321 results about "Frequency coupling" patented technology

The invention discloses a self-adaptive filtering and intelligent separation method for a multi-mode partial discharge signal, and relates to the technical field of insulation state monitoring and signal processing of power equipment, and the method comprises the following steps: S1, collecting the partial discharge signal of the power equipment in real time, and synchronously obtaining an equipment operation parameter and an environment noise signal; according to the adaptive filtering and intelligent separation method for the multi-modal partial discharge signal, the problems of modal aliasing and feature distortion caused by signal and noise time-frequency coupling in a dynamic noise environment in a traditional method are effectively solved through a cooperative mechanism of dynamic coupling degree modeling and dual-channel adversarial decoupling. The dynamic coupling path sensing module quantifies interaction characteristics of noise and signals in real time, and realizes accurate suppression of high-frequency transient interference and low-frequency periodic noise in combination with a double-channel architecture of complex field phase sensitive filtering and vibration trajectory matched filtering.

The invention discloses an electrical fire monitoring method based on inherent residual current automatic compensation, and relates to the technical field of electrical safety monitoring, and the method comprises the following steps: S100, collecting a residual current signal, executing multi-component time-frequency deconstruction processing, extracting an energy distribution characteristic through wavelet packet transformation, and extracting a mutability index in combination with short-time spectrum entropy analysis, and constructing a preliminary distribution map of higher harmonic interference, and determining boundary features of interference signals in a time domain and a frequency domain. According to the method, high-frequency interference signals are accurately positioned through time-frequency deconstruction, wavelet packet analysis and spectral entropy indexes, non-fault harmonic components are effectively eliminated in combination with amplitude-frequency coupling recognition and a recursive rejection strategy, closed-loop control is constructed by introducing an adaptive compensation and stability backtracking mechanism, accurate recognition and dynamic correction of real electric leakage risks are achieved, and the method is suitable for large-scale popularization and application. The identification precision and the safety reliability of the monitoring system in a complex industrial environment are obviously improved, and the method has good engineering adaptability.

The invention provides a multi-mode charging pile communication protocol self-adaption method and system. Wherein a communication protocol association map is generated based on the spatial topology of the building layout and the temperature dense characteristics of the charging piles, and protocol priority parameters are generated in combination with the temperature aggregation area distribution and the protocol calling frequency. The transmission frequency, the coupling parameters and the frequency change trend are dynamically adjusted through the real-time temperature data to determine the fluctuation range. And establishing a protocol scheduling rule, and performing frequency band adaptation on the communication bus protocol based on the fluctuation range, so that the charging pile in the temperature aggregation area executes the matched frequency band. And a protocol switching instruction is generated, a frequency band is directionally distributed to the target charging pile, and according to temperature data updating and instruction state loop optimization priority parameters and frequency band constraints, closed-loop control is formed. According to the technical scheme provided by the invention, closed-loop optimization of a charging pile communication protocol is realized, and the transmission efficiency and stability of a temperature aggregation area are improved.

The invention provides a virtual synchronous generator adaptive control method based on line impedance identification, and relates to the technical field of power system control. The line impedance identification-based adaptive control method for the virtual synchronous generator comprises the steps of dynamically adjusting the virtual inertia J, the damping coefficient D and the droop coefficient K of the virtual synchronous generator through a multivariable constraint optimization algorithm according to the impedance-frequency coupling model, and realizing real-time matching between the output power of the virtual synchronous generator and the impedance of a power grid. By collecting power grid signals in real time and extracting impedance characteristics, the dynamic change of power grid impedance can be timely and accurately mastered, and a reliable basis is provided for subsequent control strategy adjustment, so that real-time matching of the output power of the virtual synchronous generator and the power grid impedance is realized, power fluctuation is effectively reduced, and the stability of a power grid is improved; according to the invention, a self-adaptive modulation strategy and a multi-resonant controller are adopted, harmonic oscillation can be effectively suppressed, and the quality of output electric energy of the inverter is improved.

The invention discloses a motor rotating speed measuring method, system and equipment based on electromagnetic induction, and belongs to the field of motors. According to the embodiment of the invention, firstly, a magnetostriction resonance signal and an electromagnetic induction signal of a motor rotating shaft are obtained; performing frequency domain conversion on the magnetostrictive resonance signal to obtain magnetostrictive resonance frequency domain characteristics; performing frequency domain conversion on the electromagnetic induction signal to obtain electromagnetic induction frequency domain characteristics; frequency domain energy spectrum fusion reconstruction is carried out on the magnetostriction frequency domain characteristics and the electromagnetic induction frequency domain characteristics based on the frequency band matching relation between the magnetostriction frequency domain characteristics and the electromagnetic induction frequency domain characteristics, and a dual-frequency coupling energy spectrum is generated; and finally, determining the rotating speed of the motor rotating shaft according to the frequency position and amplitude distribution of the energy peak in the dual-frequency coupling energy spectrum, thereby improving the measurement precision of the rotating speed of the motor in a complex environment.

Disclosed in the present invention is a time-frequency multi-layer brain network-based natural hand movement EEG recognition method, comprising: (1) acquiring a multi-channel EEG signal of a natural hand movement; (2) pre-processing the multi-channel EEG signal to extract a δ wave, a θ wave, an α wave, a β wave, and a γ wave at each time point; (3) constructing a time domain multi-layer brain network by using a wSAR model; (4) calculating a frequency domain multi-layer brain network by using cross-frequency coupling (CFC); (5) combining and standardizing the time domain and frequency domain multi-layer brain network; and (6) calculating a multi-layer network metric of the multi-layer brain network and a decomposed super-adjacency matrix, and inputting same to a double-layer graph convolutional network (GCN) to fuse artificial, shallow, and deep features for classification. According to the present invention, a new time-frequency fusion multi-layer brain network is proposed by using the wSAR model and the CFC. In addition, feature information of the multi-layer brain network is comprehensively extracted by using two layers of GCNs, so that the neurophysiological mechanism of brain can be revealed and the decoding precision of the natural action EEG can be improved.

The invention relates to an electroencephalogram classification method fusing phase amplitude coupling characteristics, and belongs to the field of electroencephalogram signal processing and deep learning. The method comprises the following steps: collecting electroencephalogram signal data, and carrying out channel selection and preprocessing; performing Hilbert transform on the EEG signal of each channel, and extracting instantaneous phase and amplitude information; calculating the coupling degree of phases and amplitudes between different frequency bands, and generating a phase-amplitude coupling characteristic matrix for representing the cross-frequency coupling strength; and the PAC features and the original EEG time sequence features are jointly input into an improved Transform classification network, a self-attention mechanism is utilized to effectively model a time sequence dependence and frequency coupling relationship, multi-scale time domain, frequency domain and space features are extracted, fusion calculation is performed, and a final classification result is output. According to the method, the dependence of a traditional electroencephalogram classification method on a single frequency spectrum feature is overcome, the modeling capability of an electroencephalogram activity cross-frequency cooperation mechanism is enhanced, and the accuracy and generalization performance of electroencephalogram classification are improved.

The invention discloses a digital control uninterruptible power supply parallel operation phase locking method, which relates to the technical field of digital phase locking, and comprises the following steps of: in each analysis period, acquiring an output voltage waveform signal and a current frequency disturbance control parameter of each uninterruptible power supply, and respectively executing short-time Fourier transform to obtain an output voltage waveform signal and a current frequency disturbance control parameter of each uninterruptible power supply; and converting the time domain signal into a multi-dimensional dynamic spectrum matrix, extracting frequency and amplitude parameters of a disturbance signal, and constructing a corresponding uninterruptible power supply disturbance parameter set. According to the invention, through dynamic spectrum analysis and frequency domain coupling tensor modeling, accurate extraction of UPS modulation signal features and dynamic evaluation of resonance risk indexes are realized, in combination with a risk-driven frequency intervention mechanism, frequency coupling is broken, the frequency diversity and disturbance decoupling capability of the system are improved, and the reliability of the system is improved. The stability, the anti-interference performance and the intelligent response capability of the parallel system are obviously enhanced, and early warning and active suppression of lock point resonance are realized.

The invention discloses a detection method for judging the quality of a cable-stayed pipe, relates to the technical field of cable-stayed pipe quality detection, and is used for solving the problem of poor cable-stayed pipe quality detection precision. The optical fiber sensors are arranged on the surface of the cable-stayed pipe to collect strain data, a continuous space-time strain field is constructed, local gradient changes are extracted, identification of a micro-damage area is achieved, a node response frequency spectrum is obtained in combination with a multi-frequency-band acoustic excitation means, nonlinear energy and frequency coupling characteristics are extracted, and the micro-damage area is identified. Calculating a nonlinear display probability to locate a potential nonlinear response concentration area of the structure, constructing a topological graph model of the structure, reflecting a mechanical connection relation between nodes and monitoring abnormal response, when an abnormal structure behavior is detected, carrying out topological structure state analysis on the area where the abnormal structure behavior is located, extracting key information such as structure coupling and state propagation capability, and carrying out state analysis on the abnormal structure behavior; and high and low risk signals are generated according to a judgment result, so that the accurate identification and intelligent regulation and control of the quality change of the cable-stayed pipe are realized.

The invention relates to an intelligent sleep adjusting method and system based on closed-loop acousto-optic brain wave entrainment, electroencephalogram (EEG) signals are collected and preprocessed in real time through a wireless dry electrode, and high-precision sleep staging is achieved through a deep learning model. The system adopts a fuzzy PID (Proportion Integration Differentiation) controller to dynamically adjust acousto-optic synergistic stimulation: an acoustic module generates binaural beat signals, an optical module outputs blue light (470nm) and amber light (590nm) pulses, and the brain wave entrainment efficiency is enhanced through phase synchronization (the phase difference is less than or equal to 10 degrees) and golden section frequency coupling (fL is equal to 1.618 fA). Parameters are updated every 30 seconds through closed-loop feedback, the sound pressure level (30-50 dB) and the light intensity (10-100 lux) are adjusted according to the Weber-Fechner law, and the response delay is lt; the time is 200 ms. A three-level safety mechanism monitors gamma wave abnormity, epilepsy sample discharge and impedance overrun in real time, and triggers graded protection (alarming, cutting off light stimulation and shutdown). Clinical verifications show that the entrainment success rates of the delta wave and the theta wave respectively reach 71% and 68%, the sleep improvement effect is good, and a safe and efficient intervention scheme is provided for sleep disorders.

The invention discloses a transcranial magnetic navigation and curative effect evaluation system based on electroencephalogram power spectrum analysis, particularly relates to the technical field of electroencephalogram signal processing, and is used for solving the technical problem that existing transcranial magnetic stimulation treatment lacks interference evaluation and stimulation path optimization on a cortical collaborative area. According to the method, electroencephalogram signals of a testee in a resting state and a task state are collected, phase-amplitude coupling indexes between frequency bands are extracted, a frequency-crossing coupling matrix is constructed, a nested chain structure with modulation direction consistency is identified, a stable resonance channel group is extracted from a channel intersection set to form a collaborative active region, and the collaborative active region is constructed. And after mapping to a standard brain map, carrying out space matching with a TMS target spot, evaluating an interference risk level, formulating a stimulation avoidance strategy, recording side reactions during treatment, and constructing a curative effect scoring window in a normalization manner, so as to realize synchronous optimization of stimulation planning and curative effect evaluation.

The invention belongs to the technical field of comprehensive utilization of ocean renewable energy sources, and particularly relates to a pilot frequency coupling-based wind wave energy multi-degree-of-freedom broadband control method, which comprises the following steps of: performing frequency domain identification on wind wave environment characteristics, and constructing a wind wave pilot frequency energy coupling model; based on the motion response characteristics of the multi-degree-of-freedom floating platform, designing a pilot frequency energy regulation and conversion mechanism; a full-working-condition dynamic optimization control strategy is adopted, and cooperative operation and energy flow self-adaptive distribution of the wind energy conversion unit and the wave energy conversion unit are achieved. Aiming at the significant difference and relevance of wind energy and wave energy in frequency characteristics, time scale and spatial distribution, a pilot frequency wind wave coupling dynamic model is established, and broadband capture of low-frequency wind-induced and high-frequency wave-induced coupling energy is realized under complex sea conditions through multi-degree-of-freedom motion decoupling and state observation, so that the wind-induced and high-frequency wave-induced coupling energy is obtained. The energy utilization rate, the attitude stability and the structural safety of the system are effectively improved, and good robustness and engineering adaptability are achieved.

The invention relates to a self-adaptive alternating-current series arc fault detection system based on cloud edge cooperation, and belongs to the field of arc fault detection. The system comprises an arc fault protector which carries a lightweight arc fault detection model based on depth separable convolution and a reverse residual block, collects a high-frequency coupling signal of a line in real time, and transmits the high-frequency coupling signal to a cloud server; the cloud server receives and screens misjudgment data and unknown load combination fault data uploaded by the arc fault protector in the operation process, and updates an arc fault detection model on the arc fault protector through data enhancement and transfer learning. According to the method, the problems of misjudgment and missed judgment easily occurring when the arc fault protector faces an unknown load combination can be solved.

The invention relates to the field of gear vibration signal analysis and fault diagnosis, in particular to a gearbox gear fault detection method which comprises the steps that gear vibration signals are collected, all initial extreme points are screened, and the product of the amplitude deviation degree of the initial extreme points and local accumulated energy serves as the amplitude sudden change degree; extracting the instantaneous frequency and the instantaneous phase of each initial extreme point, and fusing the frequency consistency and the phase consistency through a geometric averaging method to obtain a time-frequency coupling index; determining the effectiveness of the initial extreme point according to the product of the time-frequency coupling index and the amplitude abrupt change degree, screening the real extreme point according to the effectiveness, extracting the inherent rotation component of the vibration signal based on the real extreme point, combining to form a feature vector, and matching the feature vector with a fault feature library to realize the fault diagnosis of the gear. According to the method, the influence of noise interference and pseudo extreme points is suppressed, and the accuracy of fault detection is improved.

The invention provides a switch cabinet dual-frequency coupling partial discharge type dynamic identification method and system and a storage medium, and the method comprises the steps: obtaining a potential partial discharge point position set of a target switch cabinet, and presetting a dual-frequency monitoring array according to the potential partial discharge point position set; deploying a synchronous acquisition system; executing dual-frequency coupling cooperative acquisition through the dual-frequency monitoring array and the acquisition system to obtain a dual-frequency signal pair array; performing space-time-frequency domain multi-dimensional feature dynamic fusion on the dual-frequency signal pair array; inputting the partial discharge characteristic parameter array fused with the space-time-frequency domain multi-dimensional characteristics into a preset partial discharge characteristic decision forest for discharge type discrimination; and carrying out dynamic weight adjustment on a discharge type judgment result. According to the invention, the space-time correlated dual-frequency monitoring array and acquisition system are deployed, and a three-level dynamic trigger mechanism is constructed, so that the cooperative capture capability of transient pulses and intermittent discharge is significantly enhanced, and the problem of leak detection of a single sensor on weak discharge signals is effectively solved.

The invention relates to the technical field of power battery health state evaluation and anomaly recognition, in particular to a remote health state analysis and anomaly recognition method for an electric bicycle battery, which comprises the following steps of: under an access working condition, applying small-signal active excitation and synchronously acquiring to form an opportunity diagnosis excitation data window; time-frequency coupling modeling including equivalent impedance modeling, harmonic wave and intermodulation fingerprint modeling, Koopman operator modeling and entropy coefficient difference is carried out, and a spectral measurement geometric distance is constructed through optimal transmission to generate a multi-domain response packet, a physical invariant vector and an uncertainty matrix; according to the geometric distance and consistency score gating and hierarchical standardization, obtaining a health state estimation record and an abnormal event set by adopting a shape-limited health mapping model; and in combination with mapping Jacobian and dual potential, generating an opportunity diagnosis incentive plan of a next period by adopting D-optimal test design, and updating a group health manifold, thereby realizing cross-environment, cross-equipment and traceable remote health assessment and early anomaly recognition.

The invention discloses an LSTM (Long Short Term Memory)-based harmonic frequency coupling model optimization method for a network construction type converter, which considers the influence of a dead zone and establishes a converter model controlled by a virtual synchronous generator based on a harmonic state space method. In the power electronic modeling process, due to approximate simplification, switch neglecting and other nonlinear relations, the model is not accurate enough, meanwhile, in the harmonic state space model modeling process, system state variable parameters need to be considered, and the precision of the model affects harmonic transmission characteristics and coupling analysis, so that the modeling efficiency is greatly improved. Errors caused by power electronic modeling are reduced through an LSTM neural network algorithm, the harmonic amplitude estimation difference is reduced, state variable parameter values in a system model are accurately estimated, the model precision is further improved, and then the harmonic coupling condition under the dead zone influence is effectively and accurately analyzed.

The invention discloses a photovoltaic grid-connected converter impedance modeling method considering frequency coupling, and the method comprises the steps: S1, carrying out the equivalence of a DC side of a grid-connected converter according to the structure of the grid-connected converter, and building a main circuit equation according to a main circuit topological structure; s2, establishing a transfer function expression under frequency disturbance for the phase-locked loop according to a frequency coupling phenomenon caused by asymmetric control of the phase-locked loop; s3, according to the voltage disturbance relation, response relational expressions after voltage disturbance is applied to the alternating-current side and the direct-current side are worked out respectively; and S4, based on the mathematical expressions of the output power, the equivalent admittance and the disturbance admittance obtained in the steps S1, S2 and S3, establishing the equivalent admittance / impedance of the photovoltaic power generation system. According to the method, multi-time scale control and full-working-condition frequency domain port characteristics of the photovoltaic power generation system can be effectively represented, and the equivalent impedance model can accurately analyze the stability of the system.

The invention discloses a personalized prediction system and device for temporal lobe epilepsy based on multi-site frequency-crossing coupling, and the system comprises a signal collection module which is used for synchronously collecting electroencephalogram signals at a plurality of sites of the brain; the signal preprocessing module is used for preprocessing the collected electroencephalogram signals, drawing a cross-frequency coupling common modulation graph and segmenting frequency bands; the feature extraction module is used for extracting phase amplitude coupling features including average coupling center phase frequency, amplitude frequency and average coupling center intensity; the prediction module is used for inputting the phase amplitude coupling characteristics into a pre-trained machine learning model and outputting a prediction result of the temporal lobe epilepsy; and the early warning module is used for generating an early warning signal according to the prediction result. According to the method, a traditional fixed frequency band analysis normal form is broken through, and phase / amplitude migration characteristics are captured through energy distribution weighting; constructing a personalized adaptation mechanism to identify epilepsy atypical cross-frequency coupling features; and the interpretability of a neural oscillation desynchronization mechanism is higher.

The invention discloses an internal-cooling-based high-frequency coupling window for a high-power cyclotron. The internal-cooling-based high-frequency coupling window is characterized in that an air knife structure is arranged at the atmosphere end of the internal-cooling-based high-frequency coupling window; two groups of conductor choke units with different lengths are arranged on two sides of a ceramic chip of the high-frequency coupling window based on internal cooling, and the two groups of conductor choke units with different lengths are equivalent to two groups of inductors and are used for forming a double-filter circuit of the high-frequency coupling window; a ceramic coating film is arranged on one side of the ceramic sheet of the high-frequency coupling window; according to the air knife structure, more heat on the surface of the ceramic chip is taken away through compressed air; on one hand, the filter unit and the conductor choke unit form a filter circuit of a high-frequency coupling window atmosphere end; according to the invention, the air knife structure is ingeniously utilized, and enhanced cooling is carried out on the ceramic wafer of the coupling window structure; the inductance value of the coupling window is adjusted by using the conductor choke unit, the standing-wave ratio coefficient of the coupling window is reduced, the surface temperature of the ceramic chip is reduced, and the risk of explosion of the coupling window is reduced.

The invention is suitable for the technical field of data processing, and provides a new energy station voltage and frequency coupling cooperative control performance online evaluation method, which comprises the following steps: acquiring a three-phase voltage signal, a frequency signal, a unit output power signal and a power grid dispatching instruction signal of a new energy station grid-connected point, and calculating a voltage and frequency coupling characteristic matrix; performing collaborative quantitative analysis to generate a collaborative parameter matrix; performing control performance evaluation on the collaborative parameter matrix based on the collaborative evaluation network structure to generate collaborative evaluation parameters; constructing a dynamic coupling constraint function, and calculating to obtain a collaborative stability boundary representing the operation state of the station; and dynamically correcting the collaborative evaluation parameters to generate a station control performance optimization sequence. And the safety, the stability and the electric energy quality of the operation of the new energy station are effectively improved.

The invention discloses a direct-driven fan system impedance modeling method considering frequency coupling, and the method comprises the steps: S1, obtaining a mathematical expression of a blade tip speed ratio and output power according to a wind turbine airflow model of a direct-driven fan; s2, according to equivalent circuit topology modeling under a dq coordinate system of the permanent magnet synchronous motor, obtaining an expression of electromagnetic torque and electromagnetic power; s3, according to the control structures of the machine-side converter and the grid-side converter of the direct-drive fan, modeling is carried out respectively, and an equivalent admittance mathematical expression of the machine-side converter and six disturbance admittance expressions in the direct-drive fan are obtained; and S4, based on the mathematical expressions of the output power, the electromagnetic power, the equivalent admittance and the disturbance admittance obtained in S1, S2 and S3, establishing the equivalent admittance / impedance of the direct-drive fan system. According to the method, multi-time-scale control and full-working-condition frequency domain port characteristics of the direct-driven fan system can be effectively represented, and the equivalent impedance model can accurately analyze the stability of the system.

The invention relates to the technical field of harmonic wave evaluation, and particularly discloses a follow-up network parallel system harmonic wave anti-interference evaluation method and system considering frequency coupling, and the method comprises the steps: obtaining grid-connected current data of a GFL converter and a GFM converter under different background harmonic waves, calculating the harmonic wave coupling admittance matrix of the GFL converter and the GFM converter, and calculating the harmonic wave coupling admittance matrix of the GFL converter and the GFM converter; obtaining a harmonic coupling admittance matrix of the parallel system at the common coupling point; calculating a power grid side harmonic coupling admittance matrix based on the harmonic coupling admittance matrix of the parallel system at the common coupling point and the power grid line impedance; and based on the power grid side harmonic coupling admittance matrix, calculating an evaluation index of the anti-background harmonic interference capability of the parallel system, and realizing evaluation of the harmonic anti-interference capability of the parallel system. According to the method, harmonic mutual impedance is introduced, the influence of harmonic coupling is fully considered, and the harmonic anti-interference capability evaluation precision is effectively improved in part of scenes.

The invention discloses an improved single-bridge arm power electronic transformer topological structure, which belongs to the field of power electronic converters and comprises a three-phase alternating current filter inductor Lg, a phase main bridge arm circuit, a three-phase common half-bridge circuit, a high-frequency series inductor capacitor L1 / C1, a high-frequency isolation transformer, a low-voltage side rectifier and a common isolation DC-DC converter. The topological structure belongs to a single-stage structure, each phase only needs a string of bridge arms, alternating-current and direct-current side power transmission is realized by directly adopting hybrid frequency conversion, the number of devices of a power electronic transformer can be reduced, and the cost is reduced; a three-phase common half-bridge circuit is introduced, so that the multi-frequency coupling degree of a bridge arm is reduced, and control is simplified; and the reasonable circuit parameter design is combined, the number of the main bridge arm sub-modules is optimized, and the efficiency can be improved. The topology is suitable for all application occasions needing conversion from medium-voltage alternating current to low-voltage direct current.

The invention relates to a DC power transmission system oscillation suppression method based on a matrix passivity theory. The method comprises the following steps: firstly, establishing a converter admittance matrix under a frequency coupling effect; then, constructing a stability criterion of the direct-current power transmission system based on the admittance matrix of the converter in combination with a matrix passivity theory; and finally, based on the stability criterion of the direct-current power transmission system, adding active damping and passive damping, adjusting corresponding parameters, and suppressing medium-high frequency oscillation of the direct-current power transmission system. The method can solve the problem of dependence on power grid side impedance modeling, constructs the stability criterion of the DC power transmission system by directly analyzing the admittance matrix characteristics of the converter, adapts to the complex situation when the AC grid side operation condition changes, and can completely suppress the medium-high frequency oscillation of the DC power transmission system. An unstable interval of a direct current system is transferred to a high frequency band through active damping, strict stability of the direct current power transmission system in middle and high frequency bands is achieved through passive damping, and the strategy cost is reduced.

The invention relates to the technical field of water turbine frequency modulation control, in particular to a water turbine primary frequency modulation control method based on an intelligent optimization algorithm, and the method comprises the steps: obtaining actual operation parameters of all collection moments in the operation process of a water turbine, and analyzing the difference degree between the high-frequency information of all actual operation parameters in the operation process of the water turbine; calculating the high-frequency coupling degree at each acquisition moment according to the random change degree of the high-frequency information of each actual operation parameter; according to the method, abnormal rotating speeds are extracted, the abnormal deviation degree of each abnormal rotating speed is obtained through the local water turbine rotating speed change dispersion degree of each abnormal rotating speed, then a rotating speed dead zone optimization value at the current collection moment is obtained, whether the opening degree of a guide vane in the water turbine is adjusted or not is judged, and optimal control parameters of a PID controller are obtained through an intelligent optimization algorithm. Therefore, primary frequency modulation control of the water turbine is realized. The stability of water turbine frequency modulation control can be guaranteed.

The invention relates to the technical field of machine learning, and discloses a transformer operation state intelligent identification method based on machine learning, which is applied to a 110kV-500kV transformer and an edge-cloud collaborative architecture of an online monitoring system of the 110kV-500kV transformer. According to the method, multi-modal features are extracted based on multi-channel cleaning data, a modal enhancement TCN model fusing kernel feature mapping and a Koopman operator is introduced, and time-frequency coupling modeling and hidden space state representation are achieved. The model realizes online recursion and working condition adaptive tracking through a Sherman-Morrison incremental updating mechanism, outputs an operation state identification result and a risk score, realizes millisecond alarm linkage through an IEC 61850 GOOSE protocol, and provides high-precision and traceable technical support for intelligent monitoring and operation and maintenance of a transformer of a transformer substation.

The invention discloses an online detection method for the crystallinity of a PET (Polyethylene Terephthalate) film by utilizing dielectric properties. The online detection method comprises the following steps: acquiring original dielectric data and real-time temperature data, acquiring space-time sampling data through parallel acquisition and processing, performing noise identification and removal processing on the data, and performing contact compensation processing to obtain high-quality dielectric characteristic data; performing temperature-frequency coupling effect separation and crystal form feature recognition on the high-quality dielectric feature data; establishing a multi-dimensional feature space through polarization mechanism quantization processing and microstructure feature extraction; carrying out crystallinity prediction by fusing a physical model and a deep learning method; and finally, obtaining a final crystallinity value through consistency check and dynamic correction. According to the method, high-precision online detection of the crystallinity of the PET film is realized through a multi-level data processing and model fusion strategy, and the method has relatively high anti-interference capability and prediction accuracy.

The invention discloses a measurement and correction method for frequency coupling sequence admittance of a network-forming converter system, and the method comprises the steps: injecting three-phase voltage disturbances with different frequency values into the network-forming converter system for a plurality of times, and obtaining output voltage and current response data; according to the voltage and current response data, obtaining a frequency-coupled sequence admittance measurement result; obtaining a compensation factor of phase correction; and performing phase correction by using the compensation factor to obtain an optimized sequence admittance result. According to the technical scheme provided by the invention, the impedance measurement of the grid-connected system is realized by adopting a grid-connected point disturbance signal injection mode, and the power grid impedance is estimated through the collected impedance data; and phase correction is carried out on the measured frequency coupling sequence admittance coupling component by considering the influence of the power grid impedance, so that the measurement precision of the frequency coupling sequence admittance of the grid-connected system of the network construction type converter is improved, the accurate analysis of the stability of the grid-connected system of the network construction type converter is facilitated, and the scheduling and operation strategies of the system are optimized.

In a general aspect, a quantum state transferring process is performed in a computing network. In some implementations, a method of transferring a quantum state between nodes in a computing network includes receiving a signal at a first node transmitted on a transmission line from a second node. The first node includes a superconducting quantum processing circuit including a tunable-frequency coupler device with a superconducting circuit loop and a first resonator device having a tunable linewidth. The first resonator device is capacitively coupled to the tunable-frequency coupler coupled to the transmission line. The second node includes a second resonator device having a fixed linewidth coupled to the transmission line. The method includes modifying the tunable linewidth of the first resonator device over time while the signal transfers a quantum state to the first resonator device from the second resonator device, by varying a magnetic flux pulse applied to the superconducting circuit loop.