13777 results about "Voltage" patented technology

The invention discloses a distribution line fault intelligent diagnosis method based on multi-source information fusion, and belongs to the technical field of power system fault detection and diagnosis, and the method comprises the steps: obtaining distribution line data and meteorological data; superposing the distribution line data with the meteorological data to generate a space-time fusion feature matrix based on the wire space displacement; dynamically adjusting a preset initial threshold value by using the space-time fusion feature matrix; and performing multi-round verification on the abnormal fluctuation of the electrical parameters of the line according to the fault judgment threshold, generating a fault positioning instruction, performing real-time matching with the topological structure of the line, correcting the priority queue of the fault section based on the switching action feedback data of the feeder terminal, and triggering the fault removal action matched with the priority. According to the method, spatial-temporal feature fusion of current, voltage, weather and spatial displacement data and a dynamic threshold adjustment mechanism are adopted, the fault type can be accurately identified, the fault section can be positioned in real time, and the diagnosis precision and the response speed in a complex environment are remarkably improved.

The invention provides a mining circuit fault self-diagnosis method and system. According to the method, current and voltage waveforms and three-dimensional vibration signals of a cable are collected, and anti-interference data are generated through self-adaptive noise reduction and time sequence synchronization; a waveform interception window is dynamically adjusted based on the correlation between the mechanical vibration intensity and the current transient rate, the wave crest slope variable quantity is extracted from the current transient segment, high-frequency harmonic energy is separated from the voltage transient segment, time-frequency analysis is carried out on the vibration signal to extract an energy sudden increase frequency point, and a mechanical damage spectrum feature set is constructed; inputting the features into a space-time correlation model, and verifying space-time consistency of current distortion and voltage abnormity to generate composite features; dynamically correcting a fault threshold based on the environmental interference factor; and finally, a short-circuit peak or open-circuit oscillation diagnosis result is output according to the association strength of the electrical and mechanical characteristics. According to the invention, accurate fault self-diagnosis of the mining cable under a complex working condition is realized.

To provide a game machine in which the influence of noise on a signal is reduced.SOLUTION: A power supply pattern for transmitting power of a specified voltage supplied from a power supply board is provided on an outer periphery of a second layer, and specific wiring that is electrically connected to the power supply pattern in the second layer and supplies power to specified components is provided in the first layer so as to extend from the outside to the inside. It is possible to suppress the influence of noise on signals transmitted by signal lines provided on the center side in the first layer. In other words, it is possible to reduce the influence of noise on signals.SELECTED DRAWING: Figure 11

An apparatus is dedicated to controlling the voltage of a battery comprising at least two modules connected in series and each comprising at least one electrochemical cell, and each coupled to voltage balancing means. The apparatus comprises measurement means for determining first voltages across the terminals of each of the modules, calculation means for measuring a second voltage across the terminals of the battery and for determining a mean voltage per module representative of the second voltage divided by the number of modules, and processor means for comparing each measured first voltage with the mean voltage per module and for delivering to the balancing means signals representative of the result of the comparison whenever the first voltage of a module is greater than the mean voltage, such that the balancing means reduce the voltage across the terminals of the module.

The invention relates to the technical field of power battery packs, and discloses a power battery pack state evaluation method and system, and the method comprises the steps: collecting an operation characteristic data set of a power battery pack under various working conditions, and constructing a hybrid evaluation model; performing parameter configuration on the double-extended Kalman filter to obtain an improved filter; acquiring real-time voltage, current and temperature data, and inputting the real-time voltage, current and temperature data into the improved filter for state calculation to obtain a charge state value and an internal parameter value; inputting the charge state value and the internal parameter value to perform multi-branch adversarial neural network analysis to obtain a fault mode type and a fault severity; according to the method, the multi-factor health index is calculated, particle swarm optimization fitting is carried out on the degradation curve of the power battery pack, the residual service life prediction value is obtained, more accurate residual service life prediction is further achieved, the precision in a wide temperature range is remarkably improved, and the difference between monomers in the power battery pack can be effectively processed.

The invention discloses a DC-DC power supply dynamic response optimization system based on digital PID, which relates to the technical field of DC-DC power supply dynamic response optimization, and comprises a voltage and current sampling module, a reference signal generation module, an output signal generation module, a digital signal generation module, a control module and an output signal generation module, the digital PID controller calculates control output according to errors, the PWM signal generation module generates corresponding PWM signals according to the control output, the power switch driving module amplifies the signals to drive a switch tube, the load monitoring module monitors load changes in real time, output of the load monitoring module can be fed back to other modules, and power supply dynamic response optimization is achieved. According to the invention, the dynamic response performance of the DC-DC power supply is remarkably improved, the output fluctuation is reduced, the recovery time is shortened, the power supply efficiency is improved, the stability is guaranteed by adopting multiple anti-interference design, peripheral elements are reduced, miniaturization is realized, the cost is reduced, fault diagnosis is realized, and the service life is prolonged.

The invention discloses a self-adaptive nanosecond driving protection method for a high-voltage IGBT power module. The method comprises the steps that data such as high-voltage side voltage, collector current, grid voltage and junction temperature are collected and preprocessed, and standardized feature vectors are obtained; extracting statistical features, frequency domain features and trend features to obtain a comprehensive feature vector; density clustering is carried out based on historical fault data, and an extreme value model is established by using generalized Pareto distribution to generate an optimized threshold value; constructing a time sequence feature matrix, predicting a fault probability by adopting a time sequence convolutional network and a long-short term memory network, and generating a protection strategy parameter; and generating an optimal driving waveform and a control signal, and updating parameters according to an actual response effect. According to the invention, predictive protection and nanosecond-level precise control of faults are realized, and the reliability and performance of the high-voltage IGBT module are remarkably improved.

The invention relates to the technical field of fault analysis, in particular to a power grid fault intelligent diagnosis and analysis system which comprises a fault signal detection module, a propagation path analysis module, a fault positioning calculation module, a fault type analysis module and a fault influence analysis module. According to the invention, by monitoring the voltage transient data of the plurality of nodes of the power grid in real time, the instantaneous offset and the time sequence difference of the voltage can be accurately calculated, so that the voltage mutation exceeding the preset threshold value can be quickly identified in the continuous time window, the early identification of the power grid fault is quicker, the risk of false alarm is reduced, and the accuracy of the early identification is improved. By analyzing the propagation path and speed of the sudden change quantity along the power grid, the fault positioning precision is improved, rapid and accurate fault source tracing is realized, and by careful analysis of the current waveform of a fault point and combination of real-time tripping current data, the fault type can be identified more accurately, the fault handling strategy can be optimized, and the fault handling efficiency is improved. And the power grid fault processing efficiency and safety are improved.

The invention relates to the technical field of artificial intelligence and data processing, in particular to a mining explosion-proof high-voltage power distribution device-oriented all-insulation state intelligent monitoring method, which specifically comprises the following steps of: acquiring training data to construct a data set; marking the data in the data set; preprocessing the data in the data set; constructing an intelligent monitoring model for the full-insulation state of the mining explosion-proof high-voltage power distribution device based on a deep probabilistic neural network, inputting the preprocessed data set into the model for training, and repeating the iterative training process until a preset iteration stopping condition is met to obtain a trained model; and inputting the preprocessed real-time data into the trained model, generating probability distributions of normal operation, slight degradation and serious degradation and corresponding uncertainty quantized values by an output layer, and obtaining a final diagnosis result according to a preset discrimination condition. According to the invention, the accuracy and anti-interference capability of insulation state diagnosis are significantly improved.

The present invention relates to the technical field of proton exchange membrane fuel cells. Disclosed are a fuel cell parameter identification method and apparatus, and a device and a storage medium. The method comprises the following steps: constructing a semi-empirical model for a proton exchange membrane fuel cell; calculating a theoretical value of an output voltage of the semi-empirical model, and constructing an objective function by means of a mean square error between the theoretical value and an actual value of the output voltage of the semi-empirical model; on the basis of the semi-empirical model, determining a plurality of parameters to be identified, and using said plurality of parameters as decision variables to construct a plurality of constraint conditions for the objective function; on the basis of the plurality of constraint conditions, constructing an optimization model for the proton exchange membrane fuel cell by using the minimization of the objective function as an optimization objective and using said plurality of parameters as variables to be solved; and solving the optimization model by means of a multi-policy sparrow search optimization algorithm, so as to obtain a plurality of optimal parameters to be identified. In the multi-policy sparrow search optimization algorithm in the present invention, Tent chaotic mapping is introduced to initialize a population, the number of populations is increased, and then the two populations are merged. An adaptive feedback mechanism is added in a follower position update stage and a vigilant position update stage, thereby reducing the convergence accuracy under a limited number of iterations. A DE / best / 1 mutation policy and a dynamic scaling factor sf are used to update the position of a sparrow. The global optimization capability and the local optimization capability of an algorithm are improved, thereby also improving the accuracy of fuel cell parameter identification.

The invention relates to the technical field of electrical fault diagnosis, and discloses an electrical system fault diagnosis method based on multiple parameters. According to the method, multi-dimensional operation parameters of an electrical system are collected, wherein the multi-dimensional operation parameters comprise voltage waveform data, current waveform data and temperature distribution data; time-frequency conjoint analysis is carried out on the multi-dimensional operation parameters, and multi-scale electrical features are extracted and comprise steady-state feature components and transient feature components; based on a historical fault case library, performing mode matching on the multi-scale electrical characteristics to generate an initial fault type set; performing confidence evaluation on the fault types in the initial fault type set by using a dynamic weight distribution algorithm, and screening out high-confidence fault types; and according to the high-confidence fault type, a fault evolution path model is constructed, and the fault evolution path model is used for describing a time sequence of fault features.

The invention discloses a storage battery capacity checking method for a parallel intelligent direct-current power supply system, and relates to the technical field of storage battery capacity checking, which comprises the following steps of: respectively deploying electromagnetic sensing modules with nanosecond-level response capability at a master control end and a slave node end of a communication bus, acquiring voltage disturbance waveform signals of the communication bus in an operation process, and performing feature extraction operation on the transient high-frequency interference pulse detected each time, and constructing a multi-dimensional feature sequence of the high-energy transient electromagnetic interference. By deploying the high-response electromagnetic sensing module and combining an active suppression mechanism of interference feature extraction and risk index driving, time alignment modeling and self-adaptive regulation and control of electromagnetic interference and communication performance are realized, the communication stability, the battery state recognition accuracy and the capacity accounting continuity of the system in a strong interference environment are effectively improved, and the system reliability is improved. And the anti-interference capability and the operation safety of the parallel direct-current power supply system are obviously enhanced.

The invention relates to the technical field of electrical equipment monitoring, in particular to a power distribution equipment state intelligent diagnosis system and method, and the system comprises a current fluctuation detection module, a voltage stability evaluation module, a heat effect diffusion analysis module, a fault point positioning module and an equipment state evaluation module. According to the invention, through extracting current change characteristics of key nodes and judging continuity of fluctuation differences, rapid identification of current anomalies can be realized, a voltage distortion region and a distribution range thereof can be positioned in combination with phase deviation and symmetry change of voltage waveforms, and the current anomalies can be identified based on a temperature gradient path and jump point screening. A hot spot diffusion trend and a thermal disturbance boundary can be described, morphological analysis and coordinate labeling are performed on the basis of multiple fault areas, multi-point concurrent positioning is realized, association between thermal disturbance and connection defects is identified in combination with temperature difference and current path change characteristics, fault diffusion trends and state disturbance blocks can be accurately presented, and the accuracy of fault diagnosis is improved. And the power distribution state identification and intervention efficiency is improved.

The invention relates to the technical field of energy storage battery diagnosis, and discloses an energy storage battery fault diagnosis method and system based on a data fusion algorithm. The method comprises the following steps: acquiring historical operation data and real-time operation data of an energy storage battery in a preset operation period, and generating a historical fault data set according to the historical operation data; performing multi-source feature fusion processing on the historical fault data set to generate a fusion feature parameter set integrating voltage, current, temperature and impedance parameter joint change features; a multi-dimensional fault space is constructed based on the parameter set, coordinate axes of the multi-dimensional fault space correspond to different parameter dimensions, and spatial position coordinates represent parameter change characteristic values; calculating the fault correlation degree of the historical fault event in the multi-dimensional fault space, and determining a fault early warning index set; and extracting real-time characteristic parameters based on the real-time operation data to form a state vector, carrying out space mapping correlation calculation on the state vector and the fault early warning index set in a multi-dimensional fault space, and outputting a real-time fault correlation factor.

The invention is suitable for the technical field of life evaluation, and provides an enhanced high-voltage circuit breaker life evaluation method, which comprises the following steps: constructing a dynamic evolution model of a contact material wear rate; constructing a nozzle degradation dynamic prediction model; constructing residual life probability distribution of the insulating material; according to the dynamic evolution model of the wear rate of the contact material, the dynamic prediction model of nozzle degradation and the residual life probability distribution of the insulating material, constructing a comprehensive life evaluation index; inputting the comprehensive life evaluation index into a preset layered prediction architecture for evaluation; wherein the first layer generates basic life distribution through a Bayesian network, the second layer outputs posterior life distribution correction parameters through a convolutional neural network, and the third layer optimizes posterior distribution through a KL divergence minimization algorithm to obtain a probability density function and a confidence interval of residual electrical life; according to the method, the error of life evaluation can be reduced on the basis of complex working conditions.

The invention provides a power equipment intelligent inspection abnormity identification method based on multi-dimensional data fusion, and the method comprises the steps: obtaining historical operation data and real-time monitoring data, fusing the historical operation data and the real-time monitoring data through a time sequence alignment method, generating a unified data set containing voltage, current, temperature and load information, and obtaining multi-dimensional equipment operation features; aiming at the outlier feature vector, adopting an adaptive neural network to adjust a weight coefficient of the health state evaluation model, generating an optimized weight parameter, and determining an optimized model structure; and performing health state evaluation on the unified data set through the optimized model structure, generating a health index scoring result of the equipment in combination with load over-limit period counting and a temperature abnormal fluctuation range, and determining an evaluation basis for guiding local maintenance of the miniature equipment.

The invention relates to the technical field of LED modules, and discloses an LED module breakpoint detection and positioning method and device, and the method comprises the steps: carrying out the voltage and current data collection of series and parallel nodes of an LED module, and obtaining an electrical feature matrix and a node topological graph; performing iterative calculation on the electrical characteristic matrix, and processing a voltage difference value and a current variation between adjacent nodes to obtain a breakpoint characteristic vector; inputting the breakpoint feature vector and the node topological graph into a multi-task neural network for multilayer convolution operation to obtain an electrical fault distribution graph; performing feature segmentation and depth feature extraction on the electrical fault distribution map to obtain fault area feature data; and establishing an electric field distribution model for the fault area feature data, calculating an electric field singular point through a least square method, and obtaining breakpoint space coordinate data, thereby improving the accuracy of LED module breakpoint detection, and realizing accurate positioning of the LED module breakpoint.

The invention relates to the technical field of electrical fault detection, in particular to an electromechanical equipment health state diagnosis method and device based on machine learning, and the method comprises the following steps: collecting the current, voltage and temperature parameters of equipment, calculating the real-time average value and standard deviation of each parameter through numerical processing, and obtaining a parameter monitoring result; and monitoring a result based on the parameters. The current, voltage and temperature parameters of the equipment are collected in real time, numerical processing is carried out on each parameter, the real-time average value and the standard deviation are calculated, and accurate basic data support is provided for state monitoring. Based on the monitoring data, parameter abnormity is judged through threshold analysis, abnormal changes can be dynamically recognized in the equipment operation process, and the timeliness and accuracy of fault discovery are improved. In combination with past performance data, the future change trend of parameters is predicted through a regression algorithm, early warning for equipment operation can be formed, and problem expansion or out-of-control is avoided.

The invention discloses a metering error correction method and system based on electric power data acquisition, and the method comprises the steps: detecting an operation state through a self-inspection and data acquisition module, and obtaining environment data, electrical parameters and error information; key feature data are selected and standardized through a data preprocessing module; an LSTM model and an SVR model are constructed through a model establishment and fusion module, and prediction results are fused to generate error compensation output; real-time error compensation is carried out on a combined prediction result by utilizing an ARIMA model and error time sequence processing through a dynamic error compensation module; the data smoothing and evaluation module is used for smoothing the voltage data and evaluating the error compensation effect; an error compensation effect is monitored through a monitoring and optimizing module, and feature selection and model parameters are optimized regularly; through the fault diagnosis and emergency module, an abnormal condition is detected, and a corresponding standby scheme is triggered. According to the invention, the accuracy and anti-interference capability of electric power metering are effectively improved, and the method is suitable for complex and changeable metering environments.

The invention relates to the technical field of power dispatching management, in particular to a power grid operation and maintenance intelligent dispatching system based on big data, which comprises a data acquisition module, an anomaly identification module, a state grading module, a trend prediction module and a path planning module. According to the method, the dynamic data of the power grid equipment are collected in real time, state monitoring and fault diagnosis of the power equipment are optimized, the abnormal state of the equipment is detected in real time according to the voltage data, the fault time node is recognized, the accuracy and efficiency of fault diagnosis are improved, and the fault diagnosis accuracy and efficiency are improved by combining the voltage phase deviation value and the temperature gradient rising rate of the equipment. According to the method, the equipment state is judged in time, the equipment overheating risk is monitored, the reliability and safety of a power grid system are improved, efficient execution of operation and maintenance tasks is ensured by predicting the equipment aging condition, optimizing the maintenance strategy, reducing the equipment outage risk and utilizing task cluster path planning and resource scheduling, and the power grid operation and maintenance efficiency and the reasonability of resource configuration are improved.

A method for online diagnosis of power battery voltage fault based on entropy algorithm, in which voltage time-series values of individual cells in a battery pack of a to-be-diagnosed vehicle are obtained to construct a first voltage data matrix; the first voltage data matrix is intercepted by a sliding time window to form a second voltage data matrix; voltage values of the second voltage data matrix are subjected to data exclusion and reconstruction to form a third voltage data matrix; Shannon entropy values of cells are calculated; each Shannon entropy value is transformed into an abnormal fluctuation evaluation coefficient of individual cells; whether there is a abnormal cell is determined; if yes, position and occurrence time of the abnormal cell are determined, and an abnormality degree is evaluated; otherwise, the time window is moved downward, and the above steps are repeated for the next iteration.

The invention relates to the technical field of power supply, in particular to a new energy station power prediction method and system based on a weather forecast large model, and the method comprises the following steps: detecting a weather forecast difference value and output fluctuation, marking an abnormal time period, calculating a voltage and frequency deviation to generate a correction threshold value, and matching phase deviation to generate a smooth reference. And analyzing inertia and response rate, applying dynamic attenuation, and collecting tie line power to adjust, predict and generate an output result. According to the method, abnormal time periods are screened through reverse correlation of meteorological difference and output fluctuation, error conduction is blocked, irradiance prediction is corrected through a voltage frequency integral quantity closed loop, power grid response is improved, actually measured wind speed rolling mean value is matched with AGC phase offset, output smoothness is optimized, inertia time delay and frequency modulation rate dynamic matching is performed, and attenuation constraints are adjusted. The frequency risk is reduced, the tie line fluctuation and deviation superimposed effect is quantified, the attenuation range is dynamically adjusted, the power transmission robustness is enhanced, and wind and light curtailment is reduced.

The invention discloses an intelligent management system and method applied to a radio frequency energy output device, and belongs to the technical field of intelligent management of the radio frequency energy output device.An integrated sensor array is deployed on a radio frequency host and a hand tool electrode, and load voltage, output current, electrode temperature and tissue impedance are synchronously collected; a dynamic thermal impedance collaborative analysis model is constructed after time synchronization alignment, a three-dimensional thermal field simulation map is generated, and a thermal accumulation trend is predicted; a double-layer control framework is constructed, a first control layer generates a dynamic power adjustment rule base based on a preset treatment target and a safety threshold value and outputs an instruction, a second control layer receives the instruction and adjusts output parameters in real time, and a strategy is adjusted by combining a model prediction result in the execution process; meanwhile, the operation state is continuously monitored, a fault classification model is constructed for anomaly detection, a fault source is positioned, and a visual report is generated; and finally, constructing a mapping relation based on historical data, and generating an energy control scheme adapted to individualized requirements through iterative optimization.

The invention provides an electric power and magnetic power dynamic joint detection method and system based on multivariable fusion, and relates to the technical field of power electronic equipment detection. According to the method, voltage, current and magnetic field sensors are arranged in a to-be-tested electromagnetic system, original physical quantity data are collected, and a multivariable time sequence data set is constructed; through preprocessing and feature extraction, a dynamic feature matrix related to electric power and magnetic power is generated; constructing a joint estimation model based on the matrix, and comparing the joint estimation model with a reference value to obtain a detection residual error; generating a state label according to the residual size and trend, and evaluating the estimation credibility in combination with a confidence interval; and a feedback mechanism is further constructed, model parameters and feature structures are optimized by adopting an adaptive algorithm, and online iteration and self-learning are realized. The method is high in detection precision, high in state recognition capability, good in adaptability and robustness, and suitable for energy flow monitoring and intelligent diagnosis of a complex electromagnetic system.

The invention discloses an energy storage power supply residual life prediction method and system based on dynamic weight distribution, and belongs to the technical field of energy storage power supply health management. The method comprises the following steps: acquiring voltage, current and temperature time sequence signals of energy storage power supply operation through a multi-source sensor, and constructing a degradation characteristic sequence by adopting a sliding window method; a degradation inflection point division stage is detected and identified by using a curvature extreme value, a historical degradation mode is matched based on a dynamic time warping algorithm, and an optimal model group is selected; respectively carrying out dynamic weight distribution on the time step length and the feature dimension by adopting a dual-channel attention network, and fusing a learnable coefficient with space-time attention output to generate a life prediction result; residual errors and feature drift are monitored and predicted in combination with an incremental learning mechanism, and model parameters are updated and a degradation knowledge base is expanded by adopting an elastic weight consolidation algorithm. According to the method, the key information capturing capability is enhanced through a space-time attention mechanism, the model adaptability is optimized in combination with incremental learning, and the energy storage power supply life prediction precision and the working condition generalization performance are remarkably improved.

The invention discloses a cable oscillation ultralow-frequency withstand voltage and partial discharge integrated test method and system, and relates to the technical field of distribution network cable detection, and the method comprises the steps: applying an oscillation ultralow-frequency voltage signal to a tested cable; the oscillation ultra-low frequency voltage signal is optimized and adjusted through a manifold learning algorithm and is used as a common excitation source for a withstand voltage test, dielectric loss detection and partial discharge detection; acquiring a voltage signal and a current signal of the tested cable under the excitation of the oscillation ultralow-frequency voltage signal, analyzing the deviation degree of the voltage signal and the current signal from the pre-established characteristic manifold, and extracting a dielectric loss characteristic signal and a partial discharge pulse signal; calculating a dielectric loss parameter based on the dielectric loss characteristic signal, determining a partial discharge position based on the partial discharge pulse signal, and generating a cable insulation state diagnosis report according to the dielectric loss parameter and the partial discharge position. According to the invention, the cable testing efficiency is improved, the equipment burden and the testing time are reduced, comprehensive evaluation and accurate fault positioning of the insulation state are realized, and a scientific basis is provided for preventive maintenance of a power system.

The invention provides a power management chip high-low side driving signal time sequence optimization method and system. The method comprises the following steps: acquiring temperature data of a power management chip under a switch switching transient working condition through an infrared thermal imaging technology, and synchronously acquiring three-dimensional temperature field distribution of junction temperatures on the surface and inside a packaging layer; based on the space continuity of a temperature field, extracting the temperature change characteristics of a power device area, converting the temperature gradient into Seebeck voltage by using a thermoelectric material embedded in a hot spot area, and constructing a dynamic thermoelectric feedback network; according to Seebeck voltage data, a coupling relation between hot carrier concentration and driving signal phase deviation is established, and disturbance of hot carrier mobility change on MOSFET switching delay is quantified; the rising / falling edge slope of the grid voltage is adjusted based on the coupling relation, the phase deviation caused by the hot carrier effect is eliminated through dynamic compensation, and the switching performance is optimized. The dynamic response precision of the power management chip is improved.

The invention discloses a low-voltage transformer area-oriented power failure fault research and positioning method and system, and relates to the technical field of electric power, and the method comprises the steps: collecting power failure event data of a low-voltage transformer area in real time; performing multi-dimensional feature analysis on the power failure event data, and constructing a transformer area topological relation network according to space-time correlation features; generating a fault feature vector based on the space-time correlation feature and the transformer area topological relation network, performing mode matching according to the fault feature vector, and determining a fault matching result; and carrying out positioning verification on the low-voltage transformer area according to a fault matching result, and generating a fault positioning parameter. Therefore, the technical effects of quickly and efficiently realizing the power failure fault diagnosis and positioning in the low-voltage transformer area and improving the positioning accuracy and reliability are achieved.

The invention discloses an ultrasonic vibrator group intelligent prediction maintenance system and method, and relates to the technical field of industrial equipment fault diagnosis and predictive maintenance, the ultrasonic vibrator group intelligent prediction maintenance system is composed of a data acquisition module, a data transmission module, a data processing module, a model analysis module, an application module, a feedback optimization module and the like, and vibration, current, voltage, resistance, temperature, power and other data are acquired in real time through a multi-source sensor; the sampling frequency is dynamically adjusted under the abnormal working condition; multi-dimensional feature fusion and fault prediction are carried out by utilizing a pre-trained Transform model, mechanical, electrical, thermal and other fault types are automatically identified, maintenance suggestions are output, a user can obtain fault early warning, residual service life and maintenance guidance through a visual interface, and an actual maintenance result is transmitted back to a system to continuously optimize the model, and compared with a traditional mode, the method has the advantages that the operation is simple, and the efficiency is high. According to the invention, accidental shutdown can be effectively reduced, the maintenance cost is reduced, the service life of equipment is prolonged, the real-time monitoring and accurate prediction of the operation state of the equipment are realized, and the production efficiency is improved.

The invention relates to the technical field of power system automation, and discloses an automatic control method and system for a multifunctional ring main unit. The method comprises the following steps: acquiring voltage, current and temperature parameters of a ring main unit in real time, analyzing load characteristics based on a clustering algorithm in combination with historical operation data, and generating a classification result including a load type, a seasonal trend and a load distribution thermodynamic diagram; a load change trend is extracted based on time sequence regression analysis, a fault prediction model is constructed by using a long short-term memory network, and a dynamic alarm threshold value is set; a grading alarm mechanism is triggered through comparison of real-time data and a model, a fault type and a positioning result are extracted in combination with a Bayesian network algorithm, the fault type is positioned, and finally automatic control is executed according to the fault type. Through data-driven intelligent analysis, the problems that a traditional ring main unit is complex in debugging and lagged in fault response are solved, the operation reliability, the maintenance efficiency and the power grid stability are remarkably improved, and predictive maintenance and self-adaptive control are achieved.