Quantum Neural Network Parameter Estimation for Photovoltaic Fault Detection

Glen Uehara, Sunil Rao, Mathew Dobson, Cihan Tepedelenlioglu, Andreas Spanias

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

In this paper, we describe solar array monitoring using various machine learning methods including neural networks. We study fault detection using a quantum computer system and compare against results with a classical computer. We specifically propose a quantum circuit for a neural network implementation for Photovoltaic (PV) fault detection. The quantum circuit is designed for two qubits. Results and comparisons are presented for PV fault detection using a classical and quantum implementation of neural networks. In addition, simulations of a Quantum Neural Network are carried for a different number of qubits and results are presented for PV fault detection.

Original languageEnglish (US)
Title of host publicationIISA 2021 - 12th International Conference on Information, Intelligence, Systems and Applications
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665400329
DOIs
StatePublished - Jul 12 2021
Event12th International Conference on Information, Intelligence, Systems and Applications, IISA 2021 - Virtual, Chania Crete, Greece
Duration: Jul 12 2021Jul 14 2021

Publication series

NameIISA 2021 - 12th International Conference on Information, Intelligence, Systems and Applications

Conference

Conference12th International Conference on Information, Intelligence, Systems and Applications, IISA 2021
Country/TerritoryGreece
CityVirtual, Chania Crete
Period7/12/217/14/21

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Information Systems
  • Information Systems and Management

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