Optimize Power Consumption Research Articles

Research on Power Consumption Model of Permanent Magnet Direct Drive Belt Conveyor System Based on Fruit Fly Optimization Algorithm-Generalized Regression Neural Network-Particle Swarm Optimization

The permanent magnet direct drive electric drum is used to replace the driving device of the traditional belt conveyor, which simplifies the structure. However, the permanent magnet direct drive electric drum still operates in a high speed and stable state after starting, and cannot transport materials reasonably, belt speed cannot match the coal quantity reasonably; the problem of energy waste is still severe. Thus, based on fruit fly optimization algorithm-generalized regression neural network-particle swarm optimization algorithm, the power consumption network model of the permanent magnet direct drive belt conveyor system is established. The relationship among belt velocity, coal transport quantity and power is obtained, and the optimal belt velocity is found with the power consumption network model. Thus, the minimum power is obtained. The algorithm selects the optimal smoothing factor by fruit fly optimization algorithm, inputs the optimal smoothing factor into generalized regression neural network and establishes the optimal power consumption model. Then establishes the matching relationship of coal quantity, optimal power, and optimal belt speed in the power consumption model by the adaptive weight particle swarm optimization. The model is compared with the network model with smoothing factors of 0.7 and 0.4. The comparisons show that the optimized model performs better, which can be better applied to establish the energy consumption model of the system.

Performance Evaluation of Penstock By-Pass System Modification on HEPP (Hydro Electric Power Plant) with CFD Analysis to Optimize Power Consumption

This paper proposes a feasibility test for the performance modification of the penstock by-pass system using CFD (Computational-Fluid-Dynamic) simulations to optimize the energy use of the water-cooling system. The main advantage of this modification is the power savings of 45%-50%. This modification of the by-pass system is carried out with the aim that the final flowrate and pressure of modification can replace the Main-Water-Supply-Pump (MWSP) operation function in the unit’s main cooling system. CFD simulation is carried out on the impact of a pressure drop of 40-4 bar with a flow of 0.3 m3/s on the by-pass pipe flow of the existing penstock system and the condition of the pipe modification to be carried out. The numerical simulation results show that the MWSP network structure can accept load flow pressures varying 40-4 bar. This simulation refers to the conditions in HEPP Saguling Indonesia and can be applied to others.

An approach for offloading in mobile cloud computing to optimize power consumption and processing time

Smart phones are widely used but they still suffer from constrained resources in term of their processing capabilities, memory capacity and battery capacity. Mobile Cloud Computing (MCC) has recently emerged to overcome the shortcomings of the standalone smartphones, where Cloud Computing (CC) is leveraged for processing capabilities and memory capacity, while the smartphone would use minimal battery power. However, task offloading from smartphones to the cloud remains an active area of research, to achieve optimized performance and resource utilization and enhance the overall Quality of Service (QoS). In this paper, we propose an approach where two servers are used alternatively, First Upload Round (FUR) offloading and Second Upload Round (SUR) offloading; both supported by a decision engine system. This approach shows better performance and less energy consumption over competing state-of-the-art approaches. The proposed approach shows reduction of the power consumption, 4G is the most improved, for example, for the file size 10 Mb the reduction of the power consumption was 93 % on 4G, compared to Wi-Fi 85 %.

Multi‐objective optimal design and performance of magnetorheological damper

AbstractIn order to solve the problems of high power consumption and low output damping force of magnetorheological dampers, the relationship model between the structural parameters and the optimization objectives of dampers is established with the low power consumption and lightweight of magnetorheological dampers. The key structural parameters of the magnetorheological damper are optimized by the multi‐objective genetic algorithm, and the optimal solution is obtained, and the optimized magnetorheological damper is tested and studied. The rationality of the selected parameters is verified by experimental study. The results show that the power consumption of magnetorheological dampers is reduced by 43 % and the damping force is increased by 30 % after optimization. There is a balance relationship between the power consumption optimization and the damping force optimization of the damper. Therefore, when finding the optimal solution, we should consider the needs of the actual situation and select the most reasonable parameters.

Design of zero bias power detectors towards power consumption optimization in 5G devices

This paper presents the design and characterization of zero bias power detectors, based on MOSFET transistors, integrated in SiGe 55-nm BiCMOS technology from ST-Microelectronics. The working frequency bands of the circuits are located in the range (38–55) GHz, dedicated to optimize the power consumption in 5G devices. Three NMOS categories available in the technology are used (GP, LP, HPA), the aim is to design several detectors based on different NMOS categories in order to compare their performances. In addition, a detector based on a stack of 6 LP transistors is designed in order to increase the dynamic range. Compared to recent works, the HPA detector exhibits a very good performance with very low noise equivalent power value (NEP) 3.8 pW/Hz and large dynamic range of 67 ​dB. The extracted voltage sensitivity values of these detectors are between (850–1400) V/W showing good agreements with the simulation results.

COMMA: A new LoRaWAN communication optimisation mechanism for mobility adaptation of IoT

Communication protocols for the Internet of Things (IoT) have been optimised to meet the specific requirements of this fast-growing domain. They have been adapted mainly to optimise power consumption, maximise coverage area and facilitate their implementation. One of these protocols is LoRaWAN, which uses the ADR algorithm (Adaptive Data Rate) and aims to reduce power consumption by reducing data rate and transmission power. The ADR algorithm has been specifically optimised for static systems, that is, systems where devices do not move or have limited mobility. In this article, we introduce COMMA (Communication Optimisation Mechanism for Mobility Adaptation), an adaptive data rate mechanism optimised for everyday mobility of devices and people. We use COMMA in telemedicine, our area of expertise, to develop a medical remote-monitoring platform for mobile patients suffering from chronic diseases.

Hybrid Precoding in Cooperative Millimeter Wave Networks

In this paper, we study the performance of cooperative millimeter wave (mmWave) networks with hybrid precoding architectures. Considering joint transmissions and BS silence strategy, we propose hybrid precoding algorithms which minimize the sum power consumption of the base stations (BSs), for both fully- and partially-connected hybrid precoding (FHP and PHP, respectively) schemes, for single-carrier and orthogonal frequency-division multiplexing systems. We reformulate the analog precoding part as an equal-gain transmission problem, which only depends on the channel information, and the digital precoding part as a relaxed convex semidefinite program subject to per-user quality-of-service constraints that gives the optimal sum power consumption in terms of the BS silence strategy. In order to reduce the complexity of the hybrid precoding algorithm with optimal BS silence strategy, we propose a sub-optimal hybrid precoding algorithm that iteratively put BSs with small power into the silent mode. The simulation results show that, depending on the parameter settings, the power consumption of the PHP may be dominated by the RF transmit power and it may result in a larger power consumption than the FHP. For the cases with 2 BSs and 4 users, implementation of the FHP and the PHP in cooperative networks reduces the required RF transmit power, compared to the case in a non-cooperative network, by 71% and 56%, respectively.

Proposing an Energy-Aware Routing Protocol by Using Fish Swarm Optimization Algorithm in WSN (Wireless Sensor Networks)

Former developments and advances in micro-electro-mechanical systems have made it possible to produce and use tiny battery-powered nodes in wireless communications. Networks consisting of such nodes which are capable of measurement are called Wireless sensor networks (WSNs). The initial objective of using nodes was related to internal applications. The initial nodes are able to sense scalar information such as temperature, moisture, pressure and the location of surrounding objects. However, sensor nodes’ power is provided by battery with limited capacity. Hence, due to limited resources, a balance should be made between precision and power optimization in these networks. In this paper, a new method is proposed which addresses the issue of optimal power consumption in WSNs. Accordingly, using fish swarm optimization algorithm, we proposed an energy-aware routing protocol in WSNs which optimizes power consumption. The proposed protocol was simulated in OPNET 11.5 simulator and compared with ERA protocol. Simulation results indicated that the proposed protocol had better performance than ERA protocol regarding power consumption, end-to-end delay, media access delay, throughput rate, the probability of successful transmission to sink and signal to noise ratio.

Game-Theoretic Demand Side Management of Thermostatically Controlled Loads for Smoothing Tie-Line Power of Microgrids

Thermostatically controlled loads (TCLs) are regarded as one of the promising resources for suppressing power fluctuations due to renewable energy (RENs). However, due to the great burdens of fully considering each users' characteristics, it is difficult to achieve unity of the individual optimal decisions and global optimum in demand-side management (DSM). This paper proposes a game-theoretic DSM that can optimize the global power consumption schedule by individual TCL user's optimization. By integrating the prediction of REN outputs into the pricing mechanism, the proposed DSM can guide the users to make their best power consumption schedules along with intermittent REN generations, thus to smooth the tie-line power of microgrids. In this paper, a novel pricing mechanism is firstly developed based on the concave N-person game theory, which is more adaptive and flexible compared with existing game-theoretic DSM. Then, an individual's power consumption optimization and its simplified model are developed considering the constraints of the TCL model and personal preferences. The simplified model can be easily solved and achieve a fast solution in the power consumption game. An implementation framework of the proposed DSM is further developed for practical application. Finally, numerical studies verify the effectiveness of the proposed models and methods.

Low-Power Retentive True Single-Phase-Clocked Flip-Flop With Redundant-Precharge-Free Operation

As basic components, optimizing power consumption of flip-flops (FFs) can significantly reduce the power of digital systems. In this article, an energy-efficient retentive true-single-phase-clocked (TSPC) FF is proposed. With the employment of input-aware precharge scheme, the proposed TSPC FF precharges only when necessary. In addition, floating node analysis and transistor level optimization are employed to further ensure the high energy efficiency of the FF without significantly increasing the area. Postlayout simulations based on SMIC 55-nm CMOS technology show that at a supply voltage of 1.2 V, the power consumption of the proposed FF is 84.37% lower than that of conventional transmission-gate flip-flop (TGFF) at 10% data activity. The reduction rate is increased to 98.53% as the data activity goes down to 0%. When the supply voltage decreases to 0.6 V, the proposed FF consumes only 0.411 fJ/cycle at 10% data activity, which is 84.23% lower than TGFF. Measurement results of ten test chips demonstrate the great energy efficiency of the proposed FF. Furthermore, the CK-to-Q delay of the proposed FF is 26.18% lower than that of TGFF at a supply voltage of 1.2 V.

Оптимизация энергопотребления системы электрообогрева железнодорожных стрелочных переводов

Оптимизация энергопотребления системы электрообогрева железнодорожных стрелочных переводов

Co‐design implementation of High Efficiency Video Coding standard encoder on Zynq MPSoC

SummaryStatistical analysis of High Efficiency Video Coding (HEVC) encoder reveals that in the motion compensation block, the interpolation filter consumes more than 30% in the encoder time in comparison with other blocks. In this paper, we start with an optimized hardware implementation of the interpolation filter on field‐programmable gate array (FPGA) based on Xilinx setup environment. In a second step, a Hardware/Software (HW/SW) co‐design implementation of HM16.7 encoder is performed on Zynq MPSoC platform to evaluate the proposed interpolation filter IP in terms of total encoder run‐time, taking advantages of both processing units (quad‐core ARM Cortex TM‐A53 processor and Programmable Logic FPGA component) available on the Zynq MPSoC. The proposed architecture of luma and chroma filters was simulated and synthesized on Xilinx XCZU7EV‐2FFVC1156 FPGA at 250‐MHz clock frequency. The synthesis results present an optimized power consumption of 3.308 mW for higher resolutions (2560 × 1600 and 1920 × 1080) at 50 fps with the use of just 1% of the FPGA resources. The experimental results of the co‐design implementation of HEVC encoder present a speedup of 2 times (41% in PeopleOnStreet sequence) in terms of processing time compared to the software alone implementation, with a an increase of 0.51% of bit rate and a very small degradation of peak signal‐to‐noise ratio (PSNR) (0.01%).

Hybrid Fuzzy-Genetic Model for Fitness-Based Performance Optimization in Wireless Networks

In recent times, the application of autonomic soft computing techniques for design and optimization of wireless access networks is progressively becoming prevalent. These computational learning techniques are capable of handling uncertain and imprecise networking information while driving toward the optimal solution set in the problem search space. The approach proposed by this paper presents the application of the fuzzy logic inference combined with the evolutionary genetic algorithm to optimize the performance parameters in wireless networks. In particular, we consider optimal bit rate allocation and transmission power consumption through the joint design of fuzzy-genetic modeling framework. The sample training data generated through simulations of IEEE 802.11 wireless access network are analyzed for optimization by supplying it to the expert hybrid model comprising of the conjunctive design of both the computational intelligent techniques. Specifically, we contemplate the binary encoding scheme, single-point crossover, reversing mutation, and two fitness functions for executing the binary genetic operations of crossover and mutation. It is generally observed that the proposed hybrid model with polynomial fitness function yields better performance with scalable network datasets than the logarithmic fitness function in terms of higher objective value. Moreover, the results obtained through simulation experiments exhibit significant throughput gains and power efficiency for the deployed fitness functions with the evolving size of training dataset. Compared with the existing methods, our hybrid learning model demonstrates performance enhancement with higher expected fitness measure, improved throughput and power efficiency.

Target tracking based on approximate localization technique in deterministic directional passive sensor network

Outdoor localization of non-cooperative moving discrete target tracking is a demanding and challenging due to inherent constraints of “target tracking wireless sensor networks” such as battery capacity, processing capacity, memory capacity. Current methods use either an active sensor or perform additional processing of the data received from multiple passive sensor nodes that increases power consumption. The work presented here proposes approximate localization of non-cooperative moving target in large open secluded area with optimal power consumption and sufficient accuracy. Binary passive infrared (PIR) sensor is used, resulting in reduced power consumption by the sensor. In order to deal with sensor’s technical limitation, a node is developed using variable range binary PIR sensors which results in variable sensing sectors. Deterministic directional, spatial–temporal domain of network topology along with activation log of node and its sectors is mapped with a dynamic target motion trajectory, resulting in sufficiently accurate target localization. Simulation results of the algorithm for “position estimation of random direction linearly moving target with positive slope motion model” indicate significant tracking of target with sufficient accuracy and reduced sensing power compared to existing methods.

Performance and power consumption optimization of a hydraulic variable valve actuation system

A Hydraulic Variable Valve Actuation (HVVA) system is studied in this work, it can continuously adjust engine valve timing and lift at any engine speeds by using a hydraulic actuation system. A detailed co-simulation model is built to reveal relationship between hydraulic pressure, engine valve lift and power consumption, it has taken flow rate, fluid property and pressure drop into consideration. Precision and accuracy of the simulation model are verified by a set of bench test. Coefficient of variation (COV) of engine lift shows that the experiment is repeatable and system robustness is verified through simulation. A modified simulated annealing optimization is conducted to search optimum operation parameters to reduce the HVVA system's power consumption and improve engine's volumetric efficiency (VE) as well as control engine valve seating speed. A pre-selecting criterion and a database retrieve process are added in the algorithm to improve the optimization's efficiency. Optimization shows that optimum pump speed and relief pressure vary from 440 rpm, 32 bar to 2200 rpm, 60 bar when engine speed increases from 500 to 3600 rpm. Corresponding system power consumption rises from 0.07 kW to 0.49 kW, valve lift has a relatively high time-area value (TAV) and an acceptable engine valve seating speed.

A Survey on Applications of Deep Learning in Cloud Radio Access Network

The necessity for high-speed and low-latency connectivity of the vast number of mobile users is rising with the immense usage of mobile applications. A cloud radio access network (C-RAN) is a promising framework for next-generation cellular communication, which can satisfy the requirements of significantly increasing data traffic and user demands. In C-RAN, the data processing unit can be centralized and virtualized in data centers and can be shared among distributed base stations. Deep learning (DL) appears to be a feasible approach for facilitating the data processing capability, resource management in the cloud, and predicting dynamic traffic in cellular communication. The convergence of C-RAN and DL is expected to bring new prospects to both interdisciplinary research and industrial applications. In this regard, different approaches have been proposed for DL-based C-RAN in the literature. This article provides a comprehensive survey of the state-of-the-art DL techniques applied in C-RAN. A brief introduction of the C-RAN architecture and DL techniques is given to provide insights into these two emerging technologies. Existing surveys are also discussed to highlight the research gap. The reviewed works are categorized into power consumption optimization, network performance maximization, and QoS maximization based on their optimization objectives. The key ideas of DL applied in the reviewed schemes are also mentioned, and the performance evaluation techniques used in the research are discussed and compared. Lastly, research challenges and open research issues are highlighted to provide future research directions.

Adaptive scheduling strategy of fog computing tasks with different priority for intelligent production lines

Abstract Fog computing has been widely deployed in intelligent production lines to provide real-time computing services for terminal devices to alleviate the transmission problem between cloud and terminal. Nevertheless, when there are many services provided in a single fog node, how to choose an optimal data processing path is a challenge to minimize optimization latency and power consumption while ensuring the reliable transmission of data with different priorities. To address the problems as mentioned above in this paper, we first propose a joint evaluation model of time latency and power consumption based on task priority. Then, we formulate a fog computing adaptive scheduling algorithm (FCAS) based on dynamic programming to obtain the optimal path for processing data with different priorities at fog nodes. Experimental results illustrate that our proposed evaluation model and algorithm have lower power consumption, high efficiency, and higher reliability.

VLSI Implementation of Green Computing Control Unit on Zynq FPGA for Green Communication

The issue of the energy shortage is affecting the entire planet. This is occurring because of massive population and industry growth around the world. As a result, the entire world is attempting to implement green networking systems and manufacture the power/energy efficient products. This research work discusses the green networking system technologies. This work introduces a power‐efficient control unit (CU) design and implemented on the Zynq SoC (System on Chip) ultrascale field programmable gate array (FPGA). The VIVADO HLx Design Suite is used to simulate and analyze the CU model which is considered as one of the key components of central processing unit (CPU), used for data communication purposes. The CU is made suitable for the green communication by making it power‐efficient. Therefore, the power consumption of the CU is analyzed for the various set frequency value ranging between 100 MHz and 5 GHz, and it is discovered that as the clock frequency rises up, the total power consumption also tends to get increased. The total power of the proposed model is reduced by 77.42%, 21.29%, and 17.93% from three models, respectively, being compared in the present paper. Final results shows that the CU is better suited to run at low frequencies to optimize power consumption.

Model investigations into assessing optimal power consumption modes for major pump stations of iron ore underground mines

The article investigates into the level of energy efficiency of main water pump stations of iron ore underground mines in case of time-of-day electricity rate. There are developed and suggested methods of analyzing the influence of pump electric capacity on electricity cost based on multifactor regressive models. The data on power consumption of iron ore mines indicates a complex character of analyzing the results obtained. However, application of information technologies enables using static materials in a new way including indices of power consumption, costs, water intake, mining depth, the number of pumps and their capacity by synthesizing mathematical models as complicated objects through in-depth procession of static materials and substantiation of the obtained results. For the first time, there are used multifactor regressive models considering multicollinearity and non-linearity of pump capacity in order to study its influence on power costs by using the elasticity factor. Analysis of mathematical simulation results relevant to static materials and applying the algorithm of studying dependency of the consumed power costs on pumps’ capacity reveals some critical values resulting in corresponding effects. The authors recommend to apply the elaborated algorithm to conducting corresponding calculations by for mining enterprises to monitor formation of the strategy of providing energy efficiency under time-of-day electricity rates.

Моделювання оптимального руху електромобіля з асинхронним електроприводом на схилах і підйомах дороги

There has been made the simulation of the optimal motion model of an electric car with a traction induction motor on the slopes and rises of the road, performing optimization on the criterion of minimum electrical losses, using the models synthesized by the authors, based on a mathematical model of the magnetization curve in the form of inverse hyperbolic sine and the variational variant of the Lagrange`s indefinite multipliers method. The simulation for three variants of conditions of the set distance passing for the allocated time, processes of movement of the electric car on the descent, and the ascent of various degrees of inclination with comparison to conditions of movement by a horizontal road section are carried out. Obtained results have been analyzed and it has been found that as for car motion by horizontal road section, that was modeled in previous authors works, the control law presented by the received mathematical model of optimal motion will provide the electric car's drive battery power consumption optimization on descent and ascent movement conditions. The simulation data and their analysis confirm the theoretical statements and the motion optimization efficiency of the obtained mathematical models also for the movement on the slopes and rises of the road.