A Robust Multi-Layer Framework for Online Condition Assessment of Power Transformers

Online continuous monitoring of dissolved gasses in transformer oil has become an emerging trend in the recent years. This way, in addition to sending the warning signal in the case of gas concentration, a fault diagnosis procedure can be conducted using standard Dissolved Gas Analysis (DGA) methods. However, since the fault diagnosis outcome and result of these methods are sometimes different, an appropriate solution should be suggested to accurately describe the transformer internal condition. To do so, a robust multi-layer framework has been proposed in this paper. Handling the measurement uncertainties and fusing the results of independent DGA methods without losing their diagnosis resolutions are the interesting features of this framework. To improve the overall accuracy of fused diagnosis result, an artificial neural network-based approach is proposed. It intelligently assigns the weight of each independent method in the fusion procedure according to its fault type detection accuracy for the range of input gasses concentrations. To evaluate performance of the proposed framework, comprehensive test studies have been conducted and the promising obtained results prove its superior performance for use in online applications.

Automated summary

In this paper, a robust multi-layer framework is proposed for online continuous monitoring of dissolved gasses in transformer oil and fault diagnosis. The framework handles measurement uncertainties and fuses the results of independent DGA methods, while improving the accuracy of the diagnosis results using an artificial neural network-based approach.