24147 results about "Electric current flow" patented technology

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.

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.

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.

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 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 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.

The invention relates to the technical field of electrical safety monitoring, in particular to an intelligent detection method and system for indoor electrical potential safety hazards, and the method comprises the following steps: constructing a synchronous data set based on electrical equipment and environment data, extracting a power current sequence, analyzing the change rate, screening abrupt change signals, and combining with environment recognition risk fragments; and extracting a fluctuation interval to judge critical offset, positioning an electrical sequence offset distance, and counting risk frequency to mark a high-incidence area to generate an alarm node list. According to the method, high-precision synchronization of power consumption behaviors and environmental conditions is realized by collecting the electric participation environment data of the electric connection points and unifying time labels, the anomaly identification accuracy is improved, the power and the current sequence change rate are extracted, the line offset is dynamically pre-warned, and the hidden anomaly in the stable stage is revealed through track mapping; a high-incidence area is positioned through frequency statistics and space mapping, closed-loop monitoring from multi-dimensional data fusion to risk area marking is achieved, and the intelligence and foresight of electrical hidden danger recognition are remarkably improved.

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

The invention discloses a grinding machine internal part temperature anomaly detection method based on vibration signal analysis, which comprises the following steps that grinding data are collected through sensor deployment, and the sensors comprise a temperature sensor, a vibration sensor, an infrared thermal imaging sensor, a magnetic resistance current sensor and an inductance type particle sensor; carrying out preprocessing and feature extraction on the collected data; model construction and training are carried out based on data of preprocessing and feature extraction; according to the method, the abnormal condition of the temperature of the part is predicted by detecting the vibration signal of the internal part, the content of metal particles in lubricating oil is detected through the oil analysis sensor, and the abrasion degree of the bearing is judged in combination with the vibration signal. And motor current harmonic characteristics are monitored, and overload or rotor imbalance problems are identified.

Air conditioning device (1) for motorhomes, comprising: - at least one first heat exchanger for exchanging heat with the air circulating through it; - a cooling circuit (8) for a cooling fluid, with a path passing through the first heat exchanger; - a compressor (7) which includes an electric motor, for circulating the cooling fluid in the cooling circuit (8); - a power supply unit that can be connected to an external power supply to receive an electrical input current or electrical input power and to generate a variable supply voltage to drive the electric motor with an electrical supply current, - a controller (11) connected to the power supply unit that is suitable for receiving input data representative of a reference value of the electrical input current or electrical input power and that is programmed to adjust the power supply unit to generate the supply voltage with a reference amplitude and / or frequency such that the electrical input current or electrical input power is below the reference value; - a positioning device designed to determine the position of the air conditioning device (1); and - a memory containing a plurality of reference values ​​corresponding to a plurality of locations, wherein the controller (11) is capable of receiving a location signal from the positioning device and of setting as input data one of the reference values ​​present in the memory depending on the location signal.

The invention relates to the technical field of chip testing, in particular to a chip aging test and analysis method, which comprises the following steps of: constructing an aging parameter matrix containing process deviation, electrical parameters and thermodynamic parameters on the basis of fusion of wafer level and packaged test data, multi-stress cooperative loading is realized through a programmable power supply and a multi-zone temperature control array, transient electric signals, thermal imaging data and leakage current frequency spectrums are acquired in real time, an electrical-thermal coupling coefficient and a process deviation sensitivity factor are extracted in a cross-dimension manner, a non-linear recession model is established, differential aging paths of chip functional blocks are evaluated, and the reliability of the chip functional blocks is improved. And based on the dynamic life prediction model and a fault early warning mechanism, generating and executing an adaptive test optimization strategy to realize early prediction and stress adjustment of multiple failure modes. According to the invention, the efficiency and accuracy of the aging test can be obviously improved, and technical support is provided for high-reliability evaluation of deep submicron and post-mole times of chips.

The invention relates to the technical field of electrical equipment fault diagnosis, and discloses an electrical equipment multi-modal fault diagnosis method based on dynamic self-adaption, and the method comprises the steps: obtaining an original multi-modal signal flow containing vibration, temperature and current signals, inputting the original multi-modal signal flow into a dynamic self-adaption diagnosis network, obtaining a fault feature matching result set, and determining each modal result. The network is trained by historical fault data, and the data comprises time domain, frequency domain and fusion feature parameters extracted from continuous multi-mode signals, and corresponding fault type labels and confidence coefficients. The network comprises a cross-modal feature fusion module, a spatial-temporal feature coding module and the like, and when the confidence coefficient of at least two modal results exceeds a dynamic threshold value, three-level early warning is triggered. The method improves the comprehensiveness, accuracy and real-time performance of diagnosis, and is suitable for fault diagnosis of electrical equipment.

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 uninsulated superconducting coil electromagnetic characteristic prediction method, system and device and a medium, and relates to the field of electrical digital data processing. Constructing a loss function based on the data fitting loss and physical constraint loss including a Kirchhoff's current law loss item, a T-A equation loss item and an E-J power law loss item; training, testing and verifying the network model by adopting a sample data set, balancing each loss item in a loss function by adopting an adaptive weight distribution strategy in the training process, and minimizing the loss function through an Adam optimization algorithm to obtain an electromagnetic characteristic prediction model; and inputting the obtained current working condition parameters into the electromagnetic characteristic prediction model to obtain an electromagnetic characteristic prediction result. According to the method, the problems of low calculation efficiency, poor physical consistency and generalization ability and the like in the existing method are solved, and a feasible tool is provided for rapid design, optimization and operation of the uninsulated superconducting coil.

The invention discloses a primary and secondary fusion pole-mounted intelligent circuit breaker. The primary and secondary fusion pole-mounted intelligent circuit breaker comprises a fault detection module, a protection action module, a reclosing module and an information reporting module, the fault detection module collects multi-dimensional electrical quantity through a current transformer and a voltage transformer, and judges a fault and identifies the type in combination with a dynamic normal range and a duration threshold. The protection action module calculates the fault distance based on the correction impedance, judges whether to act or not by combining the current flow direction and the protection range, and selects a protection level according to the fault severity; the reclosing module dynamically sets an observation time window, distinguishes instantaneous or permanent faults through a retest verification mechanism, and adaptively adjusts a reclosing strategy; and the information reporting module uploads fault related information. According to the method, the problems of high misjudgment rate, poor protection selectivity, rigid reclosing logic and the like of a traditional circuit breaker are solved, and the accuracy, quickness and self-healing capability of power distribution network fault processing are remarkably improved.

The invention relates to the technical field of power monitoring, in particular to a moisture-proof environment-friendly ring main unit online monitoring system with continuous fault indication and a ring main unit, and the system comprises an electric parameter dynamic analysis module, an environment collaborative verification module, a closed-loop control execution module and a behavior path optimization module. According to the method, the current fluctuation and the power difference value are matched through dynamic time warping, the temperature rise rate and the condensation index are calculated through linear regression, multi-dimensional verification is formed through fuzzy logic judgment, operation parameters are adjusted in real time through PID control, strategy weight is optimized through a genetic algorithm, feature extraction is reversely corrected, and the fault recognition precision and the response speed are improved; closed-loop feedback is established to continuously optimize the system state, environment and electrical parameter coupling analysis is enhanced, the misjudgment probability of a single threshold value is reduced, parameters are dynamically corrected, the adjustment real-time performance and accuracy are improved, the adaptive capacity under the complex working condition is enhanced, a complete closed loop of collection, analysis and feedback is established, and the insulation degradation judgment reliability is improved.

The invention relates to the technical field of electromagnetic relay control, in particular to an electromagnetic relay control system, which comprises a relay execution unit, a contact state monitoring unit and an intelligent viscosity elimination control unit, and is characterized in that the contact state monitoring unit acquires quantum tunneling effect impedance spectroscopy and acoustic emission energy entropy change signals in real time; the intelligent viscosity-eliminating control unit analyzes signals through a fusion welding risk multi-dimensional evaluator, triggers a three-stage control sequence, firstly starts a magnetostriction high-frequency oscillator to inject an inverted eddy current array, activates a hysteresis loop restorer to apply a gradient demagnetization potential field if the fusion welding risk multi-dimensional evaluator is not recovered, and finally, recovers the fusion welding risk multi-dimensional evaluator; when an acoustic emission signal is abnormal, a chaotic encryption message is generated through the fault feature topology generator and is linked with the magnetic solitary wave breaking device, ultrasonic frequency mechanical energy can be captured and converted into regenerative current, an adhesion evolution path is reconstructed after diagnosis, the problems of contact adhesion risk identification lagging and lack of multi-physics field cooperative control in the prior art are solved, and the reliability of the contact adhesion risk identification is improved. And the adhesion early warning success rate and the removal efficiency are improved.

The invention provides a coordination control method for a photovoltaic off-grid hydrogen production system, which comprises the following steps of: acquiring voltage and current time domain signals of a photovoltaic array, constructing an impedance frequency characteristic mapping model through tensor decomposition and a radial basis function network, and calculating converter control parameters; based on converter control parameters and environment data, temperature-sensing-free self-correction photovoltaic characteristic prediction is achieved through electrical parameter indirect temperature estimation, and an MPPT reference track and a power prediction track are generated; multi-time-scale data coordination processing of millisecond-level electrical control, second-level pressure regulation and minute-level temperature optimization is executed, potential conflicts are solved, and an execution instruction set is generated; and generating and issuing a system comprehensive control instruction by adopting a double-layer deep reinforcement learning network. According to the method, the system efficiency and stability are improved, and the performance is excellent especially under the illumination fluctuation working condition.

The invention relates to the technical field of electric energy metering and electric power system safety, and discloses an electricity larceny prevention method and system for multi-source data fusion of an electric energy meter, and the method comprises the steps: obtaining and preprocessing current and voltage waveform data and user electricity consumption behavior data; generating multi-dimensional power consumption characteristic parameters, performing screening to form a characteristic vector set, and constructing an abnormal power consumption detection reference set; dynamically verifying power utilization data based on the reference set, and analyzing a phase relation and a mismatch state of current and voltage signals; and generating an electricity consumption behavior deviation index to judge an electricity stealing behavior. The system comprises a multi-source data acquisition module, a feature modeling analysis module, an electrical relation verification module and an electricity stealing judgment module. According to the invention, through multi-source data fusion and multi-dimensional feature analysis, accurate detection of electricity larceny behaviors is realized, accuracy and reliability of electricity larceny prevention are improved, and the method is suitable for power utilization safety management of a smart power grid.

The invention relates to the technical field of cylindrical lithium ion battery production, and discloses a method for detecting insufficient welding of a cylindrical lithium ion battery, and the method comprises the following steps: S1, appearance inspection; s2, initial internal resistance benchmark test; s3, applying a short-time large-current pulse to the battery by using a pulse generator, simulating an actual load condition, and recording an instantaneous voltage drop and current response curve of the voltage of the battery end during the pulse period; s4, performing infrared thermal imaging identification and data processing; s5, learning the picture and establishing a model; s6, comparing the test picture and the data with the model; s7, dynamic internal resistance calculation; s8, cold solder joint query and sorting processing; and S9, retesting the electrical performance. According to the invention, the technologies of dynamic internal resistance measurement, nondestructive X-ray or ultrasonic detection, rate discharge test and the like are adopted, so that efficient, accurate and nondestructive detection of the cold solder joint of the cylindrical lithium ion battery is realized, and the production efficiency and the detection precision are remarkably improved.

The invention discloses a differentiated precise current distribution control method in a multi-workpiece batch electroplating process. The method comprises the following steps: acquiring multi-workpiece feature data and preprocessing the multi-workpiece feature data; workpiece difference characteristic analysis and grouping are carried out; modeling and analyzing the electric field distribution of the electrolytic cell; plating layer growth dynamic monitoring and prediction are carried out; and generating a differential current distribution strategy. A multi-frequency differential resistance spectrum acquisition system is adopted to realize real-time monitoring of plating layer growth, an improved independent component analysis algorithm fused with spatial constraint is applied to perform workpiece group resistance spectrum decoupling, and micro-area accurate current modulation is realized through a self-adaptive waveform library of an electric field distortion area. The problems that dynamic characteristics of plating layers are difficult to monitor and differentiation control of workpieces with similar characteristics is insufficient in multi-workpiece electroplating are solved, and the quality consistency of the plating layers is improved.

The invention relates to the technical field of chip testing, and discloses an LED driving chip testing method, device and equipment and a storage medium, and the method comprises the steps: carrying out the multi-point temperature excitation and constant-current output synchronous collection of an LED driving chip, and obtaining electrothermal coupling test data; performing thermal resistance network decomposition on the temperature field in the chip to obtain temperature distribution characteristic parameters; performing nonlinear function fitting on the temperature-constant current coupling relation to obtain an electrothermal coupling transfer coefficient; performing four-dimensional coupling calculation on the temperature distribution characteristic parameter and the electrothermal coupling transfer coefficient to obtain four-dimensional coupling response characteristic data; and partial differential sensitivity calculation is carried out based on the four-dimensional coupling response characteristic data, and an electrothermal coupling sensitivity analysis result is obtained.According to the method, the problem of test errors caused by time desynchrony in a traditional separated temperature-current test is solved, quantitative sensitivity analysis of electrothermal coupling parameters is achieved, and the sensitivity of the electrothermal coupling parameters is improved. Therefore, the test accuracy of the LED driving chip is improved.

The invention relates to the technical field of hybrid inverters, and discloses a hybrid inverter supporting reactive compensation and a control method thereof, and the hybrid inverter comprises a DC input unit, a three-phase full-bridge inversion unit, a grid-connected switching unit, a signal acquisition unit and a digital control unit. The direct current input unit comprises a photovoltaic interface, a battery interface and a direct current bus and is used for converging photovoltaic and energy storage electric energy; the three-phase full-bridge inversion unit is composed of a power device and an LCL filter and converts direct current into alternating current. The grid-connected switching unit comprises a static switch and an isolation transformer and is used for controlling power grid connection; the signal acquisition unit detects voltage and current signals; and the digital control unit judges a grid-connected mode or an off-grid mode according to the signal, calculates a reactive current reference value and generates a PWM (Pulse Width Modulation) control signal so as to realize reactive compensation control in the grid-connected mode and the off-grid mode. According to the invention, reactive power compensation capability is provided in both grid-connected and off-grid operation modes, and stable operation of the inverter in various working conditions is ensured.

The invention relates to the technical field of power monitoring, in particular to an intelligent power monitoring management method and system for an electrical cabinet, and the method comprises the steps: obtaining a state data point at each moment in the operation process of the electrical cabinet, the state data point comprising temperature data and residual current data; and detecting the state data points by using an isolated forest algorithm to obtain an abnormal score, and correcting the abnormal score to obtain an optimal abnormal score for monitoring the operation state of the electrical cabinet. According to the method, the initial abnormal score is corrected, so that the accuracy of the monitoring result of the isolated forest is improved.

The invention relates to the technical field of electrochemical energy storage, and discloses a method and a system for predicting the residual life of a battery of an electrochemical energy storage system. According to the method, multi-dimensional operation data such as temperature distribution, current ripples and voltage attenuation curves in the charge-discharge cycle process of a target battery pack are collected, and feature weight parameters are generated in combination with real-time working conditions and historical database matching results; a degradation characteristic mode in a full life cycle is extracted as a reference template, and a dynamic degradation characteristic space is established; constructing a multi-scale coupled life prediction model, and constructing an adaptive evaluation function by iteratively updating model parameters and reference template physical field coupling parameters; triggering cross-cycle data verification, and carrying out working condition compatibility verification; and generating a feedback matrix based on the prediction error index and the verification result, and dynamically adjusting the mapping relation between the feature weight and the reference template. The method improves the accuracy and adaptability of battery residual life prediction, and is suitable for an electrochemical energy storage system under complex working conditions.

The invention discloses a charging method and system based on battery dynamic health state evaluation and learning prediction, belongs to the technical field of electric vehicle charging, and aims to solve the technical problem of how to break through a traditional BMS information barrier, realize deep perception and accurate prediction of a microcosmic dynamic health state in a battery, and improve the charging efficiency. According to the technical scheme, when the electric vehicle is connected to a charging pile, the charging pile establishes a communication link with a BMS (battery management system) of the vehicle through a standard protocol; a current or voltage disturbance signal with a specific frequency spectrum is injected into the battery, a response signal of the battery is measured synchronously, and dynamic health state parameters of the battery of the electric vehicle are collected in real time; and analyzing the dynamic health state parameters of the battery of the electric vehicle by using a pre-trained deep learning model, predicting the health state evolution trend of the battery, and generating a multi-target charging parameter optimization scheme.