Developments of AI-Assisted Fault Detection and Failure Mode Diagnosis for Operation and Maintenance of Photovoltaic Power Stations in Taiwan

Fault detection and failure mode diagnosis are of crucial importance in operation and maintenance (O&M) of photovoltaic (PV) power stations. In this work, advanced artificial intelligence techniques are exploited to optimize these O&M tasks for 150 PV power stations in Taiwan with total powe...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on industry applications 2024-07, Vol.60 (4), p.5269-5281
Main Authors: Chang, Maoyi, Chen, Kun-Hong, Chen, Yu-Sheng, Hsu, Chung-Chian, Chu, Chia-Chi
Format: Article
Language:English
Subjects:
artificial intelligence
Data models
failure mode diagnosis
Fault detection
Inverters
Maintenance engineering
Meteorology
multiple oriented roof-top PV
operation and maintenance
Photovoltaic (PV) power station
plane of array irradiance
Power generation
Predictive models
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Fault detection and failure mode diagnosis are of crucial importance in operation and maintenance (O&M) of photovoltaic (PV) power stations. In this work, advanced artificial intelligence techniques are exploited to optimize these O&M tasks for 150 PV power stations in Taiwan with total power rating around 54 MW. First, the response of each inverter under the maximal power tracking is monitored and analyzed by machine learning algorithms in every five minutes. The alert of fault detection will be activated if the power output of each inverter is significantly different from its nominal output. Prompt notification will be sent to user by mobile devices or emails immediately. To further enhance the performance of power prediction for multiple oriented roof-top PV systems, the power prediction model will be upgraded by simulated plane of array irradiance instead of direct measurements from only one pyranometer. Two-year field test results from 74 PV power stations with 4,792 inverters indeed demonstrate the effectiveness of the proposed AI-based O&M scheme for PV power stations.
ISSN:0093-9994
1939-9367
DOI:10.1109/TIA.2024.3379319