In the below investigation, the impact of speed, feed, depth of cut, and workpiece hardness on the cutting temperature at tool-workpiece interface on hard-turning of the American Iron and Steel Institute (AISI) H13 to...
详细信息
In the below investigation, the impact of speed, feed, depth of cut, and workpiece hardness on the cutting temperature at tool-workpiece interface on hard-turning of the American Iron and Steel Institute (AISI) H13 tool steel parts will be investigated. It is worth noticing that the inclusion of workpiece hardness as an input variable in discussing cutting temperature wasn't widely investigated in the literature. Dry cutting experiments were done and the outcomes showed that the cutting temperature is highly influenced by the workpiece hardness. Also, it was noted that though the effect of depth of cut is statistically insignificant, yet it was found that the cutting temperature is an increasing function of the cutting depth. Furthermore, a predictive model for predicting cutting temperature was developed using response surface methodology (RSM) and artificial neural network (ANN) based on the inputs. The mean relative error was employed for testing the adequacy of the created predictive models, and its value was 3.56% and 0.844% for RSM and ANN respectively. Moreover, the new optimization algorithm, cuttlefish algorithm (CFA) was employed for optimizing the cutting temperature and the results were compared with those from the genetic algorithm (GA). The CFA obtained the best results at the least convergence rate.
This paper presents an efficient hybrid approach-based energy management strategy for grid-connected microgrid (MG) system. The proposed hybrid technique is the combination of both random forest (RF) and cuttlefish al...
详细信息
This paper presents an efficient hybrid approach-based energy management strategy for grid-connected microgrid (MG) system. The proposed hybrid technique is the combination of both random forest (RF) and cuttlefish algorithm (CFA) named as RFCFA. The proposed hybrid technique is utilized to decrease the electricity cost and increase the power flow between the source and load side. The MG system is tracked by the RF technique. The CFA is optimized based on the MG with the predicted load demand. MG employs two energy management strategies to reduce the impact of renewable energy prediction errors. The first strategy seeks at minimizing electricity costs during MG's operation. And the second strategy is aimed at balancing the power flow and reducing forecast error effects. In the grid-connected MG system, the objective function of the proposed technique is characterized with the inclusion of fuel cost, grid power variation, operation and maintenance cost. Battery energy storage systems (BESSs) can stabilize the output power and allow renewable power system units to operate at stable rate of output power. The proposed hybrid technique is executed in the working platform of MATLAB/Simulink, and the execution is evaluated using existing techniques such as GA, CFA and RBFNBBMO.
In general, optimization control is a relatively new trend where an optimization technique is used to tune the controller parameters for a given system under study. The optimization control is proved to he better than...
详细信息
ISBN:
(纸本)9781538659823
In general, optimization control is a relatively new trend where an optimization technique is used to tune the controller parameters for a given system under study. The optimization control is proved to he better than traditional adaptive control in many ways as discussed in this paper. Ibis research work presents optimization control schemes fir a Photovoltaic (PI) system. Two optimization techniques are proposed to find the optimum gain values of the controller in a hybrid approach. These techniques are Particle Swarm Optimization (PSO) and cuttlefish algorithm (CFA). In addition, two different controllers are considered which are the Proportional-Integral (PI) and the Proportional-Integral Acceleration (PIA) controllers. Diffrent results are presented and analysed to evaluate the dynamic performance of the proposed control schemes. Finally, robustness tests are carried out to prove the stability of the proposed controllers.
During the day, photovoltaic (PV) systems are exposed to different sunlight conditions in addition to partial shading (PS). Accordingly, maximum power point tracking (MPPT) techniques have become essential for PV syst...
详细信息
During the day, photovoltaic (PV) systems are exposed to different sunlight conditions in addition to partial shading (PS). Accordingly, maximum power point tracking (MPPT) techniques have become essential for PV systems to secure harvesting the maximum possible power from the PV modules. In this paper, optimized control is performed through the application of relatively newly developed optimization algorithms to PV systems under Partial Shading (PS) conditions. The initial value of the duty cycle of the boost converter is optimized for maximizing the amount of power extracted from the PV arrays. The emperor penguin optimizer (EPO) is proposed not only to optimize the initial setting of duty cycle but to tune the gains of controllers used for the boost converter and the grid-connected inverter of the PV system. In addition, the performance of the proposed system based on the EPO algorithm is compared with another newly developed optimization technique based on the cuttlefish algorithm (CFA). Moreover, particle swarm optimization (PSO) algorithm is used as a reference algorithm to compare results with both EPO and CFA. PSO is chosen since it is an old, well-tested, and effective algorithm. For the evaluation of performance of the proposed PV system using the proposed algorithms under different PS conditions, results are recorded and introduced.
A hybrid technique-based energy management scheme for optimal sizing of solar, wind, battery along the integral of pumped hydro storage (PHS) is presented in this paper. The suggested control scheme is the consolidate...
详细信息
A hybrid technique-based energy management scheme for optimal sizing of solar, wind, battery along the integral of pumped hydro storage (PHS) is presented in this paper. The suggested control scheme is the consolidated implementation of both Improved Dolphin Echolocation algorithm (IDEA) and cuttlefish algorithm (CFA). Searching behavior of Dolphin Echolocation algorithm (DEA) is changed through utilizing productive search capacities such as levy flight, so it is known as IDEA. The prominent intension of this work is the optimal energy management in between the source side as well as load side also the total cost function minimization through the suggested IDEA-CFA control procedure. In the proposed work, the IDEA joined with CFA develops the appraisal approach for setting specific control signals to the system as well as generating control signals to disconnected path in subject to power assortment in between the source side as well as load side. Based on equality as well as inequality constraints, the objective function is classified by system data. The suggested model is implemented at MATLAB/Simulink work site as well as execution will be evaluated along the present strategies. The annualized cost and lifetime of HRES considering the system component with capital cost, operation and maintenance cost, replacement cost and lifetime are analyzed. The system component such as PV, Wind, BESS, water pump, water turbine, and upper reservoir are analyzed. The capital cost, operation and maintenance cost, replacement cost and lifetime of PV are 865 [$/kW], 18 [$/year], 865 [$/kW], and 25 [year].
In this paper, a novel context-based 3D Otsu algorithm using human learning optimization (HLO) is proposed for multilevel color image segmentation. The performance of 3D Otsu algorithm is reported to be poor while dea...
详细信息
In this paper, a novel context-based 3D Otsu algorithm using human learning optimization (HLO) is proposed for multilevel color image segmentation. The performance of 3D Otsu algorithm is reported to be poor while dealing with between-class variances through the aid of three-dimensional histogram. To overcome this problem, the concept of context thresholding has been exploited to derive pixel intensity values and spatial information. The nature of spatial context and histogram of an image is very similar. The use of energy curve for 3D Otsu gives satisfactory results but it is more time-consuming during the process of threshold selection. HLO is a recently developed meta-heuristic optimization algorithm that involves the use of learning operators developed by mimicking human learning mechanisms. In this paper, in order to avoid an exhaustive search to obtain optimal thresholds, HLO is used. Experimental studies reported in this paper demonstrate that the proposed method is better than the histogram-based 1D Otsu, 2D Otsu, and 3D Otsu methods. These claims have been confirmed by comparing fidelity parameters such as mean error (ME), mean squared error (MSE), peak signal-to-noise ratio (PSNR), feature similarity index (FSIM), structure similarity index (SSIM) and entropy. (C) 2019 Elsevier B.V. All rights reserved.
暂无评论