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.