141 results about "Output transformer" patented technology

The invention belongs to the technical field of power distribution networks, and discloses a load prediction and early warning method and system for a transformer area containing distributed resources, and the method comprises the steps: combining a two-dimensional time sequence data sample set of a high-risk transformer area with a transformer area feature operation data set according to the transformer area and a timestamp, and generating a prediction model training sample data set; building a lightweight gradient boosting tree as a main prediction model, inputting a training sample data set for training, and optimizing hyper-parameters of the main prediction model by adopting a Bayesian optimization algorithm; and establishing a residual error correction model based on local weighted Gaussian process regression, superposing a load prediction result of a prediction day of the main prediction model of the to-be-predicted transformer area with a residual error correction value of a prediction day of the residual error correction model to obtain a final load prediction result, and outputting transformer area weight / overload early warning information. According to the method, the LGBM is adopted as the main prediction model for load prediction, the residual error correction model is adopted for residual error correction, and the robustness and adaptability of the model are improved.

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 provides a transformer construction period environmental protection risk assessment method and system, and the method comprises the steps: inputting transformer image data of a construction site in a construction period into an image recognition model, and recognizing the current abnormal coordinate information of a transformer; obtaining current abnormal state data based on the current abnormal coordinate information; inputting the current abnormal state data, the obtained current operation state parameters of the transformer and the obtained current environment parameters into a pre-constructed dynamic evaluation model for evaluation to obtain the current risk degree of the transformer; based on the current risk degree, outputting an environmental protection risk assessment result and an equipment safety monitoring result of the transformer; wherein the dynamic evaluation model is obtained by training an adaptive neural network by adopting historical abnormal state data, historical operation state parameters, historical environment parameters and historical risk degrees of the transformer. The current environmental protection risk degree of the transformer can be output in real time, and the dynamic monitoring requirement of the transformer in the construction period is met.

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 transformer multi-measuring-point working condition identification method based on deep convolution feature fusion, and belongs to the technical field of transformer detection, and the method comprises the steps: obtaining vibration signals of a plurality of preset points of a transformer, carrying out the processing based on a pre-trained transformer working condition identification model, and outputting a transformer working condition identification result, based on a Swin Transformer encoder and a depth deformable MobileNet module, a double-path collaborative architecture is adopted, a first path and a second path of a transformer working condition recognition model are constructed, and the first path and the second path are subjected to feature interaction through a cross-modal attention fusion module. According to the method, the problems of low efficiency and poor accuracy caused by neglecting vibration signal time sequence dependence, local detail features, global structures and spatial features in existing transformer multi-measuring-point working condition identification are solved.

The invention discloses a current type electric shock weeding device, a control method and a weeding system. The device comprises a battery side voltage source; the primary side full-bridge inverter circuit is connected with the battery side voltage source; the voltage doubling circuit is used for generating high-voltage direct current output; the primary side of the transformer winding is connected with the midpoint of the primary side full-bridge inverter circuit, and the secondary side of the transformer winding is connected with the voltage doubling circuit; and the phase shift control module is configured to change the phase shift duty ratio phi = theta / pi by adjusting the phase shift angle theta and control the output current. The embodiment of the invention aims to provide a DC-DC converter which is free of ripples, has a constant-current characteristic and is simple to control for the field of electric shock weeding.

The invention relates to the technical field of gas concentration detection of power equipment, and discloses a method and system for detecting the concentration of gas dissolved in oil based on an improved extreme learning machine, and the method comprises the steps: improving the extreme learning machine into a kernel extreme learning machine and a depth extreme learning machine, and respectively combining with a seagull optimization algorithm to obtain the concentration of gas dissolved in oil; constructing a model for predicting the concentration of the dissolved gas in the transformer oil, and training the model; and based on the transformer data, inputting the transformer data into the model for predicting the concentration of the dissolved gas in the transformer oil, and outputting a prediction result of the dissolved gas in the transformer oil and a fault type. According to the method, an intelligent optimization algorithm and a data processing method are combined, an extreme learning machine with high learning speed and good generalization performance is improved to form a deep extreme learning machine and kernel extreme learning machine model, an improved seagull algorithm is used for optimization, and a prediction model is formed for prediction. And the detection precision of the equipment gas concentration in the power system is improved.

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.

The utility model discloses a medium-frequency induction heating device, comprising a medium-frequency power supply which is an induction heating power supply based on high-speed DSP chip control; the output transformer is connected with the medium-frequency power supply; a cantilever crane; the heating device comprises an induction heating coil connected with the output transformer, and the induction heating coil is mounted on the cantilever crane; the far infrared temperature measurement system and the induction heating coil are integrally designed; and the industrial constant-temperature water cooler is respectively connected with the output transformer, the medium-frequency power supply and the induction heating coil. The preheating device changes the preheating mode of top bending in a steel rail factory, the whole process is automatically controlled, the possibility of uncontrollable factors existing in manual operation is eliminated, it is ensured that the temperature meets the process requirement, heating is safe, efficient and uniform, steel rail breaking is avoided, the rejection rate is reduced, and the preheating device has popularization value in the industry.

The present invention discloses an intelligent diagnosis method for partial discharge of a transformer based on an improved transformation network, which relates to the technical field of transformer fault diagnosis. The method includes collecting partial discharge signals of the transformer, preprocessing the partial discharge signals to obtain a time series; using the Markov transition field (MTF) algorithm, the Gram angular field (GASF) algorithm, and the Gram angular difference field (GADF) algorithm to convert the time series into feature images respectively; processing the feature images through an improved transformation network model to obtain a fused feature vector; and inputting the fused feature vector into a fault diagnosis model to output a fault diagnosis result of the partial discharge of the transformer. By combining an interpretable graph reasoning mechanism of environmental parameters and signal features, the present invention improves the recognition rate of oil gap discharge in tests, enabling the fault diagnosis to leap from the traditional threshold-based judgment mode to an intelligent mode based on causal logic reasoning.

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 present invention discloses an active phase shifter with temperature compensation, comprising an input transformer balun, a polyphase filter, a vector modulation module based on a Gilbert cell, an output transformer balun, and a positive temperature coefficient current source. The input transformer balun is used to convert a single-ended input signal into a differential signal; the polyphase filter is used to convert the differential signal into a quadrature signal; the vector modulation module based on the Gilbert cell is used to select polarity and perform vector synthesis on the quadrature signal; the output transformer balun is used to convert the differential signal at the output into a single-ended signal; and the positive temperature coefficient current source is used to control the gain of the Gilbert cell. By designing a temperature-dependent positive temperature coefficient current source, the present invention mitigates the temperature-dependent gain change of the vector modulation module, thereby improving the stability of the key performance of the active phase shifter within a temperature range of 55 to 85°C.

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 present application discloses a low-frequency high-power signal continuous emission system, comprising a high-voltage DC power supply, a signal processing unit, a plurality of power amplification units, a plurality of output transformers, a plurality of power switches, a plurality of output switches, a parallel connection bus and a power combining system; the signal processing unit is connected to all the power amplification units; the high-voltage DC power supply is connected to the plurality of power amplification units via the plurality of power switches, and each power amplification unit is respectively connected to one power switch; each power amplification unit is respectively connected to one output transformer, each output transformer is respectively connected to one output switch, all the output switches are connected to the parallel connection bus, and the parallel connection bus is used for connecting to an antenna; each power amplification unit is respectively connected to the output transformer. The present invention also discloses a method for a low-frequency high-power signal continuous emission system, a corresponding device and a storage medium. The present invention improves the reliability of the continuous operation of the emission system.

The invention discloses a method for identifying excitation inrush current and fault current of a transformer sent out by a double-fed wind power plant in the field of transformer relay protection, and aims to solve the technical problem that differential protection delays action and even refuses action. The method comprises the following steps: acquiring each phase current of a star-shaped side and each phase current of a triangular side of a transformer; converting each phase current of the star-shaped side and each phase current of the triangular side of the transformer to the same reference side, and calculating a differential current; the differential current is compared with a preset protection starting element setting value, and if the differential current is not larger than the preset protection starting element setting value, it is judged that the operation is normal or the protection starting condition is not met; and if the differential current is greater than the preset protection starting element setting value, starting an excitation inrush current and fault current identification process. According to the method, the magnetizing inrush current and the fault current can be accurately distinguished, frequency deviation interference is resisted, and the quickness and reliability of main protection of the transformer in a new energy scene are 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.

The invention discloses a transformer working condition identification method and system based on multi-point vibration and improved MobileNet, and belongs to the technical field of transformer detection, and the method comprises the steps: applying synchronous vibration excitation to a plurality of preset point positions of a transformer, and collecting a multi-dimensional vibration sequence; extracting frequency band energy characteristics of the multi-dimensional vibration sequence, and constructing a three-dimensional space-time matrix; inputting the three-dimensional space-time matrix into an improved MobileNetV3 network to obtain transformer working condition probability distribution; outputting a transformer working condition identification result according to the working condition probability distribution; wherein the MobileNetV3 network is improved by inserting a mixed cavity convolution block and a three-dimensional pooling layer and introducing a convolution block attention module, and the problem that a traditional single-measuring-point vibration analysis method is insufficient in sensitivity under complex working conditions is solved.

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 present invention relates to the field of medical devices, and in particular to a low-temperature plasma system capable of accurately testing impedance, comprising: a single-chip microcomputer control module, a power supply module, an output module, a DC overcurrent protection circuit, a VPK acquisition circuit, an output voltage acquisition circuit, and an output current acquisition circuit; the power supply module comprises: a DC-DC circuit, an input overvoltage and undervoltage protection circuit, and an overcurrent protection circuit; the output module comprises: an output transformer, a DC bus voltage detection circuit, an input voltage detection circuit, an output voltage detection circuit, and an output current detection circuit; the DC bus voltage detection circuit and the input voltage detection circuit are arranged on the primary side of the output transformer, and the output voltage detection circuit and the output current detection circuit are arranged on the secondary side of the output transformer.

The invention discloses a transformer fault diagnosis and prediction method and a storage medium, and the fault prediction method comprises the steps: introducing elite retention, adaptive step length, diversity protection and a local search strategy into an original ant lion optimization algorithm, and obtaining an improved ant lion optimization algorithm; optimizing the deep learning model by adopting an improved ant lion optimization algorithm to obtain an improved deep learning model; training the improved deep learning model by using the preprocessed transformer fault data set as input data to obtain a fault prediction model; obtaining and preprocessing transformer operation data; and inputting the preprocessed transformer operation data based on the fault prediction model, and outputting a transformer fault diagnosis and prediction result. The method has unique advantages in the aspects of processing time sequence data and spatial features, the parameters of the deep learning model or the hybrid model thereof are further optimized through the improved ant lion optimization algorithm, and the safety and reliability of the intelligent power grid system are effectively improved.

The invention relates to a high-frequency pulse current plastic thermal connection device which comprises an electric cabinet and a thermal connection assembly, a control circuit, a relay and a high-frequency pulse power supply are arranged in the electric cabinet, and an output transformer is arranged in the high-frequency pulse power supply. The thermal connection assembly comprises a moving device, a framework, a converter transformer and a heating head. The converter transformer is arranged in the framework and is connected with the output transformer through a low-current cable; and the heating head is arranged at the other end of the framework, is connected with the converter transformer and realizes a thermal connection function by heating the converter transformer. According to the invention, the high-frequency pulse power supply is used, so that the time required for raising the temperature of the same heating head by 200 DEG C and stabilizing the temperature is greatly shortened; a transformer for outputting large current is not needed in the electric control box, so that the weight of the electric control box can be greatly reduced; during transmission, the power line does not need large current and is transmitted to the thermal connection assembly at the rear end to be converted, so that a thinner small-current power line can be selected, and meanwhile, the power loss of the cable is greatly reduced.

The invention provides a constant-voltage digital adjustable load circuit for an ultrasonic power supply and a working method of the constant-voltage digital adjustable load circuit, the constant-voltage digital adjustable load circuit comprises a control signal processing circuit, a subtraction circuit and a voltage control circuit which are connected in sequence, and the voltage control circuit is further connected with a rectifying circuit and an overcurrent protection circuit. The input end of the rectifying circuit is connected with a transformer; and the voltage control circuit is used for receiving an output signal of the subtraction circuit and the direct-current voltage output by the rectifying circuit, controlling the primary voltage of the ultrasonic circuit to be 0-100V linear adjustable constant voltage, and outputting the direct-current voltage to the ultrasonic circuit. According to the invention, a multi-path output transformer is simplified into a single-path output transformer, and a multi-path relay required for voltage switching is removed, so that the volume of the ultrasonic power supply is reduced, the working stability is improved, the production cost is reduced, the linearly adjustable function of the output voltage is realized, the power supply function becomes stronger and more stable, and the power supply is more convenient to use. And the practicability of the power supply is greatly improved.