Photovoltaic power station joint fault diagnosis method based on asynchronous decentration federated learning

Abstract

The invention discloses a photovoltaic power station combined fault diagnosis method based on asynchronous decentralized federated learning, and relates to the field of fault diagnosis. The method includes: constructing an original local data set; preprocessing the data; building a CNN (Convolutional Neural Network) for training a local fault diagnosis model; carrying out joint fault diagnosis modeling of multiple power stations based on asynchronous decentration federated learning; and finally, evaluating the fault diagnosis accuracy, the communication efficiency and the model training efficiency of the combined fault diagnosis model. The generalization ability of the model is effectively improved; local data of a plurality of power stations are fully utilized under the condition that data privacy is guaranteed; aggregation of the global model does not need participation of a central server but is completely distributed, so that the communication and training efficiency of the model is effectively improved; and high-precision fault diagnosis of the photovoltaic module can be realized only by adopting a simple CNN (Convolutional Neural Network).

Description

technical field [0001] The invention relates to the field of fault diagnosis, in particular to a photovoltaic power plant joint fault diagnosis method based on asynchronous decentralized federated learning. Background technique [0002] The rapid consumption of traditional fossil energy has caused many problems, such as energy shortage, global warming and so on. As a renewable energy source, solar energy is attracting increasing attention due to its cleanness, high efficiency, and easy access. In practical applications, the conversion of solar radiation into electrical energy through photovoltaic systems is one of the main uses of solar energy. However, the power generation quality and efficiency of photovoltaic systems are largely affected by the state of photovoltaic modules. During the daily operation of the system, photovoltaic modules may suffer from a series of common failures, causing huge economic losses in power generation, and even causing safety accidents such a...

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Problems solved by technology

[0007] In view of the above-mentioned defects of the prior art, the technical problem to be solved by the present invention is that it is difficult for a single photovoltaic power station to collect a sufficient number and type of fault samples and multiple photovoltaic power stations The imbalance between samples is difficult to make full use of, and the problem of poor generalization performance of the model

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