Abstract
The burgeoning prognostic and health management (PHM) engineering technology with superior performance has lately received extensive attention in the academic circle. Nevertheless, the various types of faults of the power transformer often lead to less accurate predictions and the instability of the power system. To address these problems, a power transformer PHM model with a hybrid machine learning method-approach is proposed in this paper. The model uses intelligent sensors to obtain dissolved gas analysis (DGA) data for fault diagnosis of the power transformer system, so as to compress the complexity of features (gas types) in the power transformer. In particular, to enhance the robustness of the model, we adopt a modified differential evolution whale optimization algorithm (MDE-WOA) to optimize the probabilistic neural network (PNN), namely, the classification performance of the model is improved by updating the smoothing factor ( σ ) of PNN. In addition, compared with other optimization algorithms, the MDE-WOA algorithm has a lower complexity and more stable optimization process. Finally, we evaluate this model with real world data from the power transformer sensor in Jiangxi province, China. The results indicated that the proposed algorithm could achieve the highest diagnostic accuracy in the fourth iteration, its accuracy having reached 98.86%. Therefore, the proposed PNN parameter optimization meta heuristic algorithm could effectively enhance the accuracy and efficiency of the power transformer fault diagnosis.