1154 results about "Fast Fourier transform" patented technology

The invention discloses a grid-connected inverter control method based on second-order linear active disturbance rejection, and particularly relates to the field of grid-connected inverter control. Voltage and frequency error signals between the output side of the inverter and the power grid are obtained, a pre-synchronization control model is constructed, and voltage amplitude and frequency synchronization is achieved; a second-order linear active-disturbance-rejection controller is introduced in the frequency synchronization control process, an extended state observer is adopted to estimate the output frequency, the frequency change rate and a disturbance term, and a state error feedback control law is combined to generate a frequency regulation control quantity, so that accurate frequency control is realized; after grid connection, a dual-channel LADRC power regulation controller is constructed, and decoupling tracking control is carried out on active power and reactive power; meanwhile, on the basis of fast Fourier transform processing as a stable grid-connected state criterion, the method has the advantages of being high in control precision, high in anti-interference capability, good in current waveform quality, high in adaptability and the like, and is suitable for various grid-connected application scenes.

Disclosed in the present invention is a deep learning-based power distribution network load prediction method, comprising: acquiring regional load data and renewable energy power generation data to form a data set, and preprocessing the data set to obtain a first data set; using a convolutional neural network to extract a time series feature in the first data set, and converting the time series feature into a data form of a deep learning model by means of an embedding layer to obtain one-dimensional time series data; performing fast Fourier transform on the one-dimensional time series data to obtain a frequency curve, extracting amplitude values on the frequency curve to calculate corresponding periods, and selecting the corresponding periods to slice the one-dimensional time series data and form same into two-dimensional matrixes; using a two-dimensional convolutional network to perform feature extraction, reshaping the two-dimensional matrixes that have undergone feature extraction into one-dimensional arrays, and performing adaptive fusion on the one-dimensional arrays to obtain a first time series feature; and inputting the first time series feature into a fully connected layer for weight calculation and linear transformation to obtain a power distribution network load prediction result, thereby improving the stability and reliability of electric power supply.

The invention discloses a wide-frequency-domain weak signal data acquisition method, system, equipment and medium, and relates to the technical field of power system monitoring and fault diagnosis, and the method comprises the steps: collecting an input signal, analyzing the signal characteristics in real time, obtaining the frequency composition and amplitude change information of the signal, and dynamically adjusting the sampling rate according to the signal characteristics. Performing noise reduction processing on the collected signals, eliminating noise interference and retaining effective signal components, performing time alignment processing on the data to form a data set with a unified time reference, extracting multi-category features based on the data set, performing fusion judgment, identifying whether the system is in a fault state, and when it is judged that a fault occurs, judging whether the system is in a fault state or not; if yes, fault analysis and positioning are executed, and an analysis result is generated and output. According to the method, fast Fourier transform analysis is carried out on the signals, the dominant frequency and harmonic components can be accurately recognized, the energy ratio can be calculated, and a reliable data basis is provided for signal feature extraction and fault diagnosis.

The invention discloses a mixed Mamb-Attention air quality prediction model based on multi-scale decomposition and a construction method of the mixed Mamb-Attention air quality prediction model. The method comprises the following steps: firstly, constructing a multi-scale decomposition module (MSD), decomposing an input time sequence into a trend term, a season term and a residual term through a parallel sliding window group, and realizing cross-scale feature fusion by utilizing group normalization and convolution; then designing a periodic pyramid module, extracting multi-level periodic features based on fast Fourier transform (FFT), and enhancing the perception ability of the model to different time scale periodic laws; the Mama branch is used for capturing long-range dependence, the self-attention branch is used for extracting a local dynamic mode, and the output of the Mama branch and the output of the self-attention branch are fused through residual connection and layer normalization; and finally, the prediction head module completes feature aggregation and result output. The model gives consideration to long sequence modeling capability and calculation efficiency, can accurately capture multi-scale dynamic change and non-stationary features in air quality data, improves the precision and stability of air quality prediction, and has good practical value and popularization prospect.

The invention relates to the technical field of charging pile demand prediction, in particular to a charging pile demand prediction method based on frequency domain multi-head space-time attention and fluctuation propagation, and the method comprises the steps: collecting charging pile demand space-time sequence data; frequency domain conversion is carried out on the actual charging pile demand space-time sequence data through fast Fourier transform; according to a multi-head space-time attention mechanism, performing dynamic fusion on output of multi-head attention; constructing a fluctuation propagation layer based on a layered high-order differential equation, a multi-head fusion output matrix and a random turbulence model; constructing a multi-task loss function; constructing a multi-level spatio-temporal representation learning framework; and training the multi-level space-time representation learning framework according to the to-be-tested charging pile demand space-time sequence data set and the multi-task loss function, and outputting a charging pile demand prediction model. According to the method, complex features in the space-time sequence data can be fully mined, the features are enhanced by using the frequency domain information, and the accuracy of a prediction result is remarkably improved.

The invention belongs to the technical field of wireless sensing and artificial intelligence, and discloses a high-robustness non-contact accurate electrocardiogram monitoring method based on a millimeter wave radar. Firstly, the distance and angle of a potential target are obtained through distance fast Fourier transform and digital beam forming technologies, and static background removal and thoracic cavity position detection are achieved in combination with mean filtering and a two-dimensional constant false alarm rate algorithm. And then a continuous phase is extracted by using a differential cross multiplication method, a two-step heartbeat-related phase extraction scheme is designed, body micro-motion and breathing interference are removed by adopting B-spline fitting and differential operation respectively, and a stable heartbeat-related phase signal is obtained. A heart rate-guided adaptive wavelet decomposition method is designed to obtain multiband features, and time-frequency joint features are extracted through a double-branch attention mechanism and a gating fusion part. Finally, the time-frequency joint features are input into an electrocardiosignal time domain reconstruction module based on a TransUNet architecture, high-quality reconstruction of electrocardiosignals is achieved, and the method has the advantages of being non-contact, continuous and convenient.

The invention discloses a communication-detection integrated sonar signal design method and device based on multi-pulse composite modulation, and the method comprises the steps: dividing a total frame period into a detection sub-frame and a communication sub-frame, enabling the detection sub-frame to achieve the high-resolution distance measurement through employing a bidirectional linear frequency modulation (BILFM) signal, a communication sub-frame adopts a multi-frequency multi-phase composite modulation (MTPM) signal to realize high-bit-rate data transmission; after band filtering, orthogonal demodulation, low-pass filtering and down-sampling preprocessing are carried out on detection sub-frame echoes, distance and speed information is extracted by adopting fast-time low-sampling two-dimensional fast Fourier transform (2D-FFT), unambiguous Doppler frequency is recovered by utilizing the Chinese remainder theorem (CRT), and accurate speed measurement of long-distance and high-speed targets is realized; a communication subframe receiving signal is subjected to IQ demodulation after down-conversion, Costas loop carrier synchronization and matched filtering, and multi-frequency multi-phase coded data are extracted. According to the method, the time-frequency collaborative design of detection and communication is realized, the distance resolution and the speed measurement range are considered, and the signal bandwidth utilization rate and the underwater information transmission reliability are remarkably improved while the system complexity is kept low. The device comprises a processor and a memory, and an executable program for implementing the method is stored in the memory. The technology can be widely applied to the fields of unmanned underwater vehicles, ocean monitoring, underwater sensing networks and the like.

The invention relates to an encrypted traffic classification method fusing space, time sequence and frequency spectrum features, and belongs to the field of encrypted traffic classification and deep learning. The method comprises the following steps: analyzing a network original flow Pcap packet, segmenting the packet into different sessions according to a quintuple, and preprocessing each session: extracting first n data packets, and then extracting first m bytes and t time sequence data from each packet; utilizing byte data of the session to train a spatial feature module, and extracting spatial features of the session; training a time sequence feature module by using the time sequence data of the session, and extracting time sequence features of the session; the byte data of the session are converted into frequency spectrum information through fast Fourier transform, a frequency spectrum feature module is trained, and frequency spectrum features of the session are extracted; and fusing the extracted global spatial features, time sequence features and frequency spectrum features, and training by using multi-modal joint feature representation to realize fine classification of encrypted traffic. According to the invention, the precision and robustness of traffic classification can be improved.

The invention discloses a geomagnetic diurnal variation data forecasting method, system, equipment, medium and product, and relates to the technical field of geomagnetic navigation, and the method comprises the steps: collecting and preprocessing global geomagnetic station data, carrying out the fast Fourier transform of geomagnetic field diurnal variation data, solving to obtain a trigonometric function coefficient, and carrying out the calculation of the trigonometric function coefficient; according to the method, geomagnetic daily variation data of any region in the world are obtained through geomagnetic daily variation, then an association Legendre coefficient is obtained through solving, an internal source field Gaussian coefficient and an external source field Gaussian coefficient are obtained through solving according to the Gaussian theory, finally daily variation model forward modeling can be carried out according to the internal source field Gaussian coefficient and the external source field Gaussian coefficient, and geomagnetic daily variation data of any region in the world are output. According to the method, coefficient solution and model forward modeling related to spherical harmonic analysis are utilized, the problems that in the prior art, the control range of a virtual terrestrial magnetism daily variation station is limited, and data are prone to being polluted are solved, and the forecasting precision of terrestrial magnetism daily variation data is improved.

The invention belongs to the technical field of wireless communication, and particularly relates to a communication method and device based on MQAM-OFDM modulation, a medium and equipment, and the method comprises the steps: generating a random data source, carrying out MQAM modulation, generating a serial modulation signal, carrying out serial-parallel conversion and subcarrier mapping, and obtaining frequency domain subcarrier data; performing inverse fast Fourier transform on the frequency domain subcarrier data to generate a time domain OFDM symbol, and adding a cyclic prefix to obtain a sending signal; transmitting the sending signal based on a multipath channel model; receiving a sending signal and removing a cyclic prefix part in the sending signal to obtain an effective OFDM signal; performing fast Fourier transform on the effective OFDM signal to recover frequency domain data, and obtaining a frequency domain receiving symbol sequence; performing channel estimation and equalization on the frequency domain receiving symbol sequence to obtain a frequency domain symbol estimation value; and performing MQAM demodulation on the frequency domain symbol estimation value, and recovering the parallel data stream into the serial bit stream to complete data recovery.

The invention belongs to the field of structural modal identification, and particularly relates to an automatic modal parameter identification method based on a multi-scale space, which comprises the following steps: collecting a vibration data sample from a target structure, the vibration data sample comprising an acceleration signal of the structure; selecting power spectrum calculation parameters including a window function and a fast Fourier transform (FFT) length; performing frequency spectrum estimation on the acceleration signal by using interpolation power spectrum estimation to obtain frequency spectrum data; analyzing the spectrum data by using a multi-scale space algorithm, and detecting a peak value in the spectrum data to obtain a peak value frequency; through a frequency domain decomposition method, according to the peak frequency, modal parameters of the structure are calculated, the modal parameters comprise the modal frequency and the damping ratio, interpolation power spectrum estimation and a multi-scale space algorithm are innovatively combined, automatic and accurate identification of the modal parameters is achieved, and the identification precision and efficiency are remarkably improved.

The invention discloses a rectifier bridge arm fault identification method and system circuit based on multi-characteristic current waveform analysis, and relates to the technical field of rectifier bridge arm fault identification, and the method comprises the steps: collecting a current signal of an AC input side of a rectifier bridge through a current sensor, and obtaining an original current time domain waveform after filtering conditioning; performing period division on the original current time domain waveform based on a voltage synchronization signal, and outputting a positive half-cycle conduction interval and a negative half-cycle conduction interval in each power frequency period; in each power frequency period, performing square integral operation on the current waveforms of the positive half-cycle conduction interval and the negative half-cycle conduction interval to obtain a positive half-cycle energy value and a negative half-cycle energy value, and calculating an energy unbalance degree; and windowing fast Fourier transform is carried out on the original current time domain waveform, amplitudes and phases of third, fifth and seventh characteristic harmonics are extracted to form a harmonic characteristic vector, the vector is matched with a pre-stored bridge arm fault harmonic fingerprint template, and the matching degree of each fault type is obtained.

The invention discloses a crystal oscillator circuit fault classification technology based on a multi-source information fusion network, and belongs to the technical field of analog circuit fault diagnosis. Firstly, a multi-source data set of different measurement points of the crystal oscillator circuit is acquired; carrying out conversion from a time domain to a frequency domain on the data set by utilizing fast Fourier transform; extracting data fault features by using a convolutional neural network, and performing a trust distribution function under each piece of source data by using a softmax classifier; fusing different trust distribution functions under the multi-source data by adopting a D-S evidence theory to obtain a final diagnosis result and diagnosis probability output, and calculating cross entropy loss; and finally, training model parameters through back propagation to obtain a final diagnosis model. According to the method, the time-frequency transformation algorithm, the deep neural network algorithm and the information fusion algorithm are combined, a multi-source information fusion network is constructed, the defects of an existing diagnosis model in crystal oscillator circuit fault classification are overcome, and the accuracy and stability of crystal oscillator circuit fault diagnosis are remarkably improved.

The invention discloses a foggy day real-time target detection method and system based on Fourier transform image defogging, and relates to the technical field of image target detection. The method comprises the following steps: preprocessing data, normalizing the size of an image, implementing a multi-dimensional image enhancement strategy, and designing a self-adaptive amplitude enhancement module based on fast Fourier transform to carry out defogging processing on the image; feature extraction: inputting the preprocessed image into a backbone network to generate feature maps with different scales, and enhancing the spatial modeling capability of the feature maps by a spatial modeling SimAM module by using a SimAM attention mechanism; feature fusion: adding a weight convolution pyramid module into the neck network to form a feature pyramid architecture, and performing multi-scale feature fusion operation on different-scale feature maps to obtain multi-scale semantic features; information post-processing decoding and prediction are carried out, after the multi-scale semantic features are decoupled through a detection head, two independent tensors of classification and positioning are generated, and tensor information is analyzed to obtain a detection result. The accuracy of the detection model is improved.

The invention provides a denoising method for a magnetic resonance spectrum based on Transform, and relates to the technical field of signal processing and artificial intelligence, and the method comprises the steps: firstly obtaining a noisy free induction decay signal (FID) and a corresponding label signal, constructing a training data set, converting the signal into a frequency domain signal through fast Fourier transform, and carrying out the amplitude normalization; a real part and an imaginary part of a frequency domain signal are respectively projected to a high-dimensional feature space, and are respectively sent to a multi-head self-attention mechanism-based multi-layer Transform encoder through two channels, so as to capture the global correlation of a long sequence. And the coded output is separated and decoded, a real part and an imaginary part are recovered respectively, and a denoised complex frequency domain signal is obtained through combination. In the training process, a complex mean square error is used as a loss function, and dynamic learning rate scheduling and an early stop mechanism are combined to improve convergence stability and model training efficiency.

The invention discloses an SAR (Synthetic Aperture Radar) image MTF (Modulation Transfer Function) quality evaluation method and system based on a natural scene edge, and the method comprises the steps: building a selection standard system for automatic edge screening of a natural scene SAR image, and determining a high-contrast linear edge according to the standard system; automatically detecting a high-contrast linear edge in the natural scene, and intercepting a region of interest for subsequent calculation based on the position of the linear edge; gray level distribution is extracted in the normal direction of the linear edge, and an edge spread function is constructed and used for describing gray level transition response of the imaging system in the edge area; carrying out normalization and smoothing processing on the edge spread function, and carrying out spatial domain first-order differential calculation to obtain a line spread function; and performing fast Fourier transform on the line spread function to obtain a modulation degree transfer function curve, and representing the detail transfer capability of the system under different spatial frequencies. According to the method, the imaging quality of the SAR image of the natural scene can be automatically, accurately and quantitatively evaluated under the condition that manual field arrangement is not needed.

The invention provides a high-fidelity anti-compression image watermarking method and system based on spectrum-airspace decoupling, and belongs to the field of information security. Firstly, the watermark information is mapped and remodeled; a watermark encoder based on multi-granularity spectrum-spatial domain feature decoupling is constructed, discrete cosine transform is introduced to filter out high-frequency components, Haar wavelet transform is adopted to realize lossless downsampling, a fast Fourier transform dynamic filter is combined to capture global semantic features, and local texture details are combined through multi-scale spatial domain volume accumulation; designing a physical perception and visual self-adaptive dual embedding strategy, and anchoring watermark energy to an anti-compression brightness channel; constructing an anti-attack layer containing differentiable JPEG compression simulation and mixed noise simulation, and participating in network training; and constructing a decoder and designing a loss function to carry out network optimization. According to the method, the robustness of the watermark under strong compression and complex black box attacks is improved, and extremely high visual imperceptibility is realized through physical and visual constraints.

The invention provides an inhaul cable self-vibration fundamental frequency automatic identification method and system based on a harmonic product spectrum, and relates to the technical field of structure monitoring, and the method comprises the steps: receiving vibration acceleration data of an inhaul cable, carrying out the windowing processing of the vibration acceleration data through employing a Hanning window, and generating a windowed signal; carrying out fast Fourier transform on the windowed signal, and improving the frequency resolution by implementing zero filling to obtain an initial frequency spectrum; smoothing the initial frequency spectrum to form a smooth frequency spectrum; performing passband clipping on the smooth frequency spectrum to zero the passband outer amplitude to form a clipped frequency spectrum; calculating the signal-to-noise ratio of the frequency spectrum after cutting, and comparing the signal-to-noise ratio with a preset threshold value; if the signal-to-noise ratio is lower than a preset threshold value, judging that the signal quality is unqualified and terminating the processing flow; and if the signal-to-noise ratio is equal to or higher than the preset threshold value, continuing the normal processing flow. According to the invention, automatic and high-precision identification of the self-vibration fundamental frequency of the inhaul cable can be realized.

The invention discloses an ultrafast three-dimensional wave number domain imaging method based on a row-column addressing ultrasonic array, which realizes ultrafast three-dimensional ultrasonic volume imaging by combining wave number domain image reconstruction based on row-column addressing (RCA) ultrasonic array probe data acquisition. According to the method, the three-dimensional volume imaging capability and channel number advantages of an RCA ultrasonic array probe and fewer emission times of plane wave imaging (PWI) are utilized, so that the volume acquisition rate is increased, and the data volume is reduced; according to the method, wave number domain image reconstruction is executed, rapid reconstruction of three-dimensional volume data can be realized by utilizing the calculation efficiency of fast Fourier transform, and the three-dimensional imaging requirements of high resolution and high frame rate are met. Compared with a time domain delay superposition algorithm, the method has remarkable technical advantages and is expected to be popularized and applied to medical and industrial detection scenes needing real-time three-dimensional volume imaging.

The invention provides a rotating machine domain confrontation fault diagnosis method based on multi-angle feature perception, and the method comprises the steps: obtaining the original vibration signal data of a rotating machine, and constructing a label source domain data set and a multi-angle feature extraction structure; based on a multi-angle feature extraction structure, performing multi-scale parallel convolution on the original vibration signal by using three time domain branches to obtain a time domain feature vector; performing fast Fourier transform on the original vibration signal by using the three frequency domain branches to obtain a frequency domain feature vector; and splicing the time domain feature vector and the frequency domain feature vector into a fusion feature in the channel dimension, and constructing a domain adversarial learning module based on the fusion feature to perform fault diagnosis. According to the method, the essential characteristics of the signal can be deeply sensed from multi-granularity and multi-mode angles, efficient modeling of non-stationary impact, a periodic mode and a key response position is realized, and the robustness and the cross-domain generalization ability of fault diagnosis are further improved.

The invention discloses a transformer winding deformation diagnosis software system based on frequency domain transfer function analysis, relates to the technical field of transformer winding deformation diagnosis, and is used for realizing accurate online diagnosis of winding deformation. The frequency response data quality control module performs fast Fourier transform and adaptive filtering on the original frequency response signal uploaded by the distributed detection terminal to generate a transfer function data set of which the signal-to-noise ratio reaches the standard; the transfer function multi-dimensional analysis module performs longitudinal deviation and transverse consistency analysis on the data set, identifies resonant frequency deviation, amplitude change and quality factor drift, and generates a deformation suspicious degree index; the deformation trend prediction module calculates a change trend slope based on a historical suspicious degree sequence and identifies a sensitive frequency band; and the fault mode identification module carries out spectrum refinement analysis on the sensitive frequency band, matches the sensitive frequency band with a typical fault feature library, and outputs a fault type and confidence evaluation. Multi-terminal synchronous monitoring, dynamic prediction and intelligent fault identification are realized, and technical support is provided for predictive maintenance of the transformer.

The invention discloses a domestic high-power IGBT rectification system and method, and relates to the technical field of IGBT rectification, and the method comprises the steps: constructing an active front-end rectification main circuit; a direct current side hybrid harmonic modulation auxiliary circuit is integrated; performing multiple delay signal elimination operation on the sampled signal, filtering out a harmonic interference signal, and locking the frequency and phase of the fundamental voltage of the power grid through a phase-locked loop; establishing a control structure of a voltage outer loop and a current inner loop, generating an instruction amplitude of a grid-side active current and an ideal sinusoidal current reference waveform, and controlling an IGBT in an active front-end rectification main circuit in real time; real-time fast Fourier transform analysis is carried out on the actual input current of the grid side, and residual harmonic components are identified; and feedback management of regenerated energy is realized. The invention provides a fused high-adaptability active front-end IGBT (Insulated Gate Bipolar Translator) rectification method and system which can stably operate under the condition of power grid voltage distortion and realize deep harmonic suppression and efficient energy bidirectional flow.

A system and method for determining fluid quality or change in quality by training machine learning algorithms using ultrasound Fast Fourier Transform (FFT) signatures. The system includes one or more ultrasonic transducers embedded inside or outside of a pipe, piping, or vessel that allows a fluid to flow past or between the ultrasound transducers. The transducer's acoustic energy creates ultrasound pressure waves and localized heat due to acoustic cavitation. The cavitation creates bubbles that collapse creating ultrasound sonic energy in the time domain. Unique sonic time-domain signatures, distinctively associated with the characteristics of the fluids, are converted to FFTs to produce unique, well-defined frequency response signatures. Machine learning algorithms are used to identify, measure, and classify the unique frequency response signatures associated with a wide range of fluids.

The invention relates to an active sonar echo signal phase jump compensation method and device based on a synchronous serial number, and belongs to the technical field of underwater acoustic signal processing. The method comprises the following steps: adding a synchronous serial number into a wet end signal to form an expansion matrix; transmitting the extended signal to a signal processor and windowing the extended signal; traversing a synchronous serial number in the windowing signal, and detecting the continuity of the synchronous serial number to judge whether packet loss exists or not; if packet loss exists, positioning a packet loss position and performing zero padding operation; and finally, carrying out fast Fourier transform on the zero-filled signal to obtain a spectral analysis result. According to the method, accurate identification and phase compensation of the packet loss position are achieved through the synchronous serial number, the method is suitable for underwater acoustic single-frequency signals, and when the packet loss rate does not exceed 2.5%, spectrum sidelobes can be effectively restrained, and the peak value of the spectrum sidelobes is reduced to be within 10% of the packet loss state. The method has the advantages of being clear in principle, easy and convenient to operate and high in calculation efficiency, is suitable for real-time signal processing, and remarkably improves the reliability and efficiency of target detection.

The invention provides a CEEMDAN-SSA-based power distribution network fault identification method, and the method comprises the steps: receiving a traveling wave signal of a power distribution network, and carrying out the preprocessing of the traveling wave signal; decomposing the preprocessed traveling wave signal by adopting CEEMDAN to obtain an intrinsic mode function component set, and selecting a high-frequency intrinsic mode function component from the intrinsic mode function component set; performing noise reduction on the high-frequency intrinsic mode function component by using singular spectrum analysis to obtain a pure fault feature signal; analyzing and reconstructing the frequency spectrum characteristics of the pure fault characteristic signal by using fast Fourier transform, and calculating the average frequency spectrum content of the low-frequency-band signal and the amplitude ratio of the high-frequency-band signal according to the frequency spectrum characteristics; and generating a fault identification result of the power distribution network by taking the average frequency spectrum content of the low-frequency-band signal as a main criterion and taking the amplitude proportion of the high-frequency-band signal as an auxiliary criterion according to the fault traveling wave transmission characteristics. Therefore, high-precision power distribution network fault identification can be realized in a complex working condition and a high-noise environment.

Embodiments of the present application provide a system, a device, and a method for a two-variable number theoretic transform. A matrix having the two-variable number theoretic transform may be applied to a matrix having dimensions a×b, where a=2x and b=2y for x≥y. The two-variable number theoretic transform includes two stages: decomposition by rows and row-wise fast Fourier transforms (FFTs), with twiddle factors computed based on a first root α satisfying αb=−1 mod p; and decomposition by columns and column-wise FFTs, with twiddle factors computed based on a second root β satisfying β2a=2 or −2 mod p. Since each of the stages uses a different root, the number of twiddle factors is reduced.

The invention discloses a quantum double-ring cooperative current measurement device and method, and belongs to the technical field of electric measurement. The device comprises an outer ring unit used for measuring a high-frequency signal in current; the inner ring unit is used for measuring a low-frequency signal in the current; the digital signal processing unit is used for carrying out synchronous high-speed ADC sampling on the high-frequency signal measured by the outer ring unit and the low-frequency signal measured by the inner ring unit, carrying out fast Fourier transform on the sampled signal and converting the sampled signal into a frequency domain to obtain a VTMR (f) signal and a delta fNV (f) signal, and calculating the VTMR (f) signal and the delta fNV (f) signal to obtain the VTMR (f) signal and the delta fNV (f) signal. And reconstructing the full-band current waveform of the time domain. According to the invention, the measurement accuracy and reliability of the current sensor in broadband, high harmonic and strong interference scenes can be significantly improved, and the method is suitable for the fields of new energy grid connection, flexible direct current transmission, power grid protection, high-precision measurement and the like.

The invention discloses a harmonic suppression and dynamic reactive compensation control method for an energy router, which is suitable for a micro-grid and a distributed energy access system. According to the method, three-phase current and voltage signals of an alternating current port of the energy router are collected, five-time and seven-time main harmonic components are extracted through fast Fourier transform, harmonic compensation current instructions equal in amplitude and opposite in phase are generated and injected into an inverter to be output, and active counteracting of main harmonics is achieved. Meanwhile, the bus voltage deviation and the change rate thereof are detected, a reactive current instruction is adjusted on line by adopting a fuzzy self-adaptive PI controller, rapid and smooth dynamic reactive compensation is realized, and the bus voltage is stabilized. Decoupling control of active / reactive and harmonic components is realized in combination with Park conversion, and virtual impedance is introduced to enhance system damping and harmonic suppression capabilities. Compared with the prior art, harmonic suppression and voltage support can be simultaneously realized under a single control framework, the response speed is high, the stability is high, and the method is suitable for high-performance grid-connected operation of the multi-port energy router.

The invention discloses a rotating machine fault diagnosis method based on improved VMD and an energy operator, and belongs to the field of rotating machine fault diagnosis. In order to solve the defects that in the prior art, VMD decomposition parameters depend on empirical setting, and optimal IMF screening basis is insufficient, the technical scheme provided by the invention comprises the following steps: performing global optimization on penalty factors and modal decomposition number in variational modal decomposition by using a grey wolf optimization algorithm, and determining an optimal parameter combination; decomposing the collected vibration signal to obtain an intrinsic mode component; respectively calculating kurtosis and an envelope harmonic noise ratio for the intrinsic mode component, and selecting the component with the maximum fusion index as the optimal component; performing Hilbert transform on the optimal component, and calculating an energy signal; and performing fast Fourier transform on the energy signal to obtain a spectrogram and identify a fault characteristic frequency. The method is suitable for automatic identification and state monitoring of early faults of rotary mechanical equipment in industrial production.

The invention discloses a single-port detection resonator resonant frequency on-line real-time measurement system for an MEMS gyroscope, and the system comprises a closed-loop drive circuit which is used for driving a resonator to resonate and outputting a drive detection signal; the first C / V conversion circuit converts the current into a detection voltage signal, and the on-off of the first C / V conversion circuit and the negative resistance circuit is controlled by a voltage control switch; the negative resistance circuit enables the equivalent input resistance of the first C / V conversion circuit to be a negative value so as to generate positive damping feedback force on the detection resonator; the multiplier is used for carrying out multiplication demodulation on the detection voltage signal and the driving detection signal, and the angular velocity of the MEMS gyroscope is output after the signals are filtered by the low-pass filter; the upper computer controls the on-off of the voltage control switch, the frequency fd and the frequency fs are calculated through fast Fourier transform by driving the detection signal and the detection voltage signal respectively, the frequency difference is obtained by solving the difference, and the resonant frequency of the single-port detection resonator is measured on the premise that the work of the MEMS gyroscope is not affected.