Predictive Modeling of Total Real and Reactive Power Losses in Contingency Systems Using Function-Fitting Neural Networks with Graphical User Interface
Technical power losses in power systems are unavoidable, caused by factors such as transformer impedance, conductor resistance, equipment inefficiencies, line reactance, and phase imbalances. Reducing these losses is essential for improving system efficiency. This study introduces an innovative appr...
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MDPI AG
2025-01-01
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| author | Alfredo Bonini Neto Alexandre de Queiroz Giovana Gonçalves da Silva André Gifalli André Nunes de Souza Enio Garbelini |
| author_facet | Alfredo Bonini Neto Alexandre de Queiroz Giovana Gonçalves da Silva André Gifalli André Nunes de Souza Enio Garbelini |
| author_sort | Alfredo Bonini Neto |
| collection | DOAJ |
| description | Technical power losses in power systems are unavoidable, caused by factors such as transformer impedance, conductor resistance, equipment inefficiencies, line reactance, and phase imbalances. Reducing these losses is essential for improving system efficiency. This study introduces an innovative approach using Artificial Neural Networks (ANN) combined with the graphical interface to predict complete curves of real and reactive power losses in power systems under various contingencies. The key advantage of this methodology is its speed, allowing quick estimation of power loss curves both in normal and contingency conditions, whether mild or severe. ANN models excel at capturing the nonlinear behavior of power systems, eliminating the need for iterative methods commonly used in traditional approaches. The results showed that the ANN performed effectively, with a mean squared error during training below the specified threshold. For samples not included in the training set, the network accurately estimated 99% of the real and reactive power losses within the specified range, with residuals around 10<sup>−3</sup> and an overall accuracy rate of 99% between the desired and obtained outputs. Additionally, a Graphical User Interface (GUI) was implemented to facilitate user interaction, allowing for easy visualization of power-loss predictions and real-time adjustments. |
| format | Article |
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| institution | Kabale University |
| issn | 2227-7080 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Technologies |
| spelling | doaj-art-59ada43d3c4442e88c12be944980e9ed2025-01-24T13:50:45ZengMDPI AGTechnologies2227-70802025-01-011311510.3390/technologies13010015Predictive Modeling of Total Real and Reactive Power Losses in Contingency Systems Using Function-Fitting Neural Networks with Graphical User InterfaceAlfredo Bonini Neto0Alexandre de Queiroz1Giovana Gonçalves da Silva2André Gifalli3André Nunes de Souza4Enio Garbelini5School of Sciences and Engineering, São Paulo State University (UNESP), Tupã 17602-496, SP, BrazilSchool of Engineering, São Paulo State University (UNESP), Bauru 17033-360, SP, BrazilSchool of Engineering, São Paulo State University (UNESP), Bauru 17033-360, SP, BrazilSchool of Engineering, São Paulo State University (UNESP), Bauru 17033-360, SP, BrazilSchool of Engineering, São Paulo State University (UNESP), Bauru 17033-360, SP, BrazilSchool of Sciences and Engineering, São Paulo State University (UNESP), Tupã 17602-496, SP, BrazilTechnical power losses in power systems are unavoidable, caused by factors such as transformer impedance, conductor resistance, equipment inefficiencies, line reactance, and phase imbalances. Reducing these losses is essential for improving system efficiency. This study introduces an innovative approach using Artificial Neural Networks (ANN) combined with the graphical interface to predict complete curves of real and reactive power losses in power systems under various contingencies. The key advantage of this methodology is its speed, allowing quick estimation of power loss curves both in normal and contingency conditions, whether mild or severe. ANN models excel at capturing the nonlinear behavior of power systems, eliminating the need for iterative methods commonly used in traditional approaches. The results showed that the ANN performed effectively, with a mean squared error during training below the specified threshold. For samples not included in the training set, the network accurately estimated 99% of the real and reactive power losses within the specified range, with residuals around 10<sup>−3</sup> and an overall accuracy rate of 99% between the desired and obtained outputs. Additionally, a Graphical User Interface (GUI) was implemented to facilitate user interaction, allowing for easy visualization of power-loss predictions and real-time adjustments.https://www.mdpi.com/2227-7080/13/1/15continuation methodartificial intelligencetechnical power lossesestimationloading margin |
| spellingShingle | Alfredo Bonini Neto Alexandre de Queiroz Giovana Gonçalves da Silva André Gifalli André Nunes de Souza Enio Garbelini Predictive Modeling of Total Real and Reactive Power Losses in Contingency Systems Using Function-Fitting Neural Networks with Graphical User Interface Technologies continuation method artificial intelligence technical power losses estimation loading margin |
| title | Predictive Modeling of Total Real and Reactive Power Losses in Contingency Systems Using Function-Fitting Neural Networks with Graphical User Interface |
| title_full | Predictive Modeling of Total Real and Reactive Power Losses in Contingency Systems Using Function-Fitting Neural Networks with Graphical User Interface |
| title_fullStr | Predictive Modeling of Total Real and Reactive Power Losses in Contingency Systems Using Function-Fitting Neural Networks with Graphical User Interface |
| title_full_unstemmed | Predictive Modeling of Total Real and Reactive Power Losses in Contingency Systems Using Function-Fitting Neural Networks with Graphical User Interface |
| title_short | Predictive Modeling of Total Real and Reactive Power Losses in Contingency Systems Using Function-Fitting Neural Networks with Graphical User Interface |
| title_sort | predictive modeling of total real and reactive power losses in contingency systems using function fitting neural networks with graphical user interface |
| topic | continuation method artificial intelligence technical power losses estimation loading margin |
| url | https://www.mdpi.com/2227-7080/13/1/15 |
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