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" PV System Information Enhancement and Better Control of Power Systems "
Hashmy, Syed Muhammad Yousaf
Weng, Yang
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Latin Dissertation
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| Language of Document
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English
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| Record Number
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1105917
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| Doc. No
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TLpq2348304057
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| Main Entry
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Hashmy, Syed Muhammad Yousaf
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Weng, Yang
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| Title & Author
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PV System Information Enhancement and Better Control of Power Systems\ Hashmy, Syed Muhammad YousafWeng, Yang
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| College
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Arizona State University
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| Date
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2019
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| student score
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2019
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| Degree
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M.S.
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| Page No
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93
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| Abstract
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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.
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Alternative energy
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Electrical engineering
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