Research On Soft Fault Feature Extraction Method Of Buck Circuit Under Variable Operating Conditions

Buck converters have complex operating conditions in practical applications,so the need to ensure their safe,stable,and reliable operation is more and more urgent.Research on the extraction of fault characteristics of Buck circuits under variable operating conditions can reduce the losses and maintenance costs caused by circuit faults in engineering applications.Among them,the degradation of Buck circuit performance caused by the degradation of component parameters has no obvious and distinguishable fault characteristics.The method of feature extraction is difficult to achieve the desired effect,so the research on the soft fault feature extraction method of Buck circuit under variable operating conditions has practical value.The specific research contents of this article are as follows:Based on a brief analysis of the working principle of the Buck circuit and the setting of the LC filter parameters,consideration is also given to changes in operating conditions and soft faults in the capacitor.In Simulink simulation,the output voltages and inductor currents of different types of soft faults under varying operating conditions were used as fault signals.Three soft fault feature extraction methods were studied.Based on the time domain feature soft fault feature extraction,the time domain parameters of the fault signal can reflect the energy distribution and state fluctuations of the time domain of the signal,so the 14-dimensional time domain parameters of the fault signal are calculated as the fault feature parameters.And use firefly optimized support vector machine as a classifier for fault diagnosis.The results show that the time-domain characteristic parameters are suitable for soft fault diagnosis of closed-loop Buck circuits with variable operating conditions using current,voltage,or composite signals of current and voltage as fault signals.Based on the VMD fuzzy entropy soft fault feature extraction,the soft fault states of the Buck circuit under similar conditions have similarities.The difficulty of feature extraction can be solved by time-frequency analysis,and the anti-interference ability of the feature extraction method can be solved by fuzzy entropy.The VMD fuzzy entropy of the fault signal can be used as the fault characteristic parameter.Firstly,the optimal decomposition layer number is determined by the center frequency of the IMF component after VMD decomposition,and the fuzzy entropy of the IMF component of the fault signal is extracted as the fault characteristic parameter.The support vector machine optimized by Firefly is used as a classifier for fault diagnosis.The results show that this method of feature extraction,although the amount of calculation is larger than the time domain parameters,is suitable for soft fault diagnosis of open-loop Buck circuits under variable operating conditions with voltage,current and voltage composite signals as fault signals.Based on VMD wavelet packet energy entropy soft fault feature extraction,when a soft fault occurs in the Buck circuit,the energy distribution in each frequency band will change.Therefore,the MD wavelet packet energy entropy of the fault signal can be extracted as the fault feature parameter.First,the center frequency of the IMF component after VMD decomposition is used to determine the optimal number of decomposition layers,and the wavelet packet energy entropy of the fault signal IMF component is extracted as the fault characteristic parameter.The firefly optimized support vector machine is used as a classifier for fault diagnosis.The results show that although the feature extraction method is complex in calculation,it is suitable for soft fault diagnosis of open-loop Buck circuits under variable operating conditions with current,voltage,or composite signals of current and voltage as fault signals.