Microgrid anti islanding protection scheme based on deep neural network algorithm and unscented Kalman filtering
Abstract Microgrid anti-islanding protection (MAIP) is an indispensable challenge in ensuring the safe and reliable operation of microgrids. This research article proposes the unscented Kalman filtering (UKF) and deep neural network algorithm (DNN) as an innovative approach to detect and prevent isl...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-08-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-10706-7 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849333004202147840 |
|---|---|
| author | Sohaib Tahir Chauhdary Taha Saeed Khan Saad Arif Ayaz Ahmad Munam Ali Shah Jamel Baili |
| author_facet | Sohaib Tahir Chauhdary Taha Saeed Khan Saad Arif Ayaz Ahmad Munam Ali Shah Jamel Baili |
| author_sort | Sohaib Tahir Chauhdary |
| collection | DOAJ |
| description | Abstract Microgrid anti-islanding protection (MAIP) is an indispensable challenge in ensuring the safe and reliable operation of microgrids. This research article proposes the unscented Kalman filtering (UKF) and deep neural network algorithm (DNN) as an innovative approach to detect and prevent islanding events in microgrids. Initially, the UKF works as a stage-one state observer to analyze the voltage signals at the distributed generation (DG) terminal or point of common coupling (PCC). Then, the UKF-estimated voltage signal is provided to DNN for calculating the DNN residuals (DNNR) index by simply taking the vector subtraction of the UKF-estimated voltage from the measured PCC voltage. Then, the DNNR index is continually monitored on the DG terminal or PCC, and if the DNNR is more than the prespecified threshold value, the presented MAIP scheme works successfully to detect the islanding event. The presented MAIP method is proven through massive simulations on standard IEEE UL174 test beds via MATLAB/Simulink software. Results reveal that the suggested MAIP method effectively detects the islanding events in unbalanced/ balanced load generation situations. In addition, the presented MAIP scheme can discriminate between islanding/non-islanding events. The method has a very low computational burden, a very decreased non-detection zone, prompt operation, and a high accuracy of 98.5%. |
| format | Article |
| id | doaj-art-f77e4c44222f4485beb51cfb1354ef4f |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-f77e4c44222f4485beb51cfb1354ef4f2025-08-20T03:46:01ZengNature PortfolioScientific Reports2045-23222025-08-0115112310.1038/s41598-025-10706-7Microgrid anti islanding protection scheme based on deep neural network algorithm and unscented Kalman filteringSohaib Tahir Chauhdary0Taha Saeed Khan1Saad Arif2Ayaz Ahmad3Munam Ali Shah4Jamel Baili5Department of Electrical and Computer Engineering, College of Engineering, Dhofar University, Sultanate of OmanSchool of Electrical and Computer Engineering, Oklahoma State UniversityDepartment of Mechanical Engineering, College of Engineering, King Faisal UniversityDepartment of Electrical Engineering, College of Engineering, King Faisal UniversityDepartment of Computer Networks and Communication, College of Computer Science and Information Technology, King Faisal UniversityDepartment of Computer Engineering, College of Computer Science, King Khalid UniversityAbstract Microgrid anti-islanding protection (MAIP) is an indispensable challenge in ensuring the safe and reliable operation of microgrids. This research article proposes the unscented Kalman filtering (UKF) and deep neural network algorithm (DNN) as an innovative approach to detect and prevent islanding events in microgrids. Initially, the UKF works as a stage-one state observer to analyze the voltage signals at the distributed generation (DG) terminal or point of common coupling (PCC). Then, the UKF-estimated voltage signal is provided to DNN for calculating the DNN residuals (DNNR) index by simply taking the vector subtraction of the UKF-estimated voltage from the measured PCC voltage. Then, the DNNR index is continually monitored on the DG terminal or PCC, and if the DNNR is more than the prespecified threshold value, the presented MAIP scheme works successfully to detect the islanding event. The presented MAIP method is proven through massive simulations on standard IEEE UL174 test beds via MATLAB/Simulink software. Results reveal that the suggested MAIP method effectively detects the islanding events in unbalanced/ balanced load generation situations. In addition, the presented MAIP scheme can discriminate between islanding/non-islanding events. The method has a very low computational burden, a very decreased non-detection zone, prompt operation, and a high accuracy of 98.5%.https://doi.org/10.1038/s41598-025-10706-7Anti-islandingDeep neural networkMicrogrid operationHybrid microgridPassive methodsKalman filtering |
| spellingShingle | Sohaib Tahir Chauhdary Taha Saeed Khan Saad Arif Ayaz Ahmad Munam Ali Shah Jamel Baili Microgrid anti islanding protection scheme based on deep neural network algorithm and unscented Kalman filtering Scientific Reports Anti-islanding Deep neural network Microgrid operation Hybrid microgrid Passive methods Kalman filtering |
| title | Microgrid anti islanding protection scheme based on deep neural network algorithm and unscented Kalman filtering |
| title_full | Microgrid anti islanding protection scheme based on deep neural network algorithm and unscented Kalman filtering |
| title_fullStr | Microgrid anti islanding protection scheme based on deep neural network algorithm and unscented Kalman filtering |
| title_full_unstemmed | Microgrid anti islanding protection scheme based on deep neural network algorithm and unscented Kalman filtering |
| title_short | Microgrid anti islanding protection scheme based on deep neural network algorithm and unscented Kalman filtering |
| title_sort | microgrid anti islanding protection scheme based on deep neural network algorithm and unscented kalman filtering |
| topic | Anti-islanding Deep neural network Microgrid operation Hybrid microgrid Passive methods Kalman filtering |
| url | https://doi.org/10.1038/s41598-025-10706-7 |
| work_keys_str_mv | AT sohaibtahirchauhdary microgridantiislandingprotectionschemebasedondeepneuralnetworkalgorithmandunscentedkalmanfiltering AT tahasaeedkhan microgridantiislandingprotectionschemebasedondeepneuralnetworkalgorithmandunscentedkalmanfiltering AT saadarif microgridantiislandingprotectionschemebasedondeepneuralnetworkalgorithmandunscentedkalmanfiltering AT ayazahmad microgridantiislandingprotectionschemebasedondeepneuralnetworkalgorithmandunscentedkalmanfiltering AT munamalishah microgridantiislandingprotectionschemebasedondeepneuralnetworkalgorithmandunscentedkalmanfiltering AT jamelbaili microgridantiislandingprotectionschemebasedondeepneuralnetworkalgorithmandunscentedkalmanfiltering |