Neural network approach for fault diagnosis of transformers
Dissolved gas analysis (DGA) is one of the most useful techniques to detect the incipient faults of power transformer. This paper is a study of artificial neural networks (ANN) applications for the diagnosis of power transformer incipient fault. The fault diagnosis is based on dissolved gas-in-oil analysis (DGA). Using historical transformer failure data, a multi-layer perceptron (MLP) neural network is applied in this work. The proposed network can overcome the drawbacks of conventional methods. The proposed schemes are simulated and tested.
In this paper,node power of compensation method is obtained with two 8 rank matrix.Two line outage load flow is obtained by Newton. Newton load flow for normal state is used,so efficiency and accuracy of line outage analysis are improved.