Compressive Sensing in Power Engineering: A Comprehensive Survey of Theory and Applications, and a Case Study
Compressive Sensing (CS) is a transformative signal processing framework that enables sparse signal acquisition at rates below the Nyquist limit, offering substantial advantages in data efficiency and reconstruction accuracy. This survey explores the theoretical foundations of CS, including sensing...
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MDPI AG
2025-03-01
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| Series: | Journal of Sensor and Actuator Networks |
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| Online Access: | https://www.mdpi.com/2224-2708/14/2/28 |
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| author | Lekshmi R. Chandran Ilango Karuppasamy Manjula G. Nair Hongjian Sun Parvathy Krishnan Krishnakumari |
| author_facet | Lekshmi R. Chandran Ilango Karuppasamy Manjula G. Nair Hongjian Sun Parvathy Krishnan Krishnakumari |
| author_sort | Lekshmi R. Chandran |
| collection | DOAJ |
| description | Compressive Sensing (CS) is a transformative signal processing framework that enables sparse signal acquisition at rates below the Nyquist limit, offering substantial advantages in data efficiency and reconstruction accuracy. This survey explores the theoretical foundations of CS, including sensing matrices, sparse bases, and recovery algorithms, with a focus on its applications in power engineering. CS has demonstrated significant potential in enhancing key areas such as state estimation (SE), fault detection, fault localization, outage identification, harmonic source identification (HSI), Power Quality Detection condition monitoring, and so on. Furthermore, CS addresses challenges in data compression, real-time grid monitoring, and efficient resource utilization. A case study on smart meter data recovery demonstrates the practical application of CS in real-world power systems. By bridging CS theory and its application, this survey underscores its potential to drive innovation, efficiency, and sustainability in power engineering and beyond. |
| format | Article |
| id | doaj-art-5f73e89b59df44a580cae60939dcdddd |
| institution | DOAJ |
| issn | 2224-2708 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Sensor and Actuator Networks |
| spelling | doaj-art-5f73e89b59df44a580cae60939dcdddd2025-08-20T03:13:45ZengMDPI AGJournal of Sensor and Actuator Networks2224-27082025-03-011422810.3390/jsan14020028Compressive Sensing in Power Engineering: A Comprehensive Survey of Theory and Applications, and a Case StudyLekshmi R. Chandran0Ilango Karuppasamy1Manjula G. Nair2Hongjian Sun3Parvathy Krishnan Krishnakumari4Department of Electrical and Electronics Engineering, Amrita Vishwa Vidyapeetham, Amritapuri 690525, IndiaDepartment of Electrical and Electronics Engineering, Amrita School of Engineering, Coimbatore Amrita Vishwa Vidyapeetham, Ettimadai 641112, IndiaDepartment of Electrical and Electronics Engineering, Amrita Vishwa Vidyapeetham, Amritapuri 690525, IndiaDepartment of Engineering, Durham University, Durham DH13LE, UKAmsterdam Business School, University of Amsterdam, Plantage Muidergracht 12, 1018 TV Amsterdam, The NetherlandsCompressive Sensing (CS) is a transformative signal processing framework that enables sparse signal acquisition at rates below the Nyquist limit, offering substantial advantages in data efficiency and reconstruction accuracy. This survey explores the theoretical foundations of CS, including sensing matrices, sparse bases, and recovery algorithms, with a focus on its applications in power engineering. CS has demonstrated significant potential in enhancing key areas such as state estimation (SE), fault detection, fault localization, outage identification, harmonic source identification (HSI), Power Quality Detection condition monitoring, and so on. Furthermore, CS addresses challenges in data compression, real-time grid monitoring, and efficient resource utilization. A case study on smart meter data recovery demonstrates the practical application of CS in real-world power systems. By bridging CS theory and its application, this survey underscores its potential to drive innovation, efficiency, and sustainability in power engineering and beyond.https://www.mdpi.com/2224-2708/14/2/28compressive sensingsparse signal recoverysensing matricespower engineeringsmart grid |
| spellingShingle | Lekshmi R. Chandran Ilango Karuppasamy Manjula G. Nair Hongjian Sun Parvathy Krishnan Krishnakumari Compressive Sensing in Power Engineering: A Comprehensive Survey of Theory and Applications, and a Case Study Journal of Sensor and Actuator Networks compressive sensing sparse signal recovery sensing matrices power engineering smart grid |
| title | Compressive Sensing in Power Engineering: A Comprehensive Survey of Theory and Applications, and a Case Study |
| title_full | Compressive Sensing in Power Engineering: A Comprehensive Survey of Theory and Applications, and a Case Study |
| title_fullStr | Compressive Sensing in Power Engineering: A Comprehensive Survey of Theory and Applications, and a Case Study |
| title_full_unstemmed | Compressive Sensing in Power Engineering: A Comprehensive Survey of Theory and Applications, and a Case Study |
| title_short | Compressive Sensing in Power Engineering: A Comprehensive Survey of Theory and Applications, and a Case Study |
| title_sort | compressive sensing in power engineering a comprehensive survey of theory and applications and a case study |
| topic | compressive sensing sparse signal recovery sensing matrices power engineering smart grid |
| url | https://www.mdpi.com/2224-2708/14/2/28 |
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