas Microsensor of Low Power Consumption for Monitoring of Environmental Pollutants

In this work the development and microfabrication of a gas sensor is described. The microsensor consists of a thin film of SnO2 deposited on a micro-machined silicon substrate [1] . This film is heated by a microheater integrated in the microsensor to obtain a temperature between 100 and 400 °C (microhot plates). With the aim to analyze the proposed design, simulations of the mechanical (structural rigidity, maximum and minimum sizes of the structures) and thermal behaviors (distribution of temperatures and electrical powers involved) were performed [2].The manufacturing process carried out in a clean-room area involved a series of steps that include: the design and mastering of masks, photolithography operations, sputtering metal deposition, chemical attacks with dry (RIE) and wet etching (with KOH), dicing of the silicon wafer in individual sensors of 3mm x 3mm and finally, the encapsulation of the sensors by means of wire bonding (see Fig.1)It is important to note that the silicon substrate is micromachined in order to reduce its thermal inertia and the power necessary to heat the sensor film. Such a film is deposited on a silicon nitride insulating membrane that is only 250 nm thick, which significantly reduces the consumption in terms of electrical power, with respect to the known commercial sensors.The SnO2 thin films were fabricated using the technique Rheotaxial Growth and Thermal Oxidation technique (RGTO) on a microhot plate[3]. RGTO is a technique composed of two steps: depositionof a Sn thin film on the substrate maintained at the temperature slightly above the Sn melting point and oxidation at a temperature up to 400 oC. In the first step of the RGTO technique tin was deposited by sputtering DC on the central part of the membrane by means of a shadow mask. During the deposition, substrates were heated at 310 oC . Thickness of the deposited tin was 100 nm. The second step, involving the thermal oxidation of Sn thin layer, was performed in two sub-steps: 2 h at 250 oC and 60 h at 450 oC in wet air. The final result is a granular film with quasi-spherical grain shape. The microstructure was characterized by X-ray diffraction analysis and scanning electron microscopy (SEM).The characterization of the sensors with gases at different concentrations was done. In the case of ammonia in air, a linear response between 50ppm (maximum permissible limit value) and 6ppm were obtained, the latter being its detection limit (Fig 2). Also H2S were tested (Fig. 3) . The measurements were made by applying a voltage of 5 V to the heater, obtaining dissipated power between 40 and 50 mW. The operating temperature of the sensor film under these conditions was approximately 180°C. On the other hand, the sensor film is connected to a potential of 1 V. The sensor response is then measured as the variation of the electrical resistance as a function of the response time.Additionally, electronic control and signal processing was developed, suitable for incorporating sensors into portable instruments. [4]The results of this development also allow us to have a platform for the microfabrication of gas sensors at the request of other public institutions or the private sector.

Metrological support characteristics for life cycle of agricultural machines

Statements stated in this paper prove the essential role of the influence of metrological support on the quality parameters of complex equipment at each stage of the product life cycle. When designing aggregates and technological units, it is necessary to ensure the given accuracy of the sensors, while manufacturing technology, significant savings can be achieved due to the use of a more expensive and accurate measuring instrument. When operating machines, constant monitoring of power consumption, fuel, productivity, speed and other technological parameters with a given accuracy is required.

Implementation of an energy saving cloud infrastructure with virtual machine power usage monitoring and live migration on OpenStack

Cloud computing is Internet-based computing which requires more physical machines and consumes a large amount of power. By this means, that will reduce the profit of the service providers and harm the environment. How to effectively handle the power consumption of cloud computing has been an issue in recent years. When making a large number of operations, and power consumption cannot be underestimated. In this case, the usage of Virtualization that become widely in cloud computing nowadays, also need energy efficient scheduling methods. However, existing energy efficient scheduling methods of virtual machines (VMs) in the cloud cannot work well if the physical machines (PMs) are heterogeneous and their total power is considered. In this paper, we propose an implementation of a cloud infrastructure that can monitor the status of OpenStack and monitor the real-time status of a virtual machine on OpenStack. Also, achieve energy saving through live migration. The projects of monitoring include the utilization of CPU, load of memory, and power consumption. These data show in real-time, thoroughly monitor the real-time status of physical machines and virtual machines. It also records the utilization and power consumption of physical machines then show on this cloud infrastructure, to provide experimental evidence for the user as a reference. Based on the power consumption monitoring system, we can automatically allocate virtual machines on every physical machine by live migration, to balance the power consumption of every physical machine. It is not only can avoid idle and a waste of resources but also can avoid reducing machine life-time because of the physical machines always keep in high usage.

Energy-Efficient Method for Wireless Sensor Networks Low-Power Radio Operation in Internet of Things

The radio operation in wireless sensor networks (WSN) in Internet of Things (IoT) applications is the most common source for power consumption. Consequently, recognizing and controlling the factors affecting radio operation can be valuable for managing the node power consumption. Among essential factors affecting radio operation, the time spent for checking the radio is of utmost importance for monitoring power consumption. It can lead to false WakeUp or idle listening in radio duty cycles and ContikiMAC. ContikiMAC is a low-power radio duty-cycle protocol in Contiki OS used in WakeUp mode, as a clear channel assessment (CCA) for checking radio status periodically. This paper presents a detailed analysis of radio WakeUp time factors of ContikiMAC. Furthermore, we propose a lightweight CCA (LW-CCA) as an extension to ContikiMAC to reduce the Radio Duty-Cycles in false WakeUps and idle listening though using dynamic received signal strength indicator (RSSI) status check time. The simulation results in the Cooja simulator show that LW-CCA reduces about 8% energy consumption in nodes while maintaining up to 99% of the packet delivery rate (PDR).

IoT Based Energy Meter Towards Efficient Power Consumption

Electricity consumption has continued to go up rapidly at a rate faster than energy consumption. All forms of electricity generation have impact on our environment. Using electricity more efficiently reduces both the amount of fuel needed to generate electricity and the amount of greenhouse gases and other air pollution emitted as a result. This paper proposes smart, integrated power consumption monitoring system that has been implemented with the use of open source standard technology. Just like commercial energy meter, this system can monitor electrical energy consumption from a remote distance with the technology of Internet of Things (IoT). This energy monitoring system using IoT will read the energy level and the consumer can check the energy consumption and the electric bill via Cloud platform such as ThingSpeak, MQTT and mobile app. The main target of this system is to reduce the high energy consumption. The consumer can reach out his/her energy consumption at anytime and anywhere in the world.

Design Monitoring Electrical Power Consumtion at Computer Cluster

Electricity is one of the basic needs in the era of technological development, where all equipment must use electricity to operate such as computer, so that it requires a system that can monitor power consumption at computer cluster. To monitoring power consumption using WCS1800 to current sensor and microcontroller Atmega32 to data sensor process, and serial communication to send data to display at personal computer. From test system having two result, first is power consumption at computer cluster starting, where current value range is 0 to 38A with power consumption is 0 to 8360 watt. And second is power consumtion at computer cluster execution progran, current value is 27 to 40 A, with power consumption 5940 to 8800 watt. From this system has been design, the power consumption at computer cluster can be monitored and known value of energy consumption.

Smart Monitoring technology for the remote monitoring of power, gas, water and thermal energy consumption in Smart City

Smart Monitoring technology for the remote monitoring of power, gas, water and thermal energy consumption in Smart City

Performance Evaluation LoRa-GPRS Integrated Electricity usage Monitoring System for Decentralized Mini-Grids

The emerging Internet of Things (IoT) technologies such as Long-Range (LoRa), combined with traditional cellular communications technologies such as General Packet Radio Service (GPRS) offers decentralized mini-grid companies the opportunity to have cost-effective monitoring systems for the mini-grid resources. Nevertheless, most of the existing decentralized mini-grid companies still rely on traditional cellular networks to fully monitor electricity consumption information, which is not a feasible solution especially for the resource-constrained mini-grid systems. This paper presents the performance evaluation of the proposed LoRa-GPRS integrated power consumption monitoring system for decentralized mini-grid centers. Each mini-grid center consists of a network of custom designed smart meters equipped with LoRa modules for local data collection, while the GPRS gateway is used to transmit collected data from the local monitoring centre to the cloud server. Performance testing was conducted by using five electrical appliances whose power consumption data form the cloud server was compared to the same data collected by using a reference digital meter. The correlation between the two data sets was used as a key performance metric of the proposed system. The performance results show that the proposed system has a good accuracy hence providing a cost-effective framework for monitoring and managing of power resources in decentralized mini-grid centers.

Wireless Power Consumption Monitoring and Data Logging System for SMEs: A Prototype System Development and Evaluation

SMEs contribute a lot in terms of boosting a country’s economic growth and providing the majority with job opportunities. Increasing the energy efficiency of SMEs may lead them to be a more valuable asset to both local and national economy. The study specifically aims to develop a prototype of a wireless power monitoring and data logging system utilizing microcontrollers and the principle of radio frequency. Also, the study further aims to monitor and log power consumption of SME establishments, and help provide real-time feedback of its power consumption. The study used the developmental-experimental method in producing the system of the prototype. The system was evaluated in terms of its accuracy. Data from the accuracy tests indicated that the system has accuracy within the allowable percentage error. Actual applications of the system to various kinds of SMEs showed that it can withstand monitoring the average loads consumed by the SMEs tested.

Knowledge-based power monitoring and fault prediction system for smart factories

With the recent spread of the 4th Industrial Revolution, the intellectualization of industry is progressing rapidly. In particular, companies in various field are interested in converting existing factories into smart factory, and the number of cases where the smart factory template is applied is increasing. In this paper, we design and implement an IoT-based power monitoring and data collection system that enables monitoring of power consumption as well as the detection of abnormal power consumption in a smart factory. The system consists of power measurement devices, data analysis servers, and knowledge-based web and smartphone applications. The power measurement device uses IoT sensors to measure power consumption and sends collected data to the server. The server analyzes the data collected from the device using R and exploits the analysis results to provide predictions about the failure of equipment and facilities in the smart factory. From this point of view, we can expect improvement in not only cost-efficiency but also product quality.

Fault Detection and Diagnosis of Industrial Robot Based on Power Consumption Modeling

Fault detection via power consumption monitoring of industrial robots is a substantial problem considered in this article, in which the healthy measurements of power consumption and encoders data for a prespecified task are employed as a reference for comparison to diagnose the potential failures or excessive degradation in the robot joints. Since most electrical and mechanical faults directly affect the consumed energy, the proposed solution analyzes the comparison outcomes between the healthy reference data with that monitored in a real time for each individual task. To integrate the power measurements with a base station, a ZigBee-based wireless data acquisition circuit has been developed to process the joints data. This article suggests a measurement-based mathematical model called Bode equations vector fitting as a robust fitting method to estimate such power consumption patterns. The achieved estimates allow a clear distinction for the potential failures in the robot joints that affect the power rate patterns even when involving sharp fluctuations. A table-based neural network classifier is presented to indicate the faulty joint or encoder according to the time intervals that divided for the executed task. The experimental results demonstrate the performance verification and feasibility of the proposed approach in ABB-IRB-1200 robot manipulator. Note to Practitioners— Industrial machines are seeking to achieve energy optimization to verify the sustainability demand goal. Currently, many industrial robotic systems are not effectively monitored and modeled mathematically toward detecting the potential faults. In this context, a faults diagnosis method with an accurate mathematical model based on reference power patterns is proposed for monitoring the performance of that system. The proposed energy-based diagnosis technique can be readily integrated with the existent industrial robots supply and can be monitored remotely. Furthermore, no significant changes in the machine's hardware, but a reference pattern of a power consumption per each individual task per each robot, are required.

IoT Power Monitoring System for Smart Environments

In this research work, we describe the development and subsequent validation of EnerMon a flexible, efficient, edge-computing based Internet of Things (IoT) LoRa (LongRange) System to monitor power consumption. This system provides real-time information and a descriptive analytics process to provide a ‘big picture’ about energy consumption over time and identify energetic waste. The solution is based on Arduinos, current transformer sensors, Raspberry Pi as an application server and LoRa communication alongside a description and information on what is to be expected of it, describing the development process from the design phase to the validation phase with all steps in between. Due to LoRa low debit communication, an edge computing approach was implemented to create a real-time monitoring process based on this technology. This solution, with the help of descriptive analysis, allows the creation of an energetic local footprint, using a low-cost developed solution for less than 80€ per three-phases monitoring device. This solution also allows for easy installation without communication range and obstacles limitations making it easy use in different situations from big complex building to smaller consumers, such as electric boilers, or simply to measure the energetic footprint of tourists in a small local tourist apartment.

Use of by-products for partial replacement of 3D printed concrete constituents; rheology, strength and shrinkage performance

In this paper, fly ash, ladle furnace slag and limestone filler were utilized in concrete used as material for additive manufacturing (3D printing). Fly ash and ladle furnace slag were used as a replacement of cement (30% wt.) and limestone filler as a replacement of siliceous aggregates (50% wt.). Work­ability of fresh concretes that contained these by-products was measured 0, 15 and 30 minutes after mixing. Three different workability tests were conducted and compared: flow table, ICAR rheometer and an experimental method that measures the electric power consumption of the motor that rotates the screw extruder. Workability parameters that were measured were evaluated regarding printability of mixtures. Density, ultrasonic pulse velocity, compressive and flexural strength were measured on hardened concrete. Additionally, relative like­­li­hood of cracking of different concrete mixtures was estimated by per­forming restrained shrinkage test (ASTM C1581). Results showed that use of fly ash or ladle furnace slag as binder, and limestone filler as aggregate decreases slightly the mechanical properties of concrete but improve its durability re­garding cracking potential. Monitoring of electric power consumption of screw extruder motor was found to be an effective method for measuring easily real-time workability and define if a mixture is printable or not.

Real‐time power monitoring using field‐programmable gate array with IoT technology

Automation technologies are often impacted undesirably by downtime caused by machine failures. An approach to overcome machine failures is through failures prediction, achievable via the monitoring of power consumption. Unusual power consumption could serve as an alert for possible machines faults. Real-time power consumption monitoring is made possible with the deployment of Internet of Things (IoT), which brings together digital cloud computing and conventional automation technology to rapidly increase the efficiency of production. In this work, the authors have developed an IoT-based power monitoring system to detect unusual power consumption of machines and present the data to users remotely through Android application and cloud storage. The competent integration of Wi-Fi module ESP8266, CT sensor module and field-programmable gate array DE1-SoC board allows the system to accurately measure power consumption and instantaneously store the data in cloud storage. Performance of the developed system was characterised by current measurement of five different loads. The absolute errors, given by the difference between current measured by digital multimeter and the developed system, range from 0.01 to 0.26 A. The close agreement between the two measurements endorses the accuracy and reliability of the real-time power monitoring system, on top of its easy-accessibility gifted by IoT.

IoT Based Smart Room For Power Consumption Monitoring And Control

IoT Based Smart Room For Power Consumption Monitoring And Control