High-Precision Fault Diagnosis Method for Energy Storage Inverter Signals
Owing to the global trends of energy transition and carbon neutrality,ensuring the stability of energy storage inverters as key components is crucial.Specifically,the stability of power devices and heat sinks during actual operation is directly related to the reliability of the entire system.This study primarily focuses on the fault diagnosis of the vibration signals in the power module of energy storage inverters.Traditional diagnostic methods often face challenges when dealing with complex signals,and require frequent parameter adjustments.Moreover,owing to the complicated working environment of energy storage inverters,existing deep learning diagnostic methods exhibit unsatisfactory performance.This study proposes a fault diagnosis method for the power modules of energy storage inverters based on a Large Model Knowledge and Channel Attention Network(LLMCAN).First,a pre-trained Large Language Model(LLM)is utilized in the feature extraction process to leverage rich domain knowledge,thereby enhancing the analytical capability of the model for complex vibration fault signals from the power module.Second,the introduction of the channel attention network enables the model to adaptively learn the relationship between different channels in the signal.This further improves the accuracy of the fault diagnosis.The LLMCAN is validated on an energy storage inverter signal dataset containing 1 000 real-world operating data points,including normal operating conditions and nine fault modes.Based on extensive experiments,this method demonstrates excellent performance under various metric indicators,with a diagnostic accuracy rate of 99.8％.This accuracy significantly surpasses that of traditional methods.Thus,an efficient and accurate solution for the fault diagnosis of the power modules of energy storage inverters is provided.
energy storage inverterfault diagnosisdeep learningattention mechanismsignal processing
万方数据
维普
