عنوان

PV System Information Enhancement and Better Control of Power Systems

Number

TLpq2348304057

.Language of Text, Soundtrack etc

انگلیسی

Title Proper

PV System Information Enhancement and Better Control of Power Systems

General Material Designation

[Thesis]

First Statement of Responsibility

Hashmy, Syed Muhammad Yousaf

Subsequent Statement of Responsibility

Weng, Yang

Name of Publisher, Distributor, etc.

Arizona State University

Date of Publication, Distribution, etc.

2019

Specific Material Designation and Extent of Item

93

Dissertation or thesis details and type of degree

M.S.

Body granting the degree

Arizona State University

Text preceding or following the note

2019

Text of Note

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.

Alternative energy

Electrical engineering

Hashmy, Syed Muhammad Yousaf

Weng, Yang

Electronic name

p

[Thesis]

276903

a

Y