دانلود مقاله ISI انگلیسی شماره 56216
عنوان فارسی مقاله

یک مدل مزرعه بادی ژنراتور القایی دوسو تغذیه جدید کل با استفاده از گشتاور مکانیکی جبران عامل

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
56216 2013 10 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
A novel aggregated DFIG wind farm model using mechanical torque compensating factor
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Energy Conversion and Management, Volume 67, March 2013, Pages 265–274

کلمات کلیدی
ژنراتور القایی دوسو تغذیه؛ مزرعه بادی؛ مدل کل ؛ گشتاور مکانیکی جبران عامل؛ منطق فازی
پیش نمایش مقاله
پیش نمایش مقاله یک مدل مزرعه بادی ژنراتور القایی دوسو تغذیه جدید کل با استفاده از گشتاور مکانیکی جبران عامل

چکیده انگلیسی

A novel aggregated model for wind farms consisting of wind turbines equipped with doubly-fed induction generators (DFIGs) is proposed in this paper. In the proposed model, a mechanical torque compensating factor (MTCF) is integrated into a full aggregated wind farm model to deal with the nonlinearity of wind turbines in the partial load region and to make it behave as closely as possible to a complete model of the wind farm. The MTCF is initially constructed to approximate a Gaussian function by a fuzzy logic method and optimized on a trial and error basis to achieve less than 10% discrepancy between the proposed aggregated model and the complete model. Then, a large scale offshore wind farm comprising of 72 DFIG wind turbines is used to verify the effectiveness of the proposed aggregated model. The simulation results show that the proposed aggregated model approximates active power (Pe) and reactive power (Qe) at the point of common coupling more accurately than the full aggregated model by 8.7% and 12.5%, respectively, during normal operation while showing similar level of accuracy during grid disturbance. Computational time of the proposed aggregated model is slightly higher than that of the full aggregated model but much faster than the complete model by 90.3% during normal operation and 87% during grid disturbance.

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