Fault diagnosis method and system for energy storage power station based on distributed neural network

A fault diagnosis method for energy storage power stations based on distributed neural networks, which is characterized by including:

• S1. Obtain the operating data of various batteries in each energy storage station and perform preprocessing operations on the data;
• S2. Use the preprocessed power station operating parameters to use the feature extraction network in each power station client to extract power station operating fault features;
• S21. Establish a client-central server distributed learning framework;
• S22. Feature extraction model training;
• In the feature extraction network model training stage, all operating parameters of each power station are used as input data of the feature extraction network to implement the training of the feature extraction network model;
• S23. Parameter update and aggregation of the feature extraction model;
• After each client completes an iterative training, the model parameters are uploaded to the central server. The central server is based on the power plant scale coefficient βi Realize the aggregation of model parameters of each power station client;
• S24. The feature extraction network completes training;
• When the model parameters of each client server are accepted to meet the suspension condition, the training of the feature extraction network of each client server is stopped;
• The feature extraction network model is the electric energy residual sensing network BRAN. The feature extraction network model includes the electric energy residual sensing module;
• the electric energy residual sensing module includes the Mixing module and the sensing layer;
• the Mixing module is voltage, current, battery Coulomb efficiency, and battery temperature, the weighted values of the environment (temperature, humidity, light) after passing through 5 layers of convolution layers and 4 layers of deconvolution layers are used to obtain weighted fusion features;
• the perception layer implements feature sensing operations on voltage, current, battery Coulombic efficiency, and battery temperature, realize the feature sensing operation of the internal operating parameters of the battery, and extract the deep operating features inside the battery;
• finally, the weighted fusion features are deeply fused with the deep sensing features of the sensing layer to obtain the deep features of the power station's electric energy;
• the environmental parameters include ambient temperature, environment Humidity, ambient light data;
• The BRAN network implements parameter aggregation at the central server, and uses the following loss function to update and optimize the BRAN feature extraction network;
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• Among them, Ltot is the total loss function of the BRAN network, LDis is the feature distance loss function, LSca is the power plant scale loss function, LVar is the model parameter variance loss function;
• α, β, γ are the characteristic distance coefficient, power plant scale coefficient and parameter variance coefficient respectively. N is the number of models;
• li is the operating parameter;
• Ks1, Ks2 Model output features;
• Gs1, GN is a function of power plant scale;
• Vs1, Vs2 is the variance between client model parameters;
• S3. Use the aggregate feature extraction network to implement feature extraction of a single operating parameter on the client server of each power station, and train the fault diagnosis classification model;
• S4. Collect the operating parameters of the power station and input them into the feature extraction network and fault diagnosis network to realize fault diagnosis of the power station.