Controller parameters design of doubly feed induction generator using Particle Swarm Optimization

Wind energy is a promising source of electricity in the world and fastest-growing. Doubly-Fed Induction Generator (DFIG) systems dominate and widely used in wind power system because of their advantages over other types of generators, such as working at different speeds and not needing continuous maintenance. In this paper used the PI controller and Flexible AC Transmission System (FACTS) device specifically static compensator (STATCOM) to investigate the effect of the controller and FACTS device on the system. PI controller tuning by Particle Swarm Optimization technique (PSO) to limit or reduced the fault current in (DFIG) system. The responses of different kinds of faults have been presented like; two lines to ground faults and three lines to ground faults at different operating conditions. Faults are applied to three proposed controllers; the first controller is the Proportional-Integral (PI), the second controller is PI-controller based on Particle Swarm Optimization (PI-PSO) technique and STATCOM. A reactive power static synchronous compensator (STATCOM) is used, the main aim for the use of STATCOM is to improve the stability of a wind turbine system in addition to this is improving voltages profile, reduce power losses, treatment of power flow in overloaded transmission lines. The simulation programming is implemented using MATLAB program.

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Recurrent Functional-Link-Based Fuzzy-Neural-Network-Controlled Induction-Generator System Using Improved Particle Swarm Optimization

IEEE Transactions on Industrial Electronics ◽

10.1109/tie.2008.2010105 ◽

2009 ◽

Vol 56 (5) ◽

pp. 1557-1577 ◽

Cited By ~ 73

Author(s):

Faa-Jeng Lin ◽

Li-Tao Teng ◽

Jeng-Wen Lin ◽

Syuan-Yi Chen

Keyword(s):

Neural Network ◽

Particle Swarm Optimization ◽

Fuzzy Neural Network ◽

Particle Swarm ◽

Induction Generator ◽

Generator System ◽

Functional Link ◽

Swarm Optimization ◽

Improved Particle Swarm Optimization ◽

Fuzzy Neural

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Hybrid Control using Adaptive Particle Swarm Optimization and Integral Backstepping Control of Grid-connected Doubly Fed Induction Generator

Trends in Sciences ◽

10.48048/tis.2021.712 ◽

2021 ◽

Vol 18 (23) ◽

pp. 712

Author(s):

Elmostafa Chetouani ◽

Youssef Errami ◽

Abdellatif Obbadi ◽

Smail Sahnoun

Keyword(s):

Particle Swarm Optimization ◽

Wind Energy ◽

Hybrid Control ◽

Particle Swarm ◽

Backstepping Control ◽

Induction Generator ◽

Doubly Fed Induction Generator ◽

Swarm Optimization ◽

Adaptive Particle Swarm Optimization ◽

Doubly Fed

We proposed an analysis of a hybrid control of active and reactive power for a doubly-fed induction generator for variable velocity wind energy injection into the electrical grid using a combination of adaptive particle swarm optimization and integral backstepping control in this paper. The stability of the Lyapunov function is utilized to establish the latter. Six controllers are developed as part of the proposed control process: The first is concerned with the maximum PowerPoint. The stator powers are managed by the second and third regulators, which are performed by the optimal PI controller using adaptive particle swarm optimization. The DC link voltage is kept constant by the fourth controller. The fifth and sixth are employed to pilot the rotor powers and ensure that the power factor is maintained to 1. These three controllers are synthesized by using the nonlinear integral backstepping control. These control strategies show excellent results compared to field-oriented control under a variable wind speed profile and changing generator settings in a Matlab/Simulink environment. According to the test findings, using integral backstepping, the overshoot of the DC-link voltage is decreased by 99.16 %. Furthermore, the particle swarm optimization reduces its time to reach the equilibrium state to 4.3 m s and demonstrates robustness against parameter generator changes. HIGHLIGHTS The regulation of the produced power by the wind energy conversion system (WECS) based on a doubly-fed induction generator is becoming increasingly important to researchers. This system is modeled and simulated in the Matlab/Simulink software environment to apply the proposed control In order to extract the maximum power from the variable wind source, a maximum power point tracking method is developed based on the PI controller For piloting the wind energy system conversion (WECS) based on a DFIG, a combination of the integrated Backstepping controller and adaptive PSO is proposed and realized in this paper Robustness tests are established by adjusting the generator parameters, and a comparative study is conducted to verify the superiority of the suggested control over the indirect vector control GRAPHICAL ABSTRACT

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Reactive power analysis and frequency control of autonomous wind induction generator using particle swarm optimization and fuzzy logic

Energy Exploration & Exploitation ◽

10.1177/0144598719886373 ◽

2019 ◽

Vol 38 (3) ◽

pp. 755-782 ◽

Cited By ~ 1

Author(s):

Touti Ezzeddine

Keyword(s):

Fuzzy Logic ◽

Particle Swarm Optimization ◽

Reactive Power ◽

Frequency Control ◽

Renewable Energy Sources ◽

Particle Swarm ◽

Induction Generator ◽

Resistive Load ◽

Swarm Optimization ◽

Thyristor Controlled Reactor

Wind generation system is becoming increasingly important as renewable energy sources due to its advantages such as low maintenance requirement and mainly it does not cause environmental contamination. This paper presents the improvement procedure of the transient state and the regulation of the output frequency by adjusting the terminal capacitor. The aim is to provide frequency control of a self-excited induction generator in remote site using different strategies which are based on the adjustment of the reactive power at the outputs of a three-phase self-excited induction generator. A thyristor controlled reactor and a switched resistive load will be used to control reactive power. The proposed particle swarm optimization algorithm technique, location of the thyristor controlled reactor device, and parameter value are optimized simultaneously. The results obtained by this strategy will be compared with those provided by the use of Fuzzy Logic Controller. This study will be conducted through the analysis of the frequency in the steady state and transient case using a developed induction generator numerical model built using MATLAB/Simulink. Simulation and experimental results will be exposed and analyzed considering a resistive inductive load on a laboratory test bench.

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Selection of stand-alone self-excited induction generator parameters to obtain maximum allowable operating range under unbalanced operations using particle swarm optimization

International Journal of Systems Assurance Engineering and Management ◽

10.1007/s13198-020-00983-y ◽

2020 ◽

Vol 11 (3) ◽

pp. 677-689

Author(s):

Yatender Chaturvedi ◽

Sumit Kumar

Keyword(s):

Particle Swarm Optimization ◽

Particle Swarm ◽

Induction Generator ◽

Swarm Optimization ◽

Operating Range ◽

Selection Of

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Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189048 ◽

2020 ◽

Vol 39 (4) ◽

pp. 5699-5711

Author(s):

Shirong Long ◽

Xuekong Zhao

Keyword(s):

Feature Extraction ◽

Particle Swarm Optimization ◽

Deep Learning ◽

Real Time ◽

Image Recognition ◽

Particle Swarm ◽

Learning Technology ◽

Search Performance ◽

Swarm Optimization ◽

Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.

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