Research On The Application Of Deep Learning In Photovoltaic Power Stations

Deep learning is a research hotspot in recent years,and has been well applied in image,audio and data processing.This paper combines the deep learning algorithm with photovoltaic power forecasting and solar panel defect detection in photovoltaic power plant.The quality of solar panels which as the core equipment in the process of converting solar energy into electricity in photovoltaic power station will directly affect the solar energy conversion efficiency and their own service life,which is also crucial to ensure the safe,stable operation and maximize the benefits of photovoltaic power station.Therefore,it is of great practical significance to detect the defects on solar panels.This paper proposes to divide the power plant array into several ranges,and predict the photovoltaic power of each region based on the deep learning algorithm.Compare the predicted values with the real-time photovoltaic power at the corresponding time.If the deviation exceeds the setting error range,the solar panels in this range may be defective,and then the panel electroluminescence(EL)images are collected in the region,which are automatically identified and classified by using a convolutional neural network with good image classification performance in the depth learning field.Identify the defective panels and determine the type of defects,then handle the defective battery to ensure safe,efficient and continuous operation of the plant.The research contents of this paper are as follows:(1)The shortages of raw data of solar photovoltaic power and weather information are analyzed,the box plot is used to determine the location of missing values and outliers in the data,and use KNN algorithm to fill and replace missing values and outliers.Use data normalization to process the new data,and establish a solar photovoltaic power prediction data set.(2)The Dropout mechanism and mathematical principles of five activation functions,such as Sigmoid,tanh,Re LU,ELU and Softplus are analyzed.The effects of different Dropout values in different layers on the accuracy of photovoltaic power prediction results are compared by experiments,and the problems likely to occur in practical applications are pointed out.It is found that the Dropout setting in the loop layer and output layer could improve the accuracy of network prediction.The Sigmoid function in the hidden layer or the ELU function in the output layer could improve the network prediction accuracy.The Re LU function makes the network prone to neuron ’necrosis’ during model training.By changing the numbers of network layers and nodes in the GRU network,the effects of network capacity on the accuracy of prediction results and the time consumption of model training are discussed.After repeated experiments,it is found that increasing the depth of the network is more profitable than only increasing the number of nodes in every layer.After comparing the comprehensive performance of Simple RNN,LSTM,Bi LSTM and GRU in solar photovoltaic power prediction,it is found that when the data volume is very large and the precision requirement is not very strict,the GRU is the best choice for its prediction results are accurate,stable and fast-convergence.When the amount of data is relatively small but the precision requirement is strict,Bi LSTM is more suitable.(3)The classical convolutional neural network(Le Net-5)is applied to the defect detection of solar panels,then get unsatisfactory performance.Therefore,by using the visualization of the Tensorboard,the network structure and super parameters are improved,and the application effects of the classical Le Net-5 model,the improved Le Net-5 model and the support vector machine in the defect detection of solar panels are compared and analyzed.(4)The main research contents of this paper are summarized.The shortcomings of the main research work are analyzed,and the future development direction and ideas are prospected.