Power Transformer Fault Diagnonis with Hybrid Genetic Algorithm Based on Multi-Encoding
Wang Nanlan (HunanUniversity of Arts and Sciences)
A hybrid genetic algorithm based on multi-encoding method for the optimization of neural networks is put forward. This method can be used to optimize the structure and the parameters of ANN in the same training process. Through embedding a gradient descend operator into the generic algorithm, a hybrid algorithm is achieved with fast convergence and great probability for global optimization. Simulation results of power transformer fault diagnosis by dissolved gas-in-oil analysis show that it can both meet the precision request and enhance the generalization ability.