Adaptive Power Management Research Articles

Adaptive Reactive Power Management with Thyristor-Controlled Transformer and Fixed Capacitor

The objective of this article is to develop and analyse a thyristor-controlled transformer with a fixed capacitor for reactive power compensation in power systems. Reactive power compensation is crucial for enhancing the efficiency and stability of power systems by reducing power losses, improving voltage profiles, and minimizing equipment stress. Traditional compensation methods often rely on fixed capacitors, reactors, or static VAR compensators, but these systems lack the flexibility required for dynamic control of reactive power under varying load conditions. The proposed approach integrates a thyristor-controlled transformer with fixed capacitors, allowing for precise, real-time adjustment of reactive power flow. The novelty of this article lies in the hybrid configuration of the thyristor-controlled transformer and fixed capacitor, which provides a cost-effective and robust solution compared to conventional systems. Unlike traditional methods that depend solely on switching capacitors or reactors, the use of thyristors allows for fine-tuning of reactive power, offering improved performance under variable loading conditions without the need for complex control algorithms. This setup enhances the adaptability of reactive power management, thus maintaining optimal power factor and voltage regulation. The findings from the simulation and experimental results demonstrate significant improvements in power factor correction, voltage stabilization, and reduction in harmonic distortion. The proposed system exhibits a faster response time and greater control accuracy compared to existing compensation techniques. These advantages make the thyristor-controlled transformer with a fixed capacitor a promising alternative for power utilities seeking to enhance the operational efficiency and reliability of their networks. This article contributes to the advancement of reactive power compensation technologies, providing a scalable solution suitable for modern power system.

Adaptive Frequency-Based Power Management for Off-Grid Hybrid Photovoltaic Converters

The intermittency in photovoltaic systems (PV) can lead to power quality issues, especially in off-grid applications. In these cases, adding an energy storage element helps to cope with the intermittency to provide adequate power to the local load. In this scenario, combining Li-Ion batteries and supercapacitors as a hybrid energy storage system (HESS) is powerful due to the battery's high energy density and the supercapacitor's high power density. The HESS demands adequate power management to operate adequately. This paper proposes an adaptive frequency-based power management for a Li-Ion battery and supercapacitor HESS applied to an off-grid PV converter. The main idea of this strategy is to guarantee a smoother current in the battery and reduce the power dissipation in the HESS. The smoother battery current transition helps avoid excessive battery stress and improves lifespan. The proposed strategy is compared with two other strategies. The results show that the proposed approach is more efficient in reducing the HESS' power dissipation and providing smooth current variations to the battery.

Neuromorphic engineering in GaN HEMTs exploiting dendritic dislocations for neuromodulation behaviors and adaptive intelligent power forecasting systems

Gallium nitride (GaN), a prominent III-V semiconductor, plays a pivotal role in the advancement of sophisticated power systems. This study presents an innovative neuromorphic engineering approach utilizing the unique dendritic dislocations extending from bulk to channel within GaN High Electron Mobility Transistors (HEMTs) for three-dimensional charge modulation. This approach dynamically alters charge states and channel potentials in response to multiple parametric stimuli, effectively mimicking neural processing and transmission functions, as well as emulating direct and indirect neuromodulation behaviors observed in human nervous system. The advanced neuromodulation emulation enhances biomimetic diversity, laying a solid foundation for the design of intelligent systems. Comprehensive material and electrical analyses reveal the charging and discharging behaviors of the defect energy levels, underlying the neuromorphic working mechanisms. Additionally, by incorporating artificial neural network algorithms, an intelligent power forecasting system that leverages the device’s diverse neuromorphic traits is developed for real-time and adaptive power management, optimizing efficiency and accuracy in balancing supply and demand, thereby enhancing the cost-effectiveness and sustainability of power systems. The findings highlight the neuromorphic capabilities of GaN devices and open avenues for further exploration into the intelligent power management.

Computation of an Effective Hybrid DFA-SVM Approach Aimed at Adaptive PV Power Management

Predominantly focussed in environmental conditions that are dynamic in nature the energy harnessed from the photovoltaic systems has to be maintained at high efficiency for which maximum power has to be extracted so a novel hybrid DFA-SVM control has been implemented using SEPIC converter. There are many algorithms to perform this function mentioned but in order to track the power at a faster rate and to avoid oscillations at the settling peak point this new methodology has been implemented. In this paper the novel algorithm used to track the peak power is Dragon Fly Algorithm-Support Vector Machines (SVMs). The algorithm is a combination of optimization and machine learning technique, so that this new methodology can incorporate both instantaneous and steady state features. The benefits of both the optimization and supervised learning technique are used to track most efficiently the maximum power with less oscillations. The DFA-SVM technique is implemented in the controller of the DC-DC converter used to regulate the supply voltage generated by the PV. The suggested MPPT’s performance is demonstrated under demanding experimental conditions including temperature and solar irradiation fluctuations across the panel. To further illustrate the superiority of the suggested approach, its performance is contrasted with that of the P&O method, which is commonly employed in MPPT during difficult exams.

A modular multiport Landsman converter-driven hybrid EV charging station with adaptive power management system

This article presents the implementation of a hybrid charging station that utilizes renewable energy sources to power its operations. The core of this system is a modular multiport Landsman converter, which serves as the interface between diverse renewable sources, including photovoltaic panels, wind turbines and fuel cells. A sophisticated multi-agent-based energy management system is employed to address the intermittency of renewable energy supply throughout the day. Additionally, a fuzzy logic controller (FLC) facilitates the seamless charging of electric vehicles within the system. The FLC detects the state of charge (SoC) of the vehicle's battery and the battery present in the station, enabling efficient EV charging. The multi-agent energy management system with load shedding controller (MAEMLS) and FLC continuously monitor the power availability, power demand, charging time, and charging duration of the charging station. This approach reduces the charging rate for EV owners during peak demand periods. The proposed system is modelled and tested using MATLAB/SIMULINK software, and a laboratory prototype has been constructed. The results demonstrate that the anticipated converter is highly suitable for integrating hybrid energy sources and charging electric vehicles (EVs). Notably, this converter exhibits an efficiency ranging from 85 %-95.05 %, based on the availability of input DC sources.

Challenges and Solutions in High-Speed SerDes Data Path Design

Modern high-performance communication systems require high-speed Serializer/Deserializer (SerDes) data path design to meet the growing demand for data throughput and reliability due to technological advancements and data-intensive applications. This research article examines the complex problems of designing high-speed SerDes data routes and suggests novel solutions. Data fidelity across high-speed channels requires signal integrity, the first key difficulty. Attenuation, crosstalk, and EMI may degrade SerDes signal performance. Advanced methods like equalization, impedance matching, and effective shielding mitigate these impacts. Equalization, including feedforward and feedback methods, reduces channel losses and distortions, enhancing signal quality. Power consumption is another major issue as data rates and system complexity rise. Power-hungry high-speed SerDes circuits cause thermal management and energy efficiency difficulties. Adaptive power management, low-power design, and new materials with higher thermal performance and lower power dissipation are discussed in the study. Design complexity is another issue in high-speed SerDes data route design. High-precision timing and synchronization and many component integration in a small area need advanced design tools and methods. The study emphasizes simulation and modeling for complexity management and design for testability (DFT) for full verification and debugging.

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.

Fault Prediction and Awareness for Power Distribution in Grid Connected RES Using Hybrid Machine Learning

This study addresses defects in electrical power systems, focusing on short circuits that can disrupt normal operation. The method emphasis is on hybrid microgrid systems connected to the grid, crucial for efficient power management. The novel method proposes an adaptive electricity management technique for fault scenarios in grid-tied conversions, minimizing sensor requirements. Power flow involves the hybrid energy storage system (HESS), renewable energy sources (RES), the electrical grid, and loads through a parallel hybrid RES arrangement. Semiconductor switch failures are common vulnerabilities, impacting grid functionality. The novel electricity management technique considers converter failures, renewable energy availability, and HESS state of charge restrictions. Power quality goals such as harmonic reduction, reactive power support, and a power factor of one are addressed. The proposed method was evaluated using MATLAB–based Simulink, showcasing its performance. The approach employs an Adaptive Power Management Strategy (APMS) in a hybrid renewable energy system, integrating wind and solar sources with battery and supercapacitor storage. A Hybrid Machine Learning-based Active Power Management System (HML-APMS) optimizes energy production, storage, and distribution for stability, reliability, and efficiency in the power utility system. The study underscores the significance of technology integration in achieving sustainable, reliable, and cost-effective power solutions.

An adaptive power management approach for hybrid PV-wind desalination plant using recurrent neural networks

In hybrid Photovoltaic (PV)-wind desalination plants, power management is necessary because renewable energy sources such as PV and wind are highly variable. An optimal power management system also helps extend the equipment's life by preventing overloading or underloading of the system. This paper proposes a recurrent neural network (RNN) based power management for a desalination plant. In the proposed work, renewable energy sources like solar and wind are utilized to power the reverse osmosis (RO) desalination unit. The developed RNN model optimizes renewable energy sources while maintaining a stable function of the desalination process. The RNN power management module utilizes historical data to predict the power generation of the PV and wind sources and then adjusts the system's output to meet the power demand of the desalination plant while considering the limitations of the RO unit and the water profile requirements. The developed model was implemented in MATLAB tool, and the results are estimated. Simulation outcomes demonstrate that the developed model improves the hybrid system's performance in terms of resource and battery storage utilization and minimizes energy loss.

An adaptive dynamic power management approach for enhancing operation of microgrid with grid ancillary services

The increasing prevalence of photovoltaic (PV)-wind-battery-based microgrids and their integration into distribution networks have brought new challenges in grid ancillary services, such as poor power quality (PQ), grid instability, voltage and current fluctuations, and low efficiency of microgrids. These adverse effects are due to the intermittent nature of PV and wind power generation. The present work proposes an adaptive dynamic power management (ADPM) approach and enhanced instantaneous symmetrical component theory (EISCT) to improve the microgrid operation and grid ancillary services. The proposed ADPM-based EISCT not only utilizes a single grid side converter (GSC) but also reduces the sensor requirement and control structure complexity and thereby eliminates communication mismatch. The control structure, proposed in this research, has two major segments. The first segment focuses on active power management control through ADPM to facilitate efficient power flow between the utility grid and microgrid sources which helps in ensuring efficient power sharing. The second component is dedicated to synchronizing the renewable energy sources of the microgrid with the utility grid and simultaneously maintaining PQ compliance as per IEEE 519–2022 standards. The efficacy and adaptability of the proposed structure are validated through MATLAB®/Simulink® environment and the practical superiority of the system has been tested by using MicroLabBox® DS1202-based real-time hardware set-up. The experimental result shows improved performance of the developed structure under various microgrid operating conditions including dynamic loading situations, and battery system state-of-charge (SOC). The simulation and experimental results suggested that the DC-link ripples have been reduced to 2%, peak overshoot to 1.8%, settling time during the transition to 1.2s, battery response time to 0.3s, and the total harmonic distortion (THD) to less than 5%. These results demonstrated the superiority of the ADPM-based EISCT over conventional strategies.

Enhancing Performance of Hybrid Electric Vehicle using Optimized Energy Management Methodology

The fuel consumption and the fuel management strategy (PMS) of the hybrid electric vehicle are closely linked (HEV). In this study, a hybrid power management technique and an adaptive neuro-fuzzy inference (ANFIS) method are established. Artificial intelligence represents a huge improvement in electricity management across different energy sources (AI). The main energy source of the hybrid power supply is a proton exchange membrane fuel cell (PEMFC), while its electrical storage devices are a battery bank and an ultracapacitor. The hybrid electric vehicle's power management strategy (PMS) and fuel consumption are closely related (HEV). In this paper, an adaptive neuro-fuzzy inference and hybrid power management strategy (ANFIS) approach is developed. A significant advance in electricity management across multiple energy sources is artificial intelligence (AI). The proton exchange membrane fuel cell (PEMFC) serves as the primary energy source of the hybrid power supply, and the ultracapacitor and battery bank serve as its electrical storage components.

An Adaptive Power Management Tool for Sizing a PV-Battery-Hydrogen Off-Grid Electrical Energy System

An Adaptive Power Management Tool for Sizing a PV-Battery-Hydrogen Off-Grid Electrical Energy System

A Droop-Based Adaptive Power Management System for Energy Storage Integration to DC Grid Using a Modified Dual Active Bridge Converter

This article proposes an adaptive power management system (APMS) for energy storage integration to the dc grid using a modified dual active bridge (DAB) bi-directional dc–dc converter (BDC). The converter can achieve high voltage (HV) gain and zero voltage switching (ZVS) for a wide range of loading conditions with distributed voltage stress. An APMS is designed with a voltage droop control-based model reference adaptive control (MRAC). A reduced-order harmonic model of the converter is used for the APMS design. The proposed APMS does not require detailed knowledge of the system parameters and is also robust to uncertainty and multiple disturbances. The proposed converter and its control technique are verified in MATLAB/SIMULINK platform. An experimental laboratory prototype is also developed for 500 W rating, and the results are presented to validate the system’s efficacy by comparing it with conventional proportional-integral (PI) and sliding mode controllers.

An Adaptive Fully Integrated Dual-Output Energy Harvesting System With MPPT and Storage Capability

This paper presents a fully-integrated adaptive dual-output power management system with storage capability for thermoelectric energy harvesting applications. The first output delivers the load power and is regulated at 1.6 V. This output is provided by the primary path of the system that is implemented using a 5-stage reconfigurable Dickson charge pump. In case of the presence of more available power than the load demand, a secondary path is enabled to store the excess amount of energy on a supercapacitor. This provides the second output of the system that is capable of charging up to about twice the voltage of the first output. Besides storing the excess energy, a new technique using the secondary path is proposed for regulating the first output and achieving the maximum power point tracking of the input source. The proposed system is automatically reconfigured to maximize the end-to-end efficiency with the aid of a finite state machine. The system is implemented in 180nm CMOS technology. It utilizes a total on-chip flying capacitance of 2.1 nF, and operates across an input voltage range from 0.35 V to 1 V. Measurement results show that the system achieves an end-to-end efficiency of 72% at a total output power of 1.24 mW.

Analysis of Power Management for Hard Real-Time Embedded System between Adaptive Power Management Method and Idle Energy Reduction Method

Static and dynamic power management have become crucial in real-world applications of embedded systems development. While many technologies and algorithms can reduce power consumption efficiently, like dynamic voltage and frequency scaling, varying voltage and frequency make overall power consumption less efficient. The comprehensive embedded system spends time and energy on the transition between states, voltage, and frequency. To better understand how these problems can be addressed, this paper will explore two methods, the adaptive power management method and the idle state energy reduction method, to mitigate issues on the algorithm side. Furthermore, this paper is intended to give researchers and real-time embedded systems developers an overview of power management methods and to help them better develop the embedded systems.

Novel adaptive power management strategy for hybrid AC/DC microgrids with hybrid energy storage systems

Novel adaptive power management strategy for hybrid AC/DC microgrids with hybrid energy storage systems

Adaptive power management algorithm for multi-source DC microgrid system

Abstract In this work, an adaptive power management algorithm is developed for a multi-source dc microgrid system. The proposed algorithm is tested with a dc microgrid system consisting of PV, wind, batteries, supercapacitors, and variable load. The PV and wind energy systems are operated in maximum power point tracking mode to extract maximum energy from the available wind power and solar irradiation. The proposed algorithm has considered wide variation in weather conditions like zero wind velocity, zero solar irradiation, wind gust, and dynamic load change at the dc bus. For these cases, the proposed algorithm maintains the dc bus voltage constant without using a communication link. The efficacy of the proposed algorithm is tested using MATLAB-Simulink. Also, the feasibility and effectiveness of the proposed algorithm are validated using OPAL-RT control platform-based experimental studies.

Real-Time Power Management for Microgrids With Dynamic Boundaries and Multiple Source Locations

The dynamic boundary concept enables more flexible and efficient operation of microgrids with distributed energy resources (DER) that are intermittent in nature. As the integration of renewables continues to accelerate, an adaptive power management module that enables dynamic boundary operations in microgrids with an increasing number of source locations is essential for the fast and low-cost deployment of microgrid controllers. The power management module introduced in this paper is capable of handling the increased complexity in topological variations and transitions stemming from the dynamic boundaries and multiple source locations. This includes real-time operation of multiple islands with dynamic boundaries, initiation of topological transitions (merging and separation of islands), and automatic source coordination for power sharing and frequency regulation. All functions in the power management module are designed to be automatically adaptable to arbitrary microgrids with non-meshed topologies so that the deployment of the controller at new microgrid sites can be expedited with a reduced cost. The module has been implemented on NI’s CompactRIO system as an essential part of an MG controller and tested on a converter-based hardware testbed (HTB). Testing results validated the effectiveness of the algorithms under various operating conditions.

Wireless Power Transfer and Management for Medical Applications: Wireless power

Wireless power transfer (WPT) plays an important role in medical devices that require contactless energy transfer for either direct powering or recharging their small batteries. Miniaturizing medical devices and improving their WPT link robustness against different variations (e.g., distance, alignment, and load changes) also demands an efficient and adaptive power-management IC (PMIC) to convert the incoming ac power carrier into one or multiple usable dc voltage supplies. This article summarizes the key techniques for WPT and PMIC design. It discusses different modalities for WPT, including inductive, ultrasonic, capacitive, and magnetoelectric (ME) methods, with a focus on their applicability in different types of medical devices with different dimensions, implantation depths, and power requirements. It is concluded that ultrasonic and ME modalities are suitable for WPT to deeply implanted small devices with low power consumption, while inductive and capacitive methods are more suitable for WPT to large implants with shorter depth and higher power consumption. This article also summarizes different PMIC techniques for inductive WPT, particularly conventional voltage mode and more recent current-mode (and reconfigurable) techniques, with an emphasis on their suitability for different input voltage and loading conditions.

VLSI Based Architecture in ECG Monitoring for Adaptive Power Management In Wireless Bio Signal Acquisition Network

For the diagnosis and elimination of cardiovascular diseases, the ECG system has evolved as smarter healthcare professionals. As almost all of these networks are battery-operated, the whole device’s total life is greatly shortened by self-centred communication connections. This paper will present a power control technique and the accompanying VLSI design to improve the Asic ECG heart monitor’s lifespan powered by the battery. The suggested power control strategy dynamically adjusts between two transmission modes, i.e. high performance / lower energy modes, available for a device’s energy level. Because localized storage is energy, for true monitoring of QRS complexity and breathing rates assessment, a streamlined methodology focusing on slopes improvement with runtime adaptive thresholding is intended to guarantee maximum energy usage throughout limited energy level. The required communication mode is chosen based mostly on batteries and pulse rate consistency, which essentially fulfils the Wireless Body Sensor Communication network objective. Collecting appropriate ECG collections from a single customer and enhancing the longevity of the device. Response and efficiency are evaluated for the proposed method The ultra - low - power signal generator with a relaxing scope range of 1-3 m will be built to retain a bigger battery. Any transmitter and receiver for remote private communication standards have been established lately. It is not the best option for ultra-low power WBAN implementations. The Spartan 6 FPGA design has been introduced with a peak clock frequency of 269 MHz with an energy demand of 0.3mW, specially designed for actual ECG measurement techniques.