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基于自组织映射的大型光伏电站故障预测与早期检测。

Fault Prediction and Early-Detection in Large PV Power Plants Based on Self-Organizing Maps.

机构信息

i-EM S.r.l. (Intelligence in Energy Management), 57121 Livorno, Italy.

Department of Energy, Systems, Territory and Construction Engineering (DESTEC), University of Pisa, 56122 Pisa, Italy.

出版信息

Sensors (Basel). 2021 Mar 1;21(5):1687. doi: 10.3390/s21051687.

DOI:10.3390/s21051687
PMID:33804448
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7957680/
Abstract

In this paper, a novel and flexible solution for fault prediction based on data collected from Supervisory Control and Data Acquisition (SCADA) system is presented. Generic fault/status prediction is offered by means of a data driven approach based on a self-organizing map (SOM) and the definition of an original Key Performance Indicator (KPI). The model has been assessed on a park of three photovoltaic (PV) plants with installed capacity up to 10 MW, and on more than sixty inverter modules of three different technology brands. The results indicate that the proposed method is effective in predicting incipient generic faults in average up to 7 days in advance with true positives rate up to 95%. The model is easily deployable for on-line monitoring of anomalies on new PV plants and technologies, requiring only the availability of historical SCADA data, fault taxonomy and inverter electrical datasheet.

摘要

本文提出了一种新颖灵活的基于监控和数据采集（SCADA）系统中收集的数据的故障预测解决方案。通过基于自组织映射（SOM）和原始关键绩效指标（KPI）定义的数据驱动方法提供通用故障/状态预测。该模型已经在一个装有容量高达 10MW 的三个光伏（PV）工厂和三个不同技术品牌的六十多个逆变器模块的公园上进行了评估。结果表明，该方法能够有效预测平均提前 7 天的初期通用故障，真阳性率高达 95%。该模型易于部署在新的 PV 工厂和技术的异常在线监测中，只需要历史 SCADA 数据、故障分类和逆变器电气数据表的可用性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/111b/7957680/b9dbc8f0fe89/sensors-21-01687-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/111b/7957680/09e0953376d4/sensors-21-01687-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/111b/7957680/fc0d89a62ba2/sensors-21-01687-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/111b/7957680/3f521ebc2837/sensors-21-01687-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/111b/7957680/09617cfed555/sensors-21-01687-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/111b/7957680/fb4f0fcf1ab2/sensors-21-01687-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/111b/7957680/4b0cb68f91c8/sensors-21-01687-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/111b/7957680/525ba64b3940/sensors-21-01687-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/111b/7957680/b9dbc8f0fe89/sensors-21-01687-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/111b/7957680/09e0953376d4/sensors-21-01687-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/111b/7957680/fc0d89a62ba2/sensors-21-01687-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/111b/7957680/3f521ebc2837/sensors-21-01687-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/111b/7957680/09617cfed555/sensors-21-01687-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/111b/7957680/fb4f0fcf1ab2/sensors-21-01687-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/111b/7957680/4b0cb68f91c8/sensors-21-01687-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/111b/7957680/525ba64b3940/sensors-21-01687-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/111b/7957680/b9dbc8f0fe89/sensors-21-01687-g008.jpg

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本文引用的文献

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