Online Condition Assessment of Power Transformers using Neural Network

Transformer is very critical expansive component based on cost and inconvenience caused from failure. For continuous power supply a preventive maintenance is very much required. Transformer failure causes extensive damage to various other electrical equipment and supply grid which will affect a major part of electrical power system. Condition monitoring systems can help to decrease the transformer life cycle cost and to increases the reliability and availability at high level. It keeps the record of relevant data of a transformer. It tells probably time when transformer going to fail and what changes need to make to ensure that it going to last an expected number of years. Till now there is no as such system which will continuously collect of parameter data from transformer and the analysis thereof to determine the risk of unexpected incapacity and the availability of capacity. All monitoring systems only monitor the limit values for parameters. Both power transformer and distribution transformer are well maintained using the methods discus in this paper.