Optimization Control of Front-End Speed Regulation (FESR) Wind Turbine Based on Improved NSGA-II

Li, Xiaoqing; Dong, Hongbiao; Li, Hongwei; Li, Mingxue; Zhiqiang, Sun

doi:10.1109/access.2019.2908995

Cited by 16 publications

(6 citation statements)

References 8 publications

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“…e nonlinearity of the induction motor (IM) intrinsic model was shown to be significantly better managed using predictive control (MPC) optimization. e initialization and evolution of the backward learning mechanism, as well as the dynamic adjustment of the crossover probability and the variation probability in accordance with the exponential distribution, were all mentioned by Xiaoqing Li in literature [38]. e enhanced NSGA-II was used to implement variable pitch and variable torque control at their best.…”

Section: Literature Reviewmentioning

confidence: 99%

Passive Power Filter Optimization Problem Based on Adaptive Multipopulation NSGA-II and CRITIC-TOPSIS

Yuan

Liu

2022

Mathematical Problems in Engineering

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The best configuration of passive filters in extra-high voltage systems is investigated in this study using a two-stage multiobjective decision-making (MODM) framework. A collection of Pareto solutions is found using an adaptive multipopulation-modified nondominated ranking genetic algorithm (AMP NSGA-II) in the first stage. The goals taken into account include the least fundamental reactive power compensation losses, overall cost and maintenance cost, and simultaneous minimum harmonic current distortion rate. The objective weights are determined and the optimum solution is chosen in the second stage using the criteria importance interrelationship (CRITIC) and similarity ranking preference technique (TOPSIS). A number of examples show how the suggested method performs more effectively than the conventional and well-known algorithm and can identify the relationship between the objective functions, indicating that the suggested scheme has better superiority in filters and has a promising future in the solution of multiobjective problems.

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Section: Literature Reviewmentioning

confidence: 99%

Passive Power Filter Optimization Problem Based on Adaptive Multipopulation NSGA-II and CRITIC-TOPSIS

Yuan

Liu

2022

Mathematical Problems in Engineering

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“…Several evolutionary algorithms are suggested to find the nondominated Pareto solutions for a MOO. Some of them are strength Pareto evolutionary algorithm (SPEA) [34,35], niched Pareto genetic algorithm (NPGA) [36], and the improved version of nondominated sorting genetic algorithm (NSGA-II) [37]. Among these approaches, the NSGA-II shows the most effective technique to find a diverse set of solutions and calculating the solution near true Pareto optimality solutions [38,39].…”

Section: Non-dominating Sorting Binary Genetic Algorithm (Nsbga -Ii) ...mentioning

confidence: 99%

Intelligent Multiobjective NSBGA-II Control of Power Converters in DC Microgrids

Vafamand

Zarei

et al. 2021

IEEE Trans. Ind. Electron.

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“…Genetic Algorithm (GA) has been revived because of the rising demand of multi-objective optimization formulation in smart city development. With its elite retention strategy, diversity enhancement and fast convergence management with non-dominated sorting, NSGA-II is an improved evolutionary algorithm, which is analyzed to outperform most of the other multi-objective optimization algorithms [26].…”

Section: Optimization Formulation For Ipnlmoomentioning

confidence: 99%

“…The selection of the final solution for the specific application can be determined according to the requirement. In this paper, a generic selection method, reference point selection [26], is adopted to extract the final solution from PF. The reference point,p r , is determined by the maximum point of each objective, which is:…”

Section: ) Multi-objective Searching Processmentioning

confidence: 99%

Multi-Objective Optimization of Green Small Cell Allocation for IoT Applications in Smart City

Ran

Radwan

2020

IEEE Access

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Small cells (SCs) have proven their marvelous capability, especially dealing with high density of user equipment (UE) in the configuration of Internet of Things (IoT). It is widely accepted that the SCs concept is one of the prospective solutions for 5G networks, specifically targeting IoT, to attain the high demand in the future smart city. SC deployment has attracted the attention of multiple research efforts, due to its importance and potential towards enhancing future networking for IoT. However, there is still a lack of works providing SC allocation with comprehensive optimization modeling, which provides essential infrastructural platform to SC functionalities and services (e.g. offloading). The analysis of the number and positions of SCs, towards the simultaneous optimization of both energy efficiency and data rates, is a highly required research direction. SC allocation facilitates the sustainability and reliability, to support exponentially increasing number of users, as well as yielding feasibility of achieving high traffic demand and low latency performance, in addition to longer sustainability based on enhanced energy efficiency. In this paper, a smart small cell allocation scheme is proposed for IoT in smart city. Integer programming multiobjective optimization problem is formulated, and a new algorithm, based on the fusion of Branch-and-Bound (BnB) and Non-Dominated Sorting Genetic Algorithm II (NSGA-II), is developed. Energy efficiency optimization and data rate maximization are formulated, as mutual objectives to fulfill the key demands of IoT in smart city, based on the estimation of users' behaviors. Simulations are conducted, whose results show that the proposed Smart-SC allocation outperforms the different considered benchmarks, resulting in higher offered data rates, while achieving better energy efficiency.

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Optimization Control of Front-End Speed Regulation (FESR) Wind Turbine Based on Improved NSGA-II

Cited by 16 publications

References 8 publications

Passive Power Filter Optimization Problem Based on Adaptive Multipopulation NSGA-II and CRITIC-TOPSIS

Passive Power Filter Optimization Problem Based on Adaptive Multipopulation NSGA-II and CRITIC-TOPSIS

Intelligent Multiobjective NSBGA-II Control of Power Converters in DC Microgrids

Multi-Objective Optimization of Green Small Cell Allocation for IoT Applications in Smart City

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