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An Expandable Modular Internet of Things (IoT)-Based Temperature Control Power Extender
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Today, the world’s electricity consumption is growing rapidly, and therefore energy demands are also increasing. In the past few decades, various measures have been taken to improve equipment and system design to increase production and transmission efficiency and reduce power consumption. This article proposes a novel Internet of Things (IoT)-based temperature control power extender with two working modes of cooling and heating to solve power shortages. The power is turned on or off accurately and in a timely manner through a temperature-sensing element, thereby avoiding unnecessary power consumption to achieve the goal of energy-saving. This can directly power on or off the power extender through the Internet. It can also use a 2.4G Wi-Fi wireless transmission to transmit, for example, real-time temperature information, the switch status and the master–slave mode. Related data can be controlled, collected and uploaded to the cloud. Each proposed power extender’s temperature setting in a large-scale field can be set uniformly, and no staff is wasted to set the temperature separately. Taking a general industrial electric fan as an example, if it is changed to drive with this temperature control extension cable, and assuming that the industrial electric fan is activated for 900 s per hour, its power-saving rate is 74.75%
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... For example, manufactured resources such as electricity can no longer meet the needs of rapid population growth. In the past few decades, various measures have been taken to improve equipment and system design to improve production and transmission efficiency and to reduce power consumption [2]. ...
... In this article, we adopt the MQTT architecture [51]. Compared to the HTTP architecture, the MQTT architecture can reduce power consumption during transmission, and the proposed system will synchronize the time with the primary server every minute, avoiding the problems caused by time out of synchronization and data delay [2], as follows. ...
... Factors that affect the power consumption include the frequency of people entering and leaving the office, the duration of high-light T , the power of high-light mode W H , the power of low-light mode W L , the presence or absence of continuous triggering. To facilitate the deriving of the formula, we refer to calculation methods [2], [48], [49] of power consumption. Finally, consider a company as an example to calculate the total savings for all of its offices. ...
Most installed lighting systems are outdated and have poor energy efficiency. Therefore, there is an urgent need for smart LED lighting systems that are energy efficient, easy to install, and inexpensive. In this article, a leader-follower smart office lighting control system based on Internet of Things (IoT) technology is proposed to satisfy the goal of saving energy through the coordinated operation of a system counter and an infrared (IR) human movement sensor (passive infrared (PIR) sensor). When no one is present in the space, all indoor LED lights are turned to low-light mode. Otherwise, all indoor LED lights are turned to high-light mode together. In addition to change the brightness of LED lights in the same area at the same time to save energy, the parameters of the LED lights can be set directly through microcontrollers via the IoT and the internet. If a general 15 W T8 LED tube (noninduction light) is replaced with the proposed leader-follower office lighting system (assuming that the office is occupied for 10 hours a day and that the hourly low-light mode is 20 minutes), then the power-saving rate is as high as 28.13%.
... Although LED lighting prices are falling with recent advancements in manufacturing technology, LED lighting is still costly. To this end, researchers have extensively carried out many previous works [31][32][33][34][35][36][37][38] to improve the economic feasibility of LED lighting through control algorithms based on sensor and network technology. When the user is not in the room or the room is too bright, these systems can reduce energy consumption by reducing the output power of the lighting instead of processing it under the same illuminance. ...
... To facilitate the derivation of the formula, we refer to the calculation method of power consumption in [1,35]. It is assumed that pedestrians and vehicles will not stay for too long when they pass by and pass by after T seconds, which means there is no continuous trigger. ...
Reducing residential and industrial electricity consumption has been a goal of governments around the world. Lighting sources account for a large portion of the whole energy/power consumption. Unfortunately, most of the existing installed lighting systems are ancient and have poor energy efficiency. Today, many manufacturers have introduced light-controlling systems into the current market. However, existing light controlling systems may not be successfully applied to buildings, streets, and industrial buildings due to high costs and difficult installation and maintenance. To combat this issue, this article presents an easy-to-install, low-cost, Master-Slave intelligent LED light-controlling system based on Internet of Things (IoT) techniques. The benefit of using the proposed system is that the brightness of the LED lights in the same zone can be changed simultaneously to save in energy consumption. Furthermore, the parameters of the LED lights can be directly set. Moreover, the related data are collected and uploaded to a cloud platform. In this article, we use 15 W T8 LED tubes (non-induction lamps) as a case study. When the proposed system is installed in a zone with few people, the energy-saving rate is as high as 90%. Furthermore, when 12 people pass by a zone within one hour, its energy-saving rate can reach 81%. Therefore, the advantages of using the proposed system include: (1) the original lamp holder can be retained; (2) no wiring is required; and (3) no server is set up. Moreover, the goal of energy saving can also be achieved. As a result, the proposed system changes the full-dark mode of the available sensor lamp to the low power low-light mode for standby. Further, it makes the sensor lamps in the same zone brighten or low-light way simultaneously, which can quickly complete large-scale energy-saving and convenient control functions of intelligent LED lighting controlling system.
... The paper proposes a master-slave software architecture based on public or private cloud connectivity. The paper focuses on applying the concept to legacy devices and equipment to become smart machines [29]. ...
This paper presents a solution for upgrading a previous device model to an Industry 4.0 smart device, with the goal of maintaining high compatibility. A novel IoT architecture is presented that satisfies the characteristics of a smart device. We analysed existing IoT architectures and proposed a new architecture to achieve long-term security and usability. To ensure long-term security, we eliminated the possibility of device configuration outside the immediate vicinity of the device with a dedicated protocol. The security concepts of the existing architectures were also analysed and further modified. To improve compatibility with previous device models, we propose a new method to collect data from sensors by introducing a multithreaded microcontroller. We propose additional software components to ensure factory programming, maintenance, and cloud Big Data analysis. Based on our experiments, we adapted the algorithm to increase the accuracy of the temperature and flow sensors by using a temperature calibration device and known flow cycles. Measurement results are presented to confirm the successful upgrade. We designed a hardware architecture to ensure compatibility with previous and future device models. Issues with previous sensors encountered during the upgrade were discussed and resolved. A novel software architecture based on security for long-term IoT devices is proposed.
This research attempts to implement fire safety measures and ensure the safety of individuals with the preservation of property in institutions, health, and lives of citizens. Fire prevention procedures must be developed and implemented in order to assess, remove, and prevent probable causes of fires. Fire risk measures include creating ideal conditions for saving property and evacuating citizens. Preventive work with residents guarantees that the source of the ignition is identified and that emergency services are alerted as soon as possible in the event of a fire. An Unsupervised Deep Learning algorithm using the Internet of Things (IoT) concept was used to develop a fire risk estimation system. The suggested algorithm determines whether or not the scenario is safe. An alert signal is transmitted to three peripheral devices: the monitoring camera, the fire sensor due to high temperatures, and a sensor due to the increasing CO2 levels in the atmosphere. The proposed system considers the data collected by various distributed sensors and later it is processed by using a deep learning algorithm to estimate the risk level at that point, release an alarm fire risk as a signal at a usual time for safety and preserve public property. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
This paper addresses the improvement of tracking of the maximum power point upon the variations of the environmental conditions and hence improving photovoltaic efficiency. Rather than the traditional methods of maximum power point tracking, artificial intelligence is utilized to design a high-performance maximum power point tracking control system. In this paper, two artificial intelligence-based maximum power point tracking systems are proposed for grid-connected photovoltaic units. The first design is based on an optimized fuzzy logic control using genetic algorithm and particle swarm optimization for the maximum power point tracking system. In turn, the second design depends on the genetic algorithm-based artificial neural network. Each of the two artificial intelligence-based systems has its privileged response according to the solar radiation and temperature levels. Then, a novel combination of the two designs is introduced to maximize the efficiency of the maximum power point tracking system. The novelty of this paper is to employ the metaheuristic optimization technique with the well-known artificial intelligence techniques to provide a better tracking system to be used to harvest the maximum possible power from photovoltaic (PV) arrays. To affirm the efficiency of the proposed tracking systems, their simulation results are compared with some conventional tracking methods from the literature under different conditions. The findings emphasize their superiority in terms of tracking speed and output DC power, which also improve photovoltaic system efficiency.
The objective of this work is to analyze a gas injection borehole heat exchanger coupled with a desiccant assisted air conditioning system during cooling and heating operation. A common problem that occurs in air conditioning systems is peak loads, during which the cooling or heating power of the soil can be exceeded. To counteract this drawback, a gas injection borehole heat exchanger, which is capable of creating artificial groundwater flow along the heat exchanger by inducing a pressure difference inside the well, is used. Experimental results of the performance differences between a conventional and a gas injection borehole heat exchanger are presented. Under the same inlet conditions, a reduction in the outlet temperature of up to 2 °C is achieved compared with an equivalent conventional borehole heat exchanger in cooling mode. The maximum cooling power is increased by 26%. As a result, a fast and dynamic responding control of the heat transfer between the heat exchanger and the soil is possible. During winter operation, despite the lower drilling depth of the gas injection borehole heat exchanger system, the performance is within the range of a conventional system. The power increase is limited to around at a steady state. In conclusion, gas injection borehole heat exchangers can be promising in terms of reliable peak load handling within large geothermal fields.
Three-phase inverters are widely used in grid-connected renewable energy systems. This paper presents a new control methodology for grid-connected inverters using an adaptive fuzzy control (AFC) technique. The implementation of the proposed controller does not need prior knowledge of the system mathematical model. The capabilities of the fuzzy system in approximating the nonlinear functions of the grid-connected inverter system are exploited to design the controller. The proposed controller is capable to achieve the control objectives in the presence of both parametric and modelling uncertainties. The control objectives are to regulate the grid power factor and the dc output voltage of the photovoltaic systems. The closed-loop system stability and the updating laws of the controller parameters are determined via Lyapunov analysis. The proposed controller is simulated under different system disturbances, parameters, and modelling uncertainties to validate the effectiveness of the designed controller. For evaluation, the proposed controller is compared with conventional proportional-integral (PI) controller and Takagi-Sugeno-Kang-type probabilistic fuzzy neural network controller (TSKPFNN). The results demonstrated that the proposed AFC showed better performance in terms of response and reduced fluctuations compared to conventional PI controllers and TSKPFNN controllers.
This paper presents a complete design methodology of a Class-E inverter for capacitive wireless power transfer (CWPT) applications, focusing on the capacitance coupling influence. The CWPT has been investigated in this paper, because most of the literature refers to inductive power transfer (IWPT). However, CWPT in perspective can result in lower cost and higher reliability than IWPT, because it does not need coils and related shields. The Class-E inverter has been selected, because it is a single switch inverter with a grounded MOSFET source terminal, and this leads to low costs and a simple control strategy. The presented design procedure ensures both zero voltage switching (ZVS) and zero derivative switching (ZDS) conditions at an optimum coupling coefficient, thus enabling a high transmission and conversion efficiency. The novelties of the proposed method are that the output power is boosted higher than in previous papers available in the literature, the inverter is operated at a high conversion efficiency, and the equivalent impedance of the capacitive wireless power transfer circuit to operate in resonance is exploited. The power and the efficiency have been increased by operating the inverter at 100 kHz so that turn-off losses, as well as losses in inductor and capacitors, are reduced. The closed-form expressions for all the Class-E inverter voltage and currents waveforms are derived, and this allows for the understanding of the effects of the coupling coefficient variations on ZVS and ZDS conditions. The analytical estimations are validated through several LTSpice simulations and experimental results. The converter circuit, used for the proposed analysis, has been designed and simulated, and a laboratory prototype has been experimentally tested. The experimental prototype can transfer 83.5 W at optimal capacitive coupling with operating at 100 kHz featuring 92.5% of the efficiency, confirming that theoretical and simulation results are in good agreement with the experimental tests.
The research object of this paper is single-phase PWM rectifier, the purpose is to reduce the total harmonic distortion (THD) of the grid-side current. A model predictive current control (MPCC) with fixed switching frequency and dead-time compensation is proposed. First, a combination of an effective vector and two zero vectors is used to fix the switching frequency, and a current prediction equation based on the effective vector’s optimal action time is derived. The optimal action time is resolved from the cost function. Furthermore, in order to perfect the established prediction model and suppress the current waveform distortion as a consequence of the dead-time effect, the dead-time’s influence on the switching vector’s action time is analyzed, and the current prediction equation is revised. According to the experimental results, the conclusion is that, firstly, compared with finite-control-set model predictive control, proportional-integral-based instantaneous current control (PI-ICC) scheme and model predictive direct power control (MP-DPC), the proposed MPCC has the lowest current THD. In addition, the proposed MPCC has a shorter execution time than MP-DPC and has fewer adjusted parameters than PI-ICC. In addition, the dead-time compensation scheme successfully suppresses the zero-current clamping effects, and reduce the current THD.
The high frequency transformer in single-phase full-bridge DC-DC converter is prone to saturation because of the asymmetry of circuit parameters. Transformer saturation will increase power consumption, accelerate the aging of winding insulation, and even damage power switches. In order to prevent this risk, a DC bias suppression strategy is presented in this article, and the main advantage of this strategy is that the problem of transformer saturation can be completely eliminated. In this article, firstly, the DC bias and saturation mechanism of single-phase full-bridge DC-DC converter are analyzed in detail, and the Maximum Integral Value of Volt-Second Product error (MIVVSPE) is derived. Secondly, aiming at the saturation problem of single-phase full bridge DC-DC converter, a new digital integral circuit is designed to evaluate the DC bias state of transformer, and a DC bias suppression strategy is constructed to suppress the saturation of transformer. Furthermore, different from the traditional current feedback control strategy, the DC bias suppression strategy based on volt-second product error integral can be triggered before the transformer enters the saturation state, and the transformer saturation can be completely suppressed. Finally, a 30 kW single-phase full-bridge DC-DC converter for arc welding is established in lab. The experimental results show that the new DC bias suppression strategy can effectively prevent the transformer from entering the saturation state and improve the operation stability of single-phase full-bridge DC-DC arc welding inverter.
Many IoT applications require a mechanism to disseminate commands and collect responses over a wireless network in order to control and collect data from multiple embedded devices. However, severe collisions may occur if a large number of nodes attempt to respond simultaneously and promptly, not only among the responses, but also with the dissemination of commands. This is because low-power wireless network protocols for dissemination and collection have been designed separately. Tuning the parameters of one side of the protocol has clear trade-off between reliability and latency. To address this challenge, we propose SCoRe, an on-demand scheme for joint scheduling of command and responses on multihop low-power wireless networks to improve both reliability and latency simultaneously at runtime. SCoRe gathers the amount of time required by network nodes for dissemination and collection, and allocates relative timeslots to each node recursively over multihop on-demand when (and only when) disseminating a command. While doing so, information exchange occurs only between local neighbor nodes without a need for global routing table nor time synchronization. We implement SCoRe on a low-power embedded platform, and compare with well-known dissemination and collection schemes through both simulations and testbed experiments on 30 devices. Our evaluation results show that SCoRe can improve both latency and reliability without tuning the parameters for one metric, while the legacy schemes require careful parameter selection to match only one side of SCoRe, never both.
The focus of research and development on electric motorcycle range extender are system integration and energy regulation and management but the present fuel cell stack range extender still has defects, such as large volume, heavy weight and high cost. Its volume and weight will have a strong impact on the endurance of electric motorcycle. The bipolar plate takes most volume and weight of a proton exchange membrane fuel cell (PEMFC) stack and it is the key component influencing the overall power density and cost. Therefore, how to thin and lighten the bipolar plate and to enhance the performance and life of PEMFC stack is an urgent research subject to be solved for the moment and will be the key to whether the PEMFC stack range extender can be put in the electric motorcycle or not. In addition, the internal temperature, humidity, flow, voltage and current in the operation of PEMFC stack will influence its performance and life and the overall performance and life of fuel cell stack will be directly influenced by different external operating conditions. As nonuniform distribution of temperature, humidity, flow, voltage and current will occur in various regions inside the fuel cell stack, this study will use micro-electro-mechanical systems (MEMS) technology to develop a flexible five-in-one microsensor, which is embedded in the PEMFC stack range extender for real-time wireless microscopic diagnosis and the reliability test is performed, so that the actual operating condition inside the fuel cell stack range extender can be mastered instantly and correctly and the internal information is fed back instantly, the fuel cell stack range extender control system can be modified to the optimum operating parameters immediately, so as to enhance the performance and prolong the lifetime effectively.
With the introduction of Industry 4.0, and smart factories accordingly, there are new opportunities to implement elements of industry 4.0 in nonthermal processing. Moreover, with application of Internet of things (IoT), smart control of the process, big data optimization, as well as sustainable production and monitoring, there is a new era of Internet of nonthermal food processing technologies (IoNTP). Nonthermal technologies include high power ultrasound, pulsed electric fields, high voltage electrical discharge, high pressure processing, UV-LED, pulsed light, e-beam, and advanced thermal food processing techniques include microwave processing, ohmic heating and high-pressure homogenization. The aim of this review was to bring in front necessity to evaluate possibilities of implementing smart sensors, artificial intelligence (AI), big data, additive technologies with nonthermal technologies, with the possibility to create smart factories together with strong emphasis on sustainability. This paper brings an overview on digitalization, IoT, additive technologies (3D printing), cloud data storage and smart sensors including two SWOT analysis associated with IoNTPs and sustainability. It is of high importance to perform life cycle assessment (LCA), to quantify (En)—environmental dimension; (So)—social dimension and (Ec)—economic dimension. SWOT analysis showed: potential for energy saving during food processing; optimized overall environmental performance; lower manufacturing cost; development of eco-friendly products; higher level of health and safety during food processing and better work condition for workers. Nonthermal and advanced thermal technologies can be applied also as sustainable techniques working in line with the sustainable development goals (SDGs) and Agenda 2030 issued by United Nations (UN).
This paper proposes an IoT system based on wireless BLE connectivity to monitor the moisture content of wood, using a compact and low-cost moisture device that relies on a resistance measurement method valid for an ultra-wide range of resistance values. This device is digitally controlled with a BLE-incorporated micro-controller characterized by its small size and low power consumption, providing long-life battery. The proposed system consists of two main parts: first, the BLE moisture device including the moisture content measurement and wireless capability (BLE); second, the cloud-based monitoring platform, providing remote visualization and control for all the sensor nodes of the network. The complete infrastructure shows how multiple nodes can read and transmit moisture content of timber in buildings using small and unattended devices, with data saved in a central database and monitored by multiple commercial devices such as PC, smartphone, tablet, etc. The proposed system is innovative, scalable and low cost, and it can be deployed in wooden buildings and the wood industry, providing a practical solution that will help to avoid rot and other damaging effects caused by the moisture content.