Microgrid anti islanding protection scheme based on deep neural network algorithm and unscented Kalman filtering
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 ev...
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| Veröffentlicht in: | Scientific reports Jg. 15; H. 1; S. 28726 - 23 |
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| Sprache: | Englisch |
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06.08.2025
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| 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%. |
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| AbstractList | 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%. 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%.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%. 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%. |
| ArticleNumber | 28726 |
| Author | Baili, Jamel Chauhdary, Sohaib Tahir Shah, Munam Ali Ahmad, Ayaz Khan, Taha Saeed Arif, Saad |
| Author_xml | – sequence: 1 givenname: Sohaib Tahir surname: Chauhdary fullname: Chauhdary, Sohaib Tahir email: sohaibchauhdary@hotmail.com organization: Department of Electrical and Computer Engineering, College of Engineering, Dhofar University, Sultanate of Oman – sequence: 2 givenname: Taha Saeed surname: Khan fullname: Khan, Taha Saeed organization: School of Electrical and Computer Engineering, Oklahoma State University – sequence: 3 givenname: Saad surname: Arif fullname: Arif, Saad email: sarif@kfu.edu.sa organization: Department of Mechanical Engineering, College of Engineering, King Faisal University – sequence: 4 givenname: Ayaz surname: Ahmad fullname: Ahmad, Ayaz organization: Department of Electrical Engineering, College of Engineering, King Faisal University – sequence: 5 givenname: Munam Ali surname: Shah fullname: Shah, Munam Ali organization: Department of Computer Networks and Communication, College of Computer Science and Information Technology, King Faisal University – sequence: 6 givenname: Jamel surname: Baili fullname: Baili, Jamel organization: Department of Computer Engineering, College of Computer Science, King Khalid University |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40770003$$D View this record in MEDLINE/PubMed |
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| Keywords | Deep neural network Kalman filtering Anti-islanding Passive methods Microgrid operation Hybrid microgrid |
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| Snippet | Microgrid anti-islanding protection (MAIP) is an indispensable challenge in ensuring the safe and reliable operation of microgrids. This research article... Abstract Microgrid anti-islanding protection (MAIP) is an indispensable challenge in ensuring the safe and reliable operation of microgrids. This research... |
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| SubjectTerms | 639/166 639/4077 Algorithms Anti-islanding Deep neural network Fourier transforms Humanities and Social Sciences Hybrid microgrid Kalman filtering Methods Microgrid operation multidisciplinary Neural networks Passive methods Science Science (multidisciplinary) Signal processing Simulation Systems stability Voltage |
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| Title | Microgrid anti islanding protection scheme based on deep neural network algorithm and unscented Kalman filtering |
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