Application of Optimized RBF Neural Network in Power Quality Disturbance Classification
To ensure the safe operation of intelligent electronic products in buildings,a method based on an optimized RBF neural network for identifying power quality disturbances is proposed,which addresses issues such as low accuracy in power quality disturbance classification and poor noise resistance in practical engineering.Firstly,20 types of power quality disturbance signals were subjected to time-frequency domain analysis through S-transform,and the extracted disturbance time-frequency domain feature data was divided into a test set and a training set;Then,a radial basis function(RBF)neural network power quality disturbance classification model;Secondly,the Dung Beetle Optimizer(DBO)algorithm is introduced to optimize the parameters of the RBF neural network;Finally,input the divided training and testing sets into the optimized neural network for disturbance classification.Simulation and engineering experiments have shown that the proposed method has high accuracy in identifying power quality disturbances,strong noise resistance,and generalization ability.
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