Diagnosis Gangguan Permulaan Transformation Dengan JaringanSyaraf Learning Vector Quantization

The objective of this research is to find the optimum learning vector quantization (LVQ) neural network for power transformer incipient faults diagnosis based on dissolved gas in oil analysis (DGA). The research has been conducted by designing LVQ neural network topologies based on DGA. The topologies were compared each other in accuracy by varying input preprocesses. The optimum result was compared with conventional DGA methods to know the accuracy. Variables investigated are topologies, learning velocity, accuracy on training and testing data, and accuracy compared with conventional DGA methods. The research results show that LVQ neural network with topology of six nodes in competitive layer and fuzzy input preprocess has the best performance for the training and testing data compared with other topologies investigated in this research. LVQ neural network also has better performance compared with conventional DGA methods for the data investigated in this research. Thus LVQ neural network can be an alternative method in power transformer incipient faults diagnosis