عنوان
پدید آورنده
موضوع
رده
کتابخانه
محل استقرار
شماره کتابشناسی ملی
شماره
TL51909
زبان اثر
زبان متن نوشتاري يا گفتاري و مانند آن
انگلیسی
عنوان و نام پديدآور
عنوان اصلي
PV System Information Enhancement and Better Control of Power Systems
نام عام مواد
[Thesis]
نام نخستين پديدآور
Hashmy, Syed Muhammad Yousaf
نام ساير پديدآوران
Weng, Yang
وضعیت نشر و پخش و غیره
نام ناشر، پخش کننده و غيره
Arizona State University
تاریخ نشرو بخش و غیره
2019
يادداشت کلی
متن يادداشت
93 p.
یادداشتهای مربوط به پایان نامه ها
جزئيات پايان نامه و نوع درجه آن
M.S.
کسي که مدرک را اعطا کرده
Arizona State University
امتياز متن
2019
یادداشتهای مربوط به خلاصه یا چکیده
متن يادداشت
Due to the rapid penetration of solar power systems in residential areas, there has been a dramatic increase in bidirectional power flow. Such a phenomenon of bidirectional power flow creates a need to know where Photovoltaic (PV) systems are located, what their quantity is, and how much they generate. However, significant challenges exist for accurate solar panel detection, capacity quantification, and generation estimation by employing existing methods, because of the limited labeled ground truth and relatively poor performance for direct supervised learning. To mitigate these issue, this thesis revolutionizes key learning concepts to (1) largely increase the volume of training data set and expand the labelled data set by creating highly realistic solar panel images, (2) boost detection and quantification learning through physical knowledge and (3) greatly enhance the generation estimation capability by utilizing effective features and neighboring generation patterns. These techniques not only reshape the machine learning methods in the GIS domain but also provides a highly accurate solution to gain a better understanding of distribution networks with high PV penetration. The numerical validation and performance evaluation establishes the high accuracy and scalability of the proposed methodologies on the existing solar power systems in the Southwest region of the United States of America. The distribution and transmission networks both have primitive control methodologies, but now is the high time to work out intelligent control schemes based on reinforcement learning and show that they can not only perform well but also have the ability to adapt to the changing environments. This thesis proposes a sequence task-based learning method to create an agent that can learn to come up with the best action set that can overcome the issues of transient over-voltage.
اصطلاحهای موضوعی کنترل نشده
اصطلاح موضوعی
Alternative energy
اصطلاح موضوعی
Electrical engineering
نام شخص به منزله سر شناسه - (مسئولیت معنوی درجه اول )
مستند نام اشخاص تاييد نشده
Hashmy, Syed Muhammad Yousaf
نام شخص - ( مسئولیت معنوی درجه دوم )
مستند نام اشخاص تاييد نشده
Weng, Yang
شناسه افزوده (تنالگان)
مستند نام تنالگان تاييد نشده
Arizona State University
دسترسی و محل الکترونیکی
نام الکترونيکي
مطالعه متن کتاب وضعیت انتشار
فرمت انتشار
p
اطلاعات رکورد کتابشناسی
نوع ماده
[Thesis]
کد کاربرگه
276903
اطلاعات دسترسی رکورد
سطح دسترسي
a
تكميل شده
Y
