AI-Driven Signal Processing for SF6 Circuit Breaker Performance Optimization
This work presents an approach based on signal processing and artificial intelligence (AI) to identify the pre-insertion resistor (PIR) and main contact instants during the operation of high-voltage SF6 circuit breakers to help improve the settings of controlled switching and attenuate transients. F...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/18/2/377 |
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| author | Philippe A. V. D. Liz Giovani B. Vitor Ricardo T. Lima Aurélio L. M. Coelho Eben P. Silveira |
| author_facet | Philippe A. V. D. Liz Giovani B. Vitor Ricardo T. Lima Aurélio L. M. Coelho Eben P. Silveira |
| author_sort | Philippe A. V. D. Liz |
| collection | DOAJ |
| description | This work presents an approach based on signal processing and artificial intelligence (AI) to identify the pre-insertion resistor (PIR) and main contact instants during the operation of high-voltage SF6 circuit breakers to help improve the settings of controlled switching and attenuate transients. For this, the current and voltage signals of a real Brazilian substation are used as AI inputs, considering the noise and interferences common in this type of environment. Thus, the proposed modeling considers the signal preprocessing steps for feature extraction, the generation of the dataset for model training, the use of different machine learning techniques to automatically find the desired points, and, finally, the identification of the best moments for controlled switching of the circuit breakers. As a result, the models evaluated obtained good performance in the identification of operation points above 93%, considering precision and accuracy. In addition, valuable statistical notes related to the controlled switching condition are obtained from the circuit breakers evaluated in this research. |
| format | Article |
| id | doaj-art-070fbc27a03240aab111bfe6571f44f7 |
| institution | Kabale University |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-070fbc27a03240aab111bfe6571f44f72025-01-24T13:31:16ZengMDPI AGEnergies1996-10732025-01-0118237710.3390/en18020377AI-Driven Signal Processing for SF6 Circuit Breaker Performance OptimizationPhilippe A. V. D. Liz0Giovani B. Vitor1Ricardo T. Lima2Aurélio L. M. Coelho3Eben P. Silveira4Laboratory of Robotics, Intelligent and Complex Systems—ROBSIC, Itabira 35903-087, MG, BrazilLaboratory of Robotics, Intelligent and Complex Systems—ROBSIC, Itabira 35903-087, MG, BrazilCentrais Elétricas Brasileiras S/A—ELETROBRÁS, Rio de Janeiro 20091-005, RJ, BrazilLaboratory of Robotics, Intelligent and Complex Systems—ROBSIC, Itabira 35903-087, MG, BrazilLaboratory of Robotics, Intelligent and Complex Systems—ROBSIC, Itabira 35903-087, MG, BrazilThis work presents an approach based on signal processing and artificial intelligence (AI) to identify the pre-insertion resistor (PIR) and main contact instants during the operation of high-voltage SF6 circuit breakers to help improve the settings of controlled switching and attenuate transients. For this, the current and voltage signals of a real Brazilian substation are used as AI inputs, considering the noise and interferences common in this type of environment. Thus, the proposed modeling considers the signal preprocessing steps for feature extraction, the generation of the dataset for model training, the use of different machine learning techniques to automatically find the desired points, and, finally, the identification of the best moments for controlled switching of the circuit breakers. As a result, the models evaluated obtained good performance in the identification of operation points above 93%, considering precision and accuracy. In addition, valuable statistical notes related to the controlled switching condition are obtained from the circuit breakers evaluated in this research.https://www.mdpi.com/1996-1073/18/2/377high-voltage circuit breakersartificial intelligencesubstation capacitor bankcontrolled switching |
| spellingShingle | Philippe A. V. D. Liz Giovani B. Vitor Ricardo T. Lima Aurélio L. M. Coelho Eben P. Silveira AI-Driven Signal Processing for SF6 Circuit Breaker Performance Optimization Energies high-voltage circuit breakers artificial intelligence substation capacitor bank controlled switching |
| title | AI-Driven Signal Processing for SF6 Circuit Breaker Performance Optimization |
| title_full | AI-Driven Signal Processing for SF6 Circuit Breaker Performance Optimization |
| title_fullStr | AI-Driven Signal Processing for SF6 Circuit Breaker Performance Optimization |
| title_full_unstemmed | AI-Driven Signal Processing for SF6 Circuit Breaker Performance Optimization |
| title_short | AI-Driven Signal Processing for SF6 Circuit Breaker Performance Optimization |
| title_sort | ai driven signal processing for sf6 circuit breaker performance optimization |
| topic | high-voltage circuit breakers artificial intelligence substation capacitor bank controlled switching |
| url | https://www.mdpi.com/1996-1073/18/2/377 |
| work_keys_str_mv | AT philippeavdliz aidrivensignalprocessingforsf6circuitbreakerperformanceoptimization AT giovanibvitor aidrivensignalprocessingforsf6circuitbreakerperformanceoptimization AT ricardotlima aidrivensignalprocessingforsf6circuitbreakerperformanceoptimization AT aureliolmcoelho aidrivensignalprocessingforsf6circuitbreakerperformanceoptimization AT ebenpsilveira aidrivensignalprocessingforsf6circuitbreakerperformanceoptimization |