Assessing Power System Reliability Using Anomaly Detection in Daily Nighttime Light Data.
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| Title: | Assessing Power System Reliability Using Anomaly Detection in Daily Nighttime Light Data. |
|---|---|
| Authors: | Xu, Nuo1 (AUTHOR), Cao, Xin1,2 (AUTHOR) caoxin@bnu.edu.cn, Chen, Miaoying1 (AUTHOR) |
| Source: | Remote Sensing. May2026, Vol. 18 Issue 9, p1417. 27p. |
| Subjects: | Electric power system reliability, Satellite-based remote sensing, Energy consumption, Electric power failures, Sustainable development, Outlier detection |
| Abstract: | Highlights: What are the main findings? We developed a novel, self-adaptive framework for detecting power outages from daily satellite nighttime light data, eliminating the need for prior event knowledge or fixed regional thresholds. The derived satellite-based reliability index (NTPRI) shows a significant correlation with ground-measured grid reliability, validating its use as a scalable proxy metric. What are the implications of the main findings? The framework provides a transferable, remote sensing-based tool for large-scale, long-term monitoring of electricity supply reliability, which is particularly valuable for data-scarce regions. It enables rapid impact assessment of power systems following disasters and supports the tracking of infrastructure progress toward related Sustainable Development Goals (e.g., SDG 7). Power-system reliability is crucial for sustainable development, but large-scale, long-term monitoring remains challenging. Existing nighttime light (NTL)-based outage detection methods often rely on fixed thresholds or prior information, limiting cross-regional application. To address this, we develop an adaptive thresholding framework using daily NASA Black Marble data. Observations are grouped by view angle to mitigate radiometric instability, and a per-pixel dynamic baseline is constructed from high-radiance statistics, enabling robust anomaly detection without prior outage timing. From the detected anomalies, we formulate a population-weighted NTL power reliability index (NTPRI) to quantify regional electricity service reliability. Validation across six diverse outage events yields an F1 score of 0.807. National-scale analysis shows NTPRI correlates significantly with the World Bank's System Average Interruption Duration Index (SAIDI). The derived Light Anomaly Rate (LAR) further supports pixel-level frequency analysis. Together, this framework provides a transferable remote-sensing tool for large-scale power-reliability assessment in data-scarce regions, supporting disaster impact evaluation and energy vulnerability analysis. [ABSTRACT FROM AUTHOR] |
| Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 193715448 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Assessing Power System Reliability Using Anomaly Detection in Daily Nighttime Light Data. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Xu%2C+Nuo%22">Xu, Nuo</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Cao%2C+Xin%22">Cao, Xin</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> caoxin@bnu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Chen%2C+Miaoying%22">Chen, Miaoying</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. May2026, Vol. 18 Issue 9, p1417. 27p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Electric+power+system+reliability%22">Electric power system reliability</searchLink><br /><searchLink fieldCode="DE" term="%22Satellite-based+remote+sensing%22">Satellite-based remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Energy+consumption%22">Energy consumption</searchLink><br /><searchLink fieldCode="DE" term="%22Electric+power+failures%22">Electric power failures</searchLink><br /><searchLink fieldCode="DE" term="%22Sustainable+development%22">Sustainable development</searchLink><br /><searchLink fieldCode="DE" term="%22Outlier+detection%22">Outlier detection</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Highlights: What are the main findings? We developed a novel, self-adaptive framework for detecting power outages from daily satellite nighttime light data, eliminating the need for prior event knowledge or fixed regional thresholds. The derived satellite-based reliability index (NTPRI) shows a significant correlation with ground-measured grid reliability, validating its use as a scalable proxy metric. What are the implications of the main findings? The framework provides a transferable, remote sensing-based tool for large-scale, long-term monitoring of electricity supply reliability, which is particularly valuable for data-scarce regions. It enables rapid impact assessment of power systems following disasters and supports the tracking of infrastructure progress toward related Sustainable Development Goals (e.g., SDG 7). Power-system reliability is crucial for sustainable development, but large-scale, long-term monitoring remains challenging. Existing nighttime light (NTL)-based outage detection methods often rely on fixed thresholds or prior information, limiting cross-regional application. To address this, we develop an adaptive thresholding framework using daily NASA Black Marble data. Observations are grouped by view angle to mitigate radiometric instability, and a per-pixel dynamic baseline is constructed from high-radiance statistics, enabling robust anomaly detection without prior outage timing. From the detected anomalies, we formulate a population-weighted NTL power reliability index (NTPRI) to quantify regional electricity service reliability. Validation across six diverse outage events yields an F1 score of 0.807. National-scale analysis shows NTPRI correlates significantly with the World Bank's System Average Interruption Duration Index (SAIDI). The derived Light Anomaly Rate (LAR) further supports pixel-level frequency analysis. Together, this framework provides a transferable remote-sensing tool for large-scale power-reliability assessment in data-scarce regions, supporting disaster impact evaluation and energy vulnerability analysis. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/rs18091417 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 27 StartPage: 1417 Subjects: – SubjectFull: Electric power system reliability Type: general – SubjectFull: Satellite-based remote sensing Type: general – SubjectFull: Energy consumption Type: general – SubjectFull: Electric power failures Type: general – SubjectFull: Sustainable development Type: general – SubjectFull: Outlier detection Type: general Titles: – TitleFull: Assessing Power System Reliability Using Anomaly Detection in Daily Nighttime Light Data. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Xu, Nuo – PersonEntity: Name: NameFull: Cao, Xin – PersonEntity: Name: NameFull: Chen, Miaoying IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20724292 Numbering: – Type: volume Value: 18 – Type: issue Value: 9 Titles: – TitleFull: Remote Sensing Type: main |
| ResultId | 1 |
