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Health Prediction Method for Photovoltaic Inverters Based on Autoencoder and Fusion of Multiple Operating Conditions

Published: 31 July 2024 Publication History

Abstract

Photovoltaic inverter health prediction is a crucial aspect of the reliability and performance maintenance of photovoltaic power generation systems. With the rapid development of solar energy, the inverter, as one of the core components of photovoltaic power generation systems, plays a vital role in ensuring the effective conversion of energy. Traditional methods for predicting the health of photovoltaic inverters involve simple weighted summation of device-generated data or basic classification assessments. These approaches often lack precision in predicting device health. This paper proposes a data-driven health prediction method that integrates operational environment data from photovoltaic inverters with performance data during operation. Different autoencoders are trained as environmental benchmark models based on various working conditions. Real-time operational data is input into the health model to generate health scores reflecting the device's condition. Experimental results demonstrate that the constructed health model effectively fits the dataset and accurately assesses the operating status of photovoltaic inverters. By enabling real-time health assessment and prompt maintenance actions, this method provides an effective guarantee for increasing photovoltaic power generation efficiency, potentially significantly reducing maintenance costs, and enhancing system reliability and maintainability. This, in turn, contributes significantly to the sustainable development of renewable energy in the field.

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  1. Health Prediction Method for Photovoltaic Inverters Based on Autoencoder and Fusion of Multiple Operating Conditions

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    PEAI '24: Proceedings of the 2024 International Conference on Power Electronics and Artificial Intelligence
    January 2024
    969 pages
    ISBN:9798400716638
    DOI:10.1145/3674225
    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 the author(s) 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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    Published: 31 July 2024

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