A LQRI power control for DFIG tuned by a weighted-PSO

Murari, Andre L. L. F.; Rodrigues, Lucas Lima; Altuna, José Alberto Torrico; Potts, Alain Segundo; Almeida, L.A.L. de; Filho, Alfeu J. Sguarezi

doi:10.1016/j.conengprac.2018.12.014

Cited by 13 publications

(4 citation statements)

References 20 publications

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“…As shown in Figure II-C, the converter recompenses for the in equality stuck between the electrical and mechanical frequencies, where RSC handless the acquire optimized 'P' and 'Q' by means of controlling the rotor current, whereas GSC works for DC link voltage to the Unity Power Factor operation [28]. And also have a proposed power control strategy in DFIG-based WECS with regulating the reactive power along with a linear quadratic regulator with integrator (LQRI) for matching the grid requirement.…”

Section: Convertersusedindfigbasedwecsmentioning

confidence: 99%

Optimization Techniques of DFIG Controller Design for Wind Energy Conversion Systems

Bharti

2024

Preprint

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The Doubly-Fed Induction Generator (DFIG) is suggested for Wind Energy Conversion System (WECS) to extract wind power. DFIG is preferably employed due to its robustness towards variable wind and rotor speed. DFIG has the adaptable property because the system parameters are smoothly dealt with, including real power, reactive power, DC-link voltage, and the transient and dynamic responses, which are needed to analyze constantly. The analysis becomes more prominent during any unusual condition in the electrical power system. Hence, the study and improvement in the system parameters and transient response performance of DFIG are required to be accomplished using some controlling techniques. For fulfilling the task, the present work implements and compares the optimization methods for the design of the DFIG controller for WECS. The bio-inspired optimization techniques are applied to get the optimal controller design parameters for DFIG-based WECS. The optimized DFIG controllers are then used to retrieve the transient response performance of the six-order DFIG model with a step input. The results using MATLAB/Simulink show the betterment of the Firefly algorithm (FFA) over other control techniques when compared with the other controller design methods.

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Section: Convertersusedindfigbasedwecsmentioning

confidence: 99%

Optimization Techniques of DFIG Controller Design for Wind Energy Conversion Systems

Bharti

2024

Preprint

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“…Finally, the augmented model can be written as ρ(n + 1) = A aug (θ)ρ(n) + B uaug u(n) +B waug (θ)v g (n) + B raug r(n) y(n) = C aug ρ(n) (16) where ρ(n) = [x(n) ϕ(n) x r (n) ] is the augmented state vector and…”

Section: State Space Model With Uncertaintiesmentioning

confidence: 99%

Robust Control of Grid-Tied Inverters using Particle Swarm Optimization and Linear Matrix Inequalities

Borin

Cleveston

Koch

et al. 2020

2020 IEEE 14th International Conference on Compatibility, Power Electronics and Power Engineering (CPE-POWERENG)

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This paper provides a new design procedure for robust current controllers applied to LCL-filtered grid-tied inverters suitable for the integration of renewable energy sources. The design takes into account the digital implementation delay, multiple resonant controllers and operation under uncertain grid impedance. The procedure is based on the optimization of an objective function that allows to get a good trade-off between the settling of transient responses and rejection of disturbances. A particle swarm algorithm is used to find the optimal control gains and, differently from other works, here the robust stability of the closed-loop system under uncertain parameters is theoretically certified by means of linear matrix inequalities. Experimental results are shown, confirming that the closed-loop system with gains obtained by the proposed procedure presents a good tradeoff between robustness and performance, with suitable transients and grid currents with low harmonic content, complying with requirements from IEEE 1547 Standard, becoming a useful robust control design alternative for power converters in the distributed generation scenario.

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“…Em Mahmoud and Oyedeji (2018), o problema do LQRI baseado em multimodelo, referenteàs diferentes velocidades de ventos,é utilizado para o controle de turbinas eólicas. Em Murari et al (2019),é utilizado o problema do LQRI para executar o controle de potência fornecidoà rede elétrica por um gerador de indução duplamente alimentado.…”

Section: Introductionunclassified

Metodologia de Controle LQRI Fuzzy Evolutivo via Alocação de Autoestruturas em Regiões Circulares Robustas com Especificações de Desempenho

Noronha

Serra

2020

Anais Do Congresso Brasileiro De Automática 2020

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Neste artigo, é proposta uma nova metodologia de controle LQRI (Linear QuadraticRegulator with Integral Action) baseada em modelo fuzzy Takagi-Sugeno evolutivo. A metodologia de controle é dada pela obtenção de uma matriz de ganho de realimentaçãoo de estados, que garante mutualmente a otimização do funcional de otimalidade do LQRI e a alocação de autovalores em regiões circulares robustas parametrizadas em função de especicações de desempenho, utilizando uma abordagem geométrica.

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A LQRI power control for DFIG tuned by a weighted-PSO

Cited by 13 publications

References 20 publications

Optimization Techniques of DFIG Controller Design for Wind Energy Conversion Systems

Optimization Techniques of DFIG Controller Design for Wind Energy Conversion Systems

Robust Control of Grid-Tied Inverters using Particle Swarm Optimization and Linear Matrix Inequalities

Metodologia de Controle LQRI Fuzzy Evolutivo via Alocação de Autoestruturas em Regiões Circulares Robustas com Especificações de Desempenho

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