Adaptive Power Management Strategy Research Articles

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.

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 strategy-based optimization and estimation of a renewable energy storage system in stand-alone microgrid with machine learning and data monitoring

This work is carried out with the optimum design of a stand-alone hybrid energy storage system (HESS) based on solar, wind and super capacitor (SC) with battery storage system which is effectively optimized in the grid power system. The distribution of the power source is mainly considered on the Hybrid renewable energy power sources. This discourse presents an adaptive power optimizing the three-phase inverter and grid-connected hybrid renewable energy resources efficiently. In this analysis, the similar parameters are taken for the compensation such as voltage fluctuation, harmonics and Frequency imbalance by implementing Adaptive Power Management Strategy (APMS) and the obtained issues are synchronized by inverter control. All these comparative activities of the inverter are done either discretely or combined to stabilize the unbalanced impacts of a wide range of adjusted, uneven, power loss at the circulation level. A battery and SC energy management are essential for maintaining the energy sustainability in renewable energy system. Combination of solar and wind with the battery and SC is used to test the proposed stand-alone grid management. The proposed hybrid power system is designed to work under classical-based energy management and this performance is monitored with the help of the Internet of Things (IoT) and machine learning based on Polynomial Linear Regression Algorithm. The focus of the suggested HESS is reduced by the loss in stand-alone grid system with an economic performances.

Design of an Adaptive Power Management Strategy for Range Extended Electric Vehicles

The cruising distance of the range extended electric vehicle (REEV) can be further extended using a range extender, which consists of an engine and a generator, i.e., a genset. An adaptive power management strategy (PMS) based on the equivalent fuel consumption minimization strategy (ECMS) is proposed for the REEV in this paper. The desired trajectory of the state of charge (SOC) is designed based on the energy-to-distance ratio, which is defined as the difference between the initial SOC and the minimum allowable SOC divided by the remaining travel distance, for discharging the battery. A self-organizing fuzzy controller (SOFC) with SOC feedback is utilized to modify the equivalence factor, which is defined as the fuel consumption rate per unit of electric power, for tracking the desired SOC trajectory. An instantaneous cost function, that consists of the fuel consumption rate of the genset and the equivalent fuel consumption rate of the battery, is minimized to find the optimum power distribution for the genset and the battery. Dynamic programming, which is a global minimization method, is employed to obtain the performance upper bound for the target REEV. Simulation results show that the proposed algorithm is adaptive for different driving cycles and can effectively increase the fuel economy of the thermostat control strategy (TCS) by 11.1% to 16%. The proposed algorithm can also reduce average charging/discharging powers and low SOC operations for possibly extending the battery life and increasing the battery efficiency, respectively. An experiment of the prototype REEV on a chassis dynamometer is set up with the proposed algorithm implemented on a real-time controller. Experiment results show that the proposed algorithm can increase the fuel economy of the TCS by 7.8% for the tested driving cycle. In addition, the proposed algorithm can reduce the average charge/discharge powers of TCS by 7.9% and 11.7%, respectively.

Adaptive equivalent consumption minimisation strategy and dynamic control allocation-based optimal power management strategy for four-wheel drive hybrid electric vehicles

An adaptive equivalent consumption minimisation strategy and dynamic control allocation-based optimal power management strategy for a four-wheel drive plug-in hybrid electric vehicle is proposed in this paper. The equivalent factors of adaptive equivalent consumption minimisation strategy are optimised offline based on ISIGHT software over several typical driving cycles, which is integrated with AVL CRUISE and MATLAB/Simulink. To update the equivalent factor adaptively according to the predictive velocity, a neural network-based optimal equivalent factor prediction model is built, which can be used online. The torque distribution strategy considering axle load based on energy management strategy optimisation results and the vehicle dynamics control distribution is proposed: this includes two-wheel drive torque distribution, four-wheel drive torque distribution and brake torque distribution. The proposed energy management strategy is verified in New European Driving Cycle and Worldwide harmonised Light Vehicle Test Cycle driving patterns, and the simulation results show that the fuel economy of adaptive equivalent consumption minimisation strategy and dynamic control allocation-based optimal power management strategy is improved by 8.84% and 7.52% in New European Driving Cycle and Worldwide harmonised Light Vehicle Test Cycle, respectively, compared with the benchmark algorithm-based strategy.

A Self-Reliant DC Microgrid: Sizing, Control, Adaptive Dynamic Power Management, and Experimental Analysis

This paper presents an adaptive power management and control for a self-reliant dc microgrid powered by photovoltaic (PV) array and fuel cell stack that is escorted with the supercapacitor- and electrolyzer-based hybrid energy storage device. The prime operational impediments associated with this dc microgrid are the unpredictable variations in the PV generation and load demand, fuel starvation phenomenon of the fuel cell, the time constant associated with the electrolyzer operation, and state-of-charge limitation of the supercapacitor. In this paper, the above-mentioned issues are addressed by employing an adaptive dynamic power management strategy (ADPMS) that supervises the overall power flow in the system. The ADPMS enables the co-existence of high power density and high energy density storage devices to deliver the power flow required by the loads. The expeditious support extended by the supercapacitor helps in swift regulation of the dc-link voltage during unforeseen transient load/source power variations. The electrolyzer–fuel cell combination employed in this system can supplement a battery bank and can equip high energy density and self-reliance to the system. The set-reset flip-flop based fixed frequency current controller is simple and effective in accurately tracking the reference currents established by the ADPMS. The proposed ADPMS along with the control is validated for transient unforeseen load profiles and renewable generation changes via simulation studies and its performance is also validated through an experimental analysis on a laboratory-scale dc microgrid testbed.

Adaptive Power Management Strategy for Effective Volt–Ampere Utilization of a Photovoltaic Generation Unit in Standalone Microgrids

This paper proposes an adaptive power management strategy for photovoltaic (PV) generation units in a standalone microgrid. In existing microgrid configurations, the PV units are controlled using droop control technique, which ensures load sharing among the distributed generation (DG) inverters. However, the intermittency in power generation of PV-based DG poses many challenges to the standalone operation of a microgrid, the ineffective volt-ampere (VA) utilization being one among them. This paper proposes an adaptive droop control, which enables the maximum utilization of the VA rating of the interfacing inverter in a PV/battery hybrid system. With the proposed technique, as the generation goes down, the DG inverter is controlled to support the reactive power demand of the load, thereby utilizing the rating of the inverter switches effectively. The applicability of the proposed adaptive control method is analyzed using detailed simulation and real-time hardware-in-loop studies.

Adaptive Power Management Strategy for a Four-Mode Hybrid Electric Vehicle

A conventional vehicle propelled with only the internal combustion engine (ICE) is not easy to enhance the fuel economy due to the wide range operation requirement of the powertrain. However, a hybrid electric vehicle (HEV) which consists of an ICE and one or multiple electric motors can effectively improve the efficiency of the powertrain. In this paper, we propose an adaptive power management strategy (PMS) based on the equivalent fuel consumption minimization strategy (ECMS) for a four-mode HEV. The four-mode HEV which consists of an ICE and two motors provides four modes of operation, including electric vehicle (EV) mode, range extended (RE) mode, hybrid mode, and engine mode. The adaptive PMS is designed for charge sustaining such that the state of charge (SOC) can be maintained at a certain value. A self-organizing fuzzy controller (SOFC) is employed to adaptively adjust the equivalence factor of electric energy consumption based on the SOC deviation and the change of SOC deviation. An instantaneous cost function which consists of the fuel consumption of ICE and the equivalent fuel consumption of the battery is minimized to obtain the optimum power distributions of the ICE and two motors. Simulation results show that the adaptive PMS can effectively improve the fuel economy for different driving cycles.

Grid Adaptive Power Management Strategy for an Integrated Microgrid With Hybrid Energy Storage

The penetration of growing microgrid systems within the ac distribution network is leading to several challenges to have a safe and reliable operation of the power system. For the utility system, it is mandatory to maintain the voltage and frequency within the prescribed limits at the local bus under diverse conditions of the renewable energy sources (RESs), loads, and grid. With these conditions, the need for energy storage system (ESS) becomes extremely important for effective operation of critical and frequency sensitive loads. The vital role of ESS based on dc-bus regulation and monitoring of grid frequency in the microgrid is a challenging task and needs to be investigated in detail. Therefore, in this paper, the storage and microgrid are scheduled to work in a grid supportive manner. Also, these ESSs need to be operated within its safe state of charge limits. Hence, based on all the above constraints, an appropriate grid adaptive power management strategy (GA-PMS) is formulated to generate current references for RES, ESS, and microgrid-connected converters. Furthermore, this algorithm includes seamless microgrid operation under abnormal conditions, priority-based load shedding, and ensuring power quality standards at the local bus. The performance of proposed GA-PMS is tested and validated through the simulation and experimental studies.

Energy-Efficient Context Aware Power Management with Asynchronous Protocol for Body Sensor Network

MEMS sensor technology and advances in electronics, low-power processors and communication have enabled ubiquitous monitoring, providing significant opportunities for a wide range of applications including wearable devices for fitness and health tracking. However, due to the limited form factor required, there remains a challenging issue that limits even more the success of wearable devices: the limited lifetime due to the small energy storages that supply the devices. This limitation affects usability and forces the data processing to keep low-complexity to match the power constraints. As wireless communication is typically the most power hungry activity in wearable sensors devices, many techniques focus on reducing the communication power consumption. For this reason, advanced power management can be exploited to increase the lifetime of the devices. In this work, we present a wireless body area network with an adaptive power management strategy combining an ultra-low power wake up radio with context awareness. The context aware power manager is based on activity recognition, which is evaluated to decide which other nodes must be activated. The nano-power wake up receiver is used to reduce the idle listening power of the main radio and enable an asynchronous ultra-low power protocol. In order to evaluate the benefit, we present a real world application to assist elderly people in gait rehabilitation through a closed loop feedback. Experimental results demonstrate the benefit of the proposed power management in terms of energy efficiency. We evaluate the overall power consumption of the system and the lifetime extension, which can increase up to a factor of 4 depending on the amount of time the system can be placed in sleep mode.

CMDP based adaptive power management in server clusters

Many high-end computing systems use a large number of power-hungry commercial components to improve performance. Thus power reduction and energy conservation are important in these systems for the purpose of reducing operating costs. Two mechanisms are commonly applied to power reduction in these systems: Dynamic Voltage/Frequency Scaling (DV/FS) and server ON/OFF controlling: Vary-On Vary-Off (VOVF). Previous work addressed the power management issue as an optimization problem and utilized these two mechanisms either separately or together to reduce power consumption. In this paper, we construct a Constrained Markov Decision Process (CMDP) model and propose a CMDP based adaptive power management strategy in web server clusters. Our proposed strategy can greatly reduce the online computation time through an offline initialization process, the online adaptive server adjustment process will further improve system performance and deal with the dynamic changes of workload. It also combines merits from both DV/FS and VOVF mechanisms for better power reduction. The transition overhead caused by server allocation and frequency adjustment is taken into consideration in this work. The CMDP based adaptive power management strategy provides a controllable, predictable, and quantitative control over power consumption with theoretically guaranteed service performance. It is further evaluated via extensive simulations and justified by real workload trace data. The results illustrate the effectiveness and efficiency of our proposed work.

Adaptive application-driven WLAN power management

In this paper we present an adaptive application-driven power management (AADPM) strategy with online idle period length distribution learning capability for the IEEE 802.11b WLAN. We discuss its design and evaluate the performance in comparison with other power management strategies using the network simulator NS2. We simulated both the single user and multiple user scenarios. Experimental results have shown that, compared with other power management methods examined in this paper, AADPM achieved the highest energy saving in all cases and it demonstrated strong adaptability to network congestion.