PNN Neural Network Algorithm

The PNN (Probabilistic Neural Network) algorithm serves as an effective machine learning approach that achieves accurate classification of power transformer faults post-training. This algorithm operates on artificial neural network principles, utilizing probabilistic density estimation and Parzen window techniques to learn from extensive datasets. Through systematic training processes involving feature extraction and probability distribution calculations, the PNN can identify and distinguish various types of power transformer malfunctions. Key implementation aspects include: using Gaussian kernel functions for pattern layer computations, employing competitive layers for classification decisions, and optimizing smoothing parameters for probability density estimation. By implementing the PNN neural network algorithm, we significantly enhance the accuracy and efficiency of transformer fault diagnosis, thereby improving the reliability and stability of power system operations through automated fault pattern recognition.