Photovoltaic array fault diagnosis method based on fine tuning dense connection convolutional neural network

Abstract

The invention relates to a photovoltaic array fault diagnosis method based on a fine-tuning dense connection convolutional neural network, and the method comprises the steps: collecting electrical characteristic data and environment data under an actual working condition, building a model array through Simulink, and simulating the actual working condition; acquiring simulated electrical characteristic data, secondly, eliminating actual and simulated abnormal data through a sudden change point detection algorithm, acquiring complete electrical waveform data, sampling the electrical waveform data, compressing the characteristics, and splicing the electrical waveform data into a two-dimensional characteristic matrix; then, designing a dense connection convolutional neural network, pre-training the network by using a simulation training set and an Adam optimization algorithm, and then finely adjusting the network by using a small number of training sets under actual working conditions; and finally, utilizing an FT-DenseNet fault diagnosis network to detect and classify the photovoltaic power generation arrays in the to-be-tested working condition test set. According to the method, under the condition of small samples, the classification network with high precision, strong robustness and good generalization ability is obtained, and the accuracy of photovoltaic array fault detection and classification can be effectively improved.

Description

technical field [0001] The invention relates to the technical field of fault detection and classification of photovoltaic power generation strings, in particular to a photovoltaic array fault diagnosis method based on a fine-tuning densely connected convolutional neural network. Background technique [0002] In recent years, solar energy has been widely developed as a promising renewable energy source. Photovoltaic energy is a form of solar energy that plays an integral role in curbing global warming, reducing the use and emissions of fossil fuels. According to the latest announcement from the World Energy Organization, global photovoltaic installations and power generation are growing day by day. With the rapid development of the photovoltaic industry and the rapid growth of photovoltaic installed capacity, the service life and safety of photovoltaic arrays have received more and more attention. Photovoltaic power stations are mostly built in sparsely populated areas with ...

AI Technical Summary

Problems solved by technology

Such as artificial neural network (ANN), decision tree (DT), random forest (RF), probabilistic neural network (PNN), wavelet neural network (WNN), support vector machine (SVM), and other traditional machine learning algorithms, in photovoltaic Significant achievements and breakthroughs have been made in the field of fault diagnosis and detection, but they all require a large number of training samples, and it is difficult to obtain fault samples of actual photovoltaic arrays, which cannot be truly applied to large photovoltaic arrays.

Some unsupervised learning algorithms, such as density peak clustering algorithm (DPCA), fuzzy C-means algorithm (FCM) and other algorithms have achieved good results in fault diagnosis, but their accuracy is still limited

With the increasing popularity of deep learning, convolutional neural networks and cyclic neural networks have entered the sight of photovoltaic fault diagnosis. Their powerful feature extraction capabilities can obtain high-quality fault features with strong representation capabilities, which further contribute to the accuracy of classification. improved, but still requires the support of a large number of samples

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