Power Management Techniques Research Articles

Low Network Power Challenges in IoT-Based Applications in Smart Cities

The emergence of Internet of Things (IoT) technologies has led to the development of smart cities, potentially improving urban life through efficient and effective use of resources. The success of IoT-based applications in smart cities is contingent on several factors, including network power challenges. Deploying IoT-based applications in smart cities requires a reliable and efficient network infrastructure to ensure smooth and uninterrupted data communication. One of the significant challenges faced in this regard is low network power, which can significantly impact the performance of IoT devices and, consequently, the entire smart city system. This article addresses the issue of low network power in IoT-based applications in smart cities. It investigates the causes of this problem and the potential solutions that can be adopted to mitigate its impact. The article also highlights the importance of network power management in the context of IoT-based applications in smart cities. By implementing effective network power management techniques, it is possible to optimize the performance of IoT devices and extend their battery life, thus ensuring the overall efficiency and sustainability of the smart city system. The article concludes by emphasizing the need for continuous research and development to overcome the challenges of low network power and further enhance IoT technology's capabilities in smart cities.

ANN-based Maximum Power Point Tracking Technique for PV Power Management under Variable Conditions

Due to the increasing energy demand, traditional fossil fuels are gradually decaying day by day as analyzed by many researchers. Fossil fuels are not sufficient to fulfil the requirement of energy demand and it also produces greenhouse gas emissions. In this regard, worldwide research is going on related to renewable energy sources (RESs) like solar photovoltaic (SPV), wind turbines, fuel cells etc. The source of SPV is plentiful and environment friendly which converts solar radiation to non-linear electrical power. This power is not suitable for a stable system. Therefore, the maximum power point tracking (MPPT) controller is required to find the optimum maximum power point (MPP) to the load. The MPPT technology regulates the duty-cycle in favour of the DC-DC converter to continuously obtain maximum power from the SPV arrays. In the past few decades, the learning of MPPT techniques has made substantial progress in the RESs. This research article analyzes the performance of various MPPT techniques in the proposed SPV framework. The main investigation is to assess different MPPT techniques to optimize power from the SPV framework. The artificial neural network (ANN)-MPPT method has been observed to be more effective in output power production and transient response about the MPP than conventional perturb and observe (P&O)-MPPT and fuzzy logic controller (FLC)-MPPT technology.

A CNNbased energy management strategy for a Hybrid energy storage system in electric vehicles

When approaching a large-scale performance, the choice, size, and administration of an Energy Storage System (ESS) for an Electric Vehicle (EV) are crucial. As the peak-to-average power demand ratio is relatively large, particularly for an urban ride that is frequently marked by rapid deceleration as well as acceleration, the complementary characteristics of the battery and Ultra-Capacitor (UC)render this arrangement a viable Hybrid energy storage system (HESS) for EV. Enhanced dynamic responsiveness, increased miles per charge, and extended battery life provided by the HESS increase the Electric Vehicle (EV’s) effectiveness. The primary objective of the study that has been suggested is to create a smart Energy Management Strategy (EMS) for EVs. A battery package and a properly sized Ultra-Capacitor (UC) together give the required high power along with energy density. This research proposes a CNN-based power management technique to aid in efficient EMS. Additionally, by adjusting the Convolution Neural Network (CNN) classifier’s weights through Improved Honey Badger Optimization (IHBO), the adaptive approach of the Standard HBO Algorithm, the performance of the classifier is improved. In MATLAB, the suggested CNN-based model for BESS EM is simulated and experimental assessment and analysis are done in terms of converter current and battery SoC. By contrasting the suggested approach against several standardized models, the performance analysis of the proposed work is assessed to validate its performance.

Dynamic Power Allocation for Downlink NOMA

Dynamic Power Allocation for Downlink Non-Orthogonal Multiple Access (NOMA) is a critical research area in wireless communication systems. This study focuses on implementing and evaluating dynamic power allocation strategies in the context of downlink NOMA. We compare the performance of dynamic power allocation with fixed power allocation schemes to assess their impact on system throughput, interference management, and overall efficiency. Through extensive simulations and analysis in this paper using MATLAB, we demonstrate a comparison between dynamic power allocation and fixed power allocation, and the advantages of dynamic power allocation in adapting to changing channel conditions and user requirements, leading to improved system performance. Our results show the data rate and outage probability which provides valuable insights into the benefits of dynamic power allocation in downlink NOMA systems and highlight the importance of adaptive power management techniques in enhancing wireless communication networks.

Reactive Power Management in Loss and Voltage Control via Integrated Clonal Accelerated Evolutionary Programming

Optimal Reactive Power Dispatch (ORPD) plays a crucial role in maintaining voltage stability, enhance power transfer capability, improve system efficiency, regulate voltages, manage congestion, facilitate renewable energy integration, and achieve cost savings in power systems. This paper presents a new optimization technique for optimal reactive power management in power transmission system, called Integrated Clonal Accelerated Evolutionary Programming (ICAEP) algorithm to optimize the reactive power to be dispatched by the generator buses under 3 scenarios with cases. The ICAEP was developed based on hybridization of the traditional Artificial Immune System (AIS) and Evolutionary Programming (EP). The two cases demonstrate the effect of reactive load increment at the chosen load buses; while the three scenarios represent the ORPD schemes which indicate the involvement of generator buses either 3 generators, 4 generators or 5 generators. Implementation of ICAEP outperformed AIS and EP in both objective functions either in minimum voltage maximization or power loss minimization, involving all the cases under the three scenarios. The performance of the ICAEP is evaluated on the IEEE 30-bus test system. The results would be beneficial to power system operators and planning expansion and knowing the status of their systems in their utilities. The developed optimization engine is also robust and feasible for further optimization problem solving initiatives.

Learning-Based Cloud Server Configuration for Energy Minimization Under Reliability Constraint

Cloud computing has attracted wide attention from both academia and industry, since it can provide flexible and on-demand hardware and software resources as services. Energy consumption of cloud servers is the main concern of cloud service providers since reducing energy consumption can bring them a lower operation cost (and hence a higher profit) and alleviate carbon footprints to the environment. Typically, the common power management techniques for enhancing energy efficiency would make cloud servers more vulnerable to soft errors and hence adversely impact the quality of services. Thus, reliability cannot be ignored in the design of methodologies for improving the energy efficiency of cloud servers. In this article, we aim to minimize the energy consumption of cloud servers under the soft-error reliability constraint by configuring the size and speed of servers. Specifically, we first derive the expected reliability based energy consumption of cloud servers to formulate the reliability-constrained energy minimization problem. We then leverage the reinforcement learning technique to obtain an optimal server configuration solution that maximizes system energy efficiency while maintaining the system reliability constraint. Finally, we perform extensive simulation experiments to analyze the relationship between system energy consumption and server configuration under varying arrival rates and execution requirements of service requests. Comparative experiments are also performed to validate the efficacy of the proposed learning-based server configuration scheme. Results show that compared to a benchmark method, the energy saved by the proposed scheme can reach up to 31.5%.

TELECOMMUNICATIONS ENERGY EFFICIENCY: OPTIMIZING NETWORK INFRASTRUCTURE FOR SUSTAINABILITY

The global telecommunications industry is facing significant challenges due to the rapid growth in data traffic and the growing environmental concerns associated with these networks. This paper explores strategies for optimizing network design and operation towards a greener, more sustainable future. Key challenges include the environmental impact of energy consumption, which accounts for 2-3% of global electricity consumption. The paper focuses on optimizing network design and operation, exploring energy-saving techniques and innovations, and revealing advanced network management optimizations. It also explores the transformative role of renewable energy sources like solar, wind, and geothermal power in mitigating the carbon footprint of telecommunications networks. The paper evaluates on-site renewable energy generation and power purchase agreements (PPAs) as integral components of a sustainable energy strategy. The paper highlights the potential of a holistic approach to telecommunications energy efficiency, including deploying energy-efficient hardware, using green data centers equipped with advanced power management techniques, implementing intelligent power management systems, and optimizing network traffic flow. Network management optimization involves regular evaluation and optimization of infrastructure, eliminating redundancy and underutilized equipment, and employing software-defined networking (SDN) and Network Function Virtualization (NFV). Renewable energy integration is also discussed, with on-site renewable energy generation and power purchase agreements emerging as powerful tools in reducing dependence on traditional grids and fossil fuels. The integration of AI, machine learning, and blockchain technology is also explored. By implementing these strategies, the telecommunications industry can contribute significantly to global environmental goals while securing enduring economic benefits. The implications of a sustainable telecommunications infrastructure extend beyond immediate energy savings, encompassing enhanced brand image, cost-effectiveness, and compliance with evolving regulations.
 Keywords: Telecommunications, Energy Efficiency, Network Design, Green Networks, Network Management, Emerging Technologies.

A novel energy optimization framework to enhance the performance of sensor nodes in Industry 4.0

AbstractIndustry 4.0 is a term used to refer to the fourth industrial revolution, characterized by the introduction of new technologies, such as the Internet of Things, Big Data, and artificial intelligence (AI). As the number of connected devices in industrial settings grows, energy optimization of such sensors becomes increasingly essential. This paper proposes an energy optimization framework for sensor nodes in Industry 4.0. The framework is based on energy efficiency, energy conservation, and energy harvesting principles. It is designed to optimize the energy consumption of sensor nodes while maintaining their performance. The framework includes dynamic power management, scheduling, and harvesting techniques to reduce energy consumption while maintaining performance. In addition, the framework provides a comprehensive approach to energy optimization, including advanced analytics and AI to predict energy consumption and optimize energy use. The proposed model reached 96.93% sensitivity, 91.36% false discovery rate, 11.28% false omission rate, 90.12% prevalence threshold, and 91.24% threat score. The proposed framework is expected to improve the performance of sensor nodes in Industry 4.0, enabling increased efficiency and cost savings.

Design and Development of a University Outdoor Air Quality Monitoring System

An increase in air quality monitoring research using low-cost air quality sensors has been observed. Existing air quality monitoring systems provide real-time data on at most five primary pollutants, cover a short area, and could be made expandable. An air quality monitoring system utilizing a scalable, low power wide area network, centralized system, and low cost sensors is proposed. The nodes gather data on air pollutants and environmental factors. Data conversion, storage, and calculation of air quality indices were processed on the central subsystem and visualization through IoT cloud. The results suggest improvement in the design and integration of prediction models, battery, and power management techniques.

Advanced Control Technique for Optimal Power Management of a Prosumer-Centric Residential Microgrid

Advanced Control Technique for Optimal Power Management of a Prosumer-Centric Residential Microgrid

Failure Mode and Effect Analysis (FMEA) Approach Based on Avoidance of Aggregation Discrepancy

As a powerful forward-looking risk management technique, FMEA has been widely used for reliability and security analysis in engineering management fields. In practice, the implementation of FMEA involves the participation of multiple members with distinct knowledge, experience, and cultural background, thus, FMEA is a typical multicriteria group decision-making problem. However, the existence of the aggregation discrepancy issue in FMEA problems with group decision-making leads to collective evaluation conflicts that bring about decision bias for further risk management. This article proposes an AADFMEA method based on multiple-objective optimization to promote the accuracy and reliability of collective assessment information in FMEA members, in which the 10-point qualitative scale is used for evaluation. Additionally, the AADFMEA method responds to the different individual cognitions to a specific evaluation value from multiple members by constraining the risk prioritization to derive more reasonable collective risk evaluation information. Finally, two case studies are presented to show that the AADFMEA method has a good application value, and the simulation analysis was conducted to verify the effectiveness of the proposed AADFMEA approach.

Analytical review of operating system for Internet of things

A detailed comparison of operating systems designed for IoT devices based on their architecture, scheduling techniques, networking technologies, power and memory management techniques, and other functions required for IoT applications is carried out. Keywords Internet of things, operating system, wireless sensor networks, real-time operating system

A 65-nm Proactive Power Management Technique With Real-Time Machine Learning Engine for Droop Prediction and Mitigation on Microprocessors

A 65-nm Proactive Power Management Technique With Real-Time Machine Learning Engine for Droop Prediction and Mitigation on Microprocessors

A Prediction Model of Power Consumption in Smart City Using Hybrid Deep Learning Algorithm

A smart city utilizes vast data collected through electronic methods, such as sensors and cameras, to improve daily life by managing resources and providing services. Moving towards a smart grid is a step in realizing this concept. The proliferation of smart grids and the concomitant progress made in the development of measuring infrastructure have garnered considerable interest in short-term power consumption forecasting. In reality, predicting future power demands has shown to be a crucial factor in preventing energy waste and developing successful power management techniques. In addition, historical time series data on energy consumption may be considered necessary to derive all relevant knowledge and estimate future use. This research paper aims to construct and compare with original deep learning algorithms for forecasting power consumption over time. The proposed model, LSTM-GRU-PPCM, combines the Long -Short-Term -Memory (LSTM) and Gated- Recurrent- Unit (GRU) Prediction Power Consumption Model. Power consumption data will be utilized as the time series dataset, and predictions will be generated using the developed model. This research avoids consumption peaks by using the proposed LSTM-GRU-PPCM neural network to forecast future load demand. In order to conduct a thorough assessment of the method, a series of experiments were carried out using actual power consumption data from various cities in India. The experiment results show that the LSTM-GRU-PPCM model improves the original LSTM forecasting algorithms evaluated by Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) for various time series. The proposed model achieved a minimum error prediction of MAE=0.004 and RMSE=0.032, which are excellent values compared to the original LSTM. Significant implications for power quality management and equipment maintenance may be expected from the LSTM-GRU-PPCM approach, as its forecasts will allow for proactive decision-making and lead to load shedding when power consumption exceeds the allowed level

A Prediction Model of Power Consumption in Smart City Using Hybrid Deep Learning Algorithm

A smart city utilizes vast data collected through electronic methods, such as sensors and cameras, to improve daily life by managing resources and providing services. Moving towards a smart grid is a step in realizing this concept. The proliferation of smart grids and the concomitant progress made in the development of measuring infrastructure have garnered considerable interest in short-term power consumption forecasting. In reality, predicting future power demands has shown to be a crucial factor in preventing energy waste and developing successful power management techniques. In addition, historical time series data on energy consumption may be considered necessary to derive all relevant knowledge and estimate future use. This research paper aims to construct and compare with original deep learning algorithms for forecasting power consumption over time. The proposed model, LSTM-GRU-PPCM, combines the Long -Short-Term -Memory (LSTM) and Gated- Recurrent- Unit (GRU) Prediction Power Consumption Model. Power consumption data will be utilized as the time series dataset, and predictions will be generated using the developed model. This research avoids consumption peaks by using the proposed LSTM-GRU-PPCM neural network to forecast future load demand. In order to conduct a thorough assessment of the method, a series of experiments were carried out using actual power consumption data from various cities in India. The experiment results show that the LSTM-GRU-PPCM model improves the original LSTM forecasting algorithms evaluated by Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) for various time series. The proposed model achieved a minimum error prediction of MAE=0.004 and RMSE=0.032, which are excellent values compared to the original LSTM. Significant implications for power quality management and equipment maintenance may be expected from the LSTM-GRU-PPCM approach, as its forecasts will allow for proactive decision-making and lead to load shedding when power consumption exceeds the allowed level

Design Guidelines for Low Power Embedded Systems using Low Power Electronics

The design of Low Power Embedded Systems (LPES) and IoT products may include a variety of power management techniques, or they may incorporate sophisticated on-chip capabilities that assist in reduced power usage. The power management and low power consumption of embedded systems are also enabled by complex algorithms, and every Low Power System (LPS) may require a mix of approaches to avoid using extra battery power. There are several strategies we may use when creating an LPES that must be extremely power efficient while simultaneously offering the necessary degree of computational capability. It all depends on the design specifications that must be met. Then, if at all feasible, choose the appropriate low-power components from Low Power Electronics (LPE). After studying and reviewing multiple LPES real-time projects, we compiled a list of a few strategies we may use to approach low-power design and consumption for embedded systems. Following the analysis of numerous LPES real-time projects, we came up with a list of a few approaches to low-power design for embedded systems using LPE. There are additional benefits of LPES design. Less heat is generated through LPES, which is better for the environment. For 1000 LPES devices, one watt of power saved per device equals one KWH i.e. we can save 1 unit of electricity. Design with low power increases the component and system reliability. The embedded system's operating life is enhanced. In many instances, LPES designs may result in a reduction in production costs. The LPE components chosen are more affordable and inexpensive. Hence, the low-wattage power supply, LPES design is easier and less expensive.

The impact of the power management technique on the transient stability of the electrical network

The impact of the power management technique on the transient stability of the electrical network

Performance improvement through nonlinear control design and power management of a grid-connected wind-battery hybrid energy storage system

This paper presents a study on the application of nonlinear control and optimal power management techniques in a grid-connected wind energy conversion system with battery storage. The objective is to efficiently supply power to a load while considering performance constraints. The energy system under investigation comprises a wind turbine, battery, AC-grid, and load. Power is delivered to the load through various converters and a DC-link. However, the intermittent nature of wind energy and frequent load fluctuations can negatively impact the battery's lifetime and charging performance. To address these challenges, a nonlinear controller and a novel energy management system are designed. The energy management system generates multiple energy flow scenarios to achieve equilibrium in energy distribution between the load and energy sources. This equilibrium aims to minimize overall system costs, ensure DC-grid stability, and enhance power quality. Two distinct operation modes are identified: i) MPPT mode, which utilizes an optimizer to maximize power extraction, and ii) adaptive power point tracking (APPT) mode, which considers demand-driven production and the state of charge (SOC) of the storage system. The convergence of the closed-loop control is formally analyzed, and simulation results confirm the improved performance of the proposed multi-criteria design for energy management.

Modeling and Management of Hybrid System Photovoltaic-FC-Ultracapacitors Power Systems

This study shows an improved DC microgrid power management technique. The value of utilizing renewable energy has long been debatable, and this article suggests a typical DC microgrid because of the advantages it has over AC technology. A solar cell (PV), fuel cell (FC) and storage component - an ultracapacitor (UC)-make up the standard microgrid. The most important goal of the paper is to optimize the management strategy for the bus voltage with arranged power-sharing between these types of renewable energy that are used here. For variable solar irradiance and various loading conditions, the proposed management seeks to deliver high-quality energy to the load while taking into consideration the FC state. The ultracapacitor was prepared to furnish the transient period due to the FC's slow dynamics. To maintain the DC bus voltage steady in the face of load variations, a management algorithm is put into practice. To generate the references, the management controller relies on a differential flatness method. The electricity from the ultracapacitor controls the DC bus. Earlier studies showed that the circuit of the fuel cell was integrated with a standalone photovoltaic system and discovered that the fuel cell doesn't have the same properties. In the current research, observations were made on power quality elements like voltage, current, and power for energy storage systems. With several self-definition elements of the photovoltaic, fuel cell, and ultracapacitor, along with their parameters, simulation is used as the study method medium. The outcome demonstrates that since the ultracapacitor serves as the primary storage, the fuel cell integration has little impact on it, and only small changes in voltage and current were observed. Due to the fuel cell integration characteristic of providing the system a major perturbation, most perturbation from the system had been absorbed while in an ultracapacitor

Dynamic low power management technique for decision directed inter-layer communication in three dimensional wireless network on chip

3D ICs, a novel technology, might significantly impact multicore NoCs with hundreds or thousands of processing components on a single chip. Multiple 2D chips can be stacked vertically to create multiple active processing elements at various levels. Adding active device layers to 3D ICs can enhance system performance, increase functionality, and increase packing density. New architectural and IC technology advancements hinder energy-efficient design research. Achieving a balance between chip power and performance is crucial. This paper describes the “Dynamic Low Power Management Method in 3DWiNoC” (DLPM 3DWiNoC) architecture, which enables self-organized, centrally managed service management using Smart Master Agents. The approach utilizes SMA's ODA DD module for self-organized, centrally managed service management. To improve power regulation, data flow across vertical interconnects (TSVs) is reconfigured based on a dynamic evaluation of channel link use. SMA aims to reduce congestion by increasing connection utilization through high-frequency, bi-directional vertical channels via TSVs. The suggested system is modeled in MATLAB Simulink. Compared to 3D stacking, TSV stacking of vertical interconnects with the SMA method ensures low parasitic (latency and power) and higher bandwidth with higher vertical wire densities. Experimental results show that the proposed architecture decreases area overhead by 5%-7%, network latency by 12%-15%, and NoC power consumption by 15%-20% compared to the present multi-NoC design.