332 results about "Space vector" patented technology

The invention relates to the technical field of low-power-consumption AI processors, and discloses a novel real-time data processing method and chip for calculating a low-power-consumption AI processor, and the method comprises the steps: carrying out the neural mimicry classification processing of a calculation task inputted by the low-power-consumption AI processor, and obtaining a calculation task feature matrix; performing multi-dimensional mapping operation on the calculation task feature matrix to obtain a heterogeneous calculation space vector; performing core resource allocation based on the heterogeneous calculation space vector to obtain a dual-mode processing unit configuration matrix; constructing a storage and calculation integrated data path based on the dual-mode processing unit configuration matrix to obtain a parallel calculation data flow diagram; executing dynamic voltage frequency mapping on the parallel computing data flow diagram to obtain a power consumption optimization control vector; and inputting the power consumption optimization control vector into the trained pulse neural network model to carry out computing power load balancing calculation and generate computing power load balancing parameters, and the method improves the real-time data processing capability of the low-power AI processor.

The invention discloses a multi-modal data fusion method and system based on a large model, and relates to the technical field of data fusion, and the method comprises the steps: receiving multi-modal data, and carrying out the noise layering filtering and time-space alignment; mapping data to a large model hidden space through each modal lightweight encoder, extracting initial features by using a single-modal pre-training model, and converting the initial features through an adapter to generate a hidden space vector; each modal hidden space vector and a task cue word are received, a fusion feature matrix is dynamically aggregated and generated through a self-attention mechanism and a cross attention mechanism of a large model, and semantic integration of multi-modal information is realized; taking the fusion feature matrix as a soft label, and learning a cross-modal semantic mapping capability through knowledge distillation; the trained model is deployed to the edge through dynamic quantization and pruning optimization, an optimized feature matrix is input into a task-customized lightweight head network, and a final task result is output in combination with multi-modal context information. The method can break through the semantic gap between modals, and improves the fusion efficiency.

The invention discloses a hybrid modulation strategy for neutral-point voltage balance control and common-mode voltage suppression of a three-level inverter, and belongs to the technical field of power electronics. In order to solve the problems that the neutral-point voltage of a three-level inverter is unbalanced and the common-mode voltage is large under traditional modulation, the invention provides a novel modulation strategy formed by mixing virtual space vector modulation and intermittent modulation, and according to virtual space vector modulation, space vectors with small common-mode voltage are selected to construct virtual small vectors and middle vectors; adjusting the midpoint current through the midpoint voltage control parameter; dPWMA capable of suppressing common-mode voltage is selected for intermittent modulation. According to the hybrid modulation strategy, common-mode voltage suppression can be kept, when the neutral-point voltage is balanced, DPWMA is selected to reduce switching loss, and when the neutral-point voltage is unbalanced, a modulation method which is more effective for recovering balance of the neutral-point voltage is selected to achieve the purpose of faster realization of neutral-point voltage balance.

A method for recognizing a motor imagery electroencephalography (MI-EEG) signal based on a capsule network (CapsNet) is provided, and relates to the technical field of deep learning and brain-computer interfaces (BCIs). An electroencephalography (EEG) time series is mapped into a three-dimensional (3D) array form based on a spatial electrode distribution. By combining 3D convolution, a CapsNet constructs a three-dimensional capsule network (3D-CapsNet) for recognizing an MI-EEG signal. A 3D convolution module performs feature extraction from both a temporal dimension and an inter-channel spatial dimension through a plurality of layers of 3D convolution to obtain a low-level feature. The low-level feature output by the 3D convolution module is integrated through the CapsNet to obtain a high-level spatial vector containing an inter-feature relationship. A primary capsule and a motor capsule are connected through dynamic routing, and finally a CapsNet module outputs a classification result through a nonlinear activation function squash.

The invention discloses a blockage detection method, system and equipment for a flow measurement device and a medium, and relates to the field of blockage detection. The method comprises the following steps: acquiring an original pressure signal and an original flow signal of a to-be-measured flow measuring device, and generating an intrinsic mode function according to the original pressure signal and the original flow signal; extracting a time vector, a space vector and a frequency domain vector of the intrinsic mode function, obtaining an attention weight through a double-path cross attention mechanism, and performing weighted fusion on the time vector, the space vector, the frequency domain vector and the attention weight to obtain a multi-dimensional feature vector; inputting the multi-dimensional feature vector into a pre-trained blockage probability prediction model based on deep learning to obtain a blockage probability value; and constructing a state space according to the blockage probability value, the historical maintenance record and the equipment operation duration, balancing false alarm and leak detection punishment in combination with a reward function, and determining execution operation. By implementing the technical scheme provided by the invention, the accuracy and reliability of blockage detection are improved.

The provided is flux-weakening control method and system for the salient pole offset permanent magnet synchronous motor. The method includes determining the given value of electromagnetic torque according to the actual speed and the given target speed; calculating the current trajectories of MTPA and MTPV in dq coordinate system; obtaining the d′q′ coordinate system by rotating the coordinate system, and calculating the current trajectory of MTPA and MTPV in the d′q′ coordinate system to obtain the characteristic current; judging whether it is necessary to turn on the flux-weakening control, and outputting the direct axis current compensation when it turns on; judging the flux-weakening area of the motor; and generating the space vector pulse width modulation signal to drive the motor to realize the full speed range flux-weakening speed regulation of the salient pole offset permanent magnet synchronous motor.

The invention discloses a transformer fault diagnosis and root cause positioning method based on a space-time diagram neural network, and relates to the field of transformer fault diagnosis, and the method comprises the steps: S1, carrying out the preprocessing of the structure information and DGA time series data of a transformer, and obtaining a topological network structure diagram and a DGA time series; s2, obtaining a spatial vector based on a message passing mechanism of a graph convolutional network; s3, a time sequence vector is obtained in combination with a Transform encoder and multi-head self-attention; s4, obtaining space-time fusion features; s5, constructing a multi-task prediction head based on the space-time fusion feature, the fault type historical data and the fault root cause; and S6, carrying out fault detection and outputting a corresponding fault type and root cause positioning result. According to the application, the accuracy of fault type identification can be remarkably improved, and accurate positioning of the fault root cause is realized.

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 present invention relates to the technical field of electric motor control. Disclosed is a control method for electric motors in series connection, which control method solves the problems of rotation speed synchronization, energy consumption and fault protection of electric motors with axes in series connection. The control method comprises: connecting axes of n electric motors in series, and collecting basic parameters of each electric motor; obtaining a voltage space vector reference value by means of synthesis; acquiring a desired voltage output vector; implementing rotation speed synchronization and load balancing of the electric motors with axes in series connection; and monitoring, in real time, operating processes of the electric motors with axes in series connection, and resolving a fault. In the present invention, the desired voltage output vector is retained by means of a hybrid control algorithm, the rotation speed synchronization of the electric motors with axes in series connection is implemented by means of a counter closed-loop rotation speed algorithm, the load and energy consumption of the electric motors with axes in series connection are kept balanced by means of an energy consumption balancing algorithm, and a fault resolution processing scheme is selected in a combined manner by means of a rights-and-interests selection algorithm, thus greatly improving the collaboration capability of the electric motors, improving the fault detection and response capabilities, and reducing the resource consumption costs.

The invention provides a three-phase SVPWM grid-connected inverter harmonic control method based on an improved particle swarm algorithm, and relates to the field of grid-connected control. On the basis of double closed-loop control of a three-phase SVPWM grid-connected inverter, a main circuit topological structure of the grid-connected inverter is firstly provided, and a mathematical model of the grid-connected inverter is determined; then determining a double-closed-loop control strategy based on space vector modulation according to the mathematical model; and aiming at a voltage outer loop and a current inner loop of the double-closed-loop control strategy, an improved particle swarm optimization algorithm is adopted to optimize PI parameters. According to the improved inertia weight, the minimum value, the maximum value and the fitness function of the inertia weight are comprehensively considered, a harmonic suppression performance index is added to the fitness function, and the improved particle swarm optimization algorithm enhances the global search capability and the accurate search capability, so that the probability of falling into a locally optimal solution is reduced. According to the control method, the grid-connected process of the grid-connected inverter can be more stable, the anti-interference capability is enhanced, the static error is reduced, and the electric energy quality is improved.

Disclosed in the present invention is a variable coefficient equivalent SISO model based stability analysis method for a grid-connected converter system, comprising the following steps: constructing a variable coefficient equivalent SISO model; obtaining a complex space vector open-loop transfer function on the basis of the variable coefficient equivalent SISO model; and obtaining Nyquist curves under different current loop parameters or phase-locked loop parameters on the basis of the complex space vector open-loop transfer function. If the Nyquist curves surround point (-1,0), the grid-connected converter system is stable; and if the Nyquist curves do not surround point (-1,0), the grid-connected converter system is unstable. The present invention performs small disturbance modeling on the grid-connected converter system, considers the influence of mutual coupling between the phase-locked loop and the inner current loop, and derives a variable coefficient SISO open-loop transfer function capable of characterizing an MIMO system. Compared with the technical solution in the prior art that actual working conditions are not considered, the method disclosed in the present invention can more accurately analyze the system stability of the system under variable working conditions and variable parameters.

The invention discloses a converter harmonic impedance modeling method and related equipment, and belongs to the technical field of converters, firstly, space vectors of voltage and current of a grid-connected point are obtained, the space vectors can accurately reflect the magnitude and direction of the voltage and current in real time, and compared with traditional scalar modeling, more comprehensive information is provided. Based on the space vector, harmonic power under the influence of positive and negative sequence harmonic waves, reference voltage passing through a power control loop and harmonic voltage output by an inverter are obtained through sequential calculation, and modeling of a harmonic impedance model is carried out based on positive sequence harmonic voltage and negative sequence harmonic voltage of an inverter side and a grid-connected point and a power grid main circuit. A harmonic model in a coupling frequency impedance matrix form is adopted, the interaction relation between harmonic voltage and harmonic current and the coupling relation of harmonic waves with different frequencies after passing through a converter can be described more accurately, and therefore the accuracy and reliability of an analysis result are improved, and a solid model foundation is provided for harmonic power flow research.

The invention is suitable for the field of motor driving and control, and provides a dead-beat predictive current control method and system for a direct-current bias vernier reluctance motor, and the method comprises the steps: obtaining the operation data of a target motor, and carrying out the preprocessing of the obtained operation data; inputting the preprocessed operation data into an extended state observer with coupling compensation, and outputting extended state prediction parameters; inputting the extended state prediction parameter into a dead-beat prediction current control model to obtain a dq0-axis regulation voltage; carrying out coordinate transformation on the adjusting voltage of the dq0 axis, and carrying out space vector pulse width modulation to obtain a switching sequence of dual-inverter SVPWM (Space Vector Pulse Width Modulation); acting the switching sequence on a phase winding of the target motor; according to the method, disturbance caused by voltage coupling is considered in the process of predicting current and disturbance; this allows compensation of voltage coupling interference, thereby reducing the influence of interference caused by parameter changes and coupling voltage, and enhancing dynamic response capability.

The invention relates to a method for determining parameters of a double-target time interference stimulating electrode and a stimulating method. The method comprises the following steps of: inputting a lead field matrix, coordinates of two target spots and an electrode frequency; generating a random solution and multiplying the random solution with the lead field matrix to obtain a corresponding high-frequency electric field; inputting a Grossman formula, and obtaining two groups of envelope electric fields with frequencies of # imgabs0 # and delta fHz; performing Hilbert transform and space vector summation on the two groups of envelope electric fields to form a total matrix; constructing a target point matrix; solving the ratio of the average electric field intensity of the target spot matrix to the average electric field intensity of the total matrix, and if the ratio is less than b, removing a corresponding random solution; if the ratio is larger than b, the corresponding random solution is reserved, and a new random solution is generated; and iteration is carried out to a suspension condition, and if suspension is carried out, the reserved random solution is output. Through the arrangement, an electrode feasible solution for simultaneously stimulating double target spots through time interference stimulation can be effectively found.

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.

The invention is suitable for the technical field of flexible power distribution of a power system, and discloses a modular multilevel converter multi-objective optimization method, system and device based on model predictive control and space vector modulation, and a medium. Obtaining an output current reference value and a circulating current reference value; obtaining a reference voltage vector based on the output voltage reference value, and screening out a plurality of groups of redundant voltage vector sequences through a space vector diagram; performing performance evaluation through rolling optimization by combining each redundant voltage vector sequence with a multi-target cost function to obtain an optimal voltage vector sequence; and on the basis of the optimal voltage vector sequence and the corresponding duty ratio, a switching signal for driving each sub-module switching device of the MMC is generated, and multi-target cooperative control is realized. The three targets of reference output current tracking, circulating current suppression and sub-module capacitor voltage balance are met, and the dynamic response capability, efficiency and reliability of MMC system operation are comprehensively improved.

The invention discloses a method for suppressing magnetotelluric mixed noise in a shallow water area. The method comprises the following steps: reading a noisy ocean MT time sequence; setting phase space reconstruction parameters; constructing a phase-space vector to obtain a noisy data matrix; noise priori information is obtained through noise pre-estimation; constructing an original noisy data matrix after noise whitening; performing singular value decomposition to obtain a feature vector and a feature value matrix of a data covariance matrix after noise whitening; selecting a low-order principal component after noise adjustment principal component transformation to reconstruct a denoised data matrix; recovering the denoised data matrix into a time sequence; carrying out Fourier transform to obtain an ocean MT magnetic field component amplitude spectrum, and combining to obtain an ocean MT four-component amplitude spectrum; detecting the frequency point of the pulse in the four-component amplitude spectrum; and setting all amplitudes corresponding to pulse frequency points in the four-component amplitude spectrum to be 0, and performing inverse Fourier transform to obtain a processed four-component time sequence. According to the invention, the ocean magnetotelluric signals can be better processed, and the data quality is improved.

A system and method for a federated deep learning platform utilizing homomorphically-compressed and encrypted data. The system comprises multiple client devices, each with a local dataset, and a central server hosting a deep learning core. Client devices convert local data into codewords, which are also homomorphically encrypted. The central server processes these encrypted codewords without decryption, preserving data privacy. The platform supports at least two architectural variants: a conventional Transformer trained on codewords, and a Latent Transformer operating on latent space vectors. Both variants eliminate the need for embedding and positional encoding layers. The system aggregates encrypted model updates from clients, enabling collaborative learning while maintaining data confidentiality. Additional features comprise differential privacy implementation and adaptive federated optimization techniques. This innovative approach allows for efficient, privacy-preserving distributed learning across diverse datasets, addressing key challenges in federated learning such as data heterogeneity, non-IID distributions, and communication efficiency.

The invention discloses a total space vector vortex generator, which comprises a plurality of tetramer super units which have the same structure and are arranged in a periodic array at equal intervals, and each tetramer super unit comprises two first chiral metamaterial unit assemblies and two second chiral metamaterial unit assemblies which are respectively arranged along diagonal lines; the first chiral metamaterial unit assembly comprises a first patch antenna, a first dielectric layer, a metal backboard, a bonding medium, a second patch antenna and a second dielectric layer which are sequentially arranged from top to bottom; the structure of the second chiral metamaterial unit assembly is the same as that of the first chiral metamaterial unit assembly, and the in-plane rotation angle is different from that of the first chiral metamaterial unit assembly; by rotating the first patch antenna and the second patch antenna of the two chiral super units around the central point, phase distribution is independently designed in four different circular polarization transmission channels, and meanwhile, vortex beam topological charges generated in corresponding channels in transmission and reflection modes are controlled, so that the transmission bandwidth can be expanded, and the transmission efficiency is improved. And the reflection and transmission efficiency is improved.

The invention relates to the field of three-level inverters, and particularly discloses a model-free strong-robustness predictive control method and system for a three-level SNPC inverter, and the method comprises the steps: carrying out the real-time sampling and coordinate transformation of the inverter, and obtaining the current and voltage components under a two-phase static coordinate system; establishing a hyper-local model, enabling the system parameter deviation and the external disturbance to be uniformly equivalent to lumped disturbance, and designing a full-order sliding-mode observer for real-time estimation; calculating a reference voltage vector based on the disturbance estimation value and the extrapolated reference current; and an optimal double-vector combination is selected through space vector optimization, action time is solved, redundant small vectors are selected according to midpoint voltage deviation, and PWM driving signals are generated. According to the method, excellent current tracking performance and low harmonic distortion can still be kept under the conditions of parameter mismatch and working condition change, meanwhile, neutral-point potential balance on the direct-current side is achieved, and the method has the advantages of being small in calculation amount, high in robustness, easy to achieve in engineering and the like.

The invention discloses a single-stage interconnection converter port power distribution method based on a multilayer space vector modulation strategy. The method comprises the following steps: firstly, obtaining a single-stage interconnection converter alternating current side reference voltage vector through an energy management strategy, a power control loop and a current and voltage double closed loop of a hybrid alternating current and direct current micro-grid; then, the single-stage interconnection converter is subjected to equivalent decoupling modeling to form four virtual two-level converters, a four-layer space vector diagram is obtained, scaling factors for decomposing reference voltage vectors are calculated according to given reference power, and therefore the two reference voltage vectors on the alternating current side are decomposed into four independent voltage vectors; and synthesizing the four voltage vectors in a four-layer space vector diagram to obtain the duty ratio of each phase of the four two-level converters so as to obtain the switching sequence of the single-stage interconnection converter.

The invention discloses a high-robustness deadbeat predictive current control method which is suitable for current control of a dual three-phase permanent magnet motor driving system, and belongs to the technical field of permanent magnet motor control. The method mainly comprises the following steps: reconstructing a motor mathematical model by adopting a space vector decoupling matrix and Park transformation to obtain a motor voltage equation under a dqxy axis; designing a linear expansion state observer; a reference value of an output voltage vector is obtained in combination with deadbeat predictive current control; time delay in the digital control system is calculated, and angle delay is compensated; and designing a closed-loop Smith predictor for compensating a time delay link to complete high-robustness dead-beat predictive current control of the dual three-phase permanent magnet motor. The current loop design in the method does not depend on motor parameters, and the method has high parameter change robustness. By introducing the closed-loop Smith predictor, large delay of the system can be effectively compensated, and high-precision current tracking is realized under low switching frequency.

The invention discloses a method for realizing capacitor voltage balance of a four-level energy storage converter under a low modulation ratio condition, and the method comprises the steps: converting a space vector diagram of the four-level energy storage converter under an alpha-beta coordinate system into a g-h coordinate system at a low modulation ratio, and obtaining three nearest vectors for synthesizing a reference voltage vector Vref and duty ratios of the three nearest vectors; therefore, the sector judgment process is simplified, and the calculation process of the duty ratio acted by the basic voltage vector is reduced. Then, according to the small triangle where the reference voltage vector Vref is located and the duty ratio of the action of the three vectors for synthesizing the reference voltage vector Vref, a five-segment type modulation synthesis mode and a seven-segment type modulation synthesis mode are adopted, and a switch sequence corresponding to each small sector is obtained; and finally, the switching sequence corresponding to the small sector is evaluated through the target function, and the switching sequence when the target function is the minimum value is selected as the optimal switching sequence by using comparison operation. And capacitor voltage balance of the four-level energy storage converter is realized. The technical problem that an existing additional auxiliary circuit balancing method is poor in practicability can be solved.

The invention provides a display screen dynamic control method and system, and relates to the technical field of data processing, the method comprises the following steps: constructing a space coordinate system comprising a plurality of entity nodes, the entity nodes comprising a display screen preset angular point, a user binocular vision center and a handheld interaction device; analyzing a relative pose relationship among the nodes by calculating a projection dot product of a space vector corresponding to each node; constructing a dynamic topology network based on the relative pose relationship, and synchronously collecting environment illumination intensity, user fixation point coordinates and user pose data; performing time sequence synchronization and Kalman filtering preprocessing on the acquired multi-modal data to obtain an initial feature set; and generating a spatial calibration coefficient according to the distribution characteristics of the dynamic topology network, and performing normalization compensation on the initial feature set through the calibration coefficient to obtain a calibrated multi-modal sensing feature vector. According to the invention, accurate and intelligent dynamic control is realized.

The invention provides a water quantity monitoring method and device and terminal equipment, and is suitable for the technical field of data processing, and the method comprises the steps: obtaining initial monitoring signal representation information and wireless parameter adjustment interval information of a sensor; generating an initial monitoring signal state space vector and a signal state adjustment space vector according to the initial monitoring signal characterization information and the wireless parameter adjustment interval information; selecting the signal state adjustment space vector, and generating a signal state adjustment action vector; generating target monitoring signal characterization information according to the initial monitoring signal state space vector, the signal state adjustment action vector and a signal quality characterization quantity threshold value; and according to the target monitoring signal characterization information, measuring and transmitting water quantity information, and generating water quantity monitoring information. The wireless signal quality is adaptively optimized, the adaptability of the sensor to a complex and changeable water conservancy environment is improved, the stability of a monitoring signal is guaranteed, and the timeliness and accuracy of water quantity monitoring information are ensured.

The invention relates to the technical field of network communication, in particular to an intelligent network card data forwarding method and system based on an FPGA, and the method comprises the steps: after the FPGA receives a data stream, firstly extracting a time interval standard deviation of N data packets as a time feature, executing parallel hash operation on a source IP address and a destination IP address, and mapping the source IP address and the destination IP address into three-dimensional coordinates; splicing the time features and the space coordinates into a two-dimensional matrix; a time vector T and a space vector S are separated from the two-dimensional matrix, the time feature similarity is calculated through cosine similarity, the space similarity is calculated through an Euclidean distance normalization algorithm, and affinity is obtained after weighted fusion; the condition that the affinity exceeds a first preset threshold value is used as a triggering condition for micro-flow slice splitting, and a data label is added to the slice; the FPGA intelligent network card implements heterogeneous channel binding based on slice affinity, and the global crossbar switch realizes non-blocking channel switching according to priority. And the slice splitting is triggered according to the affinity threshold, so that the high-speed transmission efficiency is improved.

In accordance with an embodiment, a circuit includes: a battery monitoring circuit configured to monitor a positive supply voltage and a negative supply voltage with respect to a neutral node; an inverter configured to provide a plurality of modulated phase voltages representing a reference voltage vector; and a space vector modulator configured to generate modulated drive signals for the inverter based on the reference voltage vector, where duty cycles of the modulated drive signals depend on the monitored positive supply voltage and the monitored negative supply voltage.

The invention relates to an SVPWM (Space Vector Pulse Width Modulation) optimization method for charging pile test equipment and related equipment, and the method comprises the following steps: carrying out the real-time collection of electrical parameters of target charging pile test equipment, and obtaining working state parameters; performing spectral analysis on the working state parameters through a preset fast Fourier transform algorithm to obtain a frequency component distribution diagram; judging whether a harmonic component in a specific frequency range exists in the frequency component distribution diagram or not; if the frequency component distribution diagram does not exist, SVPWM mapping is carried out on the frequency component distribution diagram to obtain a space vector distribution diagram, and coding and modulation optimization are carried out on sector positions and switch states in the space vector distribution diagram to obtain a PWM control instruction sequence; according to the PWM control instruction sequence, an inverter circuit of the target charging pile test equipment is controlled, and output electric energy quality in a specific frequency range is obtained; the technical problem that the quality of output electric energy is reduced due to the fact that a traditional PWM modulation method is difficult to effectively suppress harmonic components is solved.

The invention discloses a dual three-phase permanent magnet synchronous motor hybrid modulation current harmonic suppression method based on voltage compensation. The method comprises the following steps: 1, calculating a required reference voltage vector by a main control unit; step 2, generating and outputting a reference voltage vector by a reference voltage vector calculation module; 3, after coordinate transformation, decomposing the reference voltage vector into two parts: an SVPWM (Space Vector Pulse Width Modulation) signal and a harmonic compensation PWM signal; 4, performing weighted fusion on the harmonic compensation PWM signal and the SVPWM signal in a PWM generation module to generate a PWM signal; and 5, the PWM signal drives the voltage source type inverter, the output voltage of each phase is decomposed according to the direct current bus voltage, the dual three-phase permanent magnet synchronous motor is driven to realize accurate torque control and low-harmonic operation, and meanwhile, the sampling signal is continuously input into the main control unit and the harmonic compensation PWM modulation module to form closed-loop regulation. According to the invention, high-efficiency and low-harmonic motor driving can be realized, and the stability and reliability of the system are greatly improved.

The invention relates to a dual three-phase permanent magnet synchronous motor turn-to-turn short circuit fault adaptive diagnosis method, and belongs to the technical field of motor fault diagnosis. A mathematical model and an adjustable model of a dual three-phase motor turn-to-turn short circuit fault are established, space vector decoupling transformation of a multi-phase motor is combined, a short circuit fault component is introduced into a harmonic sub-plane of a stator voltage equation, and a fault characteristic value is designed by using an x-y current residual error of a real motor and the adjustable model. A fundamental wave amplitude is extracted through orthogonal Fourier transform to serve as a fault degree feature, a model reference adaptive law of an observer and a segmented PI regulator based on a mapping relation between a short-circuit current residual error and a fault turn ratio is constructed, PI parameters are dynamically adjusted according to the rotating speed and the load working condition, and adaptive feedback estimation of the short-circuit turn ratio is achieved. The fault feature extraction precision and real-time performance are improved, the fault-tolerant algorithm is simplified, the parameter design of the disturbance observer is optimized, the problem of diagnosis distortion caused by working condition changes is solved, and a motor monitoring optimization solution is provided.