The international electrotechnical commission

Osborne, H. S.

doi:10.1109/ee.1953.6438478

Cited by 6 publications

(2 citation statements)

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“…Subsequently, IEC 61400-26 activity was introduced on the availability of wind turbines to develop the technological description of accessibility for wind-driven turbines. Standard operating conditions of WTGS, are allowed for inner and outer circumstances, and algorithms for operational devices established on serving of the timeline and production [128,129].…”

Section: Harmonicsmentioning

confidence: 99%

“…Hence, no deviances from the standing IEC standards are permissible. The IEC 61400-26 standard concept further splits the entity into three parts: IEC 61400-26-1 terms for time-based availability of a WTGS, IEC 61400-26-2 terms for the production-based availability of a WTGS, and IEC-61400-26-3 terms for the time and production-based availability of a WTGS [128]. Testing and assessing power supply quality features of smart grid-integrated wind power energy converters was developed by the IEC-technical committee 88, and nowadays, most wind-driven turbine industrialists supply power quality feature data accordingly [72].…”

Section: Harmonicsmentioning

confidence: 99%

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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: Harmonicsmentioning

confidence: 99%

Section: Harmonicsmentioning

confidence: 99%