Research transformer fault diagnosis early is a M.Tech project topic for Electrical Engineering. It gives students a clear starting point for research, implementation planning, and documentation.
Research transformer fault diagnosis early Project Details
| Abstract |
This study provides a framework for diagnosing faults in transformers using digital twin technologies. This study overcomes the challenges presented by the conventional methods used in operations and maintenance, and offer assistance with intelligent fault diagnosis and timely warning. The framework combines modeling, online monitoring, and diagnosis using dissolved gas analysis (DGA) to improve operational reliability and stability of the power grid. The framework includes 3D models of transformers using laser point cloud reconstruction and parametric modeling, which allows the representation of information mapping of the state of the equipment. In addition, a multi-class transformer fault diagnosis model based on Snake Optimization and Random Forest (SO-RF) has been designed using
five dissolved gas features. The performance of the model has been demonstrated using the dissolved gas data obtained from the fault case library of a power company, and the model has been compared with Support Vector Machine (SVM) and Random Forest (RF) models to show the results. The results of the experiments show that the proposed SO-RF model has achieved 93.80% accuracy, which is better than the other models. This proves that the model is a feasible option for diagnosis of transformer faults.
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| Reference Paper |
Research on transformer fault diagnosis and early warning technology based on digital twins |
| Domain |
Electrical Engineering |
| Sub-Domain |
Electrical Machines & Drives / Transformers & Machines / Condition Monitoring |
| PDF Download |
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