2002 results about "Switching frequency" patented technology

The invention discloses a charging chip real-time power scheduling control method, and relates to the technical field of new energy automobile charging pile power scheduling, and the method comprises the following steps: S1, obtaining the operation parameters of charging equipment in real time, including the voltage fluctuation value of an input power grid, the output current of a charging interface, and the temperature of a charging chip; according to the method, the power grid voltage fluctuation, battery SOC / temperature and chip working condition multi-source data are collected in real time, a multi-target optimization model of dynamic weight distribution is combined, the service life loss of the battery is minimized, the charging efficiency and the power grid load are balanced at the same time, the user charging mode and the battery state difference are taken into consideration by adopting a priority scheduling strategy, and the charging efficiency is improved. Millisecond-level power fine control is achieved through cooperative adjustment of the PWM duty ratio and the switching frequency, personalized charging requirements are met, the overall energy efficiency is improved, a dual safety defense line is constructed through a graded load reduction protection mechanism and a fuzzy control algorithm, and a power distribution scheme is intelligently reconstructed when a power grid is abnormal or a battery is overheated.

The invention provides a single-tube boost PFC circuit control method and system for single-cycle control, and relates to the technical field of data processing, and the method comprises the steps: generating a self-adaptive PWM control signal according to a duty ratio adjustment amount and a frequency compensation factor, and dynamically limiting the pulse current amplitude of a power switch tube through a peak current prediction algorithm, a load current change rate is generated based on the real-time relevance between the pulse current amplitude and the load current; setting a first monitoring point in a dynamic duty ratio change path of the PWM control signal, and extracting a duty ratio change rate and a switching frequency fluctuation parameter in real time; setting a second monitoring point in a feedback path of a load current change rate, and extracting a load current slope characteristic and a transient change amplitude in real time; and based on the parameters of the first monitoring point and the second monitoring point, generating a correction factor through a dynamic response correlation model. According to the invention, a high power factor can be realized under wide input voltage and load fluctuation.

The invention relates to the technical field of power electronics, in particular to a voltage ripple suppression and compensation method for a multi-output AC-DC switching power supply, and the method comprises the steps: obtaining the real-time voltage ripple signal of each AC-DC output channel and the working parameters of the switching power supply, and the working parameters comprise the switching frequency, the duty ratio, the inductance and capacitance parameters and the load current; inputting the real-time voltage ripple signal and the working parameters of the switching power supply into a preset coupling interference model, and generating ripple coupling characteristics between AC-DC output channels; determining a ripple suppression compensation coefficient through the ripple coupling characteristics; and based on the ripple suppression compensation coefficient and a preset PWM control signal, adjusting the output voltage of the switching power supply to suppress the ripple, thereby breaking through the limitation of a traditional method on independent processing of a single channel, and fundamentally solving the problem of a ripple suppression blind area caused by cross interference between channels.

The invention discloses a storage battery capacity attenuation trend prediction method, and belongs to the technical field of storage battery prediction. By collecting voltage, current and temperature data of each monomer in real time and combining historical capacity attenuation and internal resistance growth data, the method identifies a voltage and capacity difference value, evaluates cyclic stress non-uniform distribution, and determines a current sharing proportion and a load unbalance degree. Identifying an abnormal mode of new battery overload and aged battery deep discharge, constructing a mixing abnormal working condition identification mode, if the unbalance degree exceeds the standard, adaptively adjusting the charging and discharging time and the current switching frequency, establishing a load balance control framework, predicting the capacity attenuation rate and the residual cycle index of each monomer, and determining the capacity matching degree and the life matching degree; finally, a comprehensive residual life estimation value and a credibility interval are generated through fusion; the performance balance of the mixed battery pack is remarkably improved, the overall service life is prolonged, and the method is suitable for real-time monitoring of a battery management system.

The invention relates to the technical field of adaptive control, in particular to an adaptive control method and system for a power conversion system driven by a multi-mode neural network. The method comprises the following steps: extracting a high-order transient feature vector of a load path and a high-order transient feature vector of a voltage path based on a dual-path nonlinear coupling model, and jointly inputting the high-order transient feature vector and the high-order transient feature vector into a multi-modal neural network in combination with an aging index of a power supply for feature fusion to generate an initial switching frequency demand currently required by the power supply; evaluating the heat dissipation capability, inputting the heat dissipation capability into a preset heat dissipation capability three-state model to determine a heat dissipation state, performing temperature compensation and amplitude limiting processing on the initial switching frequency demand, and outputting the corrected switching frequency demand; according to the method and system, the frequency range can be dynamically adjusted according to the real-time heat dissipation capacity, overheating is avoided when heat dissipation is insufficient, efficient operation is kept when heat dissipation is sufficient, the magnetic saturation risk can be restrained in an emergency state, and frequency out-of-control is avoided.

The invention discloses a high-voltage integration and cooperative control method and system for an energy storage system, and the method comprises the steps: carrying out the three-dimensional space coupling design of a high-voltage box and a power conversion system PCS through a topological optimization algorithm according to the power level and heat distribution demands of the energy storage system, and generating a physical layer architecture scheme of a high-voltage all-in-one machine; based on a physical layer architecture scheme of the high-voltage all-in-one machine, constructing a collaborative optimization data set by using a timestamp alignment technology; inputting the collaborative optimization data set into a model predictive control framework to generate dynamically adjusted PCS working parameters; and according to the PCS working parameters, the internal temperature distribution of the high-voltage all-in-one machine is calculated in real time through the three-dimensional thermal field reconstruction model, the rotating speed of a cooling fan and the switching frequency of a power device are dynamically adjusted, and a cooperative control instruction is generated. According to the embodiment of the invention, the integration level and the cooperative control capability of the high-voltage energy storage system can be improved, and the efficient and stable operation of the energy storage system is realized.

The invention discloses a planning method and system of a power distribution communication access network, particularly relates to the technical field of power distribution communication network dynamic topology management, and is used for solving the problems of fault misjudgment and inaccurate positioning caused by topology update delay in the prior art. A dynamic topological data set is generated by dynamically monitoring the operation state of each optical fiber node and a link transmission signal, a real-time topological view with a dynamic version identifier is generated based on a preset topological planning strategy, and a network change area is quickly locked by combining a topological difference identification technology of an alarm moment and a historical view. Candidate fault links are screened through the conflict relation between link switching frequency and service priority, and the vibration characteristics of the construction machinery are further analyzed by using distributed acoustic sensing data and external force damage interference is eliminated; and finally, optical cable breakage faults are confirmed through optical power interruption verification, heartbeat signal loss detection and redundant optical fiber phase mirror symmetry verification. And multi-dimensional accurate positioning of the topological data fault root cause is realized.

The invention relates to a lightweight AI outbound model hot switching framework construction method. Defining a runtime context state unit to form an independent data structure; the AI model is divided into modules responsible for process reasoning execution; the module is in communication with the module responsible for dialogue memory and intention tracking state scheduling through a lightweight intermediate interface; establishing a set of semantic alignment mapping table based on the semantic difference between the models; performing semantic label conversion on a current dialogue state through a mapping table, and injecting a pseudo state vector into the new model to reconstruct an initial reasoning condition; calculating a tension score based on the intention hopping frequency, the slot position switching frequency and the user reply uncertainty; if the controller judges that the switching window is a safe switching window, hot switching logic can be triggered; a prediction module is introduced before switching to perform rehearsal on the reachable intention coverage of the new model, and switching is delayed if the risk is too high. The whole model hot switching process is not perceived by users, dialogue continuity and service continuity are guaranteed, the system resource utilization rate is improved, and model scheduling intelligence is enhanced.

The invention relates to the technical field of electric energy meter data analysis, and discloses a power consumption data analysis operation and maintenance method and system of an electric energy meter. The method comprises the following steps of: acquiring and preprocessing electricity utilization state data, namely synchronously acquiring voltage, current and power data, and performing timestamp alignment, data cleaning and resampling; classifying according to preset peak, flat and valley time intervals, and extracting time-frequency domain characteristics such as voltage fluctuation amplitude and current change rate in each interval to generate a set; detecting time period switching frequency, and marking an abnormal interval if the time period switching frequency exceeds a threshold value; generating an abnormal feature subset according to similarity decomposition of historical anomalies and current features; correcting the abnormal feature subset based on the electrical and time sequence parameter association relationship; and reversely backtracking a potential fault source along the fault association map, determining an operation and maintenance priority according to the number of abnormal parameters, position depth and the like, and generating an operation and maintenance instruction. The system comprises corresponding modules such as data acquisition and processing. According to the scheme, data can be accurately analyzed, a fault source is positioned, and the operation and maintenance efficiency is improved.

Disclosed in the present invention are a dual three-phase asymmetric stator-type double-permanent-magnet vernier electric motor and an improved model predictive current control method therefor. The electric motor comprises a stator core, stator permanent magnets, a rotor core and a rotor ferrite, wherein the stator core is formed by alternately arranging umbrella-shaped armature teeth and quasi-rectangular armature teeth in the circumferential direction, the stator permanent magnet, which is magnetized radially outwards, is adhered to each of openings on two sides of the umbrella-shaped armature teeth, and a fan-shaped auxiliary slot is formed between the umbrella-shaped armature teeth and the stator permanent magnet; and the shape of the rotor core is similar to a gear, the rotor ferrite is embedded in the rotor core using a Halbach-based alternating-pole array, and the length of the rotor ferrite is greater than the length of the rotor core and the length of the stator core. In addition, the present invention uses a model predictive current control system based on a virtual voltage vector to construct a cost function to select the voltage vector having the minimum switching frequency, and make same operate preferentially. The present invention not only improves the torque density of the electric motor, but also significantly reduces switching losses in model predictive control.

The present application relates to the technical field of safety management, in particular to a mine air compressor safe operation management method and system based on deep learning, comprising the following steps: collecting output pressure and input current, calculating slope change rate and direction switching frequency, clustering fluctuation amplitude and frequency by time period, generating working condition fluctuation label, identifying state mutation point and duration, calculating risk level label, analyzing load fluctuation coefficient, judging periodic fatigue early warning, combining synchronous rate to identify abnormal section, judging the need to switch state and generating instruction set. In the present application, by constructing the load risk level sequence on the basis of state duration and mutation point identification, the quantitative identification of multi-period load deviation is realized, the early warning period is marked by combining risk level and fatigue deviation ratio, and the abnormal load trend is judged according to the change direction of pressure and return gas flow, which strengthens the capture ability of abnormal operation section and improves the accuracy of operation safety risk identification and the adaptability of response strategy.

The invention belongs to the technical field of charging pile control, and particularly relates to an intelligent charging pile control system and method based on dynamic power distribution, and the method comprises the steps: a slave control board collects battery data in real time, and uploads the battery data to a main control board; the main control board receives the battery data, predicts the maximum allowable charging power of the battery through an LSTM model, and determines the safety boundary of the battery according to the maximum allowable charging power and the safety operation parameters of the battery; generating a power distribution strategy by adopting a Nash equilibrium algorithm based on the battery safety boundary in combination with the power grid load and the user priority; issuing the power distribution strategy to an intelligent charging module; the intelligent charging module adjusts the switching frequency of the LLC resonant converter based on the power distribution strategy to realize target power output; the intelligent charging module monitors output current, voltage and temperature in real time and feeds back monitoring data to the main control board to form closed-loop control. And flexible control and data feedback of the charging module are realized.

The invention relates to the technical field of chip design, and discloses a chip dynamic power consumption scheduling method and system based on an intelligent algorithm. The method comprises the following steps of: firstly, acquiring instruction stream data operated by a chip in real time, extracting a feature vector comprising an instruction dynamic change vector and context associated data, and determining a power consumption prediction mapping parameter according to the feature vector; and when the parameter exceeds a preset threshold value, an accurate power consumption prediction result is generated by adjusting the weight of the convolutional neural network. Subsequently, a synchronous timing demand is calculated based on the instruction switching frequency and the data dependency, and an initial power supply configuration is determined. By monitoring task load classification signals, the power consumption distribution proportion is adjusted when the signals are lower than a threshold value, the optimized power supply configuration is obtained, and the improvement index of the resource distribution efficiency is calculated according to the optimized power supply configuration. And finally, according to the index, dynamically adjusting a limiting condition of a scheduling period, and forming a self-adaptive optimization framework, thereby realizing accurate prediction and dynamic optimization scheduling of the chip power consumption.

The invention relates to the technical field of converter starting, in particular to an LLC full-bridge converter hybrid control soft start method, comprising the following steps: selecting a hybrid soft start strategy combining down-conversion starting and PWM starting, and respectively introducing nonlinear orders of a control switch frequency decline curve in the down-conversion starting process and a duty ratio rise curve in the PWM starting process; defining a mathematical formula of the change of the switching frequency and the duty ratio along with time when two nonlinear orders are different in value; establishing a simulation model, and substituting a mathematical formula that the switching frequency and the duty ratio change along with time when the two nonlinear orders are different in value into the simulation model; performing simulation testing on the nonlinear orders with different values, extracting the maximum resonance current, and determining an order combination with the best maximum resonance current suppression effect; and performing soft start of the LLC full-bridge converter based on the determined order combination. According to the invention, the current in the soft start process can be effectively suppressed, and meanwhile, quick start and device safety are considered.

The invention relates to the technical field of capacitor manufacturing and state monitoring, in particular to a capacitor aging curve optimization method based on deep learning, which comprises the following steps: step 1, multi-modal data acquisition and standardization processing; 2, modeling and training a physical constraint neural network; 3, executing a dynamic control and maintenance strategy: dynamically adjusting the switching frequency of the inverter based on a comparison result of the residual life prediction value output in the step 2 and a preset threshold value, and enabling the ripple frequency to deviate from a capacitor impedance sensitive frequency band; and according to the aging mode classification result output in the step 2, triggering a corresponding temperature sampling frequency adjustment or bus voltage reduction operation. Through fusion of multi-modal data, a physically constrained neural network model and a dynamic control strategy based on a prediction result, the aging process of the capacitor is successfully optimized, the use efficiency of the capacitor is improved, and the life cycle of the capacitor is effectively prolonged.

The invention provides a transient current capability improvement control system and method of a wind and light power generation system. The system comprises a self-synchronizing voltage source control module, a junction temperature prediction module, a transient current improvement control module, a current instruction calculation module and a transient current precise control module. According to the invention, online observation of the junction temperature of the wind-solar converter power device can be realized, the maximum short-circuit current which can be output can be accurately predicted, the DC bus voltage, the switching frequency and the modulation strategy are adjusted when the power grid has a fault, the junction temperature of the power device is reduced, and the transient current endurance capability of the power device is improved. And the transient current is accurately controlled to limit the utilization of the transient capability of the power device.

The invention relates to the technical field of data processing, and provides a dynamic load test method and device for a chiplet, and the method comprises the steps: collecting transient heat flux data generated by the chiplet in a multi-frequency load switching process to generate a thermal dynamic behavior map, calculating a thermal coupling interference index map according to the thermal dynamic behavior map, and carrying out the testing of the dynamic load of the chiplet according to the thermal coupling interference index map. And inputting the thermal coupling interference index diagram into a preset thermal stability mapping matrix, and generating a small chip dynamic load behavior evaluation parameter group. By inputting the transient heat flux data into the preset thermal stability mapping matrix to generate the small chip dynamic load behavior evaluation parameter group, the thermal stability and the overall performance of the small chip are improved, and the problem that in practical application, the prior art depends on single thermal monitoring data, and thus the reliability of the small chip is improved is solved. And when the load switching frequency is relatively high or the load disturbance is relatively large, the potential thermal coupling interference and the influence of the potential thermal coupling interference on the system performance cannot be accurately identified.

The invention relates to the technical field of inverter pulse width modulation, in particular to a neutral-point potential balance and low common-mode voltage modulation method and system based on an SVPWM inverter, and the method comprises the steps: determining a basic voltage vector based on different switching states of an NPC three-level inverter; respectively determining the midpoint current of each fundamental voltage vector, and determining the fundamental voltage vector causing midpoint potential deviation; combining with a large vector and a zero vector, and performing mode division of a single vector space region; determining an expected current according to the DC side capacitor voltage of the inverter, and constructing a target function based on the expected current; and performing region judgment according to the output synthesized voltage vector, determining a corresponding optimal division mode, obtaining each basic voltage vector of the synthesized voltage vector and a corresponding duty ratio, and determining a switching sequence to obtain a PWM wave. While the neutral-point potential balance and common-mode voltage suppression of the NPC three-level inverter are realized, the switching frequency of the inverter is effectively reduced, and the switching loss is reduced.

The invention provides a VR (Virtual Reality) video adaptive code rate control method based on multi-agent reinforcement learning, which relates to the technical field of VR, and comprises the following steps: acquiring historical video block transmission data prediction bandwidth and evaluating network stability, selecting probability distribution based on an Actor network output code rate of a multi-layer perceptron structure, and determining the probability distribution of the multi-agent reinforcement learning. Structural similarity and re-buffering indexes are calculated for the video blocks to construct a comprehensive reward function, local observation information is acquired by utilizing a multi-agent joint state vector, group information is integrated through attention weight, and network parameters are updated by adopting a strategy gradient method and an Adam optimizer to realize code rate optimization. According to the invention, the VR video transmission quality can be effectively improved, the re-buffering probability is reduced, the continuity and fluency of user watching experience are ensured, the code rate switching frequency is reduced, and the system stability is improved.

A reconfigurable flying capacitor multilevel input stage accepts a wide range of input voltages. The input stage divides and inverts a DC input voltage. Generally, output voltage regulation is achieved by adjusting the power switches' duty ratio in the input stage for non-resonant converters and by adjusting the switching frequency for resonant converters.

The invention relates to the technical field of reactive power compensation of a power system, and discloses an active filter harmonic suppression and dynamic compensation system based on IGBT (Insulated Gate Bipolar Translator) multi-mode regulation and control. The system comprises a control module; the high-frequency harmonic detection module is electrically connected with the control module and is configured to detect a power grid harmonic signal in real time at a sampling rate of 200kHz and transmit the power grid harmonic signal to the control module; the IGBT gate driving compensation circuit is electrically connected with the control module and a preset IGBT module, and the control module dynamically adjusts the dead time and the switching frequency of the IGBT module through the IGBT gate driving compensation circuit; the IGBT junction temperature observer is electrically connected with the IGBT module and the control module and is configured to monitor the junction temperature of the IGBT module in real time and trigger the control module to start a derating protection mechanism when the junction temperature exceeds 125 DEG C; wherein the control module generates a PWM (Pulse Width Modulation) compensation waveform through a model prediction control algorithm unit based on a detection signal of the high-frequency harmonic detection module, so as to drive the IGBT module to execute harmonic compensation through the IGBT gate driving compensation circuit.

The invention relates to the technical field of light storage and charging integration, in particular to an intelligent micro-grid power supply and load balance control method based on light storage and charging integration, which comprises the following steps of: synchronously acquiring a photovoltaic string output current frequency spectrum, a charging pile switching frequency set and an energy storage converter impedance curve, and outputting a mismatch characteristic frequency spectrum; the mismatch characteristic frequency spectrum marks a resonance risk frequency band; and based on the mismatched characteristic frequency spectrum, generating a light-storage-charging power coordination instruction set through a harmonic suppression collaborator, including instructing a photovoltaic string to start an active damping mode in a resonant frequency band and dynamically distributing differential switching frequency of a charging pile. According to the method, a mismatch characteristic frequency spectrum model fusing a photovoltaic current frequency spectrum, a charging pile switching frequency set and an energy storage converter impedance characteristic is constructed for the first time, a resonance risk frequency band is analyzed and judged through frequency domain coupling, and feedforward identification of inter-harmonic excitation conditions of multi-source high-frequency conversion equipment is realized; the method has higher system level adaptability and harmonic risk positioning precision.

The invention relates to the technical field of parallel three-level converter control, and provides a parallel three-level converter model prediction control method and system, and the method comprises the steps: enabling a parallel three-level converter to be modeled as a series connection of an equivalent voltage source and an equivalent filter inductor according to the Thevenin theorem, enabling the parallel three-level converter to be equivalent to a five-level converter, and enabling the parallel three-level converter to be connected in parallel; a three-layer optimization control strategy is adopted; in the first layer, a candidate vector closest to a reference voltage vector is selected as an optimal five-level basic vector so as to track a parallel total output current; in the second layer, a redundant five-level voltage vector is selected to balance the neutral-point voltage; and in the third layer, selecting the candidate switching state which enables the circulating current suppression value function to be the minimum as the optimal redundant three-level switching state so as to suppress the circulating current. The method can effectively reduce output current harmonics under low switching frequency, and balance of neutral-point voltage and effective suppression of circulating current are realized to ensure safe and stable operation of a parallel converter system.

The invention belongs to the technical field of LLC resonant converters, and provides an efficiency optimization method of a wide-range LLC resonant converter. The method comprises the following steps: firstly, obtaining a full-bridge LLC resonant converter topology and a working area; secondly, establishing a full-time-domain equation of the full-bridge LLC resonant converter topology in an inductive low-gain output working area, and setting boundary conditions of the full-time-domain equation; then, on the basis of boundary conditions, switching tube turn-off current is calculated, and the switching tube turn-off current is combined with switching tube switching frequency to be represented as switching tube turn-off loss characteristic quantity related to the inductance coefficient, the quality factor and the normalized frequency; then, carrying out characteristic analysis on the turn-off current of the switching tube, and setting constraint conditions; and finally, on the basis of the set constraint conditions, the optimal values of the inductance coefficient, the quality factor and the normalized frequency are calculated by combining a differential evolution algorithm, so that the turn-off loss of the switching tube is minimum. According to the invention, the transmission efficiency in a low-gain mode and a high-gain mode can be improved, and the turn-off loss of the switching tube is reduced.

The invention relates to a satellite-ground fusion network multi-target space-time switching decision-making method. The method comprises the following steps: S1, establishing system models based on a real environment and a Starlink satellite constellation, wherein the system models comprise a terminal mobile model, a network model and a communication model; s2, defining performance indexes of service quality and experience quality, and constructing a target function; s3, modeling a connection switching decision process: modeling the connection switching decision process as a Markov decision process; s4, proposing a space-time decision method for connection switching based on the double-mask double-depth Q network: obtaining an optimal switching strategy by utilizing continuous interaction between an intelligent agent and an environment; in the exploration stage and the utilization stage, action masks are used respectively to improve the learning efficiency of the intelligent agent in the high-dimensional action space. According to the method provided by the invention, the total switching frequency in the transmission process is reduced, the average connection duration is prolonged, the average throughput is improved, the delay is reduced, and meanwhile, dense switching in the transmission process is avoided.

The invention discloses a model prediction control method of a high-speed permanent magnet synchronous motor and a related device, and relates to the field of control, and the method comprises the steps: building a stator current equation capable of simulating the behavior of the high-speed permanent magnet synchronous motor based on the position change of a rotor in the same control period, a high control input basic voltage value is obtained by fixing motor parameters and combining a total disturbance parameter of a current loop system and a set stator current tracking target at the next moment at the same time; on the basis of the control input basic voltage value, current loop harmonic voltage compensation is carried out through the control input harmonic compensation voltage value to obtain a control input voltage value, and voltage modulation is carried out according to the control input voltage value to control the high-speed permanent magnet synchronous motor. According to the method, when the stator current equation is constructed, rotor position change parameters in the same control period are innovatively introduced, so that the motor behavior simulation precision is remarkably improved; the defect that the switching frequency is not fixed in a traditional finite set model predictive control algorithm is effectively overcome through the voltage modulation technology.

The invention discloses a soft start control method, a soft start control device and a switching power supply. The soft start control method comprises the following steps: starting at a first set frequency; comparing the output voltage Vout with the output judgment threshold Voth to judge the calculation mode of the switch preset frequency; if Vout is smaller than Voth, the switch preset frequency is calculated according to the output voltage; if Vout is greater than or equal to Voth, subtracting a fixed frequency quantity from the switching frequency of the previous judgment period as a switching preset frequency; judging whether current protection needs to be executed or not according to comparison between the resonant cavity current and a set resonant current protection threshold value; calculating the difference between the switch preset frequency and the switch frequency of the previous judgment period, comparing the difference with the maximum frequency step value and the minimum frequency step value respectively, updating the switch preset frequency, and ensuring that the switch preset frequency is adjusted within the range of the maximum frequency step value and the minimum frequency step value; and comparing the loop calculation frequency with the switch preset frequency, determining the final output switch frequency, and judging whether to end the soft start or not. The control is simple, and the monotonous rise of the output voltage within the full-load range can be realized.

The invention relates to the technical field of electric energy storage systems, and discloses an electric energy storage system hierarchical cooperative control method based on source-load-storage dynamic matching, and the method comprises the steps: obtaining a common connection point voltage sampling sequence, and calculating a second-order change rate; collecting the first-order change rate of the output current of the energy storage converter and determining a polarization constraint coefficient; switching a control mode according to a second-order change rate magnitude and a polarization constraint coefficient symbol, and utilizing a dynamic damping factor to constrain a duty ratio to adjust a step length; the dynamic damping factor is adjusted and increased when the polarization constraint coefficient exceeds a threshold value under the excitation and transformation state, so that the change rate of the output current is within the charge diffusion safety domain of the storage medium, dynamic damping is guided to implement asymmetric constraint through cooperation of trend pre-judgment and polarization stress identification, the output current is made to adapt to the charge diffusion limit, the switching frequency of the converter is reduced, and the stability of the converter is improved. Active substance precipitation is inhibited; and the total throughput of the equipment in the full life cycle is improved.

The invention relates to a high-frequency adaptive space vector pulse width modulation method, which comprises the following steps of: acquiring real-time operating parameters of a motor system to obtain a motor phase current vector and a rotor position signal; carrying out load characteristic identification on the motor system based on the motor phase current vector and the rotor position signal to obtain a load characteristic parameter set; performing frequency domain deconstruction on the load characteristic parameter set to obtain a switching frequency adaptive mapping relation; non-linear optimization is carried out on the space vector division sectors based on the switching frequency adaptive mapping relation, and a corrected space vector positioning result is obtained; and performing dead-zone adaptive compensation on the switching sequence of a power switch tube in the inverter according to a corrected space vector positioning result to obtain a high-frequency pulse width modulation waveform, thereby solving the problems that the quality of the output waveform is reduced, the harmonic distortion is increased, and the accuracy of the high-frequency pulse width modulation waveform is reduced due to the fact that the traditional SVPWM is difficult to realize accurate vector positioning under the nonlinear load and variable working conditions. And stable operation of the motor is influenced.

A method and circuit for controlling a bidirectional resonant direct-current converter are provided. The method includes obtaining an input and / or output electrical parameter of the converter; determining respective delay time periods of bridge arms in the secondary circuit based on the input and / or output electrical parameter and a desired gain of the converter; determining a switching frequency to be greater than a resonant frequency of the resonant tank based on the input and / or output electrical parameter and a preset reference signal; and turning off, in response to a secondary resonant current of the converter reaching zero, corresponding switching transistors of the switching transistors in the secondary circuit immediately after the respective delay time periods starting from a zero-crossing point; and turning on other switching transistors complementary to the turned-off switching transistors in the secondary circuit immediately after a dead time.