Power Management In Networks Research Articles

A firefly algorithm for power management in wireless sensor networks (WSNs)

In wireless sensor networks (WSNs), designing a stable, low-power routing protocol is a major challenge because successive changes in links or breakdowns destabilize the network topology. Therefore, choosing the right route in this type of network due to resource constraints and their operating environment is one of the most important challenges in these networks. Therefore, the main purpose of these networks is to collect appropriate routing information about the environment around the network sensors while observing the energy consumption of the sensors. One of the important approaches to reduce energy consumption in sensor networks is the use of the clustering technique, but in most clustering methods, only the criterion of the amount of energy of the cluster or the distance of members to the cluster has been considered. Therefore, in this paper, a method is presented using the firefly algorithm and using the four criteria of residual energy, noise rate, number of hops, and distance. The proposed method called EM-FIREFLY is introduced which selects the best cluster head with high attractiveness and based on the fitness function and transfers the data packets through these cluster head to the sink. The proposed method is evaluated with NS-2 simulator and compared with the algorithm-PSO and optimal clustering methods. The evaluation results show the efficiency of the EM-FIREFLY method in maximum relative load and network lifetime criteria compared to other methods discussed in this article.

Power management in green FFR-based heterogeneous cellular networks

The growth of mobile devices and their traffic volume make it necessary for mobile network operators to provide higher capacity. As traditional macrocell-based cellular networks could not support a massive number of mobile users, Heterogeneous Networks (HetNets) are welcomed as a coexistence of macro-cells and small-cells in 5G mobile networks. The main challenge of 5G HetNets is the management of co-tier and cross-tier interference. Fractional Frequency Reuse (FFR) is a suitable technique to mitigate the interference along with improving spectrum efficiency. On the other side, energy consumption has attracted much attention in 5G green cellular networks. Efficient power allocation and cell zooming/cell switch-off techniques are of the most important research directions in this area. However, the impact of cell zooming/switch-off on FFR-based HetNets has not been attended adequately. In this paper, a solution is presented which exploits dynamic adjustment of cell-center radius and efficient power allocation in FFR-based HetNets when conducting a cell switch-off/cell zooming. A heuristic algorithm is presented for cell-center radius adjustment problem. Also, for efficient power allocation, a multi-objective optimization problem is formulated and solved using NSGA-II algorithm Simulation results show that the proposed method reduces energy consumption compared to the baseline method and diminishes the overall interference in the system.

User-Centred Scalable Big Data Visualizer for Power Consumption Data in the Electrical Secondary Distribution Network

Establishment of Smart Grids for electrical power has been practised worldwide for the purpose of bringing reliability, security, and efficient management of electrical power networks for enhancing quality service to the society. Apart from the potential aim, smart grid has been a challenge to developing countries, including Tanzania from cost and technology point of view. Due to the use of many smart equipment involved in smart grids like Advanced Metering Infrastructure (AMI) equipped with smart meters and sensors, handling and managing big data has been a challenge. Among the challenges is the issue of visualizing the Big Data due to big volume generated with high velocity. This paper is developing a user-centered scalable big data visualizer for the electrical secondary distribution network by making use of design process model by Akanmu et al. (2017) and design activity framework by McKenna et al. (2014). The approach involves three phases: pre- development, development and post-development phase. The paper reviews several approaches in visualization and demonstrates effective big data visualization. The paper managed to visualize households’ units purchased against power consumed as well as balancing visualization of transformer phases.

Retracted: A Robust Integrated Approach for Optimal Management of Power Networks Encompassing Wind Power Plants

This paper basically concentrates on providing some significant steps for congestion management of the power systems based on an interval-based robust chance constrained transmission switching (IBRCC-TS) approach for decreasing the congestion of the system while increasing the robustness of the system against uncertainties of the wind turbines. However, the utilization of TS approach in the power system is a severe challenge, since there is no limitation over the switching rate during a certain timespan in the network as well as the power switches' failure uncertainty. Besides, the frequent switching by the TS method decreases the switch maintenance and puts the network reliability at risk. To overcome this problem, the reliability of the circuit breakers (CBs) is considered in the studied model aiming to determine the optimal number of the switching of the CBs. In addition, due to the nonlinear relation of CBs' reliability, a linearization technique is performed to linearize the CBs' reliability relation. Another step which is pursued in this paper is the utilization of energy storage system (ESS) to increase the reliability and decrease the congestion of the system. The effectiveness of the algorithm is compared with some of the well-known meta-heuristic algorithms and the proposed model is implemented on the IEEE 6-bus and 24-bus test systems. The obtained results proved the authenticity and validity of the work.

Design of power and resource management in OFDMA networks using sleep mode selection technique

This paper considers the consumption of power and resource metrics in small cell networks, which are enabled through the Sleep Mode Selection (SLS) function in Orthogonal Frequency Division Multiple Access (OFDMA) system. The proposed technique considers heterogeneous networks and operates under two different schemes that includes power management using SLS and resource management using a deactivation algorithm. Further, the limitation of low power consumption schemes leads to degradation of network performance in Orthogonal Frequency Division Multiple Access (OFDMA) networks. Hence, to improve the performance, a Semi-Markov SLS (SMD-SLS) decision making process is utilized in the present study. The performance is again improved with spectrum reuse maximization, where a small cell deactivation strategy identifies well the cells in sleep modes condition and eliminates them optimally for improved spectrum reuse. The performance is evaluated for the conventional non-sleeping mode cells and a benchmark technique. The results validates the performance of proposed model against improved resource and power management.

Improving Power and Resource Management in Heterogeneous Downlink OFDMA Networks

In the past decade, low power consumption schemes have undergone degraded communication performance, where they fail to maintain the trade-off between the resource and power consumption. In this paper, management of resource and power consumption on small cell orthogonal frequency-division multiple access (OFDMA) networks is enacted using the sleep mode selection method. The sleep mode selection method uses both power and resource management, where the former is responsible for a heterogeneous network, and the latter is managed using a deactivation algorithm. Further, to improve the communication performance during sleep mode selection, a semi-Markov sleep mode selection decision-making process is developed. Spectrum reuse maximization is achieved using a small cell deactivation strategy that potentially identifies and eliminates the sleep mode cells. The performance of this hybrid technique is evaluated and compared against benchmark techniques. The results demonstrate that the proposed hybrid performance model shows effective power and resource management with reduced computational cost compared with benchmark techniques.

A 3-Transistor Low Power Rectifier for Wideband RF Energy Harvesting with a Threshold Voltage Compensation Technique using 45 nm Technology

With the advent of modern wireless communication technology and increasing requirement of high speed network, network life-time is becoming a major area of concern. The need of network power management is gaining attention with the high data network in place and is making a paradigm shift towards green communication. Hence embedding the RF energy harvesting (EH) capability in a wireless network is becoming inevitable. To make RF EH a reality a high frequency rectifier is indeed indispensable along with other circuits in the system. The RF energy needs to be harvested from the available sources in the ambience. It is also seen that the current generation of RF sources radiates at a very low signal power. So, to successfully convert and store this energy, the rectifier must not only be able to provide a sufficiently higher percentage conversion ratio (PCE) but also be able to cater it at a lower range of signal power. This paper presents the design and analysis of a simplified 3-transistor high frequency rectifier. A threshold voltage compensation technique is also incorporated and it achieves a PCE upto 85% at -2dBm in its single stage implementation. This is observed to be one of the highest in-class efficiency as compared to recently reported designs. From the frequency response it is seen to exhibit wide band performance spanning almost all popular wireless bands. The dynamic power dissipation (DPD) is calculated to be 6.25pW at -2dB, whereas the leakage power (LP) is observed to be zero.

Formation of components of smart platform for the management of power systems and networks

Features of implementation of the Smart Grid concept in modern energy systems and networks with consideration of modern energy drivers, in particular, Internet energy, Internet of things, dispersed generation, development of customer services and aggregators of demand and supply for electricity, are considered. The basic components of the development of the electric power industry have been evaluated taking into account the requirements of energy transition, digitalization of the economy, in particular, business applications and information and communication systems. The elements of the modern technology platform Smart Grid are given. An assessment of the peculiarities of the formation of an intelligent platform for the management of energy systems and electric networks for individual energy companies is given.Features of construction of the Distributed Energy Management System (DEMS) are considered in detail. Features of DEMS are analyzed on the example of DEMS by Siemens. The planning and management functions of DEMS are considered. It is shown that DEMS controls provide control and monitoring of the power of all power generators, storage equipment, and flexible demand, as well as the ability to control to maintain a consistent electricity exchange profile.The basic functions of the Distribution Management System (DMS) are presented. The control systems developed by Oracle, ETAP and Schneider Electric have been analyzed as components of the customer-oriented power distribution management platform. Here is a description of the individual ETAP ADMS modules and the basic SE ADMS features developed by ETAP and Schneider Electric, respectively.

A Hybrid Prediction Model for Solar Radiation Based on Long Short-Term Memory, Empirical Mode Decomposition, and Solar Profiles for Energy Harvesting Wireless Sensor Networks

For power management in the energy harvesting wireless sensor networks (EH-WSNs), it is necessary to know in advance the collectable solar energy data of each node in the network. Our work aims to improve the accuracy of solar energy predictions. Therefore, several existing prediction algorithms in the literature are surveyed, and then this paper proposes a solar radiance prediction model based on a long short-term memory (LSTM) neural network in combination with the signal processing algorithm empirical mode decomposition (EMD). The EMD method is used to decompose the time sequence data into a series of relatively stable component sequences. For improving the prediction accuracy further by utilizing the current day solar radiation profile in one-hour-ahead predictions, similar solar radiation profile data were selected for training LSTM neural networks. Simulation results show that the hybrid model achieves better prediction performance than traditional prediction methods, such as the exponentially-weighted moving average (EWMA), weather conditioned moving average (WCMA), and only LSTM models.

Effective Resource Planning for Interference Aware Throughput Maximization of Wireless Body Area Networks

Cross-layer design is an in-depth analysis area in especially wireless Body Area networks and is necessary under energy and throughput limitations. To improve network performance the all the layer increases energy consumption; thus, the collaborations connecting these layers ought to be examined. This study develops an optimal interrupt aware connection planning and power management policy that assist a certain data rate on each connection in the network, including signal to interference and noise ratio(SINR) and highest transmission power controls at one another node or center.in each connection as link cost metrics Creating an interrupt-aware development problem by considering the power of each node in the routing path and the associated delay in each connection.the problem is to discover an scheduling link planning, power management and routing policy that maximizes the life span and throughput and minimizes the end-to-end delay as well as the energy link cost subject to definite controls. To provide mathematical guides to our schemes and relationships between the parameters. In our proposed work is an advance step towards our distributed joint planning, power management and routing algorithm for wireless Body Area networks utilizing cross layer design.

Artificial Neural Network power manager for hybrid PV-wind desalination system

In this paper, Artificial Neural Network (ANN) power management for a reverse osmosis desalination unit fed by hybrid renewable energy sources solar PV and wind turbine associated to battery bank as storage element is studied. The ANN power management system has as main objective to ensure the smooth transfer of the generated power by these sources under the variability and intermittency of the wind speed and the irradiation during 24 h of operation considering the limitation constraints of the RO unit and the need water profile. The design, the modeling and the control strategies of all the components are made in this study using Matlab/Simulink. The results show the ability of the ANN power manager to define the operating modes based on the proposed flow chart.

Neural network power management for hybrid electric elevator application

The present paper addresses the control and the power management of a hybrid system dedicated to an elevator application. In fact, the multi-source includes a photovoltaic generator as a main source supported by a battery-bank and a stack of super capacitors (SC). On the traction part, a permanent magnet synchronous motor (PMSM) is used to carry the elevator box. The power supervising mission is performed via a neural network (NN) routine trained with a frequency based strategy (FBS). The main objective of the applied control routines is to manage effectively the splits of the load demand. Therefore, they can provide the required power amounts in both steady-state and transient state, respecting the dynamic behavior of each source. Obviously, a fuzzy logic MPPT method has been applied to the PV side to permanently track the maximum power point through an adequate tuning of a boost converter regardless of the solar irradiance variations. Whereas, the controller of the DC–DC bidirectional converters of the battery and SC stack is based on the direct Lyapunov theory. To test the effectiveness of the proposed techniques, intensive numerical tests are done using MATLAB/Simulink Package. The obtained results prove the feasibility of the proposed approach, where the system switches smoothly between the operating modes.

Game Theoretic Solution for Power Management in IoT-Based Wireless Sensor Networks.

Internet of things (IoT) is a very important research area, having many applications such as smart cities, intelligent transportation system, tracing, and smart homes. The underlying technology for IoT are wireless sensor networks (WSN). The selection of cluster head (CH) is significant as a part of the WSN’s optimization in the context of energy consumption. In WSNs, the nodes operate on a very limited energy source, therefore, the routing protocols designed must meet the optimal utilization of energy consumption in such networks. Evolutionary games can be designed to meet this aspect by providing an adequately efficient CH selection mechanism. In such types of mechanisms, the network nodes are considered intelligent and independent to select their own strategies. However, the existing mechanisms do not consider a combination of many possible parameters associated with the smart nodes in WSNs, such as remaining energy, selfishness, hop-level, density, and degree of connectivity. In our work, we designed an evolutionary game-based approach for CH selection, combined with some vital parameters associated with sensor nodes and the entire networks. The nodes are assumed to be smart, therefore, the aspect of being selfish is also addressed in this work. The simulation results indicate that our work performs much better than typical evolutionary game-based approaches.

Reliability evaluation of PV converter based on modified hybrid power control method

With the increasing proportion of photovoltaic grid-connected systems in the global power system, many advanced active power control strategies have been introduced into power network management. In this paper, a hybrid method to optimize power control for two-stage PV converter is proposed. The proposed method adopts the mixed modulation strategy of maximum power point tracking (MPPT) control and Power Reserve Control (PRC). In this method, the power devices of the inverter have a relatively alleviated junction temperature stress, and thereby the reliability of the power devices are enhanced. The reliability of the converter using proposed method is predicted under a real mission profile in photovoltaic application.

Deriving policies from connection codes to ensure ongoing voltage stability

The management and transmission networks is becoming increasingly complex due to the proliferation of renewables-based distributed energy resources (DER). Existing control systems for DER are based on static specifications from interdependent network connection documents. Such systems are inflexible and their maintenance requires concerted effort between grid stakeholders.In this paper we present a new supplementary control approach to increase the agility of the electricity grid. The ICT system that underlies smart grids has the potential to offer, by analogy with ICT based network management, a control plane overlay for the modern smart grid. Policy-based Network Management (PBNM) is widely deployed in managed telecoms networks. We outline how PBNM can augment the management of power and energy networks and report on our initial work to validate the approach. To configure the PBNM system, we have used text mining to derive connection parameters at the LV level. In our simulations, PBNM was used in collaboration with a Volt-VAr optimisation (VVO) to tune the connection settings at each DER to manage the voltage across all the buses. We argue that the full benefits will be realised when stakeholders focus on agreeing relatively stable high-level connection policies, the policies being refined dynamically, and algorithms such as VVO that set connection parameters so they are consistent with those high-level policies. Thus faults, power quality issues and regulatory infringement can be identified sooner, and power flow can be optimised.

Learning-Based Energy-Efficient Resource Management by Heterogeneous RF/VLC for Ultra-Reliable Low-Latency Industrial IoT Networks

Smart factory under Industry 4.0 and industrial Internet of Things (IoT) has attracted much attention from both academia and industry. In wireless industrial networks, industrial IoT and IoT devices have different quality-of-service (QoS) requirements, ranging from ultra-reliable low-latency communications (URLLC) to high transmission data rates. These industrial networks will be highly complex and heterogeneous, as well as the spectrum and energy resources are severely limited. Hence, this article presents a heterogeneous radio frequency (RF)/visible light communication (VLC) industrial network architecture to guarantee the different QoS requirements, where RF is capable of offering wide-area coverage and VLC has the ability to provide high transmission data rate. A joint uplink and downlink energy-efficient resource management decision-making problem (network selection, subchannel assignment, and power management) is formulated as a Markov decision process. In addition, a new deep post-decision state (PDS)-based experience replay and transfer (PDS-ERT) reinforcement learning algorithm is proposed to learn the optimal policy. Simulation results corroborate the superiority in performance of the presented heterogeneous network, and verify that the proposed PDS-ERT learning algorithm outperforms other existing algorithms in terms of meeting the energy efficiency and the QoS requirements.

Effective Resource Planning for Interference Aware Throughput Maximization of Wireless Body Area Networks

Cross-layer design is an in-depth analysis area in especially wireless Body Area networks and is necessary under energy and throughput limitations. To improve network performance the all the layer increases energy consumption; thus, the collaborations connecting these layers ought to be examined. This study develops an optimal interrupt aware connection planning and power management policy that assist a certain data rate on each connection in the network,including signal to interference and noise ratio(SINR) and highest transmission power controls at one another node or center.in each connection as link cost metrics Creating an interrupt-aware development problem by considering the power of each node in the routing path and the associated delay in each connection.the problem is to discover an scheduling link planning, power management and routing policy that maximizes the life span and throughput and minimizes the end-to-end delay as well as the energy link cost subject to definite controls. To provide mathematical guides to our schemes and relationships between the parameters. In our Proposed work is an advance step towards our distributed joint planning, power management and routing algorithm for wireless Body Area networks utilizing cross layer design.

Robust optimal energy and reactive power management in smart distribution networks: An info‐gap multi‐objective approach

Robust optimal energy and reactive power management in smart distribution networks: An info‐gap multi‐objective approach

Coordinated Power Management in Data Center Networks

In this paper, we present a coordinated power management technique for data center networks (DCN) which we have implemented in the popular network simulator NS3. The proposed mechanism includes three key entities: (a) a local controller (LC) that manages traffic at each DCN node (switches and routers), (b) a global controller (GC) that has a global view of the DCN and provides hints to the LC's based on this knowledge, and (c) a topology aware user request assignment controller (RAC) that controls placement of the external requests on the endpoint hosts based on hints from the GC. The main goal of the coordination from the energy management perspective is to properly direct and consolidate network traffic to maximize low power (or “sleep”) opportunities for the network interfaces while avoiding link congestion. In the process of testing out our mechanisms, we have enhanced the popular NS3 package for network modeling. The key enhancements are in the accounting of energy consumption at various levels and enhanced traffic routing mechanisms. We show that these mechanisms can reduce the power consumption by up to ∼40% in the common fat-tree based DCNs using the low power idle (LPI) feature of the Ethernet. We also apply our mechanism to the Hypercube network and show that it too can save a significant amount of energy.

A Strategy for Power Generation Optimization in a Hybrid Wind-BESS Power Plant

The wind is an uncontrollable primary resource, although its energy can be stored. This fact can be used for the design of strategies for a better management of electric power networks. An option for achieving this goal is to install Battery Energy Storage Systems (BESS) in the wind farms (WF). When dealing with WFs combined with BESSs the most important is to manage the power production in order to meet the requirements of the network or those related with the owner of the plant. Both challenges constitute an optimization problem. This paper proposes an Evolutionary Algorithm (EA) to solve it, where a fitness function must be maximized under the consideration of certain constraints. The fitness function depends on the target of the power production, which may be either to help the network become more stable or to maximize the profit, assessing each scenario and accepting the best one. The constraints of the optimization problem are related to the levels of the BESSs: the maximum power transferred to or from it and the output power of the plant.