Photovoltaic panel operation state monitoring method based on sparse RBF neural network

Abstract

The invention discloses a photovoltaic panel operation state monitoring method based on a sparse RBF neural network, and aims to effectively mine nonlinear relation characteristics among measurement data of a photovoltaic panel and monitor the operation state of the photovoltaic panel on the basis of the nonlinear relation characteristics. Specifically, according to the method, a sparse RBF neural network structure is designed, real-time sampling data of the photovoltaic panel serve as input and output at the same time, and whether the running state of the photovoltaic panel is abnormal or not is reflected through generated errors. The method disclosed by the invention has the advantages that firstly, the nonlinear fitting capability of the RBF neural network is utilized, and the nonlinear feature extraction of the sample data of the photovoltaic panel in the normal operation state is realized through the established sparse RBF neural network; and secondly, according to the method, errors are generated through the sparse RBF neural network and are used for judging whether the photovoltaic panel is abnormal or not in real time, and the method is different from non-linear feature components monitored and extracted through a traditional method.

Description

Technical field [0001] The present invention relates to a photovoltaic plate operating state monitoring method, and more particularly to a photovoltaic plate operating state monitoring method based on a sparse RBF neural network. Background technique [0002] Solar energy is widely used, and is currently used in photovoltaic and photochemical technologies. Compared with traditional non-renewable energy, the solar energy has zero pollution, convenient installation, small geographic restriction, safe and reliable, and can be permanently used. The main use of solar energy has solar thermal utilization, solar thermal power generation and solar photovoltaic power generation. Among them, photovoltaic power generation technology has matured, and has been developed rapidly, and the overall conditions of energy fundamental transformation have become the main choice for people's future new energy development. With the continuous advancement of photovoltaic power generation and the simplifi...

AI Technical Summary

Problems solved by technology

Although the artificial neural network model represented by radial basis function (Radial Basis Function, abbreviation: RBF) neural network can handle nonlinear classification or regression problems well, RBF neural network is rarely used to solve the feature problem of single classification.

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