This study presents a method for regulation parameters of a distributed generation (DG) system by means of a hybrid optimisation algorithm. This aims in increasing the stability and reducing the losses and the cost of...
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This study presents a method for regulation parameters of a distributed generation (DG) system by means of a hybrid optimisation algorithm. This aims in increasing the stability and reducing the losses and the cost of generation. The hybrid algorithm which includes probability based incremental learning and micro genetic algorithm are tested among other computational intelligence techniques to validate the efficiency of the method by maximising the total social welfare and minimising the network congestion. Simultaneous optimisation of DG parameters which includes DG size, location and type is explored using generation rescheduling and with load curtailment which is vindicated on a modified IEEE distribution system and in a real time Indian utility system. Results show us that the proposed method presents advantages of low computational complexity.
This paper presents parameter and topology optimization of inductor shapes using evolutionary algorithms. The goal of the optimization is to reduce the size of inductors satisfying the specifications on inductance val...
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This paper presents parameter and topology optimization of inductor shapes using evolutionary algorithms. The goal of the optimization is to reduce the size of inductors satisfying the specifications on inductance values under weak and strong bias-current conditions. The inductance values are computed from the finite-element (FE) method taking magnetic saturation into account. The result of the parameter optimization, which leads to significant reduction in the volume, is realized for test, and the dependence of inductance on bias currents is experimentally measured, which is shown to agree well with the computed values. Moreover, novel methods are introduced for topology optimization to obtain inductor shapes with homogeneous ferrite cores suitable for mass production.
An efficient and powerful full-wave electromagnetic technique is presented to characterise and design periodic metamaterial structures. First, the spectral finite-difference time-domain (FDTD) method with periodic bou...
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An efficient and powerful full-wave electromagnetic technique is presented to characterise and design periodic metamaterial structures. First, the spectral finite-difference time-domain (FDTD) method with periodic boundary conditions and uniaxial perfect matched layer is employed to predict the performance of a mushroom-like artificial magnetic conductor (AMC) surface and further extended to characterise a negative-refractive-index material consisting of lumped and distributed transmission-line elements. Then, a new computational technique is developed to design and optimise periodic metamaterial structures by integrating the spectral FDTD method with a genetic algorithm (GA), namely the micro-genetic algorithm. This computational technique is successfully applied to design and optimise single-band and dual-band AMC structures consisting of a frequency-selective surface and a ground plane. It is demonstrated that the GA/FDTD technique is a very effective approach for the design and optimisation of periodic metarnaterial structures consisting of dielectrics and conductors of arbitrary configurations.
In planning a distribution network, after routing newly feeders, optimal placement of sectionalizers and tie switches is an important task. However, it is very difficult for planners to select the optimal place of tie...
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In planning a distribution network, after routing newly feeders, optimal placement of sectionalizers and tie switches is an important task. However, it is very difficult for planners to select the optimal place of tie switches and sectionalizers, because too many candidate locations exist. In this paper, tie switches and sectionalizers placement problem on radial distribution networks are formulated, and a microgenetic algorithm (MGA) in conjunction with fuzzy logic (FL) is proposed as solution. A FL is used to apply expert knowledge that takes into load priority and failure rate and a MGA is used to considerate reliability indices. The costs associated with installation of switches are shown. A 104-bus test system is presented and the results are compared to the solution given by other techniques. This comparison confirms the efficiency of the proposed method which makes it promising to solve complex problems of tie switches and sectionalizers placement in radial distribution networks.
The main focus of this paper is on the application of Genetic algorithm (GA) to search for an optimal solution to a reatisticafly formulated economic dispatch (ED) problem. GA is a global search technique based on pri...
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ISBN:
(纸本)078039156X
The main focus of this paper is on the application of Genetic algorithm (GA) to search for an optimal solution to a reatisticafly formulated economic dispatch (ED) problem. GA is a global search technique based on principles inspired from the genetic and evolution mechanism observed in natural biological systems. A major drawback of the conventional GA (CGA) approach is that it can be time consuming. The micro-GA (mu GA) approach has been proposed as a better time efficient alternative for some engineering problems. The effectiveness of CGA and mu GA to solving ED problem is initially verified on an IEEE 3-generating unit, 6-bus test system. Simulation results obtained on this network using CGA and mu GA validate their effectiveness when compared with the published results obtained via the classical and the Hopfield neural network approaches. Finally, both GA approaches have been successfully applied to the coordination of the Nigerian 31-bus system fed by four thermal and three hydro generating units. Herein, use has been made of the loss formula developed for the Nigerian system from several power flow studies. For the Nigerian case study, the mu GA is shown to exhibit superior performance than the CGA from both optimal generation allocations and computational time viewpoints.
Water level monitoring and forecasting are essential tasks in flood emergency response. This study proposes an Edge COMputing-based Sensory NETwork (ECOMSNet), an innovative decentralized early warning system (EWS), f...
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Water level monitoring and forecasting are essential tasks in flood emergency response. This study proposes an Edge COMputing-based Sensory NETwork (ECOMSNet), an innovative decentralized early warning system (EWS), for water level monitoring and prediction. A sensor-embedded algorithm integrates the direct step method (DSM) with a microgenetic algorithm (MGA). This algorithm predicts the water surface profile and corrects it once water level observations are available. It also meets efficiency requirements to accommodate sensor computation limitations. The errors in the predicted water surface profiles in channels with gradually varied flows are 5% in a laboratory flume experiment and below 10% in a field experiment. The ECOMSNet is an achievement of edge computing-based Internet of Things. It shows potential to increase emergency response efficiency. However, the system requires further refinement and testing if it is to adequately address rapidly varied unsteady flow in a scaled-up implementation.
Engine mount rubber (EMR) is an important vehicle component to isolate the vehicle structure from engine vibration. The paper deals with optimal design of EMR considering the material stiffness and fatigue strength of...
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Engine mount rubber (EMR) is an important vehicle component to isolate the vehicle structure from engine vibration. The paper deals with optimal design of EMR considering the material stiffness and fatigue strength of a rubber. The objective is to minimize both the weight and the maximum stress of EMR, and to maximize the fatigue life cycle subjected to constraints on the static stiffness of the rubber. A hyperelastic rubber model with a high strain range is used to accommodate the non-linear behaviour of EMR in the stress and fatigue analysis. In the context of approximate optimization, a back-propagation neural network is used to construct global response surfaces between input design variables and output responses of objective functions and constraints. A microgenetic algorithm (MGA) is adopted as a global optimizer in order to consider the inherent non-linearity of analysis model as well. A multiobjective optimization result shows improved design performances regarding the reduction in the maximum stress and the increase in the life cycle with acceptable material stiffness.
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