Hybrid optimized artificial neural network using Latin hypercube sampling and Bayesian optimization for detection, classification and location of faults in transmission lines
This paper introduces a novel hybrid approach that integrates Latin hypercube sampling (LHS) and Bayesian optimization for optimizing artificial neural networks (ANNs) in fault detection, classification, and location for transmission lines. The proposed method advances the accuracy and efficiency of...
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AIMS Press
2024-11-01
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| Series: | AIMS Electronics and Electrical Engineering |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/electreng.2024024 |
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| author | Abdul Yussif Seidu Elvis Twumasi Emmanuel Assuming Frimpong |
| author_facet | Abdul Yussif Seidu Elvis Twumasi Emmanuel Assuming Frimpong |
| author_sort | Abdul Yussif Seidu |
| collection | DOAJ |
| description | This paper introduces a novel hybrid approach that integrates Latin hypercube sampling (LHS) and Bayesian optimization for optimizing artificial neural networks (ANNs) in fault detection, classification, and location for transmission lines. The proposed method advances the accuracy and efficiency of fault diagnosis in power systems, representing a significant step forward compared to conventional approaches. The test system is a 400 kV, 50 Hz, 300 km transmission system, and the simulations were carried out in MATLAB/Simulink environment. Using the strategic insight of LHS, optimal initial points were determined, which subsequently formed the basis for the Bayesian optimization to further refine the learning rate and training epochs. Within the fault detection domain, the model showcased remarkable precision when deployed on an evaluation dataset of 168 cases, accurately detecting every instance of normal and faulty scenarios. This culminated in an astounding 100% accuracy in fault detection. In terms of fault classification, the ANN model, trained on a dataset of 495 instances, revealed perfect regression coefficients across the training, testing, and validation subsets. When tested against unseen data, it demonstrated its proficiency by correctly classifying 154 out of 154 cases, showcasing a 100% F1 score. Also, the accuracy figures in terms of fault location fluctuated between 99.826% and 99.999%, with a mean absolute percentage error (MAPE) of 0.053%. The model's mean square error (MSE) stood at 0.0083, while the mean absolute error (MAE) was calculated at 0.0717. A deep dive into diverse fault types reaffirmed the model's precision, underscoring its consistent performance across various fault scenarios. The trained models were deployed on three different transmission lines and the models exhibited remarkable precision in all the cases tested. In summary, the innovative hybrid optimized ANN model, weaving together the strengths of LHS and Bayesian optimization, signifies an advancement in the field of power system fault analysis, ushering in heightened efficiency and reliability. |
| format | Article |
| id | doaj-art-cedd57bbba3542e69528800a2e0390dc |
| institution | Kabale University |
| issn | 2578-1588 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | AIMS Electronics and Electrical Engineering |
| spelling | doaj-art-cedd57bbba3542e69528800a2e0390dc2025-01-24T01:11:32ZengAIMS PressAIMS Electronics and Electrical Engineering2578-15882024-11-018449853110.3934/electreng.2024024Hybrid optimized artificial neural network using Latin hypercube sampling and Bayesian optimization for detection, classification and location of faults in transmission linesAbdul Yussif Seidu0Elvis Twumasi1Emmanuel Assuming Frimpong2Department of Electrical and Electronic Engineering, College of Engineering, Kwame Nkrumah University of Science and Technology, Kumasi, GhanaDepartment of Electrical and Electronic Engineering, College of Engineering, Kwame Nkrumah University of Science and Technology, Kumasi, GhanaDepartment of Electrical and Electronic Engineering, College of Engineering, Kwame Nkrumah University of Science and Technology, Kumasi, GhanaThis paper introduces a novel hybrid approach that integrates Latin hypercube sampling (LHS) and Bayesian optimization for optimizing artificial neural networks (ANNs) in fault detection, classification, and location for transmission lines. The proposed method advances the accuracy and efficiency of fault diagnosis in power systems, representing a significant step forward compared to conventional approaches. The test system is a 400 kV, 50 Hz, 300 km transmission system, and the simulations were carried out in MATLAB/Simulink environment. Using the strategic insight of LHS, optimal initial points were determined, which subsequently formed the basis for the Bayesian optimization to further refine the learning rate and training epochs. Within the fault detection domain, the model showcased remarkable precision when deployed on an evaluation dataset of 168 cases, accurately detecting every instance of normal and faulty scenarios. This culminated in an astounding 100% accuracy in fault detection. In terms of fault classification, the ANN model, trained on a dataset of 495 instances, revealed perfect regression coefficients across the training, testing, and validation subsets. When tested against unseen data, it demonstrated its proficiency by correctly classifying 154 out of 154 cases, showcasing a 100% F1 score. Also, the accuracy figures in terms of fault location fluctuated between 99.826% and 99.999%, with a mean absolute percentage error (MAPE) of 0.053%. The model's mean square error (MSE) stood at 0.0083, while the mean absolute error (MAE) was calculated at 0.0717. A deep dive into diverse fault types reaffirmed the model's precision, underscoring its consistent performance across various fault scenarios. The trained models were deployed on three different transmission lines and the models exhibited remarkable precision in all the cases tested. In summary, the innovative hybrid optimized ANN model, weaving together the strengths of LHS and Bayesian optimization, signifies an advancement in the field of power system fault analysis, ushering in heightened efficiency and reliability.https://www.aimspress.com/article/doi/10.3934/electreng.2024024latinhypercubebayesianoptimizationtransmissionartificial neural network |
| spellingShingle | Abdul Yussif Seidu Elvis Twumasi Emmanuel Assuming Frimpong Hybrid optimized artificial neural network using Latin hypercube sampling and Bayesian optimization for detection, classification and location of faults in transmission lines AIMS Electronics and Electrical Engineering latin hypercube bayesian optimization transmission artificial neural network |
| title | Hybrid optimized artificial neural network using Latin hypercube sampling and Bayesian optimization for detection, classification and location of faults in transmission lines |
| title_full | Hybrid optimized artificial neural network using Latin hypercube sampling and Bayesian optimization for detection, classification and location of faults in transmission lines |
| title_fullStr | Hybrid optimized artificial neural network using Latin hypercube sampling and Bayesian optimization for detection, classification and location of faults in transmission lines |
| title_full_unstemmed | Hybrid optimized artificial neural network using Latin hypercube sampling and Bayesian optimization for detection, classification and location of faults in transmission lines |
| title_short | Hybrid optimized artificial neural network using Latin hypercube sampling and Bayesian optimization for detection, classification and location of faults in transmission lines |
| title_sort | hybrid optimized artificial neural network using latin hypercube sampling and bayesian optimization for detection classification and location of faults in transmission lines |
| topic | latin hypercube bayesian optimization transmission artificial neural network |
| url | https://www.aimspress.com/article/doi/10.3934/electreng.2024024 |
| work_keys_str_mv | AT abdulyussifseidu hybridoptimizedartificialneuralnetworkusinglatinhypercubesamplingandbayesianoptimizationfordetectionclassificationandlocationoffaultsintransmissionlines AT elvistwumasi hybridoptimizedartificialneuralnetworkusinglatinhypercubesamplingandbayesianoptimizationfordetectionclassificationandlocationoffaultsintransmissionlines AT emmanuelassumingfrimpong hybridoptimizedartificialneuralnetworkusinglatinhypercubesamplingandbayesianoptimizationfordetectionclassificationandlocationoffaultsintransmissionlines |