By integrating smart grid technology with home energy management systems, households can monitor and optimise their energy consumption. This allows for more efficient use of energy resources, reducing waste and loweri...
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This paper proposes a novel approach to address the challenges of deploying complex robotic software in large-scale systems, i.e., Centralized Nonlinear Model Predictive Controllers (CNMPCs) for multi-agent systems. T...
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This paper addresses the passive source localization problem using hybrid angle-of-arrival (AOA) and time-difference-of-arrival (TDOA) measurements observed by single stationary receiver at several time intervals, whe...
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Graph convolutional networks (GCNs) are popular for building machine-learning application for graph-structured data. This widespread adoption led to the development of specialized GCN hardware accelerators. In this wo...
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This paper presents a machine-learning-based speedup strategy for real-time implementation of model-predictive-control(MPC)in emergency voltage stabilization of power *** success in various applications,real-time impl...
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This paper presents a machine-learning-based speedup strategy for real-time implementation of model-predictive-control(MPC)in emergency voltage stabilization of power *** success in various applications,real-time implementation of MPC in power systems has not been successful due to the online control computation time required for large-sized complex systems,and in power systems,the computation time exceeds the available decision time used in practice by a large *** long-standing problem is addressed here by developing a novel MPC-based framework that i)computes an optimal strategy for nominal loads in an offline setting and adapts it for real-time scenarios by successive online control corrections at each control instant utilizing the latest measurements,and ii)employs a machine-learning based approach for the prediction of voltage trajectory and its sensitivity to control inputs,thereby accelerating the overall control computation by multiple ***,a realistic control coordination scheme among static var compensators(SVC),load-shedding(LS),and load tap-changers(LTC)is presented that incorporates the practical delayed actions of the *** performance of the proposed scheme is validated for IEEE 9-bus and 39-bus systems,with±20%variations in nominal loading conditions together with *** show that our proposed methodology speeds up the online computation by 20-fold,bringing it down to a practically feasible value(fraction of a second),making the MPC real-time and feasible for power system control for the first time.
This paper considers a free space optical (FSO) cooperative network with an energy harvesting (EH) relay with no permanent power supply. The relay implements the harvest-store-use strategy and, in addition to the ener...
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The limitations of the conventional master-slavesplitting(MSS)method,which is commonly applied to power flow and optimal power flow in integrated transmission and distribution(I-T&D)networks,are first *** that the...
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The limitations of the conventional master-slavesplitting(MSS)method,which is commonly applied to power flow and optimal power flow in integrated transmission and distribution(I-T&D)networks,are first *** that the MSS method suffers from a slow convergence rate or even divergence under some circumstances,a least-squares-based iterative(LSI)method is *** with the MSS method,the LSI method modifies the iterative variables in each iteration by solving a least-squares problem with the information in previous iterations.A practical implementation and a parameter tuning strategy for the LSI method are ***,a LSI-PF method is proposed to solve I-T&D power flow and a LSIheterogeneous decomposition(LSI-HGD)method is proposed to solve optimal power *** experiments demonstrate that the proposed LSI-PF and LSI-HGD methods can achieve the same accuracy as the benchmark ***,these LSI methods,with appropriate settings,significantly enhance the convergence and efficiency of conventional ***,in some cases,where conventional methods diverge,these LSI methods can still converge.
The work proposes a methodology for five different classes of ECG signals. The methodology utilises moving average filter and discrete wavelet transformation for the remove of baseline wandering and powerline interfer...
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A new online scheduling algorithm is proposed for photovoltaic(PV)systems with battery-assisted energy storage systems(BESS).The stochastic nature of renewable energy sources necessitates the employment of BESS to bal...
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A new online scheduling algorithm is proposed for photovoltaic(PV)systems with battery-assisted energy storage systems(BESS).The stochastic nature of renewable energy sources necessitates the employment of BESS to balance energy supplies and demands under uncertain weather *** proposed online scheduling algorithm aims at minimizing the overall energy cost by performing actions such as load shifting and peak shaving through carefully scheduled BESS charging/discharging *** scheduling algorithm is developed by using deep deterministic policy gradient(DDPG),a deep reinforcement learning(DRL)algorithm that can deal with continuous state and action *** of the main contributions of this work is a new DDPG reward function,which is designed based on the unique behaviors of energy *** new reward function can guide the scheduler to learn the appropriate behaviors of load shifting and peak shaving through a balanced process of exploration and *** new scheduling algorithm is tested through case studies using real world data,and the results indicate that it outperforms existing algorithms such as Deep *** online algorithm can efficiently learn the behaviors of optimum non-casual off-line algorithms.
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