Neural Network-based Method for Detecting the Dielectric Loss Angle of Capacitors
Accurately calculating the dielectric loss angle of capacitors is crucial for evaluating their performance and reliability in the power system.The traditional forward solving method has uncertainty in practical applications.This paper proposes a new method for identifying dielectric loss angles based on neural networks.Train a neural network model for prediction by collecting voltage,current,and dielectric loss angle data.The article elaborates on the process of solving the dielectric loss angle,including the amplitude calculation method,and incorporates capacitor disturbance factors into the deep neural network model.The simulation test is based on measured data,and the results show that even under environmental interference,the average identification error rate of the proposed method is as low as 2.74％,demonstrating superior noise resistance and accuracy stability.Compared with the traditional Hanning window harmonic analysis method,this method has significant advantages in identifying the angle of dielectric loss.This provides a new and effective way to accurately identify the dielectric loss angle of capacitors.
capacitorsdielectric loss angleneural networknoise immunityharmonic analysis method
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