Advanced fault detection in DC microgrid system using reinforcement learning
As technologies are expanding, the demand for power supply also increases. This causes the demand for power is difficult to be fulfilled as non-renewable sources are reducing. Therefore, the microgrid concept is introduced, where it is constructed with renewable energy sources, energy storage device...
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| Format: | Proceedings |
| Language: | English English |
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Institute of Electrical and Electronics Engineers
2021
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| Online Access: | https://eprints.ums.edu.my/id/eprint/32517/1/Advanced%20fault%20detection%20in%20dc%20microgrid%20system%20using%20reinforcement%20learning.ABSTRACT.pdf https://eprints.ums.edu.my/id/eprint/32517/2/Advanced%20Fault%20Detection%20in%20DC%20Microgrid%20System%20using%20Reinforcement%20Learning.pdf |
| _version_ | 1825714707256836096 |
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| author | Min, Keng Tan Kar, Leong Lee Kit, Guan Lim Ahmad Razani Haron Pungut Ibrahim Tze, Kenneth Kin Teo |
| author_facet | Min, Keng Tan Kar, Leong Lee Kit, Guan Lim Ahmad Razani Haron Pungut Ibrahim Tze, Kenneth Kin Teo |
| author_sort | Min, Keng Tan |
| collection | UMS |
| description | As technologies are expanding, the demand for power supply also increases. This causes the demand for power is difficult to be fulfilled as non-renewable sources are reducing. Therefore, the microgrid concept is introduced, where it is constructed with renewable energy sources, energy storage devices and loads. There are two types of microgrid, which are alternating current (AC) microgrid and direct current (DC) microgrid. Various research show that DC microgrid has more advantages over AC microgrid. However, DC microgrid is not widely used due to the lack of studies on it compared to AC microgrid. Besides, DC microgrid has one significant problem not fixed, which is the fault in the DC microgrid. Whenever a fault occurs, the whole DC microgrid will be affected rapidly. Therefore, this project aims to design a fault detector based on artificial intelligence to detect the fault and isolate the fault effectively. A fault detector based artificial intelligence should be implemented into the DC microgrid system to protect it. Two techniques in Artificial Immune System are being compared. The results showed that the improved Negative Selection Algorithm with variable sized detector has better performance than the general Negative Selection Algorithm with constant sized radius in detecting fault in DC microgrid system. |
| first_indexed | 2024-03-06T03:15:49Z |
| format | Proceedings |
| id | ums.eprints-32517 |
| institution | Universiti Malaysia Sabah |
| language | English English |
| last_indexed | 2024-03-06T03:15:49Z |
| publishDate | 2021 |
| publisher | Institute of Electrical and Electronics Engineers |
| record_format | dspace |
| spelling | ums.eprints-325172022-05-03T13:07:51Z https://eprints.ums.edu.my/id/eprint/32517/ Advanced fault detection in DC microgrid system using reinforcement learning Min, Keng Tan Kar, Leong Lee Kit, Guan Lim Ahmad Razani Haron Pungut Ibrahim Tze, Kenneth Kin Teo QA1-939 Mathematics As technologies are expanding, the demand for power supply also increases. This causes the demand for power is difficult to be fulfilled as non-renewable sources are reducing. Therefore, the microgrid concept is introduced, where it is constructed with renewable energy sources, energy storage devices and loads. There are two types of microgrid, which are alternating current (AC) microgrid and direct current (DC) microgrid. Various research show that DC microgrid has more advantages over AC microgrid. However, DC microgrid is not widely used due to the lack of studies on it compared to AC microgrid. Besides, DC microgrid has one significant problem not fixed, which is the fault in the DC microgrid. Whenever a fault occurs, the whole DC microgrid will be affected rapidly. Therefore, this project aims to design a fault detector based on artificial intelligence to detect the fault and isolate the fault effectively. A fault detector based artificial intelligence should be implemented into the DC microgrid system to protect it. Two techniques in Artificial Immune System are being compared. The results showed that the improved Negative Selection Algorithm with variable sized detector has better performance than the general Negative Selection Algorithm with constant sized radius in detecting fault in DC microgrid system. Institute of Electrical and Electronics Engineers 2021 Proceedings PeerReviewed text en https://eprints.ums.edu.my/id/eprint/32517/1/Advanced%20fault%20detection%20in%20dc%20microgrid%20system%20using%20reinforcement%20learning.ABSTRACT.pdf text en https://eprints.ums.edu.my/id/eprint/32517/2/Advanced%20Fault%20Detection%20in%20DC%20Microgrid%20System%20using%20Reinforcement%20Learning.pdf Min, Keng Tan and Kar, Leong Lee and Kit, Guan Lim and Ahmad Razani Haron and Pungut Ibrahim and Tze, Kenneth Kin Teo (2021) Advanced fault detection in DC microgrid system using reinforcement learning. https://ieeexplore.ieee.org/document/9573711 |
| spellingShingle | QA1-939 Mathematics Min, Keng Tan Kar, Leong Lee Kit, Guan Lim Ahmad Razani Haron Pungut Ibrahim Tze, Kenneth Kin Teo Advanced fault detection in DC microgrid system using reinforcement learning |
| title | Advanced fault detection in DC microgrid system using reinforcement learning |
| title_full | Advanced fault detection in DC microgrid system using reinforcement learning |
| title_fullStr | Advanced fault detection in DC microgrid system using reinforcement learning |
| title_full_unstemmed | Advanced fault detection in DC microgrid system using reinforcement learning |
| title_short | Advanced fault detection in DC microgrid system using reinforcement learning |
| title_sort | advanced fault detection in dc microgrid system using reinforcement learning |
| topic | QA1-939 Mathematics |
| url | https://eprints.ums.edu.my/id/eprint/32517/1/Advanced%20fault%20detection%20in%20dc%20microgrid%20system%20using%20reinforcement%20learning.ABSTRACT.pdf https://eprints.ums.edu.my/id/eprint/32517/2/Advanced%20Fault%20Detection%20in%20DC%20Microgrid%20System%20using%20Reinforcement%20Learning.pdf |
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