مدل سازی حرارتی ترانسفورماتورهای قدرت
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مدل سازی حرارتی ترانسفورماتورهای قدرت

عنوان فارسی مقاله: یادگیری مشارکتی رو به تکامل عضویت مجموعه پیشرفته با حداقل مربعات بازگشتی هسته برای مدل سازی حرارتی ترانسفورماتورهای قدرت
عنوان انگلیسی مقاله: An enhanced set-membership evolving participatory learning with kernel recursive least squares applied to thermal modeling of power transformers
مجله/کنفرانس: تحقیقات سیستم های توان الکتریکی - Electric Power Systems Research
رشته های تحصیلی مرتبط: برق
گرایش های تحصیلی مرتبط: سیستم های قدرت، برق قدرت، مهندسی کنترل، الکترونیک قدرت، ماشین های الکتریکی، الکترونیک
کلمات کلیدی فارسی: عضویت پیشرفته، سیستم های فازی رو به تکامل، ترانسفورماتورهای قدرت، مدل سازی حرارتی
کلمات کلیدی انگلیسی: Enhanced set-membership، Evolving fuzzy systems، Power transformers، Thermal modeling
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus - JCR
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.epsr.2020.106334
دانشگاه: Department of Industrial and Mechanical Engineering, Federal University of Juiz de Fora, Juiz de Fora, MG, Brazil
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2020
ایمپکت فاکتور: 4/382 در سال 2019
شاخص H_index: 114 در سال 2020
شاخص SJR: 1/042 در سال 2019
شناسه ISSN: 0378-7796
شاخص Quartile (چارک): Q1 در سال 2019
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 8
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: بله
آیا این مقاله مدل مفهومی دارد: دارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: دارد
کد محصول: E14948
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست انگلیسی مطالب

Abstract


1- Introduction


2- Problem formulation


3- Proposed models


4- Experimental results


5- Conclusions


References

نمونه متن انگلیسی مقاله

Abstract


A factor that directly impacts the lifespan of a power transformer is the hot-spot temperature, and its monitoring is vital to prevent faults, reduce costs, keep the safety, and provide a reliable service to consumers. In this paper, we propose two forecasting models to predict the hot-spot temperature of power transformers. The first is the implementation of Set-Membership in the evolving Participatory Learning with Kernel Recursive Least Squares. And the second is a combination of the evolving Participatory Learning with Kernel Recursive Least Squares and the improved version of the Set-Membership concept, named Enhanced Set-Membership. Both Set-Membership and the Enhanced Set-Membership approaches are implemented to update the rate of change of the arousal index, which is a parameter that controls the creation of rules. A data set collected from an experimental transformer is adopted to evaluate the model’s performance. The obtained results are compared with the performance of the original evolving Participatory Learning with Kernel Recursive Least Squares and with the performance of other classical models suggested in the literature. The proposals have lower errors and a competitive number of final rules, suggesting that the models are efficient approaches to modeling complex data with high accuracy.


Introduction


The power transformer is a critical equipment in power distribution [1]. It is responsible for stepped-up the voltage before to be transmitted over long distances to reduce waste, and stepped-down the voltage to provide the energy to consumers safely [2,3]. Due to the composition of a power transformer, it is the most expensive apparatus in energy distribution [4]. In the case of a power transformer’s failure, when the recovering process is possible, it is slow and inefficient [5]. Thereof, monitoring is vital to prevent faults, reduce costs, keep the safety, and provide a reliable service to consumers [6,7]. The annual spent on power transformers’ monitoring hardware will increase more than $ 642 million in eight years until 2020, according to [8,9], indicating the importance of the power transformers in power distribution. Internal failures are about 10% of the total faults, and, among them, winding and bushing defects represent approximately 44% [10]. The bushing is a fragile component constituted of four parts: insulation, conductor, connection clamp, and accessories[11,12]. In the present work, we considered power transformers composed of Resin-bonded paper bushings (RBP) [13]. The principal factor in bushing failures is the hot-spot temperature, representing 32% of the total causes [11]. The hot-spot temperature is the highest temperature near to the top of the power transformers highvoltage (HV)/low-voltage (LV) windings [7,14] and represents the main limiting factor in the load capacity of the transformer [14], since increases in this temperature reduces the lifespan of the insulation and may determine the end life of the power transformer [15]. As the estimation of the hot-spot is a complex task, many models have been proposed in the literature with the purpose of estimating the hot-spot temperature of power transformers. Among them, the most commonly used in practice is the model based on the IEEE Standard C57.91-2011 [16] which is based on transient heating equations and specific thermal characteristics and parameters of power transformers. This deterministic model is imprecise due to assumed simplifications, and consequently, the power transformer must operate below the maximum capacity to prevent damages [7].

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