Power converters are essential components in modern life,being widely used in industry,automation,transportation,and household appliances.In many critical applications,their failure can lead not only to financial loss...Power converters are essential components in modern life,being widely used in industry,automation,transportation,and household appliances.In many critical applications,their failure can lead not only to financial losses due to operational downtime but also to serious risks to human safety.The capacitors forming the output filter,typically aluminumelectrolytic capacitors(AECs),are among the most critical and susceptible components in power converters.The electrolyte in AECs often evaporates over time,causing the internal resistance to rise and the capacitance to drop,ultimately leading to component failure.Detecting this fault requires measuring the current in the capacitor,rendering the method invasive and frequently impractical due to spatial constraints or operational limitations imposed by the integration of a current sensor in the capacitor branch.This article proposes the implementation of an online noninvasive fault diagnosis technique for estimating the Equivalent Series Resistance(ESR)and Capacitance(C)values of the capacitor,employing a combination of signal processing techniques(SPT)and machine learning(ML)algorithms.This solution relies solely on the converter’s input and output signals,therefore making it a non-invasive approach.The ML algorithm used was linear regression,applied to 27 attributes,21 of which were generated through feature engineering to enhance the model’s performance.The proposed solution demonstrates an R^(2) score greater than 0.99 in the estimation of both ESR and C.展开更多
This paper presents a method for estimating the parameters of DC-link capacitors in three-level NPC voltage source inverters(3L-NPC-VSI)used in grid-tied systems.The technique uses the signals generated by the intermo...This paper presents a method for estimating the parameters of DC-link capacitors in three-level NPC voltage source inverters(3L-NPC-VSI)used in grid-tied systems.The technique uses the signals generated by the intermodulation caused by the PWM strategy and converter topology interaction to estimate the capacitor parameters of the converter DC-link.It utilizes an observer-based structure consisting of a recursive noninteger sliding discrete Fourier transform(rnSDFT)and an RLS filter improved with a forgetting factor(oSDFT-RLS)to accurately estimate the capacitance and equivalent series resistance(ESR).Importantly,this method does not require additional sensors beyond those already installed in off-the-shelf 3L-NPC-VSI systems,ensuring its noninvasiveness.Furthermore,the oSDFTRLS estimates capacitor parameters in the time-frequency domain,enabling the tracking of capacitor degradation and predicting potential faults.Experimental results from the laboratory setup demonstrate the effectiveness of the proposed condition monitoring method.展开更多
文摘Power converters are essential components in modern life,being widely used in industry,automation,transportation,and household appliances.In many critical applications,their failure can lead not only to financial losses due to operational downtime but also to serious risks to human safety.The capacitors forming the output filter,typically aluminumelectrolytic capacitors(AECs),are among the most critical and susceptible components in power converters.The electrolyte in AECs often evaporates over time,causing the internal resistance to rise and the capacitance to drop,ultimately leading to component failure.Detecting this fault requires measuring the current in the capacitor,rendering the method invasive and frequently impractical due to spatial constraints or operational limitations imposed by the integration of a current sensor in the capacitor branch.This article proposes the implementation of an online noninvasive fault diagnosis technique for estimating the Equivalent Series Resistance(ESR)and Capacitance(C)values of the capacitor,employing a combination of signal processing techniques(SPT)and machine learning(ML)algorithms.This solution relies solely on the converter’s input and output signals,therefore making it a non-invasive approach.The ML algorithm used was linear regression,applied to 27 attributes,21 of which were generated through feature engineering to enhance the model’s performance.The proposed solution demonstrates an R^(2) score greater than 0.99 in the estimation of both ESR and C.
基金funded by the Brazilian National Council for Scientific and Technological Development—CNPq(CNPq grant number 405997/2022-1)supported by the EMBRAPII VIRTUS Competence Center in Intelligent Hardware for Industry—VIRTUS-CC(MCTI grant number 055/2023).
文摘This paper presents a method for estimating the parameters of DC-link capacitors in three-level NPC voltage source inverters(3L-NPC-VSI)used in grid-tied systems.The technique uses the signals generated by the intermodulation caused by the PWM strategy and converter topology interaction to estimate the capacitor parameters of the converter DC-link.It utilizes an observer-based structure consisting of a recursive noninteger sliding discrete Fourier transform(rnSDFT)and an RLS filter improved with a forgetting factor(oSDFT-RLS)to accurately estimate the capacitance and equivalent series resistance(ESR).Importantly,this method does not require additional sensors beyond those already installed in off-the-shelf 3L-NPC-VSI systems,ensuring its noninvasiveness.Furthermore,the oSDFTRLS estimates capacitor parameters in the time-frequency domain,enabling the tracking of capacitor degradation and predicting potential faults.Experimental results from the laboratory setup demonstrate the effectiveness of the proposed condition monitoring method.
