Research on Fault Diagnosis Technology of Low-Voltage Distributed Photovoltaic Grid Connection Based on Machine Learning

With the wide application of distributed photovoltaic power generation system, its stability and reliability have become the key factors in the energy supply system. In this paper, the fault diagnosis technology of low-voltage distributed photovoltaic grid connection based on machine learning (ML) is studied, aiming at improving the operation and maintenance efficiency and fault handling ability of photovoltaic system. Firstly, the collected data are preprocessed, including data cleaning, denoising, normalization and feature extraction to ensure the accuracy and availability of the data. Through statistical analysis, signal processing and wavelet transform, the key indicators reflecting the fault characteristics of photovoltaic system, such as voltage volatility and current harmonic content, are extracted. Then, this paper constructs a convolutional neural network (CNN) model based on deep learning (DL) for fault diagnosis. The model is optimized by back propagation algorithm and gradient descent method, and the hyperparameter is optimized by cross validation and grid search to improve the generalization ability of the model. The experimental results show that the CNN model achieves 95% accuracy and 0.94 F1 score on the independent test set, which shows a high ability to identify all kinds of faults. This study not only provides technical support for the stable operation of photovoltaic system, but also contributes a new perspective to the sustainable development of energy and technological innovation.