93 results about "Output transformer" patented technology

The invention discloses a 10kV distribution transformer on-load voltage regulation control method and system, and relates to the technical field of transformer on-load voltage regulation, and the method comprises the steps: collecting the three-phase voltage and current of a low-voltage side of a distribution transformer and a tap switch gear signal, and loading a preset voltage regulation control mode and an oscillation region parameter; based on the acquired three-phase voltage and current of the low-voltage side of the distribution transformer and the tap switch gear signal, calculating a feeder comprehensive impedance parameter and a load reactive power parameter, and outputting a transformer operation state index; according to the transformer operation state index, the voltage regulation control mode and the oscillation area rule, current voltage out-of-limit and reactive power deviation are judged, and a voltage regulation strategy triggering instruction is generated according to the judgment result; and calculating upper and lower limit values of feed line correction voltage based on the voltage regulation strategy trigger instruction, and controlling the tap switch to execute voltage regulation action. According to the method, a combined calculation scheme of feeder comprehensive impedance and load reactive power is fused, and dynamic optimization of the triggering condition of the voltage regulation strategy is achieved.

The invention discloses a transformer state recognition method and device based on multi-scale time-frequency characteristics. The method comprises the steps that voiceprint signals generated when a transformer operates are collected; processing the voiceprint signal, and extracting a multi-scale time-frequency feature to obtain a time-frequency tensor; the time-frequency tensor is input into a pre-constructed multi-scale time-frequency model, and the multi-scale time-frequency model is constructed based on multi-scale convolution, a Transform neural network and a double-branch attention mechanism; and outputting a transformer state identification result through the multi-scale time-frequency model. According to the method, the voiceprint feature expression capability can be remarkably improved by extracting the multi-scale time-frequency features and the multi-scale time-frequency model, so that the transformer state recognition precision and speed are improved, and stable real-time recognition of the transformer state can still be realized in a complex noise environment.

The invention discloses a power grid transformer technical renovation and overhaul time domain interval evaluation method. The method comprises the steps of 1, establishing an evaluation basic data set through multi-source data acquisition and data preprocessing; 2, screening key influence variables, quantifying variable influence weights by adopting regression modeling, and realizing accurate quantification of the health state of the transformer in combination with a TOPSIS algorithm; 3, forming a dynamic fault rate model; 4, constructing a system dynamics-Monte Carlo cooperation model, a fault tree-Bayesian network dynamic and static diagnosis model and a long and short term memory network time sequence prediction model; 5, setting an optimization target and a constraint condition, and outputting an optimal time point and a reasonable time domain interval of technical renovation and overhaul of the transformer by adopting a multi-target optimization solution method; and 6, model evaluation precision is tested back and verified through historical data, and key parameters of the model are subjected to feedback adjustment by adopting a particle swarm optimization algorithm. The method effectively solves the problems that a traditional method is low in evaluation precision, single in target and poor in dynamic adaptability, and is convenient to popularize and use.

The invention discloses a defect coupling low-noise amplifier. The low-noise amplifier solves the problem that the performance of a low-noise amplifier in the prior art is influenced by a grid source inductance coupling coefficient. Forward coupling of an input inductor and a grid inductor and reverse coupling of the input inductor and a source inductor are achieved through a unique defect coupling transformer matching network, meanwhile, reverse coupling of a grid inductor and the source inductor is achieved, and the coupling coefficient of the grid-source inductor is reduced through coupling counteracting inductance; by combining a differential common-source transconductance amplification stage, a four-order magnetic coupling resonance unit, a differential transconductance amplification stage with a neutralizing capacitor and an output transformer matching network, the characteristics of high gain bandwidth and low noise are realized; simulation and actual tests verify that the low-noise amplifier can effectively reduce the noise coefficient and broaden the input matching range, has good compatibility and wide application prospects based on a conventional integrated circuit production process, can be applied to a phased array system, and can also form a receiver system with other circuits.

The invention discloses an operation state identification method and device of a transformer, a medium and equipment. According to the method, the real-time operation data, the historical data and the physical attribute parameters of the converter transformer are acquired, and the data are combined with the preset multi-physical-field high-fidelity simulation model to carry out multi-physical-field solution, so that a plurality of predicted values are obtained. Meanwhile, a time sequence neural network model based on an LSTM-Transform network obtained by training real-time and historical data is utilized to predict a temperature residual value. And dynamically adjusting the fusion weight of the first temperature prediction value and the actual monitoring value by comparing the residual error of the first temperature prediction value and the actual monitoring value of the simulation model, and generating a mixed temperature prediction value. And finally, the mixed temperature prediction value is input into a knowledge layer decision-making system, the system uses a preset threshold value judgment and similarity matching algorithm to output an operation state identification result of the transformer, and the problem that the operation state of the transformer cannot be accurately and efficiently identified in the prior art is effectively solved.

This invention discloses a method and system for real-time analysis of transformer operating status oriented towards edge computing, specifically relating to the field of power equipment condition monitoring and edge computing data processing technology. The method includes acquiring load values, oil temperature values, winding temperature values, cooling status values, and status monitoring values ​​collected from the transformer site. It generates three types of change symbols (increase, decrease, and remain unchanged) for each type of value according to the sampling order, and binds each change symbol to its corresponding sampling sequence number, outputting ordered transformer operating data. By converting the load values, oil temperature values, winding temperature values, cooling status values, and status monitoring values ​​collected from the transformer site into change symbols with sampling sequence numbers, it identifies load change segments or cooling change points and extracts the current temperature rise response segment. Then, it compares the current temperature rise response segment with existing temperature rise response segments in response order, and generates transformer operating status analysis results based on the order differences formed by oil temperature reversal, winding temperature changes, and status monitoring value changes.

The invention provides an anti-magnetic saturation control method for an inverter, which belongs to the field of power electronics and comprises the following steps of: sampling a primary side current instantaneous value of a transformer at an output voltage zero-crossing moment of the inverter; calculating the deviation between the current instantaneous value and a transformer primary side current sampling value half a period ahead of the zero-crossing moment, judging whether the deviation exceeds a set threshold value or not, if so, determining a compensation direction according to the deviation and the modulation wave, updating the current compensation amount according to the deviation and the compensation direction, injecting the updated compensation amount into the modulation wave, and outputting the updated compensation amount. The updated compensation amount is attenuated; if not, injecting the current compensation amount into the modulation wave, and attenuating the current compensation amount; the invention also provides an inverter anti-magnetic saturation control device and an inverter system. The DC magnetic bias of the inverter output transformer can be eliminated.

A valve amplifier (105, 305) comprising an output transformer (110, 310) and a capacitor (210, 410) connected in parallel with a secondary winding (205, 405) of the output transformer (110, 310) Systems (100, 300), methods, and valve amplifier accessories (325, 500, 600) are also provided.

This invention discloses a millimeter-wave broadband high harmonic suppression tripler, comprising an input matching network, a time-domain shaping tripler unit, and a differential common-source output stage. The proposed time-domain shaping tripler unit employs transistor pairs with different gate widths and bias voltages to superimpose two sets of voltage signals of different amplitudes in the time domain, thereby enhancing the inherent harmonic suppression of the tripler unit. Furthermore, by connecting a common-mode impedance control in series at the center tap of the primary inductor coil of the output transformer of the tripler unit, the output power of the third harmonic is further improved. The input matching network achieves single-ended to differential conversion, with its primary and secondary coil center taps grounded and open-circuited respectively in a counterintuitive manner, improving differential-mode balance and common-mode stability. Simultaneously, by using a series-coupled inductor, the quality factor of the transistor input impedance is reduced, improving the bandwidth performance of the tripler.

The invention provides a power transformer thermal aging health state estimation method and system. The method comprises the steps of obtaining health index monitoring data of transformer thermal aging; inputting the health state monitoring data into a multiple linear regression model, and outputting a classification label of the health state of the transformer based on the multiple linear regression model; inputting the health state monitoring data into a Bayesian deep neural network model, and outputting probability prediction of transformer health state classification labels and uncertainty estimation of prediction results based on the Bayesian deep neural network model; and simulating and calculating the combined weight of the multiple linear regression model and the Bayesian deep neural network model through a Monte Carlo Markov chain method, and generating a final health state prediction value of the transformer according to the combined weight, the output of the multiple linear regression model and the output of the Bayesian deep neural network model. According to the scheme, the interpretability of the thermal aging health state evaluation process can be guaranteed, and the accuracy and reliability of evaluation can be improved.

The invention relates to a transformer insulation health state comprehensive evaluation method based on a type-2 hyper-fusion cloud spectrum, and belongs to the technical field of power equipment state monitoring and intelligent diagnosis. The method takes electrical, thermal, chemical and gas operation indexes as unified input, and faces an engineering reality of'multi-source acquisition error, sample fluctuation and state boundary fuzziness'. And outputting a health state score, a membership degree vector and a corresponding state grade judgment result of the transformer through a closed-loop structure of global mixed weighting-single index types-2 cloud spectrum uncertainty expression-multi-source evidence fusion and type dimensionality reduction-cross-index nonlinear aggregation-comprehensive cloud spectrum reconstruction and similarity decision-making. According to the method, various operating state parameters of different dimensions can be fused, and accurate classification evaluation of the operating state of the transformer is realized while the data fuzziness and uncertainty expression ability is maintained.

Apparatus and methods for radio frequency (RF) amplification are disclosed. In certain embodiments, an RF amplifier includes an output node configured to output an RF output signal, a main amplifier stage including a differential output, a first differential balun combiner configured to provide a first single-ended RF signal to the output node based on combining a first differential RF signal from the differential output of the main amplifier stage, an auxiliary amplifier stage including a differential output, a transformer component, and a second differential balun combiner configured to generate a second single-ended RF signal based on combining a second differential RF signal from the differential output of the auxiliary amplifier stage. The second differential balun combiner provides the second single-ended RF signal to the output node through the transformer component.

This invention discloses a general testing device and method for the weak grid adaptability of power electronic grid-connected equipment, including a converter module, a main control module, an input transformer, and an output transformer. The converter module includes a rectifier circuit unit and its filter circuit, and an inverter circuit unit and its filter circuit. The main control module controls the converter module and includes a rectifier control unit, an inverter control unit, and a host computer that are communicatively connected to each other. The rectifier control unit is connected to the rectifier circuit unit, and the inverter control unit is connected to the inverter circuit unit. The rectifier control unit and the inverter control unit receive and execute input commands from the host computer to adjust the operating state of the converter module. The inverter control unit is based on the equivalent grid impedance transfer function of a generalized second-order integrator. Using this invention, a wide-frequency domain simulation of AC grid impedance can be achieved, greatly improving the test accuracy.

The invention discloses a transformer fault detection method based on double-flow auditory feature fusion and a random forest, and the method comprises the steps: obtaining a real-time voiceprint signal during the operation of a transformer, carrying out the pre-emphasis, framing and windowing of the real-time voiceprint signal, and obtaining a voiceprint frame; extracting Mel frequency cepstrum coefficient characteristics of the voiceprint frame based on a Mel filter bank; extracting Gammatone frequency cepstrum coefficient characteristics of the voiceprint frame based on a Gammatone filter bank; calculating time domain statistical index characteristics of the voiceprint frames; constructing a joint feature, inputting the joint feature into a pre-trained random forest model, and calculating a Gini importance score so as to obtain an anti-noise robust feature subset; and inputting the anti-noise robust feature subset into a random forest classifier, and outputting a fault type identification result of the transformer by using an integrated voting mechanism of a multi-decision tree. The method can solve the problems that in the prior art, feature extraction is single, noise immunity is poor, attention to transient impact is lacked, and a feature dimension reduction and classification method has defects.

The invention relates to the technical field of intelligent operation and maintenance of power equipment, and discloses an AmRMR-based transformer adaptive fault diagnosis method and system, and the method comprises the steps: obtaining the detection data of dissolved gas in transformer oil, and constructing a candidate ratio feature set; respectively carrying out standardization processing on the key gas concentration characteristics and the candidate ratio characteristic set; performing discretization operation on the standardized candidate ratio feature set; performing redundancy compression and information contribution evaluation on the discretized candidate ratio features based on an AmRMR algorithm, and outputting a key ratio feature set; and inputting the key gas concentration characteristics and the key ratio characteristic set into a pre-constructed DSD-DQN fault diagnosis model, and outputting a transformer fault diagnosis result. According to the invention, the highest engineering risk that the fault is misjudged to be normal can be effectively avoided, the recognition capability of minority samples such as serious faults is remarkably improved, and transformer fault diagnosis considering engineering safety and diagnosis accuracy is realized.

The invention relates to harmonic conversion gain reduction for mixer circuits. The mixer circuit may include a first mixer transistor configured to receive a first oscillation signal, a second mixer transistor configured to receive a second oscillation signal, and a harmonic conversion gain suppression filter coupled between a source terminal of the first mixer transistor and a source terminal of the second mixer transistor. The harmonic conversion gain suppression filter may be configured to suppress a harmonic conversion gain of the mixer circuit. The mixer circuit may also include a third mixer transistor configured to receive the first oscillation signal, a fourth mixer transistor configured to receive the second oscillation signal, and another harmonic conversion gain suppression filter coupled between a source terminal of the third mixer transistor and a source terminal of the fourth mixer transistor. The mixer circuit may also include an output transformer and a notch filter interposed between coils of the output transformer.

The application discloses a kind of based on multivariate information fusion and transformer winding fault monitoring method and system of deep learning, method includes: obtaining original vibration signal in the process of transformer operation, input to the transformer fault diagnosis model constructed in advance, output transformer winding fault type;The process for being constructed in advance by transformer fault diagnosis model is: obtaining original vibration signal in the process of transformer operation, constructs initial signal data set and is divided into multiple data samples;Composite feature vector of fusion time domain, frequency domain and time-frequency domain features is extracted from data sample;Convolutional neural network is constructed, and channel attention mechanism and spatial attention mechanism are introduced in convolutional neural network, and the deep learning model of fusion attention mechanism is constructed;Composite feature vector is used to train and evaluate deep learning model, and the final transformer winding fault diagnosis model is obtained.The application has the advantages of high fault diagnosis precision.

The application discloses a transformer sound signal processing method based on CEEMDAN, collects a noisy transformer sound signal for processing, obtains a signal matrix and carries out pretreatment, obtains a pretreated signal, uses a correlation coefficient and a permutation entropy algorithm to screen the pretreated signal, inputs the screened signal into a CEEMDAN model for denoising processing, inputs the denoised signal into a Deep Focusing U-Net model for processing, obtains a target source signal, constructs a transformer target voiceprint library, and registers and trains the target source signal with a ResNet34 model, the trained model is used for processing the target source signal and comparing with the registered signal, when the comparison result is higher than a judgment threshold, the target source signal is input into the voiceprint library for identification, and the category of the transformer target sound source is output. The method combines the correlation coefficient and the permutation entropy to screen the signal component, and improves the accuracy of voiceprint identification.

The application provides a radio frequency power amplifier, which comprises a signal input end, a driving amplifier realized by a CMOS process, a power amplifier realized by a GaAs HBT process, a power combining network and a signal output end which are electrically connected in sequence and integrated on the same substrate; the driving amplifier is electrically connected in sequence by a first-stage amplifier, an inter-stage balun network, a second-stage amplifier and a second-stage output transformer network; the input end of the first-stage amplifier is connected to the signal input end, and the output end of the inter-stage balun network is connected to the input end of the second-stage amplifier; the radio frequency power amplifier is used for converting a single-ended signal amplified by the first-stage amplifier into a differential signal and outputting a first differential signal and a second differential signal. The radio frequency power amplifier has small insertion loss, high output power, improved efficiency and linearity.

A drive device includes: a first circuit including an alternating current (AC) power supply, a primary winding constituting a first side of an output transformer, and an input line connecting the primary winding and the AC power supply to each other; a second circuit including a pair of electrodes detachably connected to an ultrasound treatment instrument, a secondary winding constituting a second side of the output transformer, and a pair of output lines connecting the pair of electrodes and the secondary winding; and a pair of third circuits each having one end respectively connected to the output lines and another end connected to a ground. Each of the pair of third circuits includes an inductor being a passive element, and a capacitor being a passive element connected in series to the inductor. Each of the pair of third circuits is connected to each of the output lines.

The invention relates to the technical field of motors and control, in particular to a stator power supply method of a long stator linear motor, which effectively reduces partial voltage of winding leakage reactance by subdividing a segmented winding into a winding branch A and a winding branch B and combining'pre-switching-on 'and'delayed switching-off' of uncovered segments of a vehicle. In addition, through star-shaped connection of center end points of winding branches A and B and adoption of a double-path power supply for power supply, at least two adjacent branches are ensured to be supplied with power at any time. The output transformer is arranged for the single frequency converter, the two secondary sides of the transformer share the same primary side excitation transformer, and dynamic load balancing is achieved. For doubly-fed linear motor control, a power supply adopts a variable control mode. According to the method, under the technical condition that the step changing control frequency and the power supply interval are kept basically unchanged, under the set winding voltage withstanding level, stator power supply section division is more effectively regulated and controlled, higher rotor counter electromotive force can be overcome for operation, and then the maximum operation speed of a vehicle is increased.

The utility model provides an LLC circuit in a vehicle-mounted charger. The LLC circuit comprises a switch controller U1, an output transformer T1, a switch tube Q3 and a switch tube Q4. The switch tube Q3 and the switch tube Q4 are N-type MOS (Metal Oxide Semiconductor) tubes; one end of the capacitor C1 is connected with high voltage HV, and the other end is grounded; one end of the resistor R1 is connected with high voltage HV, the other end of the resistor R1 is connected with an input voltage detection end of the switch controller U1, one end of the capacitor C2 and one end of the resistor R2, and the other end of the capacitor C2 and the other end of the resistor R2 are grounded; the soft start discharge end of the switch controller U1 is connected with one end of a resistor R3, the other end of the resistor R3 is connected with one end of a resistor R4 and one end of a capacitor C3, and the other end of the capacitor C3 is grounded; the highest frequency clamping end of the switch controller U1 is grounded through a resistor R5; the timing setting end of the switch controller U1 is grounded through a resistor R6 and a capacitor C4 which are connected in parallel. The utility model has the advantages of complete protection function and large output current, and can meet the charging requirement of a high-capacity vehicle-mounted battery.

The application relates to the technical field of pumped storage control, in particular to a grounding protection detection device applied to a static frequency converter and a detection method thereof, which comprises an inverter cabinet and an AC reactor, an output transformer and an output transformer input switch S1 are arranged between the inverter cabinet and the AC reactor, the neutral point of the output transformer is short-circuited and grounded through a grounding resistor R; the output transformer short-circuiting comprises a grounding detection device for detecting the voltage between the two ends of the grounding resistor R; when the output transformer is put into use, the grounding resistor R is automatically put into use and detects the grounding fault through the grounding detection device, the safe and reliable operation of the SFC is ensured, and the situation that the grounding protection is misoperated due to two-point grounding during the operation of the SFC is avoided.

The application provides a transformer detection method and device based on artificial intelligence and computer vision, the method comprising: acquiring a live photo of a transformer; detecting the live photo by using a pre-trained transformer detection model to obtain a detection result of the transformer; the transformer detection model comprising a residual network for extracting features of the live photo, and a polarized self-attention (PSA) module and a fully convolutional network (FCN) module for decoding the features extracted by the residual network; and outputting a transformer abnormality prompt information when the detection result indicates that the transformer is in an abnormal state. According to the scheme, the live photo of the transformer is automatically analyzed by the transformer detection model to obtain a corresponding detection result, the detection efficiency is improved, and real-time detection of the transformer is realized.

The invention discloses a comprehensive optimization method and system for improving the transient current overload capacity of a hybrid transformer. The hybrid transformer comprises a transformer side converter and a power grid side converter. In a low-voltage ride-through state, based on the adjusted reference voltage, reference current and modulation frequency, the controller outputs pulse signals of IGBTs in the transformer side converter and the power grid side converter; moreover, the grid voltage and the junction temperature average value of the IGBTs in the transformer side converter and the power grid side converter are obtained, and the grid voltage target value of the IGBTs in the transformer side converter and the power grid side converter is determined under the constraint condition that the junction temperature average value of the IGBTs in the low-voltage ride-through process does not exceed the maximum allowable value; based on the pulse signal of the IGBT and a grid voltage target value, generating a driving signal to control a transformer side converter and a power grid side converter; and the overload capacity of the hybrid transformer during low-voltage ride-through is improved.

The invention discloses a CEEMDAN-based transformer sound signal processing method, which comprises the following steps of: collecting noise-containing transformer sound signals, processing, obtaining a signal matrix, preprocessing, obtaining preprocessed signals, screening the preprocessed signals by using a correlation coefficient and a permutation entropy algorithm, inputting the screened signals into a CEEMDAN model for denoising processing, and obtaining a CEEMDAN model. And inputting the denoised signal into a Deep Focus U-Net model for processing to obtain a target source signal, constructing a transformer target voiceprint library, registering the target sound source signal and training a ResNet34 model, processing the target source signal and comparing the target source signal with the registered signal by the trained model, and when the comparison result is higher than a judgment threshold value, determining that the transformer is in the target voiceprint library, and if the comparison result is higher than the judgment threshold value, determining that the transformer is in the target voiceprint library. And if yes, inputting the target source signal into a voiceprint library for recognition, and outputting the category of the transformer target sound source. The method combines the correlation coefficient and the permutation entropy to screen the signal component, and improves the accuracy of voiceprint recognition.

The invention relates to a transformer fault detection method and device based on multi-modal data fusion. The method comprises the steps that operation state sensing data of multiple modes of different data sources of a transformer are acquired, initial operation state features are extracted from the operation state sensing data of each mode, each mode has a corresponding operation state type, and the initial operation state features are extracted from the operation state sensing data of each mode; aligning each initial operation state feature to a target dimension, carrying out fusion based on an attention weight, carrying out fusion based on a data source, obtaining target operation state features, determining an initial fault detection result, corresponding to each operation state type, of the transformer according to each target operation state feature, and obtaining a fault detection result of the transformer according to probability distribution of each initial fault detection result. And fusing the initial fault detection results corresponding to the operation state types to obtain target fusion data, and outputting a target fault detection result of the transformer according to the target fusion data. By adopting the method, high-precision, high-applicability and high-reliability transformer fault detection can be provided.

The present application belongs to the field of integrated circuit chip design and artificial intelligence technology, and provides a multi-input and output Transformer model chip architecture and a calculation method to solve the problem that current chips are difficult to perform professional acceleration calculation on large model operators. The multi-input and output Transformer model chip architecture comprises an encoder module, a decoder module, a linear calculation module, a softmax calculation module and a pipeline stage controller. The pipeline stage controller is configured to control the synchronization of the input data stream of the encoder module and the input data stream of the decoder module, and the synchronization of the same level data stream of the multi-head attention acceleration calculation module in the encoder module and the multi-head attention acceleration calculation module in the decoder module, so as to realize the pipeline stage calculation of the entire Transformer model, thereby improving the calculation efficiency of the Transformer model.

The present application relates to the technical field of transformer fault diagnosis, and particularly relates to a transformer state detection method and system based on vibration characteristic analysis, which comprises the following steps: establishing an analysis and calculation model of a current transformer according to the type of the transformer by using a finite element method to obtain a modal matrix; obtaining original vibration signals in the operation process of the transformer, decomposing the original vibration signals into inherent modal components according to an adaptive variational modal decomposition method, and determining a matching index value by using the modal matrix and the inherent modal components; calculating the modal energy proportion, the correlation index and the average correlation index of the current mode according to the inherent modal components, and constructing a modal effectiveness evaluation function; training a multi-classification support vector machine as a fault diagnosis model, and finally outputting a transformer fault classification result. The present scheme realizes precise non-invasive perception, early fault capture and strong environmental adaptability of the internal mechanical state of the transformer winding, the iron core and the like, and meets the industrial application requirements of online real-time monitoring.

The invention relates to the technical field of power systems, in particular to a transformer hot-spot temperature inversion method and system and electronic equipment. Comprising the following steps: acquiring external temperature measurement sampling point temperature, high-voltage side current, load rate and environment temperature in the operation process of the transformer; inputting the external temperature measurement sampling point temperature, the high-voltage side current, the load rate and the environment temperature into a decoupling convolution operator neural network, wherein the decoupling convolution operator neural network is integrated with a pre-trained multi-source physical prior operator; and performing hot-spot temperature inversion calculation by using the decoupling convolution operator neural network, and outputting the internal hot-spot temperature and the top oil temperature of the transformer. The method does not need to depend on internal temperature label data which is difficult to obtain, can effectively fuse multi-source heterogeneous physical priori knowledge, and meets the requirements of an engineering site for real-time performance, precision and low-cost deployment.