Abstract: The invention belongs to the field of polymers, and relates to biaxially oriented polypropylene dielectric film, a modified polypropylene material and use thereof. The preparation raw materials of the biaxially oriented polypropylene dielectric film comprise a modified polypropylene grafted with an alkenyl-containing functional monomer. The modified polypropylene grafted with an alkenyl-containing functional monomer comprises a structural unit derived from polypropylene as a matrix phase and a structural unit derived from an alkenyl-containing functional monomer as a dispersion phase. The ash content of the modified polypropylene grafted with an alkenyl-containing functional monomer is less than 50 ppm.

Abstract: A polyethylene composition has a polyethylene matrix resin and a pore-forming agent. The content of the pore-forming agent is 30-110 parts by weight on the basis of 100 parts by weight of the polyethylene matrix resin. The pore-foaming agent contains maleic anhydride copolymer microspheres, a copolymer in the microsphere comprises a structural unit A from maleic anhydride, a structural unit B from a vinyl-containing comonomer M and an optional cross-linking structure unit from a cross-linking agent, and the average particle size of the maleic anhydride copolymer microspheres is 500-2000 nm. The polyethylene composition can be used for preparing a breathable film without using a coupling agent, a dispersing agent and/or a surfactant, and achieves uniform air permeability and a high water vapor transmission rate. The film can be used for breathable composite products such as sanitary products, medical products, and food packaging or building products.

Abstract: The invention belongs to the field of insulating materials, in particular cable insulating materials, and particularly relates to a modified flexible polypropylene insulating material, and preparation method and use thereof. The modified flexible polypropylene insulating material comprises a propylene-based continuous phase, and a rubber phase and a grafted phase derived from an unsaturated bond-containing polymerizable monomer dispersed in the propylene-based continuous phase; wherein, the modified flexible polypropylene insulating material has a content of xylene solubles of 10-55 wt %, preferably 15-45 wt %, more preferably 18-40 wt %, and still more preferably 20-40 wt %, based on the total weight of the modified flexible polypropylene insulating material; the content of structural units derived from the unsaturated bond-containing polymerizable monomer and in a grafted state in the modified flexible polypropylene insulating material is 0.3-6 wt %, and preferably 0.

Abstract: A power electronic circuit fault diagnosis method based on Extremely randomized trees (ET) and Stack Sparse auto-encoder (SSAE) algorithm includes the following. First, collect the fault signal and extract fault features. Then, reduce the dimensionality of fault features by calculating the importance value of all features using ET algorithm. A proportion of the features to be eliminated is determined, and a new feature set is obtained according the value of importance. Further extraction of fault features is carried by using SSAE algorithm, and hidden layer features of the last sparse auto-encoder are obtained as fault features after dimensionality reduction. Finally, the fault samples in a training set and a test set are input to the classifier for training to obtain a trained classifier. And mode identification, wherein the fault of the power electronic circuit is identified and located by the training classifier.

Abstract: A fault diagnosis method for power electronic circuits based on optimizing a deep belief network, including steps. (1) Use RT-LAB hardware-in-the-loop simulator to set up fault experiments and collect DC-link output voltage signals in different fault types. (2) Use empirical mode decomposition to extract the intrinsic function components of the output voltage signal and its envelope spectrum and calculate various statistical features to construct the original fault feature data set. (3) Based on the feature selection method of extreme learning machine, remove the redundancy and interference features, as fault sensitive feature data set. (4) Divide the fault sensitive feature set into training samples and test samples, and primitively determine the structure of the deep belief network. (5) Use the crow search algorithm to optimize the deep belief network. (6) Obtain the fault diagnosis result.

Abstract: A transformer simulation system and a measurement method for setting value simulation are disclosed. The transformer simulation system includes an oil tank, a heating device, a pump body, a first oil pipe, a second oil pipe and a first flow sensor. The heating device is disposed in the oil tank. The oil tank is provided with a first inlet and a first outlet, the first outlet is connected to the first oil pipe, and the first inlet is connected to the second oil pipe. An end of the first oil pipe away from the oil tank is a first mounting end for being connected to an oil inlet of a gas relay, and an end of the second oil pipe away from the oil tank is a second mounting end for being connected to an oil outlet of the gas relay. The pump body is connected to the first oil pipe or the second oil pipe, and the first flow sensor is disposed in the first oil pipe.

Abstract: The disclosure provides a silicon photovoltaic cell scanning eddy current thermography detection platform and a defect classification method. The technical solution adopted by the disclosure is: firstly, fixing the position of the electromagnetic inductive coil and the thermal imager, and using the main conveyor belt to carry the silicon photovoltaic cell to move forward on the production line to form a scanning eddy current heating of the silicon photovoltaic cell. Secondly, the defect temperature information is obtained through the thermal imager in terms of thermal image sequences. Thirdly, the feature extraction algorithms are used to extract the silicon photovoltaic cell defect features. Finally, the image classification algorithms are used to classify the silicon photovoltaic cell defects, and the sorting conveyor belts are used to realize the automatic sorting of silicon photovoltaic cells with different types of defects on the production line.

Abstract: A transformer simulation system and a measurement method for setting value simulation are disclosed. The transformer simulation system includes an oil tank, a heating device, a pump body, a first oil pipe, a second oil pipe and a first flow sensor. The heating device is disposed in the oil tank. The oil tank is provided with a first inlet and a first outlet, the first outlet is connected to the first oil pipe, and the first inlet is connected to the second oil pipe. An end of the first oil pipe away from the oil tank is a first mounting end for being connected to an oil inlet of a gas relay, and an end of the second oil pipe away from the oil tank is a second mounting end for being connected to an oil outlet of the gas relay. The pump body is connected to the first oil pipe or the second oil pipe, and the first flow sensor is disposed in the first oil pipe.

Abstract: A power electronic circuit troubleshoot method based on a beetle antennae optimized deep belief network algorithm including the following steps is provided. Output current signals of DC bus of a three-phase PWM rectifier under different switching device open circuit failure modes are collected as an original data set. Intrinsic mode function components of the output current signals under different switching device open circuit failure modes are extracted using empirical mode decomposition to construct an original failure feature set. Fault feature is selected based on extra-trees to generate final fault dataset. A structure of a deep belief network is optimized using a beetle antennae algorithm. An optimized deep belief network is trained using a training set and an obtained failure recognition result is verified using a testing set.

Abstract: A power electronic circuit fault diagnosis method based on Extremely randomized trees (ET) and Stack Sparse auto-encoder (SSAE) algorithm includes the following. First, collect the fault signal and extract fault features. Then, reduce the dimensionality of fault features by calculating the importance value of all features using ET algorithm. A proportion of the features to be eliminated is determined, and a new feature set is obtained according the value of importance. Further extraction of fault features is carried by using SSAE algorithm, and hidden layer features of the last sparse auto-encoder are obtained as fault features after dimensionality reduction. Finally, the fault samples in a training set and a test set are input to the classifier for training to obtain a trained classifier. And mode identification, wherein the fault of the power electronic circuit is identified and located by the training classifier.

Abstract: The disclosure provides a silicon photovoltaic cell scanning eddy current thermography detection platform and a defect classification method. The technical solution adopted by the disclosure is: firstly, fixing the position of the electromagnetic inductive coil and the thermal imager, and using the main conveyor belt to carry the silicon photovoltaic cell to move forward on the production line to form a scanning eddy current heating of the silicon photovoltaic cell. Secondly, the defect temperature information is obtained through the thermal imager in terms of thermal image sequences. Thirdly, the feature extraction algorithms are used to extract the silicon photovoltaic cell defect features. Finally, the image classification algorithms are used to classify the silicon photovoltaic cell defects, and the sorting conveyor belts are used to realize the automatic sorting of silicon photovoltaic cells with different types of defects on the production line.

Abstract: A fault diagnosis method for power electronic circuits based on optimizing a deep belief network, including steps. (1) Use RT-LAB hardware-in-the-loop simulator to set up fault experiments and collect DC-link output voltage signals in different fault types. (2) Use empirical mode decomposition to extract the intrinsic function components of the output voltage signal and its envelope spectrum and calculate various statistical features to construct the original fault feature data set. (3) Based on the feature selection method of extreme learning machine, remove the redundancy and interference features, as fault sensitive feature data set. (4) Divide the fault sensitive feature set into training samples and test samples, and primitively determine the structure of the deep belief network. (5) Use the crow search algorithm to optimize the deep belief network. (6) Obtain the fault diagnosis result.

Abstract: A fault diagnosis method for a series hybrid electric vehicle AC/DC (Alternating Current/Direct Current) converter, implementing identifying and diagnosing of an open circuit fault of a power electronic components in an AC/DC converter, and including the following steps: first, establishing a simulation model for a series hybrid electric vehicle AC/DC converter, and selecting a DC bus output current as a fault characteristic; then classifying fault types according to a quantity and locations of faulty power electronic components; next, decomposing the fault characteristic, that is, the DC bus output current by means of fast Fourier transform to different frequency bands, and selecting harmonic ratios of the different frequency bands as fault diagnosing eigenvectors; and finally, identifying the fault types by using a genetic algorithm-based BP (Back Propagation) neural network.

Abstract: A fault diagnosis method for a series hybrid electric vehicle AC/DC converter, implementing identifying and diagnosing of an open circuit fault of a power electronic components in an AC/DC converter, and including the following steps: first, establishing a simulation model for a series hybrid electric vehicle AC/DC converter, and selecting a DC bus output current as a fault characteristic; then classifying fault types according to a quantity and locations of faulty power electronic components; next, decomposing the fault characteristic, that is, the DC bus output current by means of fast Fourier transform to different frequency bands, and selecting harmonic ratios of the different frequency bands as fault diagnosing eigenvectors; and finally, identifying the fault types by using a genetic algorithm-based BP neural network.