Power Management Issues Research Articles

Sweat‐powered, skin‐adhesive multimodal sensor for long‐term and real‐time sweat monitoring

AbstractThe importance of continuous healthcare management has significantly accelerated the development of wearable devices for monitoring health‐related physical and biochemical markers. Despite extensive research on wearable devices for physiological and biochemical monitoring, critical issues of power management and device/skin interfacial properties restrict the advancement of personalized healthcare and early disease detection. Here, we report a multimodal sweat monitoring device featuring a real‐time display and long‐term data analysis based on self‐powered format of sweat‐activated batteries (SABs). The polyvinyl alcohol‐sucrose (PVA‐Suc) hydrogel serves as the key component for the SAB, offering not only great long‐term adhesive properties for conformable wearability but also significant power generation capabilities. A maximum current density of 44.06 mA cm−2 and a maximum power density of 21.89 mW cm−2 can be realized for the hydrogel based SAB. The resulting device integrates an advanced colorimetric and electrochemical sensor array to measure pH levels, glucose concentrations, and chloride ion levels in human sweat, with data wirelessly transmitted by Near Field Communication. The self‐powering features and multiple mode sensing function offer sufficient power to support real‐time monitoring of metabolic biomarkers in sweat, with the ability to visually observe changes in the colorimetric sensors for long‐term data monitoring.

Green energy solution for grid integrated solar PV system for water storage towers

In India, water storage towers are present almost everywhere, especially across small towns to rural areas. The storage tanks either utilize diesel powered or utility grid fed induction motor (IM) water pumps. The large spaces available with water storage towers can be utilized to plant solar PV panel for large storage towers water pumping operation, hence, making a switch from diesel or grid run storage towers to solar operated storage towers. This paper presents a robust control of grid integrated solar PV-based water pump system with efficient and smooth transition between standalone and grid interfaced modes. A smart DC link voltage control is presented for distributed generation system (DGS), which provides full control on DC link voltage for both modes of operation at all dynamic load conditions. An enhanced phase-locked loop (EPLL) control is utilized for grid source converter (GSC) in grid control operation, making it flexible to different grid scenarios and power quality. It works for harmonics suppression, load balancing and grid side converter control. An enhanced phase-locked loop (EPLL) is implemented for synchronization control algorithm, which provides an accurate estimation of voltage angle even during frequency ramps and exhibiting superior performance particularly in terms of robustness against DC offsets. The primary requirement for smooth operation of grid-tied solar PV system is effective power management between different sources. The issues of power management and power quality, are discussed in this work, and solutions are presented. The implemented control algorithms are tested successfully for dynamic solar insolation, varying load conditions, unbalance in load, sudden grid outage condition. The system appropriateness is validated by experimental results.

Implementation of a Power Management System on Combat Robots based on a Hybrid Energy Storage System

This paper presents a power management (PMS)-based resource regulation system for combat robots equipped with batteries, supercapacitor banks, and solar cells. Hybrid energy storage source arrangements are needed to increase operational duration and increase the range of combat robots. Integration of multiple devices with different characteristics requires the implementation of a PMS. Distribution of the load of a combat robot by solving a power management problem, formulated as a finite quadratic program (CQP). Optimization of parameters is carried out, such as speed, drag coefficient and operating point of robot components, which are then regulated by the PMS controller. Data information during robot maneuvering by the PMS is measured and takes into account power management issues during the control period. In this research simulation, variations in weight factor parameters are given to determine various difficulties, namely regenerative power management. The simulation results show that the combat robot power management control system is capable of optimally generating electrical power loads for all components. In another study, a comparison of CQP performance in power management is comparable to benchmark performance after being analyzed based on nonlinear model predictive control.

Triboelectric Nanogenerator Enabled Sweat Extraction and Power Activation for Sweat Monitoring

AbstractWearable sweat sensors can detect and monitor various substances in sweat, providing valuable information for healthcare monitoring and clinical diagnostics. Recent advances in flexible electronic technologies have enabled the development of wearable sweat sensors that can measure sweat rate and biochemical substances in real time, although several challenges remain, such as power management and sweat extraction issues. Here, a passive sweat extraction strategy as well as a self‐powered monitoring system (SEMS) is reported to be designed for sedentary individuals, i.e., elders. The SEMS system comprises a wearable triboelectric nanogenerator (TENG) for sweat extraction, a sweat‐activated battery (SAB) as the integrated power source, carbachol‐loaded iontophoresis electrodes for sweat extraction, microfluidics with biosensors for detecting physiological information in sweat, and near field communication (NFC)‐based wireless microelectronics for data communication, processing, and collection. By tapping the TENG, sedentary people can passively extract sweat based on the iontophoresis process, allowing the sensors to detect biological information in sweat. The good flexibility of the SEMS device enables real‐time and non‐invasive detection of sweat analytes in a wearable format. This system offers a new strategy of sweat collection and analysis for the elderly group, and therefore can help to understand human physiology and personalize health monitoring deeply.

Power Performance Evaluation of Standalone Renewable Energy Source Energy Management Using Pass Filter

Hybrid energy storage systems have shown promise in enhancing solar panel systems reliability and efficiency. However, managing the power distribution balance with multiple energy storage units remains a challenge. This research addresses power distribution balancing during solar irradiation intermittency by employing a first-order filter with lowpass and highpass characteristics. The study aims to investigate energy and power management issues for off-grid electrical systems using this filter. Results from numerical simulations and experiments with a hybrid energy storage setup comprising a battery and supercapacitor show that the first-order filter effectively allocates the first-order signal to the main energy storage and subsequent orders to the supporting energy storage. During increased intermittency, the battery contributes 62 W and stores 387 W, while the supercapacitor contributes 167 W and stores 297 W. Conversely, during reduced intermittency, the battery stores 390 W and contributes 62 W, and the supercapacitor stores 295 W and contributes 164 W. The findings demonstrate the filter's efficacy in optimizing power distribution balance within hybrid energy storage systems. However, using the supercapacitor as the main energy storage does not result in higher power efficiency compared to the battery. In conclusion, the First Order Filter presents a viable solution for addressing power distribution challenges in hybrid energy storage systems, contributing to improved energy and power management for off-grid electrical systems. Further research on cost-effectiveness, maintenance, and environmental impact is warranted for practical implementation

Gaussian Process Approximate Dynamic Programming for Energy-Optimal Supervisory Control of Parallel Hybrid Electric Vehicles

We propose an energy-efficient supervisory control method for the power management of parallel hybrid electric vehicles (HEVs) to improve the fuel economy and reduce exhaust gas emissions. Plug-in HEVs ((P)HEVs) have multiple power sources (e.g., an engine and motor) that should be cooperatively operated to meet the required instantaneous traction power for the desired vehicle speed while satisfying their physical limits. Because the efficiencies of the engine and motor vary with different operating speeds and torques, the main issue of energy-efficient power management is to allocate the power demand among the power sources by achieving maximum power conversion efficiencies and satisfy the operating limits. For an efficient power allocation, an optimal control problem is formulated, and a global solution is found through deterministic dynamic programming (DP). Owing to the curse of dimensionality and uncertainties in real driving, DP solutions are not directly applicable in real time. To resolve the limitations of DP, we employ a non-parametric Bayesian function approximation technique using a Gaussian process (GP). The offline DP solutions obtained from a set of real vehicle driving test data were used to learn a state-dependent probabilistic value function through Gaussian process regression. For online implementations, a receding horizon control scheme was applied for the feedback control of the power management. In comparison with the existing charge sustaining strategy and charge depleting and charge sustaining mixed controllers, we recorded fuel efficiency improvements of over 4.8% and 7.3%, respectively, in a mixed urban-suburban route.

Power System Planning and Quality Control

The optimum planning of the electrical power expansion and, accordingly, controlling the power quality are recent critical issues in power management [...]

Problems and Prospects of Flying Rotor Drones Particularly Quadcopters

Rotor type drones are used as a source for acquiring intelligence from areas which are remotely located. This intelligence can be used for ensuring crop insurance, knowing post-disaster assessments, knowing information of restricted security zones, etc. Apart from various advantages, rotor type drones, like quadcopters, have certain drawbacks also. These drawbacks need to be researched and addressed in detail so that the information can be acquired in a manner which is deliberate and very effective, while obtaining information from various sensors attached to the drones. These drawbacks are the problems pertaining to sound of propellers, selection of flight controller, power management issues, flying in non-conducive weather, collision avoidance, videography during night and extended communication ranges, which have been discussed in this paper.

An optimal control for power management in super capacitors/battery of electric vehicles using Deep Neural Network

Nowadays, Hybrid Energy Storage Systems (HESS) is gaining popularity as a result of their superior system efficiency and battery lifetime when compared to solitary energy sources. To maximize the energy management for electric vehicles, HESS like batteries and super capacitors (SCAP) are used, which has two objectives: (i) first the voltage of SCAP reference can be determined viaincluding real-time dynamics of load and (ii) optimize the power flow by reducing the magnitude variation of battery power & power loss. A sophisticated model of the DC-DC converter is taken into account in this HESS power management issue, which includes both conduction and switching losses. Also, the optimization issue is then quantitatively tackled for varied drive cycles utilizing a Proportional Integral Derivative(PID) controller.Therefore, to train and predict the control parameters of HESS, a Deep Neural Network (DNN) is used that consists of layers of neurons between the input and output layers, which fuse the feature extraction process with an increase in accuracy. Hence, to generate the optimal controllerparameters of HESS, it's planned to propose a novel meta-heuristic algorithm called the Squirrel Search with Improved Food Storage (SS-IFS) Optimizer, which is the conceptual improvement of the standard Squirrel Search Algorithm (SSA).Finally, the superiority of the proposed approach is demonstrated using various metrics.

Prioritized Shortest Path Computation Mechanism (PSPCM) for wireless sensor networks.

Routing Protocol for Low-power and Lossy Networks (RPL), the de facto standard routing protocol for the Internet of Things (IoT) administers the smooth transportation of data packets across the Wireless Sensor Network (WSN). However, the mechanism fails to address the heterogeneous nature of data packets traversing the network, as these packets may carry different classes of data with different priority statuses, some real-time (time-sensitive) while others non-real-time (delay-tolerant). The standard Objective Functions (OFs), used by RPL to create routing paths, treat all classes of data as the same, this practice is not only inefficient but results in poor network performance. In this article, the Prioritized Shortest Path Computation Mechanism (PSPCM) is proposed to resolve the data prioritization of heterogeneous data and inefficient power management issues. The mechanism prioritizes heterogeneous data streaming through the network into various priority classes, based on the priority conveyed by the data. The PSPCM mechanism routes the data through the shortest and power-efficient path from the source to the destination node. PSPCM generates routing paths that exactly meet the need of the prioritized data. It outperformed related mechanisms with an average of 91.49% PDR, and average power consumption of 1.37mW which translates to better battery saving and prolonged operational lifetime while accommodating data with varying priorities.

Optimal Power Flow Analysis Based on Hybrid Gradient-Based Optimizer with Moth–Flame Optimization Algorithm Considering Optimal Placement and Sizing of FACTS/Wind Power

Optimal power flow (OPF) is one of the most significant electric power network control and management issues. Adding unreliable and intermittent renewable energy sources to the electrical grid increase and complicates the OPF issue, which calls for using modern optimization techniques to solve this issue. This work presents the optimal location and size of some FACTS devices in a hybrid power system containing stochastic wind and traditional thermal power plants considering OPF. The FACTS devices used are thyristor-controlled series compensator (TCSC), thyristor-controlled phase shifter (TCPS), and static var compensator (SVC). This optimal location and size of FACTS devices was determined by introducing a multi-objective function containing reserve costs for overestimation and penalty costs for underestimating intermittent renewable sources besides active power losses. The uncertainty in the wind power output is predicted using Weibull probability density functions. This multi-objective function is optimized using a hybrid technique, gradient-based optimizer (GBO), and moth–flame optimization algorithm (MFO).

The gravitational search algorithm for incorporating TCSC devices into the system for optimum power flow

<span lang="EN-US">This paper proposes a gravitational search algorithm (GSA) to allocate the thyristor-controlled series compensator (TCSC) incorporation with the issue of reactive power management. The aim of using TCSC units in this study is to minimize active and reactive power losses. Reserve beyond the thermal border, enhance the voltage profile and increase transmission-lines flow while continuing the whole generation cost of the system a little increase compared with its single goal base case. The optimal power flow (OPF) described is a consideration for finding the best size and location of the TCSCs devices seeing techno-economic subjects for minimizing fuel cost of generation units and the costs of installing TCSCs devices. The GSA algorithm's high ability in solving the proposed multi-objective problem is tested on two 9 and 30 bus test systems. For each test system, four case studies are considered to represent both normal and emergency operating conditions. The proposed GSA method's simulation results show that GSA offers a practical and robust high-quality solution for the problem and improves system performance.</span>

Fast algorithms for test optimization of core based 3D SoC

The use of 3D-IC technology has become quite widespread in designing core-based systems-on-chip (SoCs). Concomitantly, testing of cores and inter-layer through-silicon-vias (TSVs) spanning through different layers of 3D chips has become an important problem in the manufacturing cycle. Testing 3D-SoCs is more challenging compared to their 2D counterparts because of the complexity of their design and power management issues. Also, the test procedure demands substantially more power than what is required in the normal functional mode, and hence, stringent thermal constraints during test need to be fulfilled to safeguard future performance and reliability of the chip. Since the overall 3D infrastructure depends on routing layer assignments, core allocation, and the geometry of TSV locations, these parameters should be given due consideration while designing the test-access-mechanism (TAM) that aims for minimizing overall test time satisfying power and TSV constraints. In this paper, we present a three-stage algorithm for reducing the test time in automated post-bond core-based 3D-SoCs, under a set of given constraints on test power, TAM-width, and the number of available TSVs. The proposed algorithm, when run on several ITC-02 SoC benchmarks, outperforms the algorithms presented in earlier work with respect to CPU-time, and additionally, reduces test time in many instances.

Power and Thermal Management Issues for Portable Processors

Power and Thermal Management Issues for Portable Processors

Enhanced development of communication between the network and the end user by eliminating the interference signals inMIMOchannel

AbstractThough the key technology for 5G communication is the massive Multiple input and multiple output (MIMO), it has a major drawback when the user generated inference signals are to be handled by several other users. When these interference signals are used in various users, issues concerning system ability, power management issues and QoS related issues arises with the involvement of MIMO channel. Though internet of things (IoT) making use of MIMO is still under development, the requirements of the IoT is quite different when compared to several other connections. Thus, taking into account the demands, the solutions were framed with the help of the proposed system. One such solution is the Partition Square and Cross‐Processing method that can possess several antennas at the given destination. Based on this solution obtained the data rate can be drastically increased correspondingly reducing the power consumed thus producing the most desirable result with high signal to noise ratio (SNR). Best victory is another such solution that solves the issues relating to power reduction thereby providing the best solution with minimum SNR. These solutions produced by the proposed system still has some disadvantages with respect to the following case: when the number of destination antennas is less than the number of source antennas. This case is considered as most common in the model proposed. For the above‐mentioned case, near optimal solution is produced by both the algorithms. In this article, the transmitters are represented by the source antenna and the receiver is represented by the destination antenna. Thus, with the help of IoT connectivity, major benefits can be bought to the massive MIMO channel.

Simulation of Cloud Platform Supporting Wireless Sensor Network

WSN has been an area of interest for a lot of researchers as well as the industry specialist due to its high usage in healthcare, military applications and in robotics. The focus area of WSN has been energy consumption. Integration of Cloud Computing in WSN has been observed by couple of research scholars as Cloud has been observed to be struggling against the power management issue. This paper presents a novel solution of power management in Cloud computing which uses the concept of clustering borrowed from WSN. The jobs are grouped or Clustered as per the algorithmic architecture of WSN and the minimization of migration is attempted using Genetic Algorithm (GA). Artificial Neural Network (ANN) is used as a conjunction to GA for energy efficiency. MATLAB is used as a simulation tool and consumed energy has been evaluated as the major evaluation parameter.

IEC 61850 Modeling of UPFC and XMPP Communication for Power Management in Microgrids

Integration of distributed renewable energy sources in microgrids presents many challenges to grid such as voltage, stability and power management issues. In literature different control strategies to improve the performance of microgrids integrated with renewable energy sources proposed. FACTS based controller such as Unified Power Flow Controller (UPFC) has also been employed in microgrids to improve the power management and transient stability. Coordinated control strategies between UPFC controller and DERs have been developed. However, the underlying communication for realizing these strategies has not been discussed. This paper presents IEC 61850 and XMPP based communication for coordinating UPFC and DERs in microgrid for its stable operation. IEC 61850 information model for UPFC controller is developed. Furthermore, the XMPP protocol is utilized to provide the scalability for communication network in microgrids with high penetration of DERs. Additionally, XMPP also provides improved security along with the scalability. IEC 61850 message exchanges over XMPP communication between different components of microgrid for power management is demonstrated.

Power charging management strategy for electric vehicles based on a Stackelberg game

Increasing electric vehicles (EV) charging efficiency and reducing charging cost are important issues of EVs power management and key factor that affect the spread of EVs. Researching the competitive relationship between power grids and EV users is the key to solving this problem. Therefore, the authors propose a charging control strategy that is based on a 1-N-type Stackelberg game. In the game, power grid, retailers, and users are all able to do power decision making. Thus, the charging strategy here can flexibly meet different demands of power grid, retailers, and users. The equilibrium of the game model is solved by the inverse induction method. The benefits are compared by simulation, by the disordered charging process, and by charging control methods based on static time-sharing electricity prices. The influence of the parameters on the charging process in the game model is analysed, and the feasibility of the proposed method is verified. Results show that load peak is reduced by 34.9% while users' charging costs are reduced by 25.1%. The approaches they proposed can effectively solve the competition between the two.

Distributed Power Management for Networked AC–DC Microgrids With Unbalanced Microgrids

This paper investigates the issue of power management networked ac–dc microgrids (MGs) interconnected by interlinking converters with the consideration of unbalanced single-/three-phase ac MGs as well as power quality improvement. An integrated hierarchical distributed coordinated control approach is developed, which mainly consists of an up-layer event-triggered method of power sharing among MGs, and an event-triggered dynamic power flow routing approach to navigate the power flow among phases of the single-/three-phase ac MGs to balance the power of the MG. With the proposed control method, balanced output phase powers for the three-phase distributed generation (DGs) and enhanced voltage quality at the point of common coupling and DG terminals can be achieved besides proportional active power sharing among MGs and reduced communication. Simulation results are presented to demonstrate the proposed control method.

Linear matrix inequality approach in stability improvement through reactive power control in hybrid distributed generation system

Stability of a standalone hybrid power system (HPS) in a smart grid is always a challenging task. Further, the operational stability of the power system depends on the associated communication infrastructure. Therefore, it is always crucial to pick up a controller that can assure system's stability along with performance, despite disturbances like (load and input wind variations) with communication delays. Present study focuses on reactive power management and voltage stability issues of an isolated HPS. The stability aspects of HPS are improved through reactive power compensation, by custom power devices like static var compensator. The control aspects of SVC as well as the whole hybrid system are taken care by H ∞ linear matrix inequalities approach. Further, H-infinity control, Lyapunov stability along with linear matrix inequalities techniques estimate the delay boundary of controllers. The iterative performance of the proportional–integral–derivative controllers, and robust H ∞ damping controller of the HPS, are designed through LMI approach. Later experimental study of the HPS is done, with a prototype model in dSPACE real-time control environment. In this case, dSPACE 1104 is added for data acquisition and control. Adaptability and robustness of the proposed controllers are verified under fluctuating loads and uncertain wind power input.