Intelligent approach for active and reactive power control in doubly fed induction generator wind turbine system

Pandey, Kamlesh; Bharath, Kurukuru Varaha Satya

doi:10.1109/iicpe.2016.8079418

Cited by 2 publications

(1 citation statement)

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“…The researchers in [151] proposed a theoretical regulatory methodology for DFIG wind turbine systems to overcome the IGBT switching operations' power quality issues and behavior. Researchers carried out the active/reactive power control and the DC-link voltage control at variable wind speeds with the help of back-to-back converters.…”

Section: Ai Methodologies In Dfig Power Converter Control Systemmentioning

confidence: 99%

Artificial Intelligence Control System Applied in Smart Grid Integrated Doubly Fed Induction Generator-Based Wind Turbine: A Review

Behara

Saha

2022

Energies

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Wind-driven turbines utilizing the doubly-fed induction generators aligned with the progressed IEC 61400 series standards have engrossed specific consideration as of their benefits, such as adjustable speed, consistent frequency mode of operation, self-governing competencies for voltage and frequency control, active and reactive power controls, and maximum power point tracking approach at the place of shared connection. Such resource combinations into the existing smart grid system cause open-ended problems regarding the security and reliability of power system dynamics, which needs attention. There is a prospect of advancing the art of wind turbine-operated doubly-fed induction generator control systems. This section assesses the smart grid-integrated power system dynamics, characteristics, and causes of instabilities. These instabilities are unclear in the wind and nonlinear load predictions, leading to a provisional load-rejection response. Here, machine learning computations and transfer functions measure physical inertia and control system design’s association with power, voltage, and frequency response. The finding of the review in the paper indicates that artificial intelligence-based machine and deep learning predictive diagnosis fields have gained prominence because of their low cost, less infrastructure, reduced diagnostic time, and high level of accuracy. The machine and deep learning methodologies studied in this paper can be utilized and extended to the smart grid-integrated power context to create a framework for developing practical and accurate diagnostic tools to enhance the power system’s accuracy and stability, software requirements, and deployment strategies.

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Section: Ai Methodologies In Dfig Power Converter Control Systemmentioning

confidence: 99%