A hybrid approach for power quality event identification in power systems: Elasticnet Regression decomposition and optimized probabilistic neural networks
The transformation of traditional grid networks towards smart-grid and microgrid concepts raises many critical issues, and quality in the power supply is one of the prominent ones that needs further research. Developing and applying power quality (PQ) recognition methods with efficient and reliable...
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| Published in: | Heliyon Vol. 10; no. 18; p. e37975 |
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| Main Authors: | , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
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England
Elsevier Ltd
30.09.2024
Elsevier |
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| ISSN: | 2405-8440, 2405-8440 |
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| Abstract | The transformation of traditional grid networks towards smart-grid and microgrid concepts raises many critical issues, and quality in the power supply is one of the prominent ones that needs further research. Developing and applying power quality (PQ) recognition methods with efficient and reliable analysis are essential to the fast-growing issues related to modern smart power distribution systems. In this regard, a hybrid algorithm is proposed for PQ events detection and classification using Elasticnet Regression-based Variational Mode Decomposition (ER-VMD) and Salp Swarm Algorithm optimized Probabilistic Neural Network (SSA-PNN). The Elasticnet Regression (ER) process is suggested to modify the conventional VMD approach instead of the Tikhonov Regularization (TR) method to enhance performance and obtain better band-limited intrinsic mode functions. This idea results in robust and effective reconstruction features and helps to obtain accurate classification using the classifier. In the classification stage, a Salp Swarm Algorithm (SSA) based PNN is used for the PQ event, considering the relevant features obtained from ER-VMD. The system parameters often influence PNN performance, and SSA is used to determine the ideal values to improve the PNN's capacity for more accurate classification. The numerical values of the accuracy percentage, percentage of sensitivity, and percentage of specificity in the case of real-time data are found as 98.58, 100, and 98.46, respectively. The acquired comparison findings demonstrate the effectiveness and robustness of the proposed technique in terms of rapid learning speed, smaller computational complexity, robust performance for anti-noise conditions, and accurate identification and categorization. |
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| AbstractList | The transformation of traditional grid networks towards smart-grid and microgrid concepts raises many critical issues, and quality in the power supply is one of the prominent ones that needs further research. Developing and applying power quality (PQ) recognition methods with efficient and reliable analysis are essential to the fast-growing issues related to modern smart power distribution systems. In this regard, a hybrid algorithm is proposed for PQ events detection and classification using Elasticnet Regression-based Variational Mode Decomposition (ER-VMD) and Salp Swarm Algorithm optimized Probabilistic Neural Network (SSA-PNN). The Elasticnet Regression (ER) process is suggested to modify the conventional VMD approach instead of the Tikhonov Regularization (TR) method to enhance performance and obtain better band-limited intrinsic mode functions. This idea results in robust and effective reconstruction features and helps to obtain accurate classification using the classifier. In the classification stage, a Salp Swarm Algorithm (SSA) based PNN is used for the PQ event, considering the relevant features obtained from ER-VMD. The system parameters often influence PNN performance, and SSA is used to determine the ideal values to improve the PNN's capacity for more accurate classification. The numerical values of the accuracy percentage, percentage of sensitivity, and percentage of specificity in the case of real-time data are found as 98.58, 100, and 98.46, respectively. The acquired comparison findings demonstrate the effectiveness and robustness of the proposed technique in terms of rapid learning speed, smaller computational complexity, robust performance for anti-noise conditions, and accurate identification and categorization. The transformation of traditional grid networks towards smart-grid and microgrid concepts raises many critical issues, and quality in the power supply is one of the prominent ones that needs further research. Developing and applying power quality (PQ) recognition methods with efficient and reliable analysis are essential to the fast-growing issues related to modern smart power distribution systems. In this regard, a hybrid algorithm is proposed for PQ events detection and classification using Elasticnet Regression-based Variational Mode Decomposition (ER-VMD) and Salp Swarm Algorithm optimized Probabilistic Neural Network (SSA-PNN). The Elasticnet Regression (ER) process is suggested to modify the conventional VMD approach instead of the Tikhonov Regularization (TR) method to enhance performance and obtain better band-limited intrinsic mode functions. This idea results in robust and effective reconstruction features and helps to obtain accurate classification using the classifier. In the classification stage, a Salp Swarm Algorithm (SSA) based PNN is used for the PQ event, considering the relevant features obtained from ER-VMD. The system parameters often influence PNN performance, and SSA is used to determine the ideal values to improve the PNN's capacity for more accurate classification. The numerical values of the accuracy percentage, percentage of sensitivity, and percentage of specificity in the case of real-time data are found as 98.58, 100, and 98.46, respectively. The acquired comparison findings demonstrate the effectiveness and robustness of the proposed technique in terms of rapid learning speed, smaller computational complexity, robust performance for anti-noise conditions, and accurate identification and categorization.The transformation of traditional grid networks towards smart-grid and microgrid concepts raises many critical issues, and quality in the power supply is one of the prominent ones that needs further research. Developing and applying power quality (PQ) recognition methods with efficient and reliable analysis are essential to the fast-growing issues related to modern smart power distribution systems. In this regard, a hybrid algorithm is proposed for PQ events detection and classification using Elasticnet Regression-based Variational Mode Decomposition (ER-VMD) and Salp Swarm Algorithm optimized Probabilistic Neural Network (SSA-PNN). The Elasticnet Regression (ER) process is suggested to modify the conventional VMD approach instead of the Tikhonov Regularization (TR) method to enhance performance and obtain better band-limited intrinsic mode functions. This idea results in robust and effective reconstruction features and helps to obtain accurate classification using the classifier. In the classification stage, a Salp Swarm Algorithm (SSA) based PNN is used for the PQ event, considering the relevant features obtained from ER-VMD. The system parameters often influence PNN performance, and SSA is used to determine the ideal values to improve the PNN's capacity for more accurate classification. The numerical values of the accuracy percentage, percentage of sensitivity, and percentage of specificity in the case of real-time data are found as 98.58, 100, and 98.46, respectively. The acquired comparison findings demonstrate the effectiveness and robustness of the proposed technique in terms of rapid learning speed, smaller computational complexity, robust performance for anti-noise conditions, and accurate identification and categorization. |
| ArticleNumber | e37975 |
| Author | Cherukuri, Murthy Rout, Pravat Kumar Samanta, Indu Sekhar Swain, Kunjabihari Prokop, Lukas Misak, Stanislav Blazek, Vojtech Panda, Subhasis Bajaj, Mohit |
| Author_xml | – sequence: 1 givenname: Indu Sekhar surname: Samanta fullname: Samanta, Indu Sekhar organization: Department of Computer Science and Engineering, Siksha’ O’ Anusandhan University, India – sequence: 2 givenname: Pravat Kumar surname: Rout fullname: Rout, Pravat Kumar organization: Department of Electrical and Electronics Engineering, Siksha’ O’ Anusandhan University, India – sequence: 3 givenname: Kunjabihari surname: Swain fullname: Swain, Kunjabihari organization: Department of Electrical and Electronics Engineering, NIST University, India – sequence: 4 givenname: Murthy surname: Cherukuri fullname: Cherukuri, Murthy organization: Department of Electrical and Electronics Engineering, NIST University, India – sequence: 5 givenname: Subhasis surname: Panda fullname: Panda, Subhasis organization: Department of Electrical Engineering, Srinix College of Engineering, Ranipatna, Balasore, Odisha, India – sequence: 6 givenname: Mohit orcidid: 0000-0002-1086-457X surname: Bajaj fullname: Bajaj, Mohit email: thebestbajaj@gmail.com organization: Department of Electrical Engineering, Graphic Era (Deemed to be University), Dehradun, 248002, India – sequence: 7 givenname: Vojtech surname: Blazek fullname: Blazek, Vojtech organization: ENET Centre, VSB—Technical University of Ostrava, 708 00, Ostrava, Czech Republic – sequence: 8 givenname: Lukas surname: Prokop fullname: Prokop, Lukas organization: ENET Centre, VSB—Technical University of Ostrava, 708 00, Ostrava, Czech Republic – sequence: 9 givenname: Stanislav surname: Misak fullname: Misak, Stanislav organization: ENET Centre, VSB—Technical University of Ostrava, 708 00, Ostrava, Czech Republic |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/39328549$$D View this record in MEDLINE/PubMed |
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| Issue | 18 |
| Keywords | Salp swarm algorithm Power quality events Variational mode decomposition Probabilistic neural network Power quality indices |
| Language | English |
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| Title | A hybrid approach for power quality event identification in power systems: Elasticnet Regression decomposition and optimized probabilistic neural networks |
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