A New Intelligent Estimation Method Based on the Cascade-Forward Neural Network for the Electric and Magnetic Fields in the Vicinity of the High Voltage Overhead Transmission Lines
The evaluation and estimation of the electric and magnetic field (EMF) intensity in the vicinity of overhead transmission lines (OHTL) is of paramount importance for residents’ healthcare and industrial monitoring purposes. Using artificial intelligence (AI) techniques makes researchers able to esti...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-10-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/13/20/11180 |
| _version_ | 1797574902674358272 |
|---|---|
| author | Shahin Alipour Bonab Wenjuan Song Mohammad Yazdani-Asrami |
| author_facet | Shahin Alipour Bonab Wenjuan Song Mohammad Yazdani-Asrami |
| author_sort | Shahin Alipour Bonab |
| collection | DOAJ |
| description | The evaluation and estimation of the electric and magnetic field (EMF) intensity in the vicinity of overhead transmission lines (OHTL) is of paramount importance for residents’ healthcare and industrial monitoring purposes. Using artificial intelligence (AI) techniques makes researchers able to estimate EMF with extremely high accuracy in a significantly short time. In this paper, two models based on the Artificial Neural Network (ANN) have been developed for estimating electric and magnetic fields, i.e., feed-forward neural network (FFNN) and cascade-forward neural network (CFNN). By performing the sensitivity analysis on controlling/hyper-parameters of these two ANN models, the best setup resulting in the highest possible accuracy considering their response time has been chosen. Overall, the CFNN achieved a significant 56% reduction in Root Mean Squared Error (RMSE) for the electric field and a 5% reduction for the magnetic field, compared to the FFNN. This indicates that the CFNN model provided more accurate predictions, particularly for the electric field than the proposed methods in other recent works, making it a promising choice for this application. When the model is trained, it will be tested by a different dataset. Then, the accuracy and response time of the model for new data points of that layout will be evaluated through this process. The model can predict the fields with an accuracy near 99.999% of the actual values in times under 10 ms. Also, the results of sensitivity analysis indicated that the CFNN models with triple and double hidden layers are the best options for the electric and magnetic field estimation, respectively. |
| first_indexed | 2024-03-10T21:28:38Z |
| format | Article |
| id | doaj.art-b28b9231dfb74b69a7dc4d39b67096ad |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T21:28:38Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-b28b9231dfb74b69a7dc4d39b67096ad2023-11-19T15:29:39ZengMDPI AGApplied Sciences2076-34172023-10-0113201118010.3390/app132011180A New Intelligent Estimation Method Based on the Cascade-Forward Neural Network for the Electric and Magnetic Fields in the Vicinity of the High Voltage Overhead Transmission LinesShahin Alipour Bonab0Wenjuan Song1Mohammad Yazdani-Asrami2Propulsion, Electrification & Superconductivity Group, James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UKPropulsion, Electrification & Superconductivity Group, James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UKPropulsion, Electrification & Superconductivity Group, James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UKThe evaluation and estimation of the electric and magnetic field (EMF) intensity in the vicinity of overhead transmission lines (OHTL) is of paramount importance for residents’ healthcare and industrial monitoring purposes. Using artificial intelligence (AI) techniques makes researchers able to estimate EMF with extremely high accuracy in a significantly short time. In this paper, two models based on the Artificial Neural Network (ANN) have been developed for estimating electric and magnetic fields, i.e., feed-forward neural network (FFNN) and cascade-forward neural network (CFNN). By performing the sensitivity analysis on controlling/hyper-parameters of these two ANN models, the best setup resulting in the highest possible accuracy considering their response time has been chosen. Overall, the CFNN achieved a significant 56% reduction in Root Mean Squared Error (RMSE) for the electric field and a 5% reduction for the magnetic field, compared to the FFNN. This indicates that the CFNN model provided more accurate predictions, particularly for the electric field than the proposed methods in other recent works, making it a promising choice for this application. When the model is trained, it will be tested by a different dataset. Then, the accuracy and response time of the model for new data points of that layout will be evaluated through this process. The model can predict the fields with an accuracy near 99.999% of the actual values in times under 10 ms. Also, the results of sensitivity analysis indicated that the CFNN models with triple and double hidden layers are the best options for the electric and magnetic field estimation, respectively.https://www.mdpi.com/2076-3417/13/20/11180artificial intelligencecascade-forward neural networkfield estimationoverhead transmission linefeed-forward neural network |
| spellingShingle | Shahin Alipour Bonab Wenjuan Song Mohammad Yazdani-Asrami A New Intelligent Estimation Method Based on the Cascade-Forward Neural Network for the Electric and Magnetic Fields in the Vicinity of the High Voltage Overhead Transmission Lines Applied Sciences artificial intelligence cascade-forward neural network field estimation overhead transmission line feed-forward neural network |
| title | A New Intelligent Estimation Method Based on the Cascade-Forward Neural Network for the Electric and Magnetic Fields in the Vicinity of the High Voltage Overhead Transmission Lines |
| title_full | A New Intelligent Estimation Method Based on the Cascade-Forward Neural Network for the Electric and Magnetic Fields in the Vicinity of the High Voltage Overhead Transmission Lines |
| title_fullStr | A New Intelligent Estimation Method Based on the Cascade-Forward Neural Network for the Electric and Magnetic Fields in the Vicinity of the High Voltage Overhead Transmission Lines |
| title_full_unstemmed | A New Intelligent Estimation Method Based on the Cascade-Forward Neural Network for the Electric and Magnetic Fields in the Vicinity of the High Voltage Overhead Transmission Lines |
| title_short | A New Intelligent Estimation Method Based on the Cascade-Forward Neural Network for the Electric and Magnetic Fields in the Vicinity of the High Voltage Overhead Transmission Lines |
| title_sort | new intelligent estimation method based on the cascade forward neural network for the electric and magnetic fields in the vicinity of the high voltage overhead transmission lines |
| topic | artificial intelligence cascade-forward neural network field estimation overhead transmission line feed-forward neural network |
| url | https://www.mdpi.com/2076-3417/13/20/11180 |
| work_keys_str_mv | AT shahinalipourbonab anewintelligentestimationmethodbasedonthecascadeforwardneuralnetworkfortheelectricandmagneticfieldsinthevicinityofthehighvoltageoverheadtransmissionlines AT wenjuansong anewintelligentestimationmethodbasedonthecascadeforwardneuralnetworkfortheelectricandmagneticfieldsinthevicinityofthehighvoltageoverheadtransmissionlines AT mohammadyazdaniasrami anewintelligentestimationmethodbasedonthecascadeforwardneuralnetworkfortheelectricandmagneticfieldsinthevicinityofthehighvoltageoverheadtransmissionlines AT shahinalipourbonab newintelligentestimationmethodbasedonthecascadeforwardneuralnetworkfortheelectricandmagneticfieldsinthevicinityofthehighvoltageoverheadtransmissionlines AT wenjuansong newintelligentestimationmethodbasedonthecascadeforwardneuralnetworkfortheelectricandmagneticfieldsinthevicinityofthehighvoltageoverheadtransmissionlines AT mohammadyazdaniasrami newintelligentestimationmethodbasedonthecascadeforwardneuralnetworkfortheelectricandmagneticfieldsinthevicinityofthehighvoltageoverheadtransmissionlines |