42697 results about "Electric current flow" patented technology

A superconducting distributed bidirectional current driver system includes multiple bidirectional current drivers, a bidirectional current load being operatively coupled between two adjacent bidirectional current drivers. Each of the bidirectional current drivers includes first and second superconducting latch circuits. The first superconducting latch circuit in a first one of the bidirectional current drivers and the second superconducting latch circuit in a second one of the bidirectional current drivers coupled to the current load are selectively activated by first and second activation signals, respectively, to establish a first current path through the current load in a first direction. The second superconducting latch circuit in the second one of the bidirectional current drivers and the first superconducting latch circuit in the first one of the bidirectional current drivers are selectively activated to establish a second current path through the current load in a second direction opposite the first direction.

The invention provides a power grid fault waveform identification and intelligent relay protection rapid control method and system, and relates to the technical field of electric power, and the method comprises the steps: collecting the voltage and current waveform data of a power grid, carrying out the digital filtering and noise elimination processing, carrying out the Fourier transform and wavelet transform, extracting the time domain and frequency domain features, and constructing a combined feature vector. And a deep learning model including a convolutional neural network and a long-short-term memory network is used to identify and evaluate the type, position and amplitude of the power grid fault based on the feature vector database. And then, a multi-target particle swarm optimization algorithm for constructing a target weight matrix based on historical fault data is adopted to optimize fault isolation time, a power supply recovery rate and system stability to obtain a control strategy, and a relay protection device setting value is adjusted according to the control strategy to execute fault isolation and power grid topology reconstruction.

The invention discloses a computing power load stabilizing and energy-saving management system based on a flywheel energy storage system, and belongs to the technical field of energy storage and management, and the system comprises a data collection module which is used for collecting and calculating a real-time current harmonic spectrum of a cluster and server rack vibration data to generate an electric energy quality characteristic spectrum; the prediction modeling module is used for establishing a prediction model output computing power demand dynamic distribution matrix based on multi-dimensional space-time correlation; the optimization control module is used for analyzing a peak load phase in the power demand dynamic distribution matrix to generate a dynamic coordination control strategy; and the power execution module is used for executing the dynamic coordination control strategy to adjust the phase parameter of the reverse electromotive force of the flywheel so as to offset the voltage harmonic component of the server bus. Harmonic phase compensation is realized through dynamic reverse electromotive force modulation, the calculation load prediction precision can be effectively improved, and the energy utilization efficiency and the equipment operation reliability are remarkably improved.

Pulsed electromagnetic field systems include planar microcoil arrays integrated into clothing. The planar microcoil arrays are connected to a neuromodulation controller and configured to generate an electrical current and transmit that electrical current, in accordance with a particular stimulation protocol, to each of the planar microcoil arrays.

The invention discloses an energy storage battery pack equalization control method, which belongs to the field of energy storage battery packs, and comprises the following steps: S1, constructing a multi-physics field-aging coupling model; s2, based on a multi-physical field-aging coupling model, space-time-frequency domain joint state estimation and unbalanced pattern recognition are realized; s3, formulating optimal equalization topology and control parameters according to space-time-frequency domain joint state estimation and an unbalanced mode recognition result; s4, establishing an electric-thermal-fluid multi-physics field coupling control model, and optimizing equalization current regulation and control, liquid cooling flow velocity and cooling fan rotating speed based on the optimal equalization topology and control parameters; and S5, optimizing the full life cycle of the digital twin drive. By adopting the equalization control method for the energy storage battery pack, the efficient coordination of the energy storage battery pack from dynamic modeling, accurate state estimation and intelligent equalization decision to full life cycle management is realized, and the equalization efficiency and the system reliability are remarkably improved.

An apparatus for use in a magnetic induction wireless power transfer system comprises at least one booster coil positioned adjacent an active coil of a magnetic induction wireless power transfer system and a capacitor electrically connected to the booster coil. A capacitance of the capacitor is selected such that a current in the booster coil is approximately equal to a current in the active coil during wireless power transfer. The apparatus may comprise at least one shielding coil positioned adjacent an active coil of a magnetic induction wireless power transfer system, a capacitor electrically connected to the shielding coil, and a conductor positioned adjacent the shielding coil opposite the active coil. The conductor encompasses the shielding coil.

The invention relates to a closed-loop multi-guide multi-mode time domain interference electrical stimulation system and method. The method comprises the steps that a software part determines stimulation parameters; the automatic electrode position adjusting device adjusts the spatial position of the multi-conductive-electrode device, and the multi-conductive-electrode device stimulates the position of a target spot; the current output precision and impedance monitoring module monitors the precision and impedance information of electrode output current, and the core main control and functional circuit module adjusts a control signal according to the precision and impedance information; when the multi-conducting-electrode device conducts stimulation, the electroencephalogram device synchronously collects electroencephalogram signals and uploads the electroencephalogram signals to the electroencephalogram analysis algorithm module of the software part, the electroencephalogram analysis algorithm module evaluates and feeds back the effectiveness and the effect of stimulation, and the time domain interference electrical stimulation function module and the target area dynamic adjustment algorithm module optimize stimulation parameters according to the effectiveness and the effect of stimulation. Through the arrangement, a real-time closed-loop feedback control system is formed, continuous monitoring, real-time optimization and adjustment of electrical stimulation parameters and dynamic adjustment of a stimulated brain target region are realized, and the accuracy and safety of current output are ensured.

The invention relates to the technical field of motor control, in particular to a permanent magnet motor current control system based on predictive compensation, which comprises a thermal field identification module, a response offset identification module, a correction track generation module, an amplitude limiting interval decision module and a compensation instruction output module. According to the method, a dynamic prediction correction mechanism is established based on thermal field characteristics and a response error trend, real-time adjustment of a current track of the permanent magnet motor is realized, a three-dimensional space thermal load curved surface is constructed by means of a stator temperature rise curve, a permanent magnet temperature change rate and driving torque fluctuation, and transient influence of thermal working condition change on magnetic performance of the motor is reflected. A multi-dimensional prediction compensation path is integrally constructed through interaction among multiple thermal field parameters, electromagnetic response variables and time sequence feature points, the response precision and control stability of the permanent magnet motor under the dynamic working condition are improved, the magnetic performance attenuation influence caused by thermal drift is reduced, and meanwhile the tracking consistency under current track disturbance is improved.

The invention discloses an integrated energy system operation management method based on load prediction, and belongs to the technical field of energy system management, and the method specifically comprises the steps: collecting operation parameters of energy use equipment, the parameters comprising current waveform characteristics, surface temperature distribution and medium flow change; analyzing a time sequence change rule of the operation parameters, and extracting characteristic indexes related to equipment aging; establishing an energy consumption prediction correction model according to the characteristic indexes, and dynamically adjusting a theoretical energy consumption calculation value of the energy use equipment; inputting the corrected theoretical energy consumption calculation value into an energy distribution optimization model to generate a load distribution instruction of the energy network; after executing the load distribution instruction, comparing the deviation between the actual energy consumption and the correction theoretical value, and updating the parameter weight of the energy consumption prediction correction model; according to the invention, continuous and stable operation and optimal management of the integrated energy system in the equipment aging process are realized.

The present invention concerns a novel magnetic stimulation device and method with optimized energy usage and improved performance comprising: a magnetic field generating device, a first energy storage device and a second energy storage device connected in series to form an electrical oscillating resonant circuit; an energy source coupled to the first energy storage device; and a switch to allow charging of the first energy storage device from the energy source and initiating electrical oscillation of the resonant circuit, wherein after the initiation of the electrical oscillation the two energy storage devices repetitively exchange electric charge through the magnetic field generating device due to the electrical oscillation of the resonant circuit and a time varying magnetic field is generated. The time varying magnetic field induces electric currents in biological tissues that stimulate them.

The invention provides a battery health state assessment method and system based on a BMS, and the method comprises the steps: presetting different voltage sampling ranges and resolutions for different load current intervals, judging the current interval to which the load current belongs when the load current changes, and dynamically switching to the corresponding range and resolution; when the current changes violently, the voltage sampling frequency is improved, more data points are obtained, and when the current changes gently, the sampling frequency is reduced, and data redundancy is reduced; filtering and fusing the internal resistance estimated value by adopting a Kalman filtering algorithm, dynamically tracking the change trend of the internal resistance, and adaptively adjusting the parameters of a filter according to the working state and the aging degree of the battery; in combination with working condition parameters including the temperature, the current and the SOC of the battery, a multiple regression model of the internal resistance and the working condition parameters is established, the multiple regression model is trained and optimized through a machine learning method, and online self-adaptive correction of the internal resistance is achieved.

The invention discloses a multi-dimensional time sequence photovoltaic grid-connected harmonic current prediction control method, and particularly relates to the technical field of power system control. Grid-connected original multi-dimensional electrical signals are collected and decomposed into multi-dimensional frequency signals through Fourier transform, the characteristic components of an independent harmonic source are separated through the blind source separation and non-negative matrix factorization technology, and the frequency and amplitude characteristics of the characteristic components are extracted; then, a multi-dimensional time sequence prediction model is constructed through a recurrent neural network, dynamic behaviors of multiple harmonic sources are predicted at the same time, and frequency domain data are reconstructed and frequency aliasing errors are corrected in combination with high-frequency compensation and multi-scale analysis; and finally, inputting a prediction result into a photovoltaic inverter control system, dynamically generating a compensation current opposite to a target harmonic wave, realizing effective offset of the harmonic wave, accurately processing the multi-source harmonic wave problem in a complex power grid environment, and greatly improving the harmonic wave suppression capability, the electric energy quality and the system operation stability of a photovoltaic grid-connected system.

The invention discloses an intelligent monitoring and early warning system for the safety state of an electrical cabinet, and belongs to the technical field of electrical variable measurement, and the system comprises a data collection module which is used for obtaining multi-source sensing data of the electrical cabinet, and the multi-source sensing data comprises a current waveform parameter, an infrared temperature distribution parameter and a mechanical vibration spectrum parameter; the health feature evaluation module is used for generating a health state feature vector of the electrical cabinet by using the multi-source sensing data; the instruction generation module is used for outputting an early warning decision instruction according to a matching result of the health state feature vector and a preset fault propagation rule base; and the safety protection module is used for executing a safety protection action when the early warning decision instruction meets a preset execution condition. A multi-source sensing data fusion analysis technology is adopted to construct an electrothermal mechanical composite feature vector, a fault propagation rule base is coupled to realize cross-domain conduction path modeling, composite hidden dangers such as contact deterioration and the like can be accurately identified in a fault incubation period, and graded active safety protection measures are triggered.

The invention provides a power battery charging and discharging management optimization method and system based on deep learning, and the method comprises the steps: firstly obtaining real-time battery state data including a battery temperature sequence, a voltage fluctuation sequence and a current change sequence during the charging and discharging of a target power battery, and inputting the real-time battery state data into a pre-trained deep learning model; a battery temperature time-varying characteristic, a voltage fluctuation periodic mode and a current change abnormal fluctuation interval are analyzed through different branches of a deep learning model, and a dynamic optimization strategy which is matched with a current charging and discharging stage and contains a charging and discharging rate adjustment threshold value and the like is generated based on a fusion analysis result of the characteristics. And finally, charging and discharging parameters are adjusted in real time according to the dynamic optimization strategy, so that the charging and discharging parameters meet preset battery health degree constraint conditions and energy efficiency maximization is realized, thereby intelligently optimizing charging and discharging management of the power battery, improving the performance of the battery and prolonging the service life of the battery.

The invention relates to the field of welding process control, and discloses a pipeline all-position automatic TIG (Tungsten Inert Gas) welding method which comprises the following steps: collecting pipeline geometric parameters, welding position angles and material attribute data in real time through multi-source data fusion; constructing a Gaussian process regression dynamic response model, coupling a nonlinear mapping relationship among the process parameters, the molten pool morphology and the corrosion tendency index, and dynamically adjusting the weight of the model based on the welding position angle; a hierarchical strategy of Bayesian optimization and model prediction control is adopted to generate a parameter solution set meeting the fusion depth constraint and the corrosion threshold value, and the molten pool oscillation frequency is used as feedback to correct the current in real time; the heat accumulation evolution trend is predicted through a hidden Markov chain, and parameter closed-loop migration and trajectory compensation are achieved in combination with molten pool flow field coupling correction. The problems of uneven forming quality and corrosion risk caused by space displacement, dissimilar metal interface effect and dynamic disturbance in all-position welding are solved.

The invention belongs to the technical field of battery monitoring, and discloses an electric vehicle power battery health condition real-time monitoring method and system, and the method comprises the steps: obtaining multi-parameter data such as voltage, current, temperature, internal resistance and the like through a battery management system, and carrying out the synchronous collection of the data through a preset sampling frequency, and obtaining a multi-dimensional time series data set; aiming at the multi-dimensional time sequence data set, adopting a feature extraction algorithm to separate dynamic change features of voltage, current, temperature and internal resistance to obtain a multi-parameter feature set; if the deviation of the battery health state output by the dynamic mapping relation model exceeds a preset threshold value, analyzing a fluctuation mode in the multi-parameter feature set through an anomaly detection algorithm, and judging whether a potential degradation risk exists or not; and according to the comprehensive representation of the battery health state, a time sequence prediction algorithm is adopted to analyze the future change trend of the multi-parameter feature set, and a short-term prediction value of the battery health state is generated.

The invention discloses an intelligent cable fault diagnosis and positioning method and system, and relates to the technical field of intelligent operation and maintenance of a power system. The method is used for accurately identifying and positioning high-resistance faults and external damage. According to the method, electric field, current, temperature and vibration signals are synchronously collected, a multi-source fusion enhanced signal flow is constructed, and multi-physical field features are extracted; based on a coupling mechanism of an electromagnetic-thermal field and a mechanical-electric field, generating a fault type label and a space coordinate; executing targeted impedance correction for different fault types, establishing a dynamic topology network, and inputting a time-space diagram neural network to output a preliminary positioning result; and multi-source verification is carried out by further fusing salinity dielectric, a harmonic thermal field, a vibration electric field and stress topological information, a high-confidence-coefficient fault positioning result is finally output, a closed-loop diagnosis mechanism is formed, and the fault recognition accuracy and the system adaptability under complex working conditions are improved.

Systems and methods are provided herein for monitoring electrical activity in ex vivo, resting donor organs. Systems and methods provided herein also include providing electrical stimulation to the donor tissue to maintain or increase viability during storage or transport. Donor organs can include ex vivo, resting hearts. Electrical activity can be used to determine donor organ viability, especially in instances where visual evaluation of a beating heart is not possible. The same electrodes or other sensing devices used to measure electrical activity can be used to apply a voltage / current to the tissue as well in order to maintain viability of the organ. In some embodiments, stimulation or depression may be provided in response to the measured electrical activity.

A lithium battery includes: a housing in which an accommodation space is formed; a core disposed in the accommodation space; a negative cover disposed at a first end of the housing; a positive cover disposed at a second end of the housing; a positive current collecting disk disposed on a side of the positive cover close to the core; and a support member disposed between the positive cover and the positive current collecting disk and abutting against the positive current collecting disk.

The invention relates to the technical field of power batteries, in particular to a battery aging state detection system, method and device. The method comprises the following steps: obtaining battery pack structure design data of the unmanned aerial vehicle; according to the battery pack structure design data, carrying out single battery health monitoring, and generating a multi-parameter feature vector; establishing a data mapping relation of the battery monomer indexes through the multi-parameter feature vectors, thereby obtaining initial health state data of the battery pack; collecting charging and discharging sample data of the unmanned aerial vehicle under different flight conditions; a dynamic current excitation sequence is constructed based on charging and discharging sample data, and current load changes in different flight stages are simulated, so that a multi-working-condition current disturbance sequence is generated. By simulating the load change of the unmanned aerial vehicle in different flight stages and combining the dynamic response capture capability of the high-frequency current sensor, the electrochemical characteristics of the battery can be extracted in real time, and the aging state of the battery under the complex working condition can be accurately identified.

The invention discloses a smart power grid line loss intelligent detection method based on Internet of Things big data, and relates to the technical field of smart power grid electric energy metering, and the method comprises the following steps: S1, extracting a voltage signal and a current signal of each electric energy metering node in a set time window, carrying out the frequency domain transformation to obtain a frequency spectrum, calculating the energy concentration index of a high-frequency band, and calculating the energy concentration index of the high-frequency band; and identifying a high-frequency harmonic interference time section according to the change of the energy concentration degree. According to the method, a closed-loop mechanism of high-frequency harmonic interference identification, error traceability, cause discrimination and dynamic correction is constructed, fragment-level and node-level error compensation is realized, and the accuracy and the stability of line loss anomaly identification of the smart grid are improved by combining electric quantity consistency verification and trend model updating; and the reliability and the intelligent level of the system in a complex interference environment are obviously enhanced.

Aspects of transcranial magnetic stimulation (TMS) devices are described. In one example, the TMS device includes a magnetic flux shutter that includes a multi-turn coil and a magnetic film. The magnetic film extends in a loop to encircle a region, and the multi-turn coil is continuously wound around the magnetic film. The TMS device also includes a magnet that is positioned in the region within a boundary of the magnetic film, with the magnet generating a magnetic flux. The TMS device also includes a current source coupled to the multi-turn coil. The current source is configured to deliver a current to the multi-turn coil, to vary a permeability of the magnetic film and a density of the magnetic flux extending outside the region of the magnetic flux shutter during operation.

The invention provides a photovoltaic module and a photovoltaic system, and relates to the technical field of photovoltaics. The photovoltaic module comprises an insulating part, a conductive back plate and a back contact battery piece. The back contact battery piece comprises a collector grid line; the combined parts and the non-combined parts extend in the second direction and are alternately distributed in the first direction, and each of the combined parts and the non-combined parts comprises a part of the collector grid line; the conductive back plate comprises a conductive layer and an insulating layer located between the conductive layer and the back contact battery piece; a through hole is formed in a position, corresponding to at least one combination part, in the insulating layer; the through hole is filled with a bonding material; the current collection grid line of one polarity in the combination part is electrically connected with the conductive layer through a combination material, and the current collection grid line of the other polarity is provided with an insulating part and is insulated from the conductive layer through the insulating part; for one through hole, the ratio of the width of the insulator to the width of the through hole is greater than 1 and less than or equal to 50. The photovoltaic module has the advantages of small hidden crack risk, easy mass production, long service life and good current collection effect.

The invention relates to the technical field of electric power operation and maintenance, and particularly discloses a power distribution network fault line selection method considering topological structure change, and the method comprises the steps: collecting zero-sequence current data, converting the zero-sequence current data into graph structure data, and providing input information representing the topology and current characteristics of a power distribution network for a model; learning the adjacency matrix and the node features by using a graph attention network until the model can accurately predict the probability of the single-phase earth fault of the power distribution network; based on the node fault probability output by the model, a threshold value is set to identify and position a specific fault line; and inputting field zero-sequence current data into the trained model so as to quickly and accurately judge the fault position of the power distribution network. According to the power distribution network fault line selection method considering topological structure changes, the efficiency and accuracy of fault line selection can be improved, and therefore effective fault monitoring can be achieved in a wider power distribution network environment.

A device for deep transcranial magnetic stimulation regulation treatment is provided. The device includes an upper computer control software, configured to send a stimulation instruction to a system main control module according to predetermined parameter data; a system main control module, configured to send a control enable signal to a stimulation coil excitation power module when receiving the stimulation instruction; a stimulation coil excitation power module, configured to provide a current to a deep stimulation coil module according to the control enable signal; and a deep stimulation coil module, configured to generate a superimposed magnetic field and transmit the superimposed magnetic field to a target region. The deep stimulation coil module includes multiple coil groups. Each coil group includes two coils. There is an overlapping part among magnetic fields of the multiple coil groups, and the overlapping part corresponds to a predetermined target region.

The invention discloses an electrical equipment operation state monitoring method, system and terminal, full life cycle health management of equipment is realized through multi-dimensional perception and intelligent analysis, a composite sensor network can be constructed from the level of the method, and high-frequency current, ultrahigh frequency, fiber grating temperature vibration, multi-parameter gas and acoustic sensors are integrated. Electromagnetic characteristics, mechanical states, environmental parameters and voiceprint characteristics are covered; the adaptive signal processing technology performs classification and noise reduction on multi-source data, and the three-dimensional digital twin model realizes time-space fusion of a temperature field, a vibration field, an electric field and a sound field; a lightweight space-time convolutional network is deployed to fuse a time domain waveform, a spectrogram and spatial distribution characteristics for diagnosis, and a hidden semi-Markov model and a particle filter algorithm are combined to dynamically predict the residual service life of equipment. The system architecture comprises a self-organizing sensor network with edge computing capability, a time-sensitive industrial communication network and a containerized analysis engine, and supports mixed reality visual interaction.

The invention discloses a battery thermal runaway detection system based on an early warning function, and relates to the technical field of battery thermal runaway, and the system comprises a supervision center which is used for carrying out the real-time supervision of the data information of a plurality of batteries, carrying out the early warning of the time data of the battery thermal runaway, and facilitating the timely issuing of warning information and notification announcements, and a monitoring device which is used for monitoring the data information of a plurality of batteries. The monitoring device is used for collecting thermal runaway parameters of each detection node of a plurality of batteries, the thermal runaway parameters comprise battery voltage, temperature, current and pressure, and the protection device is used for carrying out delayed protection operation on the monitoring device. The battery thermal runaway detection system based on the early warning function establishes a thermal runaway early warning model by fusing multi-parameter data of heat, force, electricity and the like and historical data of a thermal runaway phenomenon and by utilizing various learning algorithms, monitors battery parameters and sets simple judgment standards and threshold values such as a non-working state current threshold value and the like, so that the battery thermal runaway early warning model is established. Early detection of battery thermal runaway is realized, and monitoring and early warning effects are achieved.

The invention discloses a distributed power supply access-oriented power distribution network fault distance measurement method and system, and relates to the technical field of power systems, and the method comprises the steps: collecting the electrical parameters and operation states of distributed power supply access nodes in a power distribution network in real time, building a dynamic manifold model based on an ecological niche theory, and carrying out the calculation of the dynamic manifold model; adaptively adjusting manifold learning neighborhood parameters according to power fluctuation data included in the electrical parameters, and updating node ecological niches to reconstruct a dynamic manifold model; based on the reconstructed dynamic manifold model, fault features are extracted from three scales of a current harmonic component, a feed line inter-harmonic propagation path and whole network voltage influence, and a three-dimensional feature vector is generated through fusion of a graph correlation algorithm; based on the expanded fault sample library and the power fluctuation data, constructing a fault transfer relation model to predict a ground fault and a short circuit risk area; a fault source is modeled by adopting a topological neural network, and a fault point distance measurement value is output through state prediction and strategy deduction.

The invention discloses an electrical switch cabinet detection method based on the Internet of Things, and particularly relates to the technical field of electrical switch cabinets. A sensor is installed inside or outside a switch cabinet, temperature, humidity, current, voltage and gas concentration are collected, data are transmitted to an edge computing node for preprocessing by using a wireless communication technology, the preprocessed data are uploaded to a cloud platform, storage and trend analysis are performed at a cloud end, and the data are analyzed based on big data analysis and an artificial intelligence algorithm. According to the invention, the system can predict potential faults, a user can check the operation state and the prediction result of the equipment in real time through the cloud platform, and carries out the emergency shutdown or equipment scheduling operation remotely according to the early warning information, thereby effectively solving a reliability problem caused by communication interruption or data delay in an existing Internet of Things system, and improving the safety and intelligent level of the monitoring of the switch cabinet.

The invention relates to the technical field of battery management systems, in particular to a distributed battery state estimation and compensation method based on voltage and current sampling, which comprises the following steps: constructing an original characteristic matrix by synchronously acquiring voltage and current waveforms and extracting ripple characteristics and slope abrupt change points, and generating a characteristic tensor with environment correction through a coupling compensator; then constructing a state parameter space based on adaptive particle injection and an electrochemical-thermodynamic coupling model; adopting a multi-source data fusion engine to realize distributed collaborative estimation of a health state index and a charge state confidence interval, and completing SOC, SOH and temperature trend prediction and residual compensation correction through a multi-state dynamic prediction model; and based on the prediction result and the uncertainty boundary, constructing an energy compensation optimization model, and searching a Pareto optimal compensation strategy. The method can be widely applied to intelligent management and control in the fields of battery energy storage systems and electric power equipment.