Power Outage Estimation: The Study of Revenue-Led Top Affected States of U.S
The electric power systems are becoming smart as well as complex with every passing year, especially in response to the changing environmental conditions. Resilience of power generation and transmission infrastructure is important to avoid power outages, ensure robust service, and to achieve sustain...
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9288696/ |
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| author | Naveed Taimoor Ikramullah Khosa Muhammad Jawad Jahanzeb Akhtar Imran Ghous Muhammad Bilal Qureshi Ali R. Ansari Raheel Nawaz |
| author_facet | Naveed Taimoor Ikramullah Khosa Muhammad Jawad Jahanzeb Akhtar Imran Ghous Muhammad Bilal Qureshi Ali R. Ansari Raheel Nawaz |
| author_sort | Naveed Taimoor |
| collection | DOAJ |
| description | The electric power systems are becoming smart as well as complex with every passing year, especially in response to the changing environmental conditions. Resilience of power generation and transmission infrastructure is important to avoid power outages, ensure robust service, and to achieve sustained economic benefits. In this study, we employ a two-stage model to estimate the power outage in terms of its intensity as well as the duration. We identify the top three potentially critical states of United States of America, not merely based on duration of the power outage, but by also incorporating outage related revenue loss. In the proposed model, the first stage classifies the intensity of the outage event while the second stage predicts the duration of the outage itself. Moreover, the key predictors are characterized and their association with outage duration is illustrated. We use a comprehensive and publicly available dataset, which provides the information related to historical power outage events, such as electricity usage patterns, climatological annotations, socio-economic indicators, and land-use data. Our rigorous analysis and results suggest that the power outage interval is the function of several parameters, such as climatological condition, economic indicators as well as the time of the year. The proposed study can help the regulatory authorities taking appropriate decisions for long term economic paybacks. It can also help disaster management authorities to take risk-informed resilient decisions for system safety. |
| first_indexed | 2024-12-16T14:42:28Z |
| format | Article |
| id | doaj.art-96a3e5ce694a437595fe3c5ba12b5647 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T14:42:28Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-96a3e5ce694a437595fe3c5ba12b56472022-12-21T22:27:51ZengIEEEIEEE Access2169-35362020-01-01822327122328610.1109/ACCESS.2020.30436309288696Power Outage Estimation: The Study of Revenue-Led Top Affected States of U.SNaveed Taimoor0https://orcid.org/0000-0003-2419-595XIkramullah Khosa1https://orcid.org/0000-0002-9149-9358Muhammad Jawad2https://orcid.org/0000-0003-3730-2128Jahanzeb Akhtar3https://orcid.org/0000-0002-9429-8412Imran Ghous4https://orcid.org/0000-0002-8215-4315Muhammad Bilal Qureshi5https://orcid.org/0000-0002-0030-8959Ali R. Ansari6https://orcid.org/0000-0001-5090-7813Raheel Nawaz7https://orcid.org/0000-0001-9588-0052Department of Electrical and Computer Engineering, COMSATS University Islamabad, Lahore Campus, Lahore, PakistanDepartment of Electrical and Computer Engineering, COMSATS University Islamabad, Lahore Campus, Lahore, PakistanDepartment of Electrical and Computer Engineering, COMSATS University Islamabad, Lahore Campus, Lahore, PakistanDepartment of Electrical and Computer Engineering, COMSATS University Islamabad, Lahore Campus, Lahore, PakistanDepartment of Electrical and Computer Engineering, COMSATS University Islamabad, Lahore Campus, Lahore, PakistanDepartment of Electrical Engineering, COMSATS University Islamabad, Abbottabad Campus, Abbottabad, PakistanDepartment of Mathematics and Natural Sciences, Gulf University of Science and Technology, Mishref Campus, Kuwait City, KuwaitDepartment of Operations, Technology, Events and Hospitality Management, Manchester Metropolitan University (MMU), Manchester, U.KThe electric power systems are becoming smart as well as complex with every passing year, especially in response to the changing environmental conditions. Resilience of power generation and transmission infrastructure is important to avoid power outages, ensure robust service, and to achieve sustained economic benefits. In this study, we employ a two-stage model to estimate the power outage in terms of its intensity as well as the duration. We identify the top three potentially critical states of United States of America, not merely based on duration of the power outage, but by also incorporating outage related revenue loss. In the proposed model, the first stage classifies the intensity of the outage event while the second stage predicts the duration of the outage itself. Moreover, the key predictors are characterized and their association with outage duration is illustrated. We use a comprehensive and publicly available dataset, which provides the information related to historical power outage events, such as electricity usage patterns, climatological annotations, socio-economic indicators, and land-use data. Our rigorous analysis and results suggest that the power outage interval is the function of several parameters, such as climatological condition, economic indicators as well as the time of the year. The proposed study can help the regulatory authorities taking appropriate decisions for long term economic paybacks. It can also help disaster management authorities to take risk-informed resilient decisions for system safety.https://ieeexplore.ieee.org/document/9288696/Predictionpower outagesnatural disastersclassificationsupport vector machinerandom forest |
| spellingShingle | Naveed Taimoor Ikramullah Khosa Muhammad Jawad Jahanzeb Akhtar Imran Ghous Muhammad Bilal Qureshi Ali R. Ansari Raheel Nawaz Power Outage Estimation: The Study of Revenue-Led Top Affected States of U.S IEEE Access Prediction power outages natural disasters classification support vector machine random forest |
| title | Power Outage Estimation: The Study of Revenue-Led Top Affected States of U.S |
| title_full | Power Outage Estimation: The Study of Revenue-Led Top Affected States of U.S |
| title_fullStr | Power Outage Estimation: The Study of Revenue-Led Top Affected States of U.S |
| title_full_unstemmed | Power Outage Estimation: The Study of Revenue-Led Top Affected States of U.S |
| title_short | Power Outage Estimation: The Study of Revenue-Led Top Affected States of U.S |
| title_sort | power outage estimation the study of revenue led top affected states of u s |
| topic | Prediction power outages natural disasters classification support vector machine random forest |
| url | https://ieeexplore.ieee.org/document/9288696/ |
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