Efficient Artificial Neural Network for Smart Grid Stability Prediction
According to the stability process of smart grids, which starts by gathering information of consumers, and then evaluating this information based on specifications of a power supply, and finally, information of a price is sent to the consumers as a report about the utilization. From this perspective...
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| Format: | Article |
| Language: | English |
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Wiley
2023-01-01
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| Series: | International Transactions on Electrical Energy Systems |
| Online Access: | http://dx.doi.org/10.1155/2023/9974409 |
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| author | Saeed Mohsen Mohit Bajaj Hossam Kotb Mukesh Pushkarna Sadam Alphonse Sherif S. M. Ghoneim |
| author_facet | Saeed Mohsen Mohit Bajaj Hossam Kotb Mukesh Pushkarna Sadam Alphonse Sherif S. M. Ghoneim |
| author_sort | Saeed Mohsen |
| collection | DOAJ |
| description | According to the stability process of smart grids, which starts by gathering information of consumers, and then evaluating this information based on specifications of a power supply, and finally, information of a price is sent to the consumers as a report about the utilization. From this perspective, this process is too much time consuming, thus it should predict a smart grid stability via artificial intelligence (e.g., neural networks). Recent advances in the accuracy of neural network have effective solutions to solving the smart grid stability prediction issues, but it remains necessary to develop high performance neural networks that give higher accuracy. In this paper, an artificial neural network (ANN) is proposed to predict a smart grid stability for Decentral Smart Grid Control (DSGC) systems. This neural network is applied to a dataset aggregated from simulations of grid stability, executed on a four-node network with star topology, and engaged in two classes of grid stability–stable and unstable. Keras framework is used to train the proposed neural network, and a hyperparameter tuning method is utilized to achieve high accuracy. Receiver operating characteristic (ROC) curves and confusion matrices are experimentally utilized to evaluate the performance of the proposed neural network. The neural network provides high performance, with a testing loss rate of 0.0619, and a testing accuracy of 97.36%. The weighted average recall, precision, and F1-score for the proposed neural network are 98.02%, 98.03%, and 98.02%, respectively, while the area under the ROC curves (AUCs) is 100%. This neural network with the utilized dataset indeed provides an accurate and quick approach of predicting grid stability to analyze DSGC systems. |
| format | Article |
| id | doaj-art-d9da1861373c4e849dd06d7090328552 |
| institution | Kabale University |
| issn | 2050-7038 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Transactions on Electrical Energy Systems |
| spelling | doaj-art-d9da1861373c4e849dd06d70903285522025-02-03T06:45:10ZengWileyInternational Transactions on Electrical Energy Systems2050-70382023-01-01202310.1155/2023/9974409Efficient Artificial Neural Network for Smart Grid Stability PredictionSaeed Mohsen0Mohit Bajaj1Hossam Kotb2Mukesh Pushkarna3Sadam Alphonse4Sherif S. M. Ghoneim5Department of Electronics and Communications EngineeringDepartment of Electrical EngineeringDepartment of Electrical Power and MachinesDepartment of Electrical EngineeringUFD PAIDepartment of Electrical EngineeringAccording to the stability process of smart grids, which starts by gathering information of consumers, and then evaluating this information based on specifications of a power supply, and finally, information of a price is sent to the consumers as a report about the utilization. From this perspective, this process is too much time consuming, thus it should predict a smart grid stability via artificial intelligence (e.g., neural networks). Recent advances in the accuracy of neural network have effective solutions to solving the smart grid stability prediction issues, but it remains necessary to develop high performance neural networks that give higher accuracy. In this paper, an artificial neural network (ANN) is proposed to predict a smart grid stability for Decentral Smart Grid Control (DSGC) systems. This neural network is applied to a dataset aggregated from simulations of grid stability, executed on a four-node network with star topology, and engaged in two classes of grid stability–stable and unstable. Keras framework is used to train the proposed neural network, and a hyperparameter tuning method is utilized to achieve high accuracy. Receiver operating characteristic (ROC) curves and confusion matrices are experimentally utilized to evaluate the performance of the proposed neural network. The neural network provides high performance, with a testing loss rate of 0.0619, and a testing accuracy of 97.36%. The weighted average recall, precision, and F1-score for the proposed neural network are 98.02%, 98.03%, and 98.02%, respectively, while the area under the ROC curves (AUCs) is 100%. This neural network with the utilized dataset indeed provides an accurate and quick approach of predicting grid stability to analyze DSGC systems.http://dx.doi.org/10.1155/2023/9974409 |
| spellingShingle | Saeed Mohsen Mohit Bajaj Hossam Kotb Mukesh Pushkarna Sadam Alphonse Sherif S. M. Ghoneim Efficient Artificial Neural Network for Smart Grid Stability Prediction International Transactions on Electrical Energy Systems |
| title | Efficient Artificial Neural Network for Smart Grid Stability Prediction |
| title_full | Efficient Artificial Neural Network for Smart Grid Stability Prediction |
| title_fullStr | Efficient Artificial Neural Network for Smart Grid Stability Prediction |
| title_full_unstemmed | Efficient Artificial Neural Network for Smart Grid Stability Prediction |
| title_short | Efficient Artificial Neural Network for Smart Grid Stability Prediction |
| title_sort | efficient artificial neural network for smart grid stability prediction |
| url | http://dx.doi.org/10.1155/2023/9974409 |
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