1833 results about "Current wave" patented technology

The invention discloses an intelligent control system for a current transformer, and particularly relates to the technical field of power system monitoring, which comprises a fluctuation identification unit, a redundancy acquisition unit, an error fitting unit, a protection delay unit and a fault response unit, realizes real-time identification and classification of current fluctuation, and has standby path high-speed sampling and fusion signal construction capabilities. The fluctuation trend is judged through error regression, a protection strategy is dynamically set, response is delayed to avoid misoperation, finally, a high-level protection instruction is triggered and a fault event is output when offset overruns or alignment fails, and the data accuracy and the system response stability in the power supply switching process are guaranteed; according to the method, accurate identification of current fluctuation is realized through multi-dimensional feature extraction, data reliability is enhanced by using redundant sampling and error fitting, and by combining delay control and a hierarchical response mechanism, the risk of misoperation is effectively reduced, the fault judgment accuracy and the system protection stability are improved, and the method is suitable for a complex power supply switching scene.

The invention discloses a switching loss evaluation method and system based on a semi-soft switching modulation technology, and relates to the technical field of information. According to the method, parasitic inductance parameters and buffer capacitance parameters of a device are obtained, a resonant circuit working model is established, an initial voltage waveform and an initial current waveform are obtained by solving the model, and corresponding voltage stress and current stress are calculated; a parasitic inductance parameter and a buffer capacitance parameter of the device are optimized by using a genetic algorithm; adjusting the design of the buffer circuit according to the optimized device parameters, carrying out the simulation of the resonance process again, carrying out the frequency domain analysis of the optimized resonance voltage waveform and resonance current waveform, and optimizing the device parameters and the design of the buffer circuit. And finally, semi-soft switching modulation simulation is carried out, a temperature coefficient mapping relation between simulation switching loss and actually measured switching loss is constructed, an optimal design scheme of the device is determined, the switching loss of the device is effectively reduced, and it is ensured that the device works in a safe working area.

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 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 relates to the technical field of industrial automatic detection, in particular to an intelligent electromagnetic valve fault detection method and system based on multi-parameter analysis, and the method comprises the following steps: S1, synchronously collecting the dynamic current waveform, vibration spectrum information and environment temperature data; s2, extracting a current characteristic value and vibration energy distribution, and calculating a temperature drift amount; s3, the current characteristic value, the vibration energy distribution and the temperature drift amount are input into a multi-parameter incidence matrix, and a coupling deviation coefficient representing the coupling relation between parameters is generated; s4, calculating a comprehensive health index of the electromagnetic valve based on the coupling deviation coefficient; and S5, matching the comprehensive health index with a preset fault feature library, and outputting a corresponding fault type and a positioning result. According to the method, by fusing the multi-source characteristic parameters and introducing the health index evaluation mechanism, accurate recognition and positioning of the electromagnetic valve faults are achieved, and the detection accuracy and the response efficiency are improved.

The invention relates to the technical field of intelligent power grids, and provides an intelligent operation centralized control center intelligent scheduling interaction method and system, and the method comprises the steps: generating a heat distribution gradient through environment noise filtering, reflecting the temperature field change of equipment, obtaining a voltage amplitude envelope through fundamental wave and harmonic wave separation, and monitoring the power quality; a current fluctuation coefficient is extracted by combining segmented deviation analysis to evaluate a line load state, and frequency fluctuation parameters are captured by using time domain statistics to track system stability. By constructing a dynamic association map, heterogeneous data such as thermodynamics, electrical quantity, load characteristics and synchronous states are mapped to power grid topological nodes, and a cross-dimension association relationship is established to realize whole-network state coupling analysis. An overload risk, a voltage out-of-limit probability and a system instability threshold are quantified through a hierarchical evaluation mechanism, a multi-target optimization constraint condition is formed, and finally a scheduling strategy considering equipment safety, economy and power supply reliability is generated. When the power system operates, intelligent scheduling is carried out according to the operation state of the power grid.

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 circuit breakers, in particular to a circuit breaker fault diagnosis method and system for switching an alternating current filter in an extra-high voltage converter station, and the method comprises the following steps: S1, synchronously collecting multiple parameters: synchronously collecting vibration acceleration signals, opening and closing coil current waveforms, arc light intensity and environment temperature and humidity parameters; s2, time sequence feature extraction: performing time-frequency domain decomposition on the vibration acceleration signal to obtain a mechanical action time sequence spectrum; s3, operation mode classification: determining the operation mode of the circuit breaker; s4, three-dimensional fault matrix construction: generating a three-dimensional fault matrix including spatial distribution characteristics; s5, dynamic threshold analysis: generating a diagnosis threshold interval dynamically adjusted along with the environmental parameters; and S6, comprehensive diagnosis output: outputting a mechanical jam grade, a contact ablation degree and an insulation deterioration early warning signal. According to the invention, the mechanical, electromagnetic and insulation characteristics of the circuit breaker can be reflected more comprehensively, and reliable guarantee is provided for safe and stable operation of a high-voltage power grid.

The invention discloses a power transmission channel risk early warning evaluation method and system, and relates to the technical field of power transmission channel abnormal condition early warning, and the method comprises the steps: analyzing the current waveform distortion degree caused by a nonlinear load in a power transmission line through monitoring the voltage harmonic distortion rate, and evaluating the influence of harmonic waves on the insulation aging and heating loss of the line and equipment; the real-time gradient of the temperature of the wire is analyzed, abnormal heating conditions of different sections of the wire are judged, and the abnormal rising degree of the temperature of the wire and potential thermal damage accumulation caused by overload and poor contact factors are evaluated; by monitoring and analyzing multi-dimensional indexes such as the voltage harmonic distortion rate, the lead temperature real-time gradient, the sag dynamic deviation rate and the corona discharge intensity, the defect of a traditional monitoring means in the aspect of abnormal heating risk assessment is overcome.

The invention relates to the technical field of electric power monitoring analysis, and discloses an intelligent analysis system for electric power monitoring data based on a mutual inductor. The system comprises a mutual inductor data acquisition and structuring module which acquires a current waveform, a voltage waveform and a harmonic component in real time, and generates a standardized monitoring data unit through preprocessing, field mapping and association identification establishment; the theoretical monitoring value calculation module constructs a dynamic calculation model according to historical power data, and an input standard data unit outputs a theoretical value; the rule conformity verification module is used for matching the equipment type with the operation rule and generating single compliance judgment; a multi-dimensional difference analysis module compares a theoretical value with a measured value from time domain deviation, frequency domain deviation and waveform distortion, and generates an equipment-level difference coefficient matrix; the association network construction module is used for constructing a multi-monitoring-point association map according to the equipment identifier and the position information; and the anomaly positioning and strategy generation module combines the map, compliance judgment and a difference matrix, calculates a risk score, generates an anomaly probability distribution map, positions anomaly and matches a monitoring strategy.

The invention relates to the technical field of earth leakage protection monitoring, and discloses an earth leakage circuit breaker remote monitoring system integrated with the Internet of Things. An electric leakage characteristic modeling module of the system obtains real-time monitoring data, extracts current waveform characteristic parameters, identifies an abnormal waveform mode and a first occurrence time sequence, and generates an original electric leakage characteristic template; the network path generation module performs topological sorting on the monitoring nodes based on the template, establishes an equipment connection path and constructs an Internet of Things topological path; a path anomaly analysis module extracts a node sequence and screens abnormal transmission paths to obtain an abnormal path set; the risk level judgment module collects terminal node risk tags and obtains a risk level tag group through matching; and the topological structure output module counts associated nodes, divides abnormal paths, establishes a mapping relation and generates a remote monitoring topological table. The system improves the accuracy and efficiency of electric leakage monitoring, and is suitable for a complex power network environment.

The invention discloses an electric energy meter fault detection method and device, terminal equipment and a computer readable storage medium, and the method comprises the steps: obtaining the voltage data, current data and temperature and humidity data of an electric energy meter in a current sampling time period, obtaining the historical feedback layer gain of the electric energy meter in a previous sampling time period, and determining a final gain parameter; multiplying the final gain parameter by the current bandwidth and the current cut-off frequency of the anti-interference filter to obtain the updated bandwidth and cut-off frequency of the anti-interference filter; when the anti-interference filter works based on the updated bandwidth and cut-off frequency, acquiring a voltage fluctuation value and a current fluctuation value filtered by the anti-interference filter; and comparing the voltage fluctuation value with a voltage fluctuation threshold value, comparing the current fluctuation value with a current fluctuation threshold value, if the voltage fluctuation value is greater than the voltage fluctuation threshold value and the current fluctuation value is greater than the current fluctuation threshold value, judging that the electric energy meter has a high-interference fault, otherwise, judging that the electric energy meter does not have the high-interference fault.

The invention relates to the technical field of distribution line fault detection, in particular to a tree line discharge fault identification method based on fault traveling wave current characteristics, which comprises the following steps of: based on a fault traveling wave current signal, performing high-frequency sampling on a current waveform on a distribution line, dividing the signal into a plurality of time slices, the current fluctuation characteristic of each time period is analyzed by calculating the time and frequency component change trend of each fragment, time-frequency changes are matched step by step, and a time-frequency fragment set is generated. The current waveform is sampled at high frequency, and the time-frequency change of the current waveform is analyzed in a segmented manner, so that the current fluctuation characteristics are careful to capture, and the influence of external interference on signals is avoided. By gradually reconstructing the waveform, the smoothness of waveform transition and the integrity of the signal are ensured, and signal distortion caused by waveform discontinuity is avoided. The feature points are screened and corrected to ensure that the points can accurately reflect the actual fault condition, and the possibility of false alarm or missing alarm is reduced.

The invention discloses a method and a device for calculating an electromagnetic-heat flow field under a turn-to-turn short circuit fault of a converter transformer. The method comprises the following steps: establishing a converter transformer multi-physical field numerical calculation theoretical model based on circuit-electromagnetic-heat flow coupling; based on the multi-physical field numerical calculation theoretical model, establishing a converter transformer electromagnetic-heat flow field model under the turn-to-turn short circuit fault; setting turn-to-turn short circuit faults at different positions in the electromagnetic-heat flow field model of the converter transformer; and performing winding current waveform, voltage waveform and electromagnetic field analysis and heat flow field analysis of the converter transformer under the set turn-to-turn short circuit fault.

The invention relates to the technical field of the Internet of Things, in particular to a short circuit fault monitoring system and method based on the Internet of Things. According to the method, fault mode identification is carried out based on current characteristic waveforms, prediction is carried out based on historical data, current waveform data are captured in real time by using a high-speed sampling current sensor, and abnormal changes in the current waveforms are identified and marked; an RNN deep learning model is constructed, features are extracted from waveforms, the features are compared with known fault modes, potential short circuit faults are recognized, the constructed deep learning model conducts reasoning based on real-time data, abnormal waveform features are quickly recognized, and therefore quick response is achieved, the fault detection time is greatly shortened, and the fault detection efficiency is improved. According to the method, short-circuit faults in a high-speed circuit are effectively handled, power flow data of multiple nodes are processed in real time by adopting a distributed data aggregation and analysis technology, a Bayesian network model is constructed, potential short-circuit faults are identified when power flow is abnormal, and specific nodes and paths where the faults occur are positioned.

The invention relates to a signal emission control method and device based on optical communication and a signal emission laser. The method comprises the following steps: acquiring output light intensity data of the vertical cavity surface emitting laser in a preset temperature range and a bias current range, and determining a bistable working characteristic curve; acquiring a vibration resonance gain signal; gaussian distribution transformation and target phase sampling are performed on the vibration resonance gain signal, and a signal nonlinear compensation model is established; applying a driving current pulse synchronous with communication data, symmetrically sampling the output signal subjected to transient response control at a rising edge and a falling edge, and constructing a signal quality evaluation vector; and inputting a support vector regression model, establishing a mapping relation between the light intensity characteristic parameter and the driving control parameter, and generating a real-time control signal of the laser. According to the invention, the self-adaptive accurate control of the vertical cavity surface emitting laser is realized, and the stable operation of the signal emitting laser under the temperature and bias current fluctuation condition is ensured.

The invention discloses a modular reclosing circuit breaker protection system based on intelligent electrical cloud coordination, particularly relates to the technical field of intelligent power distribution network relay protection, and is used for solving the problems that fault diagnosis is delayed and a protection strategy cannot be generated in a fault clearing time window due to resource competition in a cloud coordination protection system. A dynamic preemption priority is generated based on power grid topology vulnerability and associated circuit breaker collaborative requirements by monitoring the load state of cloud computing node resources in real time; credibility attenuation control is carried out in combination with a fault current waveform distortion rate; when the resource preemption necessity exceeds a threshold value, freezing a periodic task thread and releasing computing resources, and opening up a high-priority communication channel at the same time; sending a fault recording compression instruction to a target circuit breaker terminal to obtain an edge preprocessing feature vector; and protection strategy optimization is executed in the released resources to generate reclosing parameters, and the reclosing parameters are issued and executed through a high-priority channel, so that quick response of a fault diagnosis task is realized.

The invention discloses a switch loop data extraction method, device and equipment based on phase locking, and the method comprises the steps: carrying out the interval change analysis of operation data in a ring main unit, and recognizing a communication abnormal node through a phase locking technology; associating a mechanical vibration source based on the communication abnormal nodes, detecting a vibration conduction path, extracting current fluctuation characteristics, and determining position deviation; the operation current intensity is adjusted in combination with fluctuation characteristic parameters, and accurate off-position control is achieved through reverse braking force; constructing a dynamic threshold reference based on frequency domain analysis, and generating a complete operation record according to the dynamic threshold reference and the accurate parking data; performing typed grouping and transmission optimization on the data, and controlling data switching by adopting a gradually migrated beat sequence; and finally, through signal reconstruction and data fusion technologies, a switch loop data set with continuous time sequence and complete space is generated. The method can effectively cope with communication interference, mechanical vibration and other complex environmental factors, and improves the integrity and accuracy of data acquisition.

The invention discloses an intelligent electrical equipment maintenance task decision support system and method, particularly relates to the field of motor maintenance, is used for solving the problem of running-in mismatching of new and old parts, and comprises the following steps: collecting multi-dimensional performance parameters of the new and old parts, constructing a performance feature database, and detailedly describing key parameter differences; a personalized running-in curve is generated by using a support vector machine regression algorithm, the load change rate, the rotating speed adjustment step length and the running-in time are accurately set, and vibration and current fluctuation caused by initial mismatching of new and old parts are avoided; by monitoring the temperature, the vibration spectrum and the current fluctuation of the equipment in real time, the load and the rotating speed are automatically adjusted, and the running-in process is ensured to be stable; the frequency spectrum sharpness index and the time sequence entropy difference index are combined, running-in state classification analysis is carried out, and potential anomalies are recognized in time; in an ambiguous state, running-in is optimized by adjusting PID parameters, it is ensured that new and old parts are gradually matched, the operation stability of equipment is improved, and faults and damage caused by poor running-in for a long time are avoided.

The invention discloses a pulsed eddy current detection method and system for a thermal power pipeline with a coating layer, and relates to the technical field of pulsed eddy current nondestructive detection.The pulsed eddy current detection method comprises the steps that pulsed eddy current detection simulation and experiment are conducted under the condition of a three-dimensional simulation model and a test piece, and excitation current waveform, amplitude and frequency parameters are determined; an induced voltage signal is collected, peak values, time points and attenuation characteristics are extracted, the corresponding relation between the wall thickness, the coating layer thickness and the welding seam structure and detection signals is constructed, a parameterized database is constructed, and temperature correction is completed; and optimizing a sensor structure and a signal acquisition scheme according to a parameterized database, obtaining detection data in a pipeline operation state, completing two-dimensional imaging of wall thickness distribution and welding seam positioning, and forming detection information covering a straight pipe, an elbow and a welding seam area. According to the method, wall thickness detection and welding seam positioning of the thermal power pipeline under the conditions of a thick wall and a thick coating layer are achieved through the full-process design of test piece modeling, simulation experiment and imaging collection.

The invention relates to the technical field of audio processing, in particular to an intelligent power amplifier calibration method, system and device. The method comprises the following steps: monitoring corresponding voltage and current waveforms of a power amplifier in real time and carrying out load impedance response analysis to obtain power amplifier load impedance; acquiring a power amplifier output audio signal, and performing spectrum analysis and audio distortion detection to obtain a power amplifier output audio distortion degree; performing gain self-adaptive adjustment on the power amplifier based on the power amplifier output audio distortion degree to obtain power amplifier output self-adaptive adjustment gain; performing frequency response optimization corresponding to different frequency bands on the power amplifier output audio frequency spectrum to generate power amplifier response optimization output frequencies corresponding to different frequency bands; and performing intelligent feedback calibration on the power amplifier based on the power amplifier output adaptive adjustment gain and the power amplifier response optimization output frequency corresponding to different frequency bands to generate an intelligent calibration power amplifier. The overall performance and the use experience of the power amplifier can be improved.

The invention belongs to the technical field of electrical safety, and particularly relates to a human body electric shock protection method and system based on multi-parameter fusion, multi-dimensional characteristic parameters are formed by extracting a high-frequency harmonic component, a voltage abrupt change slope and a magnetic field intensity fluctuation value of a residual current waveform, and the weight is dynamically adjusted according to the instantaneous change rate of each characteristic, so that the human body electric shock protection is realized. Generating a weighted feature vector; and a comprehensive criterion value is calculated in combination with a preset rule base and is compared with a dynamic threshold value, so that whether the circuit breaker is triggered to act or not is determined. According to the method, through fusion analysis of a plurality of physical characteristics, the identification capability of the system in a complex electromagnetic environment is improved, the probability of misoperation caused by external interference is effectively reduced, the action accuracy and stability of the circuit breaker are improved, and the power utilization safety and the power supply continuity are guaranteed.

The invention discloses an intelligent lighting street lamp system and a control method thereof, and the method comprises the steps: obtaining an LED drive current and a dual-energy output parameter, and generating a PWM drive characteristic through frequency decomposition; performing pre-dimming compensation on the LED based on the human eye brightness sensitivity to obtain a luminous flux control feature and an energy prediction feature; obtaining LED junction temperature change data and carrying out thermal characteristic analysis to obtain LED driving compensation characteristics; dividing an illumination control time window and dynamically adjusting an LED driving current waveform to obtain a time sequence driving characteristic; and fluctuation analysis is carried out according to the real-time luminous flux data, and linkage adjustment is carried out on PWM driving characteristics, luminous flux control characteristics, LED driving compensation characteristics and time sequence driving characteristics. According to the invention, the rapid and accurate adjustment of the LED driving current can be realized, the influence of the dual-energy output fluctuation on the illumination quality is effectively inhibited, and the accurate matching problem of the energy supply and the illumination demand in a key time window is solved.

The invention relates to the technical field of power supply system management, and discloses a centralized management method and management system for a power supply system. The method comprises the following steps: monitoring a current fluctuation sequence and voltage offset data of each node of a power supply network in a continuous operation period, and collecting an equipment state log text and load change time sequence information; identifying a time point of abnormal sudden change of current fluctuation, and extracting a characteristic parameter set of a power supply quality reduction event in combination with a voltage offset data change amplitude; matching a historical abnormal event library, positioning key operation record fragments strongly related to load change in the log, and generating a mapping relation table of equipment operation behaviors and power supply quality fluctuation through semantic analysis and time sequence alignment; and correcting the parameters of the load prediction model, outputting and executing a power supply priority dynamic adjustment strategy, and triggering automatic calibration of equipment operation parameters. According to the method, comprehensive control of the power supply system is realized, abnormity can be identified and processed in time, association between operation and quality is clear, and stability and flexibility of operation of the power supply system are improved.

The invention discloses a synchronous monitoring and early warning method for substation ground current micro-power consumption, and relates to the technical field of substation monitoring, and the method comprises the steps: obtaining a ground current data set; performing dynamic time warping on the ground current data set to obtain a time sequence synchronization data set; performing time sequence analysis based on the time sequence synchronization data set, and determining a plurality of ground current characteristics; traversing the time sequence synchronization data set to carry out current fluctuation learning, and generating a ground current prediction data set; and dynamically monitoring the grounding network of the transformer substation, and generating a plurality of feedback response signals to intelligently trigger the alarm of the transformer substation. The technical problems that in the prior art, ground current data of multiple ground points cannot be collected in real time, real-time abnormal fluctuation cannot be accurately captured, potential risks of ground current cannot be effectively recognized in advance, and the early warning instantaneity and accuracy are insufficient are solved, low-power-consumption continuous monitoring of the ground current is achieved, and the early warning efficiency is improved. The technical effect of improving the real-time performance and accuracy of monitoring and early warning is achieved.

The invention relates to the technical field of data processing, in particular to a motor performance monitoring data processing system which comprises a signal registration module, a rhythm judgment module, a trend lag module, an effect recognition module and a label scheduling module. According to the method, the waveband gravity center is established by identifying extreme points, trend comparison is performed, a gravity center alignment structure between different channels is constructed, the time period rhythm stability is judged based on the sampling interval change trend, abnormal paragraphs are marked, and the response lag time period is positioned in combination with the rotation speed slope change and the current fluctuation behavior. And detecting the sequence and direction relationship of a plurality of parameter fluctuation frames, identifying the dense segments which alternately act to construct a label labeling index, and adjusting the state output sequence according to the occurrence frequency of different labels in a continuous time period, thereby realizing the identification and dynamic sorting scheduling of the response structure relationship among different time sequence behaviors. And the time sequence consistency identification capability and the parameter linkage structure expression capability of the monitoring data in the continuous abnormal state are improved.

The invention discloses a distributed photovoltaic access power distribution network relay protection analysis method and system, and belongs to the technical field of photovoltaic grid connection, and the method specifically comprises the steps: injecting a dynamic resonance detection signal at the AC side of a current converter of a photovoltaic grid-connected system, and enabling the frequency to be matched with the natural resonance frequency of a target section; analyzing the energy focusing effect of the dynamic resonance detection signal in the power distribution network, and generating a resonance energy distribution map; when the energy density of a certain section in the resonance energy distribution map exceeds a preset threshold value, the photovoltaic output current waveform is adjusted to be a discontinuous waveform containing periodic intermittent cutoff; based on the reflection delay difference of the waveform in the fault section, constructing a space-time mapping model of a fault point, and analyzing and determining a fault position; according to an output result of the space-time mapping model, controlling a photovoltaic side circuit breaker and a power grid side relay to execute a selective tripping action based on waveform phase synchronization; according to the invention, the sensitivity and reliability of distributed photovoltaic access power distribution network relay protection are improved.

The invention discloses a high aspect ratio micropore pulse abrupt change current copper filling method and system, and belongs to the technical field of printed circuit board manufacturing, and the method comprises the steps: obtaining micropore structure parameters, and generating a gradient pulse abrupt change current waveform; monitoring a deposition thickness parameter of the orifice of the micropore in real time, and when the deposition thickness parameter exceeds a preset thickness threshold value, activating the reverse peak clipping pulse group; generating a resonant composite sound field in the electroplating bath; detecting the concentration distribution of the additive in the micropores in real time, and generating a dynamic adjusting instruction according to the concentration distribution of the additive; the independent injection rate of the accelerator and the inhibitor is controlled according to the dynamic adjusting instruction; and based on the gradient pulse abrupt change current waveform, the resonant type composite sound field and the dynamic adjusting instruction, the copper filling process is executed till the target deposition parameters are achieved. According to the method, a multi-field coupling mechanism of gradient pulse dynamic peak clipping, composite sound field synergistic mass transfer and additive concentration real-time regulation and control is adopted, and defect-free high-reliability copper filling can be achieved.

The invention discloses a digital calibration system and method for dynamic response parameters of a surge protection circuit, relates to the technical field of power electronic protection and test calibration, and is used for solving the problem that an existing surge protection circuit calibration method cannot reflect device degradation and dynamic response mismatch. Surge event voltage and current waveforms are collected through a synchronous trigger mechanism, non-stationary signal features are extracted through combined time-frequency analysis, and a voltage-current phase trajectory diagram is constructed to generate a response feature matrix; performing phase-space reconstruction on the matrix, calculating Kolmogorov entropy quantification system stability, and establishing a material degradation transfer function in combination with an energy absorption sequence to generate a grain boundary degradation index; key parameters are screened through L1 regularization regression, and a quantitative relation between a grain boundary degradation index and a protection threshold value is established to realize adaptive threshold value calculation; and executing strategy switching from calibration to predictive maintenance according to the degradation trend and threshold fluctuation change, and outputting a maintenance signal to complete dynamic accurate calibration and reliable operation of the surge protection circuit.

The invention belongs to the technical field of circuit monitoring, and particularly relates to an AI fault identification method and system for an electric power circuit, and the method comprises the steps: constructing a cross-modal data flow with a space-time mark through the synchronous collection of a thermal imaging sequence and a three-phase current waveform, extracting the characteristics of a multi-physical field through combining the characteristics of a wire material, and carrying out the recognition of an AI fault. And establishing a thermal-electric coupling relationship by using space-time alignment convolution, further analyzing and calculating correlation strength between temperature anomaly and current fluctuation through a sliding window, constructing a dynamic threshold model, generating a fault probability thermodynamic diagram, and finally outputting a graded early warning signal. Compared with the prior art, the method has the advantages that deep fusion analysis of thermal imaging and electrical signals is realized, the recognition precision and response speed of potential fault points are improved, the abnormal state of the wire joint can be found and accurately positioned earlier, the misjudgment rate is effectively reduced, and the safety and stability of power grid operation are improved.