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: An analog-circuit fault diagnosis method based on continuous wavelet analysis and an ELM network comprises: data acquisition: performing data sampling on output responses of an analog circuit respectively through Multisim simulation to obtain an output response data set; feature extraction: performing continuous wavelet analysis by taking the output response data set of the circuit as training and testing data sets respectively to obtain a wavelet time-frequency coefficient matrix, dividing the coefficient matrix into eight sub-matrixes of the same size, and performing singular value decomposition on the sub-matrixes to calculate a Tsallis entropy for each sub-matrix to form feature vectors of corresponding faults; and fault classification: submitting the feature vector of each sample to the ELM network to implement accurate and quick fault classification.

Abstract: A Deep Belief Network (DBN) feature extraction-based analogue circuit fault diagnosis method comprises the following steps: a time-domain response signal of a tested analogue circuit is acquired, where the acquired time-domain response signal is an output voltage signal of the tested analogue circuit; DBN-based feature extraction is performed on the acquired voltage signal, wherein learning rates of restricted Boltzmann machines in a DBN are optimized and acquired by virtue of a quantum-behaved particle swarm optimization (QPSO); a support vector machine (SVM)-based fault diagnosis model is constructed, wherein a penalty factor and a width factor of an SVM are optimized and acquired by virtue of the QPSO; and feature data of test data are input into the SVM-based fault diagnosis model, and a fault diagnosis result is output, where the feature data of the test data is generated by performing the DBN-based feature extraction on the test data.

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 method for estimating the junction temperature on-line on an insulated gate bipolar transistor (IGBT) power module, including the following steps. Estimate the junction temperature by the temperature sensitive electrical parameter method, set the space thermal model of the extended state, and apply the Kalman filter to the junction temperature estimation. The temperature sensitive electrical parameter method estimates the junction temperature of the IGBT power module in real time, selects the IGBT conduction voltage drop VCE(ON) as the temperature sensitive electrical parameter, and provides a VCE(ON) on-line measuring circuit. The power loss of the diode and IGBT and the estimated value of junction temperature obtained by the temperature sensitive electrical parameter method are taken as the input of the Kalman filter, and measurement noise and process noise are considered to obtain an optimal estimated value of junction temperature.

Abstract: A power transformer state evaluation method is provided. The transformer is evaluated by the following steps: selecting an evaluation parameter, establishing a power transformer evaluation parameter system and collecting relevant parameter data; using the KLEE method to calculate the relative importance between the parameters, and then obtaining the weight of each parameter; establishing a collection of comments; finally determining the state level of the power transformer through the cloud model. The invention is applied to the technical field of power transformer state evaluation, and remedies the defects of existing transformer state evaluation methods, which are computationally complex and unable to achieve a balance between ambiguity and randomness, thereby improving the accuracy and objectivity of the transformer evaluation. The evaluation calculation is simple, and the subjective and objective aspects are taken into consideration.

Abstract: A transformer winding fault diagnosis method based on wireless identification sensing includes the following steps: collecting transformer winding vibration signals in a normal state and when a fault occurs; denoising the transformer winding vibration signals by using singular entropy, and randomly dividing the denoised transformer winding vibration signals in the normal state into two groups, where one group is training data, and the other group is original measurement data; using the denoised transformer winding vibration signals obtained when the fault occurs as original threshold data; reconstructing the original threshold data to obtain reconstructed threshold data, and obtaining a transformer winding residual error threshold when the fault occurs; and reconstructing the original measurement data to obtain reconstructed measurement data, obtaining corresponding residual error data, and comparing the residual error data with the residual error threshold, to implement fault diagnosis on the transformer win

Abstract: A space vector pulse width modulation (SVPWM) method for suppressing a common-mode voltage of a multiphase motor includes the following steps: (1) dividing all basic vectors of the multiphase motor into q types, and selecting therefrom x types having equal common-mode voltage magnitude of which an absolute value is smallest; (2) for each type in the x types of basic vectors, structuring y classes of auxiliary vectors according to an optimization model; (3) synthesizing reference vectors by virtue of the auxiliary vectors to obtain functioning time of basic vectors functioning in each switching period; and (4) obtaining an optimal functioning sequence of the basic vectors functioning in each switching period with fewest switching operations of a converter as a purpose. The present invention may effectively suppress a magnitude and frequency of the common-mode voltage of the multiphase motor without increasing calculation complexity or reducing other performance indexes.

Abstract: An analog circuit fault feature extraction method based on a parameter random distribution neighbor embedding winner-take-all method, comprising the following steps: (1) collecting a time-domain response signal of an analog circuit under test, wherein the input of the analog circuit under test is excited by using a pulse signal, a voltage signal is sampled at an output end, and the collected time-domain response signal is an output voltage signal of the analog circuit; (2) applying a discrete wavelet packet transform for the collected time-domain response signal to acquire each wavelet node signal; (3) calculating energy values and kurtosis values of the acquired wavelet node signals to form an initial fault feature data set of the analog circuit; and (4) analyzing the initial fault feature data by the parameter random distribution neighbor embedding winner-take-all method, to acquire optimum low-dimensional feature data.

Abstract: A nonlinear model transformation solving and optimization method for partial discharge positioning based on multi-ultrasonic sensor includes the following steps: (1) constructing a spatial rectangular coordinate system in a transformer, and setting a position of each ultrasonic sensor; (2) constructing a positioning model on the basis of an arrival time positioning method to obtain a nonlinear positioning equation set for solving a position of a PD source; (3) eliminating second-order terms in the nonlinear positioning equation set to transform the nonlinear positioning equation set into a linear equation set; (4) obtaining multiple sample initial values of a coordinate of the PD source; (5) screening the multiple sample initial values; (6) performing clustering processing on the multiple effective sample initial values by adopting an improved K-means algorithm; and (7) selecting a class with most cluster elements, and calculating a mean of the elements of the class to finally determine an optimal coordinate

Abstract: A space vector pulse width modulation (SVPWM) method for suppressing a common-mode voltage of a multiphase motor includes the following steps: (1) dividing all basic vectors of the multiphase motor into q types, and selecting therefrom x types having equal common-mode voltage magnitude of which an absolute value is smallest; (2) for each type in the x types of basic vectors, structuring y classes of auxiliary vectors according to an optimization model; (3) synthesizing reference vectors by virtue of the auxiliary vectors to obtain functioning time of basic vectors functioning in each switching period; and (4) obtaining an optimal functioning sequence of the basic vectors functioning in each switching period with fewest switching operations of a converter as a purpose.

Abstract: A 5G-oriented multidimensional adaptive MIMO system including a transmitting antenna array, a first rotary table, a broadband vector signal generator, a receiving antenna array, a second rotary table, a broadband vector signal analyzer and a data acquisition terminal is provided. The broadband vector signal generator and the broadband vector signal analyzer are respectively connected with the transmitting antenna array and the receiving antenna array. The data acquisition terminal is connected with the broadband vector signal generator and the broadband vector signal analyzer. In addition, a method for adjusting radiating modes of antenna ports by using the 5G-oriented multidimensional adaptive MIMO system is also provided.

Abstract: A Deep Belief Network (DBN) feature extraction-based analogue circuit fault diagnosis method comprises the following steps: a time-domain response signal of a tested analogue circuit is acquired, where the acquired time-domain response signal is an output voltage signal of the tested analogue circuit; DBN-based feature extraction is performed on the acquired voltage signal, wherein learning rates of restricted Boltzmann machines in a DBN are optimized and acquired by virtue of a quantum-behaved particle swarm optimization (QPSO); a support vector machine (SVM)-based fault diagnosis model is constructed, wherein a penalty factor and a width factor of an SVM are optimized and acquired by virtue of the QPSO; and feature data of test data are input into the SVM-based fault diagnosis model, and a fault diagnosis result is output, where the feature data of the test data is generated by performing the DBN-based feature extraction on the test data.

Abstract: A transformer winding fault diagnosis method based on wireless identification sensing includes the following steps: collecting transformer winding vibration signals in a normal state and when a fault occurs; denoising the transformer winding vibration signals by using singular entropy, and randomly dividing the denoised transformer winding vibration signals in the normal state into two groups, where one group is training data, and the other group is original measurement data; using the denoised transformer winding vibration signals obtained when the fault occurs as original threshold data; reconstructing the original threshold data to obtain reconstructed threshold data, and obtaining a transformer winding residual error threshold when the fault occurs; and reconstructing the original measurement data to obtain reconstructed measurement data, obtaining corresponding residual error data, and comparing the residual error data with the residual error threshold, to implement fault diagnosis on the transformer win