Abstract:
Power transformers are a critical part of the power system. The early-stage fault detection
of Power transformer is essential for the protection and prevention of further technical
and financial losses. Dissolved Gas Analysis (DGA) is a commonly used diagnosis tool for
keeping track of transformer status,but the existing DGA methods are based on expertise
and personal experience,so their reliability can never be guaranteed,which can lead to
unreliable diagnosis.Nowadays,artificial intelligence-based techniques are widely used to
enhance DGA fault detection accuracy.Here,the machine learning model tries to overcome
the deficiency of conventional DGA by converting DGA into a pattern recognition problem
by establishing a connection between gas concentration and incipient faults. In this thesis,
a new deep learning based Bidirectional Long short-term Memory(BLSTM) is introduced
for multi-class classification of transformer fault and the model’s performance is compared
with that of other deep learning models.
