This study implements a potent Multiobjective Multi-Verse Optimization algorithm to solve the highly complicated combined economic emission dispatch and combined heat and power economic emission dispatch problems. Sol...
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This study implements a potent Multiobjective Multi-Verse Optimization algorithm to solve the highly complicated combined economic emission dispatch and combined heat and power economic emission dispatch problems. Solving these problems operates the power system integrated with cogeneration plants economically and reduces the environmental impacts caused by the pollutants of fossil fuel-fired power plants. A chaotic opposition based strategy is proposed to explore the search space extensively and to generate the initial populations for the multiobjective optimization algorithm. An effective constraint handling mechanism is also proposed to enable the population to remain within the bounds and in the feasible operating region of the cogeneration plants. The algorithm is applied to standard test functions, four test systems including a large 140 bus system considering valve-point effects, ramp limits, transmission power losses, and the feasible operating region of cogeneration units. The Pareto Optimal solutions obtained by the algorithm are well spread and diverse when compared with other optimization algorithms. The statistical analysis and various performance metrics used indicate the algorithm converges to true POF and is a viable alternative to solve the highly complicated combined economic emission dispatch and combined heat and power economic emission dispatch problems. (C) 2020 Elsevier B.V. All rights reserved.
This paper presents a novel methodology that utilizes gesture recognition data, which are collected with a Leap Motion Controller (LMC), in tandem with the Spotted Hyena-based Chimp Optimization Algorithm (SSC) for fe...
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This paper presents a novel methodology that utilizes gesture recognition data, which are collected with a Leap Motion Controller (LMC), in tandem with the Spotted Hyena-based Chimp Optimization Algorithm (SSC) for feature selection and training of deep neural networks (DNNs). An expansive tabular database was created using the LMC for eight distinct gestures and the SSC algorithm was used for discerning and selecting salient features. This refined feature subset is then utilized in the subsequent training of a DNN model. A comprehensive comparative analysis is conducted to evaluate the performance of the SSC algorithm in comparison with established optimization techniques, such as Particle Swarm Optimization(PSO), Grey Wolf Optimizer(GWO), and Sine Cosine Algorithm(SCA), specifically in the context of feature selection. The empirical findings decisively establish the efficacy of the SSC algorithm, consistently achieving a high accuracy rate of 98% in the domain of gesture recognition tasks. The feature selection approach proposed emphasizes its intrinsic capacity to enhance not only the accuracy of gesture recognition systems and its wider suitability across diverse domains that require sophisticated feature extraction techniques.
Photovoltaic generation systems under partial shading conditions are difficult to optimize using conventional maximum power point tracking (MPPT) algorithms. Most of the techniques developed for these conditions, fail...
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ISBN:
(纸本)9781538679951
Photovoltaic generation systems under partial shading conditions are difficult to optimize using conventional maximum power point tracking (MPPT) algorithms. Most of the techniques developed for these conditions, fail to track dynamically the MPP and leads to energy losses during normal operation. This paper presents hybrid MPPT techniques combining global and conventional MPPTs to extract the most available energy from the system under any condition. The methods are compared through simulations using Matlab/ Simulink, where the tracking factor (TF) and power characteristics during time are evaluated.
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