Modified P&O Approach Based Detection of the Optimal Power-Speed Curve for MPPT of Wind Turbines

2020 ◽  
pp. 137-155
Author(s):  
Liuying Li ◽  
Yaxing Ren ◽  
Jian Chen ◽  
Kai Shi ◽  
Lin Jiang
Keyword(s):  
Wind Turbines ◽  
Optimal Power

scholarly journals Optimal power flow for distribution networks with distributed generation

2015 ◽  
Vol 12 (2) ◽  
pp. 145-170 ◽  
Author(s):  
Jordan Radosavljevic ◽  
Miroljub Jevtic ◽  
Dardan Klimenta ◽  
Nebojsa Arsic
Keyword(s):  
Distributed Generation ◽  
Wind Turbines ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Distribution Networks ◽  
Point Estimate ◽  
Photovoltaic Systems ◽  
Multi Objective ◽  
Point Estimate Method ◽  
Optimal Power

This paper presents a genetic algorithm (GA) based approach for the solution of the optimal power flow (OPF) in distribution networks with distributed generation (DG) units, including fuel cells, micro turbines, diesel generators, photovoltaic systems and wind turbines. The OPF is formulated as a nonlinear multi-objective optimization problem with equality and inequality constraints. Due to the stochastic nature of energy produced from renewable sources, i.e. wind turbines and photovoltaic systems, as well as load uncertainties, a probabilisticalgorithm is introduced in the OPF analysis. The Weibull and normal distributions are employed to model the input random variables, namely the wind speed, solar irradiance and load power. The 2m+1 point estimate method and the Gram Charlier expansion theory are used to obtain the statistical moments and the probability density functions (PDFs) of the OPF results. The proposed approach is examined and tested on a modified IEEE 34 node test feeder with integrated five different DG units. The obtained results prove the efficiency of the proposed approach to solve both deterministic and probabilistic OPF problems for different forms of the multi-objective function. As such, it can serve as a useful decision-making supporting tool for distribution network operators.

Optimal Power Sharing Control of Wind Turbines

2017 ◽  
Vol 32 (1) ◽  
pp. 824-825 ◽  
Author(s):  
Yujun Li ◽  
Zhao Xu ◽  
Ke Meng
Keyword(s):  
Wind Turbines ◽  
Power Sharing ◽  
Optimal Power
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