Intelligent Fault Detection System for Microgrids
The dynamic features of microgrid operation, such as on-grid/off-grid operation mode, the intermittency of distributed generators, and its dynamic topology due to its ability to reconfigure itself, cause misfiring of conventional protection schemes. To solve this issue, adaptive protection schemes t...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-03-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/13/5/1223 |
| _version_ | 1817991811783720960 |
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| author | Cristian Cepeda Cesar Orozco-Henao Winston Percybrooks Juan Diego Pulgarín-Rivera Oscar Danilo Montoya Walter Gil-González Juan Carlos Vélez |
| author_facet | Cristian Cepeda Cesar Orozco-Henao Winston Percybrooks Juan Diego Pulgarín-Rivera Oscar Danilo Montoya Walter Gil-González Juan Carlos Vélez |
| author_sort | Cristian Cepeda |
| collection | DOAJ |
| description | The dynamic features of microgrid operation, such as on-grid/off-grid operation mode, the intermittency of distributed generators, and its dynamic topology due to its ability to reconfigure itself, cause misfiring of conventional protection schemes. To solve this issue, adaptive protection schemes that use robust communication systems have been proposed for the protection of microgrids. However, the cost of this solution is significantly high. This paper presented an intelligent fault detection (FD) system for microgrids on the basis of local measurements and machine learning (ML) techniques. This proposed FD system provided a smart level to intelligent electronic devices (IED) installed on the microgrid through the integration of ML models. This allowed each IED to autonomously determine if a fault occurred on the microgrid, eliminating the requirement of robust communication infrastructure between IEDs for microgrid protection. Additionally, the proposed system presented a methodology composed of four stages, which allowed its implementation in any microgrid. In addition, each stage provided important recommendations for the proper use of ML techniques on the protection problem. The proposed FD system was validated on the modified IEEE 13-nodes test feeder. This took into consideration typical features of microgrids such as the load imbalance, reconfiguration, and off-grid/on-grid operation modes. The results demonstrated the flexibility and simplicity of the FD system in determining the best accuracy performance among several ML models. The ease of design’s implementation, formulation of parameters, and promising test results indicated the potential for real-life applications. |
| first_indexed | 2024-04-14T01:18:20Z |
| format | Article |
| id | doaj.art-e913ab958aae4429b78b5beeb7206c31 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-14T01:18:20Z |
| publishDate | 2020-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-e913ab958aae4429b78b5beeb7206c312022-12-22T02:20:47ZengMDPI AGEnergies1996-10732020-03-01135122310.3390/en13051223en13051223Intelligent Fault Detection System for MicrogridsCristian Cepeda0Cesar Orozco-Henao1Winston Percybrooks2Juan Diego Pulgarín-Rivera3Oscar Danilo Montoya4Walter Gil-González5Juan Carlos Vélez6Department of Electrical and Electronic Engineering, Universidad del Norte, Barranquilla 080001, ColombiaDepartment of Electrical and Electronic Engineering, Universidad del Norte, Barranquilla 080001, ColombiaDepartment of Electrical and Electronic Engineering, Universidad del Norte, Barranquilla 080001, ColombiaDepartment of Electrical and Electronic Engineering, Universidad del Norte, Barranquilla 080001, ColombiaFaculty of Engineering, Universidad Distrital Francisco José de Caldas, Bogotá D.C. 11021, ColombiaSmart Energy Laboratory, Universidad Tecnológica de Bolívar, Cartagena 131001, ColombiaDepartment of Electrical and Electronic Engineering, Universidad del Norte, Barranquilla 080001, ColombiaThe dynamic features of microgrid operation, such as on-grid/off-grid operation mode, the intermittency of distributed generators, and its dynamic topology due to its ability to reconfigure itself, cause misfiring of conventional protection schemes. To solve this issue, adaptive protection schemes that use robust communication systems have been proposed for the protection of microgrids. However, the cost of this solution is significantly high. This paper presented an intelligent fault detection (FD) system for microgrids on the basis of local measurements and machine learning (ML) techniques. This proposed FD system provided a smart level to intelligent electronic devices (IED) installed on the microgrid through the integration of ML models. This allowed each IED to autonomously determine if a fault occurred on the microgrid, eliminating the requirement of robust communication infrastructure between IEDs for microgrid protection. Additionally, the proposed system presented a methodology composed of four stages, which allowed its implementation in any microgrid. In addition, each stage provided important recommendations for the proper use of ML techniques on the protection problem. The proposed FD system was validated on the modified IEEE 13-nodes test feeder. This took into consideration typical features of microgrids such as the load imbalance, reconfiguration, and off-grid/on-grid operation modes. The results demonstrated the flexibility and simplicity of the FD system in determining the best accuracy performance among several ML models. The ease of design’s implementation, formulation of parameters, and promising test results indicated the potential for real-life applications.https://www.mdpi.com/1996-1073/13/5/1223fault detectormicrogridmachine learning-based techniques |
| spellingShingle | Cristian Cepeda Cesar Orozco-Henao Winston Percybrooks Juan Diego Pulgarín-Rivera Oscar Danilo Montoya Walter Gil-González Juan Carlos Vélez Intelligent Fault Detection System for Microgrids Energies fault detector microgrid machine learning-based techniques |
| title | Intelligent Fault Detection System for Microgrids |
| title_full | Intelligent Fault Detection System for Microgrids |
| title_fullStr | Intelligent Fault Detection System for Microgrids |
| title_full_unstemmed | Intelligent Fault Detection System for Microgrids |
| title_short | Intelligent Fault Detection System for Microgrids |
| title_sort | intelligent fault detection system for microgrids |
| topic | fault detector microgrid machine learning-based techniques |
| url | https://www.mdpi.com/1996-1073/13/5/1223 |
| work_keys_str_mv | AT cristiancepeda intelligentfaultdetectionsystemformicrogrids AT cesarorozcohenao intelligentfaultdetectionsystemformicrogrids AT winstonpercybrooks intelligentfaultdetectionsystemformicrogrids AT juandiegopulgarinrivera intelligentfaultdetectionsystemformicrogrids AT oscardanilomontoya intelligentfaultdetectionsystemformicrogrids AT waltergilgonzalez intelligentfaultdetectionsystemformicrogrids AT juancarlosvelez intelligentfaultdetectionsystemformicrogrids |