Development of machine learning-based algorithm to determine the condition in transformer oil
One very popular and useful electric device in daily life is a transformer, and it is one of the greatest components of the power network system. The main fault of these transformers can purpose considerable damage. This not only disrupts other functions of the power supply, rather than caused...
| 第一著者: | |
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| フォーマット: | 学位論文 |
| 言語: | 英語 英語 英語 |
| 出版事項: |
2021
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| 主題: | |
| オンライン･アクセス: | http://eprints.uthm.edu.my/6955/ |
| Abstract | Abstract here |
| _version_ | 1855520642461335552 |
|---|---|
| author | Mohsen Al-Katheri, Hussein Hasan |
| author_facet | Mohsen Al-Katheri, Hussein Hasan |
| author_sort | Mohsen Al-Katheri, Hussein Hasan |
| description | One very popular and useful electric device in daily life is a transformer, and it is one
of the greatest components of the power network system. The main fault of these
transformers can purpose considerable damage. This not only disrupts other functions
of the power supply, rather than caused very large losses. The interpretation of
dissolved gas analysis (DGA) is used to detect incipient faults in transformer oil. This
paper aims to develop a model for taking into consideration the results obtained from
DGA to investigate the condition of transformer oil fault. Machine Learning (ML)
algorithm have been utilized to detect the fault more accurate. Classification learning
app used to train DGA data divided into three categories fault, Not determined (N/D)
and stray gassing. Three different types of ML algorithm have achieved high accuracy
of 93.0%, 95.4% and 97.7% support vector machine (SVM), Naïve Bayes algorithm
(NB), K-nearest neighbour (KNN) respectively. Graphical User Interface (GUI) has
overall the system by testing and verified with many different user data and performed
a correct classification. |
| format | Thesis |
| id | uthm-6955 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | English English English |
| publishDate | 2021 |
| record_format | EPrints |
| record_pdf | Restricted |
| spelling | uthm-69552022-04-18T01:21:30Z http://eprints.uthm.edu.my/6955/ Development of machine learning-based algorithm to determine the condition in transformer oil Mohsen Al-Katheri, Hussein Hasan TK2000-2891 Dynamoelectric machinery and auxiliaries. Including generators, motors, transformers One very popular and useful electric device in daily life is a transformer, and it is one of the greatest components of the power network system. The main fault of these transformers can purpose considerable damage. This not only disrupts other functions of the power supply, rather than caused very large losses. The interpretation of dissolved gas analysis (DGA) is used to detect incipient faults in transformer oil. This paper aims to develop a model for taking into consideration the results obtained from DGA to investigate the condition of transformer oil fault. Machine Learning (ML) algorithm have been utilized to detect the fault more accurate. Classification learning app used to train DGA data divided into three categories fault, Not determined (N/D) and stray gassing. Three different types of ML algorithm have achieved high accuracy of 93.0%, 95.4% and 97.7% support vector machine (SVM), Naïve Bayes algorithm (NB), K-nearest neighbour (KNN) respectively. Graphical User Interface (GUI) has overall the system by testing and verified with many different user data and performed a correct classification. 2021-02 Thesis NonPeerReviewed text en http://eprints.uthm.edu.my/6955/1/24p%20HUSSEIN%20HASAN%20MOHSEN%20AL-KATHERI.pdf text en http://eprints.uthm.edu.my/6955/2/HUSSEIN%20HASAN%20MOHSEN%20AL-KATHERI%20COPYRIGHT%20DECLARATION.pdf text en http://eprints.uthm.edu.my/6955/3/HUSSEIN%20HASAN%20MOHSEN%20AL-KATHERI%20WATERMARK.pdf Mohsen Al-Katheri, Hussein Hasan (2021) Development of machine learning-based algorithm to determine the condition in transformer oil. Masters thesis, Universiti Tun Hussein Onn Malaysia. |
| spellingShingle | TK2000-2891 Dynamoelectric machinery and auxiliaries. Including generators, motors, transformers Mohsen Al-Katheri, Hussein Hasan Development of machine learning-based algorithm to determine the condition in transformer oil |
| thesis_level | Master |
| title | Development of machine learning-based algorithm to determine the condition in transformer oil |
| title_full | Development of machine learning-based algorithm to determine the condition in transformer oil |
| title_fullStr | Development of machine learning-based algorithm to determine the condition in transformer oil |
| title_full_unstemmed | Development of machine learning-based algorithm to determine the condition in transformer oil |
| title_short | Development of machine learning-based algorithm to determine the condition in transformer oil |
| title_sort | development of machine learning based algorithm to determine the condition in transformer oil |
| topic | TK2000-2891 Dynamoelectric machinery and auxiliaries. Including generators, motors, transformers |
| url | http://eprints.uthm.edu.my/6955/ |
| work_keys_str_mv | AT mohsenalkatherihusseinhasan developmentofmachinelearningbasedalgorithmtodeterminetheconditionintransformeroil |