Optimize Power Consumption Research Articles

Fuzzy Power Converter for Rainfall Detection System

The efficient utilization of energy resources is of paramount importance in modern society, and the demand for intelligent systems to optimize power consumption is steadily increasing. This paper presents a novel approach to address this need by introducing a Fuzzy Power Converter (FPC) for a Rainfall Detection System (RDS). The FPC is designed to enhance the performance of the RDS by adapting the power supply based on the prevailing weather conditions, thereby ensuring energy efficiency. This work outlines the development and implementation of the FPC, demonstrating its capability to significantly reduce power consumption in RDS applications. The FPC employs fuzzy logic control to intelligently modulate the power supply to the RDS, thereby improving energy efficiency and prolonging the system's lifespan. Experimental results in MATLAB showcase the effectiveness of the proposed FPC, indicating substantial energy savings and reduced environmental impact.

Using machine learning methods to forecast the number of power outages at substations

Forecasting in the energy sector is of great importance for suppliers and for consumers. Optimum power consumption depends on many factors. Due to natural or any other external conditions, accidents are possible. In order to minimize emergency consequences, it is necessary to be prepared for possible outages in advance in order to reduce the time for their elimination and decision-making. This article considers the problem of forecasting power outages at substations. The enterprise provided a summary table of outages at substations due to natural disasters on specific days. To solve the problem, a machine learning method was chosen – binary classification. Five different algorithms were considered. The models were tested on data from the first half of 2022. The most effective algorithm for 20% of the test sample was the binary classification algorithm using generalized additive models (GAM). This algorithm is also one of the best with a sample of 50%. A model has been prepared for further use in predicting the probability of outages at the enterprise. The model can be used in other organizations; for this, it is first necessary to train the model on the data of the corresponding region.

Self-Supervised Online and Lightweight Anomaly and Event Detection for IoT Devices

The increasing number of Internet of Things (IoT) devices and low-cost sensors have facilitated developments in large-scale monitoring applications. However, the accuracy of low-cost sensors remains questionable. Monitoring applications, such as environmental monitoring, try to detect “interesting” data points or patterns, known as anomalies, that do not conform to the norm. These include erroneous data caused by hardware failures or malicious attacks, and nonerroneous data due to unexpected phenomenon, caused by events, such as unexpected high traffic volume. Traditionally, IoT devices collect raw data and periodically upload them to the cloud for processing, which includes anomaly detection. However, the increasing processing capabilities of IoT devices have made the on-device anomaly detection possible in an online and real-time manner. In this article, multivariate long short-term memory (LSTM) autoencoder is proposed for anomaly and event detection in IoT devices. In addition, the proposed approach integrates smart inference, based on a game-theoretical approach, which dynamically changes the period of detection based on the stability of the data, aiming to optimize power consumption and elongate the lifetime of the device. The proposed anomaly and event detection model was simulated and implemented on an STM32H743 Nucleo board, and results show the robustness of the model regardless of the number of anomalies and events present.

Effect of Interchanging Red and Blue LED Irradiation Sequence on Growth of Lettuce

Light-emitting diode (LED) is commonly employed as supplementary light sources to enhance plant growth and quality. Nowadays, a combination of red (R) and blue (B) LED sources is mainly used since it can boost plant photosynthesis rate and biomass. Generally, both light sources are continuously irradiated for 16–18 hours for plant photoperiod, which may result in high electricity costs. Therefore, interchanging use of red and blue LED irradiation is introduced to reduce the use of electricity, thus minimising the operating cost. In this study, a growth chamber with red and blue LED was constructed and its intensity and power distribution were assessed. The effect of blue and red LED irradiation sequence on plant growth parameters and chlorophyll content was examined. Three light treatments over a period of 16 hours (RB16 - 16 hours constant simultaneous (R) and (B) light, R4/B4 - 4 hours interval interchanging R and B light and R8/B8 - 8 hours interval interchanging R- and B-light) were exposed to lettuce under controlled room temperature. Results revealed that the different light treatments influence lettuce plant morphology such as plant height, leaf width and leaf number. However, the results are similar for fresh and dry mass, and chlorophyll concentration. It is found that, both plant height and leaf width were the highest under R4/B4 and RB16, respectively. Such findings would be extremely valuable information on the use of interchanging LED irradiation in plant development for commercial production, resulting in optimized power consumption.

A novel power consumption optimization framework in 5G heterogeneous networks

The fifth-generation (5G) mobile networks have the capacity to handle the dynamic traffic demands of the user equipment (UE). One approach is the dense deployment of small base stations (s-BSs), which can control the dynamic traffic demands. We consider s-BSs which are interconnected through the mmWave backhaul (BH) link to transfer traffic from the s-BS to the core network through the macro base station (MBS). In this setting, the network power consumption is affected by the way UEs are connected to the base stations and the traffic routes through the BH links. The main objective of this paper is to minimize the power consumption of the heterogeneous network (HetNet) with the intelligent backhauling and s-BS/BH link sleeping (IBSBS) framework. The proposed framework provides the minimized power consumption in HetNets through UE association, backhauling, and sleep s-BS/BH links. The UE association and backhauling uses a heuristic function based intelligent backhauling algorithm to assess the minimum power consumption from the s-BS to the MBS while considering the power and capacity constraints of the network. The load sharing based s-BS sleeping algorithm dynamically changes the states (active/sleep) of the s-BSs according to their loads without compromising UE demands. Different distributions of UEs and different data rates over the HetNets architecture are considered for performance evaluation. The evaluation results of the proposed framework outperform the state-of-the-art algorithms in terms of network energy efficiency, power consumption, and the number of active s-BSs/BH links.

A green energy circular system with carbon capturing and waste minimization in a smart grid power management

Sustainable electricity production is an energy-efficient strategy aimed at both economic progress and environmental conservation. The power generation sector is the major contributor to global emissions and waste. The scope of circular economy in the power production sector with investments in waste minimization and emission reduction was not discussed in previous studies. In this context, a circular sustainable smart electric supply chain system is introduced in this paper with an aim of maximizing the profit with the minimum amount of emissions and waste generated from the power generation units. The power plant in this system consists of four power generation units that generate electricity from coal, waste, wind, and solar plants. A smart grid management system is used in this system for an efficient power supply according to the demands of the customer, and it enhances the system to distribute the power from renewable energies. In this system, the carbon dioxide from the thermal plants is captured and converted to natural gas to reduce emissions. Municipal and industrial waste is transformed into composite fuel to generate electricity. The profit for each unit and the customer’s demand for electricity depends on the circularity index. Linear demand versus linear and logistical cases of unit profit was considered. This study theoretically and numerically maximizes the profit of the system with optimal power consumption, circularity index of electricity, and green and waste minimization investment as per the carbon cap policy. To find the optimal strategies for maximum profit, an algorithm was developed. Sensitivity analysis is done to determine the fluctuations in the profit, emission cost, waste cost, circularity index, and power consumption due to the variations in parameters. The wise allocation of power generation from each unit and reduce energy loss by installing efficient storage devices will result in a higher profit. A system with the high efficacy of green techniques to reduce emissions, a smart grid to minimize waste, higher taxes to regulate emissions, and a rise in the amount of production of electricity from renewable sources will result in a greener power system that gains more profit than conventional systems. The key findings state that electric supply chain systems can obtain a good profit in a more sustainable manner than conventional systems.

Research on Low Power BIST Based on LFSR Reseeding

With the increasing scale and complexity of the internal circuit, the function of the chip is more powerful, but it will bring serious problems to the test of the chip. The internal power consumption of the chip in the test mode is much higher than that in the normal working mode, especially in the process of built-in self-test, the excessive power consumption will damage the circuit under test and lead to the failure of the chip. The low power test vector generation technology reduces the test power by preprocessing the test vector set. However, the modification of the test vector set results in the low failure coverage in the test process. LFSR replaying technology is a common method of generating test vectors in built-in self-test. It can improve the coverage of test faults by loading test vector seeds into linear feedback shift register. However, while improving the fault coverage, the technology will generate high test power consumption in the circuit under test. In design for testability (DFT), it is a hot topic to generate low-power test vectors by combining LFSR reseeding technology with low-power test vector generation technology. Aiming at the problem of high power consumption caused by test vectors in built-in self-test, this paper proposes a low power test vector generation method based on LFSR reseeding. On the basis of studying the influence of test vector on dynamic test power consumption, the linear correlation between test vector seed and test vector is analyzed deeply. A model of dynamic test power consumption optimization based on Hamming distance sorting test vector seed is proposed to realize the design of low-power test vector seed generation algorithm. Combined with LFSR reseeding technology, a low-power test vector generator based on test vector seed sorting is designed. The simulation design of test vector generator is based on ISCAS85 and ISCAS89. The experimental results show that the total number of test vector seed storage bits is reduced by 64.39%, the average fault coverage is 97.42%, the average area overhead is 4.32%, and the dynamic test power consumption is reduced by 44.21%. Compared with other schemes, the proposed low-power test vector generation technology based on LFSR reseeding has some comprehensive advantages in reducing the number of seed storage bits, improving fault coverage, reducing circuit area overhead and reducing power consumption.

Enhancing Asset Security by Integrating Internet of Things on Non-Powered Assets

Rapid proliferation of the Internet of things (IoT) has helped solve a myriad of problems across different sectors. Whilst powered assets rely on their own power source to enhance asset monitoring, a need exists to develop an asset security system for non-powered assets. Since an IoT device has network layers, it can be used with the physical layer to solve this problem. As a result, a method of enhancing asset security with IoT was implemented. A GSM chip for SMS and server connection was used to communicate the battery status and location information obtained from a GPS chip. A method of detecting tamper on the device was implemented through infrared sensors. A microcontroller was the heart of the system as it interfaced with all other devices to form the IoT system. The result of the study was an efficient prototype of an IoT asset tracking device which communicates through SMS and it logs location data to a remote server through GPRS connection. There is a room for improvement in terms of optimizing power consumption to lengthen the duration before a recharge is required.

Optimizing power consumption during fattening of young animals

Optimizing power consumption during fattening of young animals

Preparation of polyhydroxyalkanoates using <i>Pseudomonas helmanticensis</i> in non-sterile media containing glycerol and sodium dodecyl sulfate

Biosynthetically-produced Pseudomonas poly-3-hydroxyalkanoates (PHAs) are a promising substitute for conventional plastics. Costs involved with the production of PHAs can be reduced by optimizing power consumption, which can be achieved using nutrient media without preliminary steam sterilization. Cultivation of Pseudomonas bacteria resistant to sodium dodecyl sulfate (SDS) on SDS-containing non-sterile media yields a biomass consisting predominantly of a PHA producer. SDS plays the role of an antimicrobial agent that inhibits the growth of foreign microorganisms. In this work, an SDS-resistant culture of Pseudomonas helmanticensis and media containing glycerol and SDS were used. The concentrations of carbon (glycerol) and nitrogen sources were optimized using an experiment performed according to a central composite rotatable design. The concentration of substrate C and the C/N ratio between the glycerol and nitrogen content were varied. The dependence of the degree of substrate conversion in PHA on C and C/N was derived in the R programming environment. The constructed model adequately describes the experimental data at a significance level of 0.05 (adequacy variance of the regression equation 4.1×10-2; R2 =0.98). According to the constructed model, the conversion of glycerol to PHA equals 6.9±0.4%. Under optimized conditions (0.61 g/L nitrogen source; 8.4 g/L glycerol; 96 h), P. helmanticensis converts 7.0% of the substrate to PHA with an average monomer unit length. Using a 16S rRNA metagenomic assay, the proportion of foreign bacteria in P. helmanticensis cultures on non-sterile media containing 0.5 g/L SDS was shown to be 2%.

Cloud Data Centre Energy Utilization Estimation

Due to the growth of the internet and internet-based software applications, cloud data center demand has increased. Cloud data centers have thousands of servers that are 24×7 working for users; it is the strong witness of enormous energy consumption for the operation of the cloud data center. However, server utilization is not remaining the same all the time, so, from an economic feasibility point of view, energy management is an essential activity for cloud resource management. Some well-known energy management techniques for cloud data centers generally used are dynamic voltage and frequency scaling (DVFS), dynamic power management (DPM), and task scheduling-based techniques. The present work is based on an analytical approach to integrating resource provisioning with sophisticated task scheduling; the authors estimate energy utilization by cloud data centers using iDR cloud simulator. The work is intended to optimize power consumption in the cloud data center.

Federated learning for energy constrained devices: a systematic mapping study

Federated Machine Learning (Fed ML) is a new distributed machine learning technique applied to collaboratively train a global model using clients local data without transmitting it. Nodes only send parameter updates (e.g., weight updates in the case of neural networks), which are fused together by the server to build the global model. By not divulging node data, Fed ML guarantees its confidentiality, a crucial aspect of network security, which enables it to be used in the context of data-sensitive Internet of Things (IoT) and mobile applications, such as smart Geo-location and the smart grid. However, most IoT devices are particularly energy constrained, which raises the need to optimize the Fed ML process for efficient training tasks and optimized power consumption. In this paper, we conduct, to the best of our knowledge, the first Systematic Mapping Study (SMS) on Fed ML optimization techniques for energy-constrained IoT devices. From a total of more than 800 papers, we select 67 that satisfy our criteria and give a structured overview of the field using a set of carefully chosen research questions. Finally, we attempt to provide an analysis of the energy-constrained Fed ML state of the art and try to outline some potential recommendations for the research community.

An Improved Sparrow Search Algorithm for Solving the Energy-Saving Flexible Job Shop Scheduling Problem

Due to emerging requirements and pressures related to environmental protection, manufacturing enterprises have expressed growing concern for adopting various energy-saving strategies. However, environmental criteria were usually not considered in traditional production scheduling problems. To overcome this deficiency, energy-saving scheduling has drawn more and more attention from academic scholars and industrial practitioners. In this paper, an energy-saving flexible job shop scheduling problem (EFJSP) is introduced in accordance with the criterion of optimizing power consumption and processing costs simultaneously. Since the classical FJSP is strongly NP-hard, an Improved Sparrow Search Algorithm (ISSA) is developed for efficiently solving the EFJSP. In the ISSA, a Hybrid Search (HS) method is used to produce an initial high-quality population; a Quantum Rotation Gate (QRG) and a Sine–Cosine Algorithm (SCA) are integrated to intensify the ability of the ISSA to coordinate exploration and exploitation; the adaptive adjustment strategy and Variable Neighborhood Search (VNS) are applied to strengthen diversification of the ISSA to move away from local optima. Extensive computational experiments validate that the ISSA outperforms other existing algorithms in solving the EFJSP due to the advantages of intensification and diversification mechanisms in the ISSA.

Simulation of Soil Cutting and Power Consumption Optimization of a Typical Rotary Tillage Soil Blade

The rotary tillage knife roller, as one of the typical soil-touching parts of the tillage equipment cutting process, is in direct contact with the soil. During the cutting process, there are problems related to structural bending, deformation, and high power consumption, caused by impact and load, and it is difficult to observe the micro-change law of the rotary tillage tool and soil. In view of the above problems, we took the soil of the cotton experimental field in Shihezi, Xinjiang, and the soil-contacting parts of the rotary tillage equipment, specifically the rotary tiller roller, as the research subject. Using the finite-element method (FEM) to simulate the structure of the rotary tiller with different bending angle parameters, we obtained its average stress and deformation position information, and obtained a range linear relationship between the bending angle and the structural performance of the rotary tiller tool. Using discrete element method (DEM)-based simulation to build the corresponding contact model, soil particle model, and soil–rotary tillage knife roll interaction model to simulate the dynamic process of a rotary tillage knife roll cutting soil, we obtained the change rules of the soil deformation area, cutting process energy, cutting resistance, and soil particle movement. By using the orthogonal simulation test and the response surface method, we optimized the kinematic parameters of the rotary tiller roller and the key design parameters of a single rotary tiller. Taking the reduction of cutting power consumption as the optimization goal and considering the influence of the bending angle on its structural performance, the optimal parameter combination was obtained as follows: the forward speed was 900 m/h, the rotation speed was 100 rad/min, the bending angle was 115°, and the minimum power consumption of the cutter roller was 0.181 kW. The corresponding average stress and deformation were 0.983 mm and 41.826 MPa, which were 15.8%, 13%, and 7.9% lower than the simulation results of power consumption, stress, and deformation under the initial parameter setting, respectively. Finally, the effectiveness of the simulation optimization model in reducing power consumption and the accuracy of the soil-cutting simulation were verified by a rotary tilling inter-field test, which provided theoretical reference and technical support for the design and optimization of other typical soil-touching parts of tillage and related equipment, such as disc harrow, ploughshare, and sub-soiling shovel.

A Power-Aware Real-Time System for Multi-Video Treatment on FPGA with Dynamic Partial Reconfiguration and Voltage Scaling

As the energy consumption is an evaluating factor for System-On-Chip (SOC) design, this paper presents a power-aware architecture for a real-time multi-video system on FPGA. This architecture aims to optimize power consumption for a multi-video system on ARM-based architectures. The proposed architecture uses dynamic reconfiguration and voltage scaling to create a power-aware system for real-time multi-video processing with minimal power dissipation. Dynamic partial reconfiguration was used to optimize the utilization of resources and reduce dynamic power consumption. Voltage scaling was also used to optimize dynamic power consumption, by configuring the blocks to use the minimum necessary voltage for normal operating conditions. The proposed architecture focused on the Zynq platform. The results showed power savings of up to 70% concerning performance and real-time constraints.

An efficient bi-objective approach for dynamic economic emission dispatch of renewable-integrated microgrids

To overcome the challenges of conventional power systems, such as increasing power demand, requirements of stability and reliability, and increasing integration of renewable energy sources, the concept of microgrids was introduced and is currently one of the most important solutions for solving the mentioned problems. Generally, microgrids have two operating modes, namely grid-connected and islanded modes. Based on the literature and its unique characteristics, the islanded mode is more challenging than the other one. In this paper, a new self-adaptive comprehensive differential evolution (SACDE) algorithm is proposed for solving economic load dispatch (ELD) and combined economic emission dispatch (CEED) problems, achieving optimal power consumption in isolated microgrids. Initially, SACDE is employed for solving the ELD problem as a single-objective function, meaning that the operational cost is just considered as the objective function, and thereby, the resources are scheduled accordingly. Then, a multi-objective platform based on SACDE is also proposed to solve the CEED problem. It means two objective functions, including operational cost and emission, are simultaneously optimized. For evaluating the performance of the proposed method, three different scenarios under various cases are considered. According to the results, when SACDE is employed to solve the single objective function (cost minimization) problem, it has better performance than other methods. In terms of the bi-objective scheme (cost and emission minimization), SACDE is significantly superior to the price penalty factor technique which is frequently used in previous studies.

Distribution Voltage and Output Power Sharing Control for DC Microgrids using Fuzzy Control and Gain-Scheduling

Abstract: A dc microgrid has gained popularity in recent years due to dc sources such as solar panels, fuel cells, and batteries. Interconnections can be made without AC to DC conversion, which improves system efficiency. Furthermore, when a utility grid is experiencing voltage sags or blackouts, A continuous supply of high-quality power is possible. Parallel operations have been proposed with several types of droop controls and microgrids, including DC and AC, were also used in some cases. Sharing the storage unit outputs via a gain scheduling scheme would result in unbalanced storage energy. Therefore, this research integrates fuzzy control with gain-scheduling techniques to create a new voltage control system that can both share energy and optimize power consumption. A microgrid consists of a variety of distributed energy sources used for the energy storage system. Fuzzy control is used to maintain power quality. Controlling fuzzy is based on different rules with the goal of maintaining constant droop.

Study of Energy-Efficient Optimization Techniques for High-Level Homogeneous Resource Management

Resource management efficiency can be a beneficial step toward optimizing power consumption in software-hardware integrated systems. Languages such as C, C++, and Fortran have been extremely popular for dealing with optimization, memory management, and other resource management. We investigate novel algorithmic architectures capable of optimizing resource requirements and increasing energy efficiency. The experimental results obtained with C++ can be extended to other programming languages as well. We emphasize the inherent drawbacks of memory management operators. These operators are intended to be extremely generic in their application, just as the concept of dynamic memory is. As a result, they are unable to take advantage of the various optimization techniques and opportunities that specific use cases present. Each source code file is modeled after its own distinct memory usage pattern, which can be used to speed up memory management routines. Such concepts are frequently time-consuming and costly to implement; consequently, they are not the primary concern of application developers, as they require manual development and integration. We intend to address this gap by providing a suite of memory management algorithms that enable dramatic performance improvements at the source code level while allowing for seamless integration across multiple use cases. The techniques have been evaluated on several performance parameters, and results have been presented. In this paper, we have compared a variety of memory allocation techniques and compared their space and energy efficiency requirements. Three variants of SSDAM, SSDAM-E, and DLLOM strategies have been evaluated and compared against the base performance of new and delete operators. SSDAM-E, SSDAM with new delete operators, and DDLOM improve the memory consumption by the factors of 8.01, 7.0, and 4.0, respectively. In the worst case, SSDAM-E gave an average running time of 5.650 sec faster than the DLLOM average time of 7.496 sec. As far as energy efficiency is considered, SSDAM-Original and SSDAM-E-Original attain 100%, in comparison with the base efficiency of 12.48% characterized by new/delete operators.

Experimental and numerical analysis of hovering multicopter performance in low-Reynolds number conditions

Unmanned Aircraft Systems (UAS) are state of the art in the aerospace industry and are involved in many operations. Although initially developed for military purposes, commercial applications of small-scale UAS, such as multicopters, are abundant today. Accurate engineering tools are required to assess the performance of these vehicles and optimize power consumption. The thrust and power curves of the rotors used by small-scale UAS are essential elements in designing efficient aircraft. The scarcity of experimental data and sufficiently accurate prediction models to evaluate rotor aerodynamic performance in the flight envelope are primary limitations in UAS science. In addition, for small-scale rotors at usual rotation rates, chord-based Reynolds numbers are typically smaller than 100,000, a flow regime in which performance tends to degrade. In this paper, experimental data on small-scale multicopter propulsion systems are presented and combined with a Computational Fluid Dynamics (CFD) model to describe the aerodynamics of these vehicles in low Reynolds numbers conditions. We use the STAR-CCM+ software to perform CFD simulations adopting both a dynamic-grid, time-accurate analysis and a static-grid, steady-state technique that solves the Navier-Stokes equations in a suitable framework. Comparing numerical simulation results on a conventional UAS propeller with related experimental data suggests that the proposed approach can correctly describe the thrust and torque coefficients in the range of Reynolds numbers characterizing the UAS flight envelope.

Design and Installation of High Capacity Centralized Coolant Pump for Optimization of Power Consumption using VFD Controller

This paper discuss the pumping system study involved detailed discussions with Maintenance Engineers. The flow, pressure and power measurements for the pumps were done with the advanced instruments available with GIN. A total of around 2 Pumps were studied during the audit period. We find that there is a good amount of saving potential available on replacement of the pumps with Energy Efficient pumps call it a variable speed drive, adjustable frequency drive, adjustable speed drive, AC drive, inverter drive or something else, a variable frequency drive (VFD) can reduce the energy usage of an electric motor up to 90%, extend motor life, deliver a payback in as little as 6 months and often qualify for energy-saving financial incentives. Since HVAC consumes the most energy in a building and motors account for the majority of HVAC energy usage, using VFDs on pump and fan motors can result in substantial energy cost reductions.