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Temporal convolutional networks for fault diagnosis of photovoltaic systems using satellite and inverter measurements

Published: 17 November 2021 Publication History

Abstract

Over time, photovoltaic (PV) systems become increasingly susceptible to faults. Early fault detection and identification not only limits power losses and increases the systems lifetime, but also prevents more serious consequences, such as risk of fire or electrical shock. Although several accurate fault diagnosis methods have been proposed in literature, most PV systems remain unmonitored as the installations are not equipped with the required sensors. In this work, we propose a fault diagnosis technique that does not require on-site sensors. Rather, weather satellite and inverter measurements are used as inputs for the proposed machine learning model. As no dedicated sensors are needed, our method is widely applicable and cost-effective. A temporal convolutional neural network is developed to accurately identify 6 common types of faults, based on the past 24 h of measurements. The proposed approach is tested extensively on a simulated PV system, taking into account multiple severities of each fault type, and reaches an accuracy of over 86%.

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Cited By

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  • (2024)A Data-Driven Coordinated Active and Reactive Dispatching Strategy for PhotovoltaicsData Science10.1007/978-981-97-8749-4_15(202-213)Online publication date: 31-Oct-2024

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Published In

cover image ACM Conferences
BuildSys '21: Proceedings of the 8th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation
November 2021
388 pages
ISBN:9781450391146
DOI:10.1145/3486611
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 17 November 2021

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Author Tags

  1. fault diagnosis
  2. photovoltaics
  3. temporal convolutional network

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BuildSys '21 Paper Acceptance Rate 28 of 107 submissions, 26%;
Overall Acceptance Rate 148 of 500 submissions, 30%

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  • (2024)A Data-Driven Coordinated Active and Reactive Dispatching Strategy for PhotovoltaicsData Science10.1007/978-981-97-8749-4_15(202-213)Online publication date: 31-Oct-2024

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