عنوان

PV System Information Enhancement and Better Control of Power Systems

شماره

TLpq2348304057

زبان متن نوشتاري يا گفتاري و مانند آن

انگلیسی

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

نام ناشر، پخش کننده و غيره

Arizona State University

تاریخ نشرو بخش و غیره

2019

نام خاص و کميت اثر

93

جزئيات پايان نامه و نوع درجه آن

M.S.

کسي که مدرک را اعطا کرده

Arizona State University

امتياز متن

2019

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.

موضوع مستند نشده

Alternative energy

موضوع مستند نشده

Electrical engineering

مستند نام اشخاص تاييد نشده

Hashmy, Syed Muhammad Yousaf

مستند نام اشخاص تاييد نشده

Weng, Yang

نام الکترونيکي

فرمت انتشار

p

نوع ماده

[Thesis]

کد کاربرگه

276903

سطح دسترسي

a

تكميل شده

Y