scholarly journals Efficient Power Management of Heterogeneous Soft Real-Time Clusters

2008 ◽  
Author(s):  
Leping Wang ◽  
Ying Lu
Keyword(s):  
Real Time ◽  
Power Management ◽  
Efficient Power

Safe and efficient power management of hard real-time networks-on-chip

Integration ◽  
2019 ◽  
Vol 65 ◽  
pp. 1-17 ◽  
Author(s):  
Thawra Kadeed ◽  
Sebastian Tobuschat ◽  
Adam Kostrzewa ◽  
Rolf Ernst
Keyword(s):  
Real Time ◽  
Power Management ◽  
Networks On Chip ◽  
On Chip ◽  
Hard Real Time ◽  
Efficient Power

scholarly journals MPC-informed ECMS based real-time power management strategy for hybrid electric ship

2021 ◽  
Vol 7 ◽  
pp. 126-133
Author(s):  
Peilin Xie ◽  
Sen Tan ◽  
Josep M. Guerrero ◽  
Juan C. Vasquez
Keyword(s):  
Real Time ◽  
Power Management ◽  
Management Strategy ◽  
Power Management Strategy ◽  
Electric Ship ◽  
Hybrid Electric

scholarly journals Efficient Prediction of Network Traffic for Real-Time Applications

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Muhammad Faisal Iqbal ◽  
Muhammad Zahid ◽  
Durdana Habib ◽  
Lizy Kurian John
Keyword(s):  
Power Consumption ◽  
Real Time ◽  
Power Management ◽  
Dynamic Resource Allocation ◽  
Computation Complexity ◽  
Real Time Traffic ◽  
Double Exponential ◽  
Efficient Prediction ◽  
Dynamic Resource ◽  
Double Exponential Smoothing

Accurate real-time traffic prediction is required in many networking applications like dynamic resource allocation and power management. This paper explores a number of predictors and searches for a predictor which has high accuracy and low computation complexity and power consumption. Many predictors from three different classes, including classic time series, artificial neural networks, and wavelet transform-based predictors, are compared. These predictors are evaluated using real network traces. Comparison of accuracy and cost, both in terms of computation complexity and power consumption, is presented. It is observed that a double exponential smoothing predictor provides a reasonable tradeoff between performance and cost overhead.

scholarly journals Real-Time Implementation of the Predictive-Based Control with Bacterial Foraging Optimization Technique for Power Management in Standalone Microgrid Application

Energies ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1723
Author(s):  
Félix Dubuisson ◽  
Miloud Rezkallah ◽  
Hussein Ibrahim ◽  
Ambrish Chandra
Keyword(s):  
Real Time ◽  
Power Management ◽  
Renewable Energy Sources ◽  
Optimization Technique ◽  
Euler Method ◽  
Small Scale ◽  
Diesel Generator ◽  
Bacterial Foraging Optimization ◽  
Time Model ◽  
Bacterial Foraging

In this paper, the predictive-based control with bacterial foraging optimization technique for power management in a standalone microgrid is studied and implemented. The heuristic optimization method based on the social foraging behavior of Escherichia coli bacteria is employed to determine the power references from the non-renewable energy sources and loads of the proposed configuration, which consists of a fixed speed diesel generator and battery storage system (BES). The two-stage configuration is controlled to maintain the DC-link voltage constant, regulate the AC voltage and frequency, and improve the power quality, simultaneously. For these tasks, on the AC side, the obtained power references are used as input signals to the predictive-based control. With the help of the system parameters, the predictive-based control computes all possible states of the system on the next sampling time and compares them with the estimated power references obtained using the bacterial foraging optimization (BFO) technique to get the inverter current reference. For the DC side, the same concept based on the predictive approach is employed to control the DC-DC buck-boost converter by regulating the DC-link voltage using the forward Euler method to generate the discrete-time model to predict in real-time the BES current. The proposed control strategies are evaluated using simulation results obtained with Matlab/Simulink in presence of different types of loads, as well as experimental results obtained with a small-scale microgrid.

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