Multi-objective proportional–integral–derivative optimization algorithm for parameters optimization of double-fed induction generator-based wind turbines

2021 ◽  
pp. 107673
Author(s):  
Linfei Yin ◽  
Qi Gao
Keyword(s):  
Wind Turbines ◽  
Optimization Algorithm ◽  
Parameters Optimization ◽  
Induction Generator ◽  
Proportional Integral Derivative ◽  
Proportional Integral ◽  
Multi Objective ◽  
Double Fed Induction Generator

Fractional order frequency proportional-integral-derivative control of microgrid consisting of renewable energy sources based on multi-objective grasshopper optimization algorithm

2021 ◽  
pp. 014233122110346
Author(s):  
Abdulsamed Tabak
Keyword(s):  
Fractional Order ◽  
Optimization Algorithm ◽  
Frequency Control ◽  
Frequency Deviation ◽  
Control Signal ◽  
Proportional Integral Derivative ◽  
Proportional Integral ◽  
Multi Objective ◽  
Grasshopper Optimization Algorithm ◽  
Grasshopper Optimization

In recent years, fractional order proportional-integral-derivative (FOPID) controllers have been applied in different areas in the academy due to their superior performance over conventional proportional-integral-derivative (PID) controllers. When the literature is reviewed, it has been observed that lack of studies that use swarm-based and multi-objective optimization algorithms together with FOPID controllers in frequency control of micro-grid. The load frequency control (LFC) problem is considered as two objectives in order to eliminate the complications that occur when only the frequency deviation is minimized. In our study, a method called MOGOA-FOPID in which both the frequency deviation and the control signal are minimized together for the frequency control in the microgrid is proposed. By using the multi-objective grasshopper optimization algorithm (MOGOA), both the frequency deviation and the control signal are minimized together, and thus, it is aimed to limit the battery capacity, reduce the flywheel jerk and avoid high diesel fuel consumption as well as an effective frequency control. In order to obtain a more realistic system, not only the photovoltaic (PV) solar and wind power but also the load demand is taken in a stochastic structure. Then, the results of the proposed MOGOA-FOPID are compared with the results of multi-objective genetic algorithm (MOGA)-based PID/FOPID and MOGOA-PID and its superiority is demonstrated. Finally, robustness tests of the system are performed under the perturbed parameters and outperform of MOGOA-FOPID over other methods is seen.

A multi-objective artificial electric field optimization algorithm for allocation of wind turbines in distribution systems

2021 ◽  
pp. 107278
Author(s):  
Amirreza Naderipour ◽  
Zulkurnain Abdul-Malek ◽  
Mohd Wazir Bin Mustafa ◽  
Josep M. Guerrero
Keyword(s):  
Electric Field ◽  
Wind Turbines ◽  
Optimization Algorithm ◽  
Distribution Systems ◽  
Multi Objective

scholarly journals Design optimization of multi-objective proportional–integral–derivative controllers for enhanced handling quality of a twin-engine, propeller-driven airplane

2020 ◽  
Vol 12 (6) ◽  
pp. 168781402092317
Author(s):  
Mohsen Rostami ◽  
Joon Chung ◽  
Hyeong Uk Park
Keyword(s):  
Genetic Algorithm ◽  
Design Optimization ◽  
Proportional Integral Derivative ◽  
Optimum Result ◽  
Proportional Integral ◽  
Multi Objective ◽  
Directional Motion ◽  
The Stability ◽  
Handling Quality

Herein, the design optimization of multi-objective controllers for the lateral–directional motion using proportional–integral–derivative controllers for a twin-engine, propeller-driven airplane is presented. The design optimization has been accomplished using the genetic algorithm and the main goal was to enhance the handling quality of the aircraft. The proportional–integral–derivative controllers have been designed such that not only the stability of the lateral–directional motion was satisfied but also the optimum result in longitudinal trim condition was achieved through genetic algorithm. Using genetic algorithm optimization, the handling quality was improved and placed in level 1 from level 2 for the proposed aircraft. A comprehensive sensitivity analysis to different velocities, altitudes and centre of mass positions is presented. Also, the performance of the genetic algorithm has been compared to the case where the particle swarm optimization tool is implemented. In this work, the aerodynamic coefficients as well as the stability and control derivatives were predicted using analytical and semi-empirical methods validated for this type of aircraft.

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