1250 results about "Electric parameter" patented technology

The invention relates to the technical field of power distribution protection cabinet fault diagnosis, and discloses an intelligent fault diagnosis and rapid isolation method for a power distribution protection cabinet, and the method comprises the steps: synchronously collecting electrical parameters, environment states and equipment motion signals in the cabinet as multi-source monitoring data when the power distribution protection cabinet operates; parameter fluctuation, state change and action time sequence features are extracted after preprocessing, cross-data association analysis is carried out through a multi-dimensional association model to generate an association feature set, a fault diagnosis information set is formed based on the identified abnormal types, fault positions and development trends, an isolation strategy is formulated according to severity levels and influence ranges, and a fault diagnosis result is obtained. And generating a rapid isolation instruction and dynamically adjusting along with fault evolution. According to the method, multi-source data fusion analysis is realized, faults can be accurately diagnosed and rapidly isolated, the operation safety and reliability of the power distribution protection cabinet are improved, and the method is suitable for fault processing of the power distribution protection cabinet in a power system.

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

The invention discloses a digital intelligent switch cabinet state comprehensive sensing system based on AI, and the system comprises a multi-source data collection module, a data preprocessing and synchronization module, an edge calculation feature extraction module, an AI intelligent fusion recognition module, an expert rule diagnosis module, and a cloud comprehensive evaluation and decision module. Various types of sensors are deployed to respectively acquire environmental parameters, electrical parameters and partial discharge signals generated in the operation process of the switch cabinet to form an original multi-modal data stream. The method has the advantages that the recognition precision and response speed of the complex operation state of the switch cabinet are improved, hidden faults under multi-modal data mismatch can be effectively found, and the misjudgment and missed judgment risks are reduced. Meanwhile, a closed-loop diagnosis system is constructed, intelligent evaluation and interpretable feedback of fault types, positions and trends are achieved, scientificity and reliability of operation and maintenance decisions are enhanced, and the method is suitable for intelligent upgrading of an electric power system.

This invention pertains to an intelligent electronic device designed for sophisticated monitoring and analysis of electrical power distribution. The device integrates at least one sensor for detecting electrical parameters from a distribution system to a load, coupled with an analog-to-digital converter to transform sensed analog signals into digital data. A processing module, linked to the converter, employs customized moving average filters to calculate the frequency of electrical power distribution, enhancing measurement accuracy by adjusting for transient fluctuations. It retrieves frequency threshold settings from memory to define operational bounds, logging frequency data around identified deviation events when measurements surpass these thresholds. This system facilitates precise frequency analysis and robust event logging, providing a comprehensive solution for maintaining electrical power distribution within defined standards.

An offshore wind power plant composite ring current collection system topology planning method based on an N-1 safety criterion comprises the following steps that S1, wind power plant basic data including wind turbine and transformer substation node coordinates, wind turbine rated power, cable electrical parameters and the like are obtained; s2, generating a candidate cable set and a cable cross avoidance constraint pair set based on the basic data; s3, a scene set containing a normal scene and all single cable fault scenes is constructed, an optimization model with the minimum total cost of the whole life cycle as the target is established, and cable construction / operation variables and direct current power flow power balance, node degrees and cable cross avoidance constraints are integrated; s4, solving the model to obtain an initial topology scheme; and S5, carrying out full fault set N-1 security verification on the initial scheme, and bringing a violation scene into a fault set to resolve until the scheme meets an N-1 criterion. According to the method, through non-predefined topological optimization and planning, reliability and economical efficiency are taken into consideration, calculation complexity is reduced, and the method can be expanded to a cross-substation scene.

The invention relates to the technical field of power equipment state monitoring, in particular to a power equipment operation state evaluation and prediction method based on big data, which comprises a multi-source data acquisition module, a feature engineering processing module, an intelligent evaluation and prediction module and a decision support output module. By setting a multi-source sensing end, when the operation state of the power equipment is evaluated, the definiteness of state evaluation of different types of equipment is ensured by formulating multi-modal data fusion standard parameters and setting different state sensing weights for different types of equipment; and meanwhile, real-time state sensing is performed by fusing electrical parameters, mechanical vibration, thermal distribution and environmental stress data, so that whether a sensing blind area problem caused by a single data dimension occurs in an equipment evaluation process or not can be detected in real time, the comprehensiveness and accuracy of equipment operation state evaluation are ensured, and state sensing fragmentation errors are further reduced.

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 photovoltaic systems, and particularly discloses a fault identification method and system for a photovoltaic system, and the method comprises the steps: collecting data in real time through environment, electrical parameters and an equipment state monitoring sensor, carrying out the cleaning and standardization, extracting time domain, frequency domain and time frequency features, and screening a feature subset through a correlation analysis and feature importance sorting algorithm; detecting and classifying faults by using a hybrid model composed of an isolated forest algorithm and a random forest classifier; positioning a fault subsystem and analyzing a root cause by means of a hierarchical diagnosis strategy, a graph neural network and a Bayesian reasoning algorithm; and periodically updating the model based on the new data. The method can quickly and accurately identify and position faults, adapts to a complex environment, reduces the operation and maintenance cost, and improves the operation reliability and operation and maintenance efficiency of a photovoltaic system.

The invention relates to the technical field of electrical variable measurement, in particular to a control system and a control method of a battery measuring instrument. The method comprises the following steps: acquiring single voltage, total voltage, current and internal resistance data of a battery pack to obtain initial electrical parameters; collecting the surface temperature of the battery pack to obtain an initial thermodynamic parameter; performing synchronous time sequence integration on the initial electrical parameters and the initial thermodynamic parameters, and unifying formats to obtain a battery running state monitoring data set; and performing dynamic threshold determination and trend analysis on each parameter based on the battery operation monitoring data set to obtain an abnormal state identification result. By constructing a complete intelligent monitoring system covering data acquisition, processing, abnormity identification, state evaluation, communication packaging and visualization, high-precision real-time monitoring, fault pre-judgment and remote communication of the running state of the battery are realized, and the synchronism, the accuracy and the adaptability of the system and the intelligence and the practicability of field application are remarkably improved.

The invention discloses a power distribution network dynamic multi-target regulation and control method and system based on graph-model driving, and belongs to the technical field of power system automation. The method comprises the following steps: generating a dynamic topological graph model with a time mark correction label based on an acquired power distribution network topological state and preset power grid graph model verification; in combination with the dynamic topological graph model and the electrical parameters, executing state estimation and security risk analysis, and generating a security risk space-time distribution matrix; based on the security risk space-time distribution matrix, generating a multi-target strategy set for simultaneously optimizing a network loss index, a voltage fluctuation index and a load balancing index; and executing the multi-target strategy set, collecting power grid state response data after execution, calculating a control effect index, and judging whether to iteratively update the dynamic topological graph model or not. The method is used for solving the problems that topology modeling is static, state estimation granularity is coarse, risk assessment lacks a spatio-temporal evolution mechanism and strategy closed-loop capacity is weak in a traditional method.

The invention discloses an intelligent diagnosis method for electric power secondary equipment based on digital twinning, which relates to the technical field of operation and maintenance of electric power equipment, and comprises the following steps: uniformly accessing real-time data of the electric power secondary equipment and carrying out timestamp standardization to complete cross-channel alignment; constructing an equipment-level digital twinborn body, and calculating a key business volume, a contrast deviation of an output and a field volume and a credible interval; cross-channel transition parameters, key electrical parameters and link state parameters are extracted under the unified time axis and secondary network topology, and a constraint quantity set is generated; and constructing an evidence chain on the topology based on the contrast deviation and the constraint quantity, executing root cause convergence and conflict stripping, and forming a diagnosis conclusion with a confidence level and processing steps. Through unified time reference and multi-domain twinborn contrast, triple constraint quantity weighting and evidence chain reasoning, cooperative constraint of time sequence consistency, physical consistency and safety boundary is realized, positioning precision and closed loop efficiency are improved, false alarm and missing alarm are reduced, strong isolation of simulation and production links is guaranteed, and visual tracing is realized.

The invention discloses a power equipment overheating fault diagnosis method, system, equipment and medium, and relates to the field of fault diagnosis. According to the method, a visible light image and an infrared image are enhanced through a preset double-flow generative adversarial network to obtain a target visible light image and a target infrared image, and a device semantic mask graph is generated according to the target visible light image; projecting temperature data in the target infrared image to each segmented power device according to the device semantic mask map to form a device temperature distribution map; determining static temperature characteristics and dynamic temperature characteristics of each power device according to the device temperature distribution diagram, and generating a space-time temperature evolution diagram according to the static temperature characteristics, the dynamic temperature characteristics and the physical connection relationship among the power devices; and predicting an overheating fault probability, a fault type and a potential fault source of each power device according to the space-time temperature evolution diagram and the electrical parameters. By implementing the technical scheme provided by the invention, the overheating fault prediction accuracy of the power equipment is improved.

The invention provides a power grid energy conservation and loss reduction system based on dynamic reactive compensation and a reliability optimization method, and belongs to the technical field of power grid energy conservation and loss reduction. Comprising a data acquisition module which is used for acquiring real-time electrical parameters of a power grid; the reactive power demand calculation module is in communication connection with the data acquisition module and is used for receiving the real-time electrical parameters of the power grid transmitted by the data acquisition module, and the reactive power demand calculation module is further used for calculating the real-time electrical parameters of the power grid based on an improved compensation demand prediction algorithm. And according to the real-time electrical parameters of the power grid, dynamically calculating a reactive compensation amount and a reactive compensation type, and generating a compensation instruction by combining the reactive compensation amount, the reactive compensation type and a preset threshold value. The beneficial effects are that the compensation instruction is generated according to the reactive compensation type and the preset threshold value, and the compensation execution module switches the compensation device according to the compensation instruction, thereby realizing reactive dynamic adjustment, and enabling the power grid to achieve the effects of energy saving and loss reduction.

The invention relates to the field of on-line monitoring of electrical equipment, in particular to an on-line monitoring method of the electrical equipment, which comprises the following steps: acquiring multi-dimensional monitoring parameters of the electrical equipment, including an electrical parameter, a temperature parameter and an insulation parameter; performing feature extraction on the collected multi-dimensional monitoring parameters to obtain feature parameters corresponding to the monitoring parameters, and constructing a feature vector set; based on historical normal operation data and real-time acquisition data of the electrical equipment, establishing a dynamic threshold model, and calculating a threshold corresponding to the characteristic parameter through the dynamic threshold model; feature parameters in the feature vector set are compared with corresponding thresholds output by the dynamic threshold model, and whether the electrical equipment is abnormal or not is judged; when the abnormity exists, outputting an abnormity monitoring result; and if not, returning to S1. According to the invention, by collecting multi-dimensional parameters, calibrating, correcting, de-noising and extracting features, and combining historical and real-time data to build a model and verify, data preprocessing, threshold adaptation and anomaly judgment are realized, and the monitoring reliability is improved.

The invention discloses a power distribution network multi-source load intelligent optimization control method, and relates to the technical field of power system power distribution networks, and the method comprises the following components: S1, building a digital twin model, S2, optimization control strategy preview, S3, strategy verification and evaluation, S4, strategy adjustment and optimization, and S5, practical application and feedback. According to the method, intelligent optimization control over the multi-source loads of the power distribution network is achieved by building the digital twin model and optimizing control strategy rehearsal, the topological structure, the electrical parameters and the real-time operation state of the physical power distribution network can be accurately mapped, the optimization control strategy is formulated according to the load priority and the collaborative weight of distributed energy, and the optimization control over the multi-source loads of the power distribution network is achieved. The operation efficiency and stability of the power distribution network are improved, the reliability and safety of load supply are ensured, and potential problems can be found and solved in time through simulation operation and strategy verification of the digital twin model, so that risks and faults possibly occurring in the operation process of the power distribution network are effectively avoided.

The invention discloses a metering box abnormal power consumption behavior diagnosis method and system based on edge calculation, and belongs to the technical field of power monitoring, and the method comprises the steps: collecting the voltage, current and time mark data of a metering box in real time through an edge end, synchronously obtaining the physical state information of a box door state and the like, generating a space-time feature matrix through an electric parameter space-time coupling analysis method, and carrying out the calculation of the time-space feature matrix; inputting an electric parameter feature mask pruning algorithm to obtain an optimized feature set, triggering an edge-sensing sleep wake-up linkage device when current abnormity is monitored, and collecting magnetic field data; the edge end calculates a total-branch electric energy difference value, combines the magnetic field data and a box door state, and constructs an electricity consumption abnormity classification determination tree through a metering error-electricity stealing behavior coupling diagnosis method to distinguish electricity stealing behaviors; an intermittent abnormal trajectory splicing algorithm is adopted to process fragmented abnormal data to generate an electricity stealing trajectory, and the electricity stealing trajectory is fed back to an electric parameter space-time coupling analysis method to dynamically update the space-time feature weight; according to the invention, the abnormity diagnosis precision and real-time performance are improved, and the energy consumption is reduced.

The invention belongs to the technical field of intelligent traffic and new energy power supply, particularly relates to a traffic equipment solar power supply fault diagnosis system, and aims to solve the problems that a solar power supply system is not timely in fault diagnosis, low in precision and difficult to distinguish instantaneous interference and continuous faults. The system collects multi-source data through an environment sensing and electrical parameter monitoring module, generates a power deviation sequence and extracts time sequence characteristics by combining dynamic expected power modeling with actual output comparison; the fault identification module adopts a multi-level logic discrimination and 12-hour continuous verification mechanism, accurately identifies photovoltaic panel pollution, storage battery aging, poor line contact and controller faults, and distinguishes instantaneous interference; and the decision alarm module generates graded alarms according to fault types and grades, and realizes accurate positioning and operation and maintenance scheduling in linkage with geographic information. The system also has the functions of internal resistance pulse detection, dual-channel redundancy sampling, adaptive threshold adjustment and model self-learning, and the diagnosis accuracy and the operation and maintenance efficiency are significantly improved.

The invention discloses a remote real-time monitoring and fault early warning method for a power supply operation state, and relates to the technical field of power electronics and energy management, and the method comprises the steps: collecting the temperature data of a key part of a power supply through an optical fiber Bragg grating sensor array, and analyzing the wavelength offset; electrical parameters are collected through a differential MEMS current / voltage sensor, and a permalloy / ferrite composite shielding layer is arranged in the sensor; collecting a mechanical vibration signal through a piezoelectric ceramic vibration sensor; for device data from different manufacturers, Modbus and CAN heterogeneous protocols are matched and analyzed through a regular expression, and the Modbus and CAN heterogeneous protocols are converted into a unified RDF triple format; inputting temperature, current and vibration data into a space-time alignment module, and constructing a semantic mapping relation by adopting an ontology; calculating a sensor confidence coefficient weight, and performing weighted fusion to generate an equipment health index; operating a lightweight LSTM model at an edge node, and detecting current harmonic and vibration resonance frequency band abnormity in real time; cOMSOL multi-physical field simulation is combined at the cloud end, and the evaporation rate of the capacitor electrolyte is predicted.

The invention discloses an electrical automation detection device for power distribution automation equipment, and belongs to the technical field of intelligent detection of power distribution equipment. Comprising a multi-source data acquisition module which is used for acquiring multi-dimensional electrical parameters and environment information data of the power distribution automation equipment in real time and realizing comprehensive perception of the operation state of the equipment; the load prediction module is used for executing short-term and medium-term prediction of the equipment load based on historical data and the real-time operation state; the prediction error analysis module responds to the prediction result output by the load prediction module and generates a corresponding error compensation coefficient; the self-adaptive threshold calculation module is used for dynamically calculating an optimal detection threshold adaptive to the current operation condition; and the fault detection and judgment module is used for intelligently evaluating and judging the running state of the equipment based on the optimal detection threshold value output by the self-adaptive threshold value calculation module so as to realize accurate identification and positioning of a fault.

A power transmission line fault locating method and system based on traveling wave front fitting, and a storage medium and an electronic device. The method mainly comprises the following steps: on the basis of electrical parameters and control parameters of an actual project, constructing an electromagnetic transient simulation model for a power transmission system, setting metallic grounding faults along the entire length of a power transmission line at preset step sizes, and performing simulation (S1); recording fault occurrence moments, and, at a preset sampling frequency, collecting a voltage at an installation position of a fault locating device under each fault condition, so as to calculate a line-mode fault voltage under each fault condition (S2); performing function fitting on the line-mode fault voltage calculated at each fault position to obtain a fitting coefficient (S3); performing power function fitting on the fault positions and the fitting coefficients under various preset step size conditions to obtain a fault positioning function (S4); and when a power transmission line protection device of the actual project detects a line fault, performing fitting on a voltage collected by the fault locating device at a target sampling frequency to obtain a fitting coefficient, and substituting the fitting coefficient into the fault locating function to calculate a fault distance (S5).

The invention discloses a multi-time-scale coordination control device and method for a novel electric power system, and relates to the technical field of electric power automation. Aiming at the problems of difficulty in multi-source data collaboration, complexity in control strategy optimization and insufficient scene adaptability in a novel power system, the device realizes flexible deployment and mobile operation through a high-strength aluminum alloy frame integrated modular functional assembly; the environment monitoring module is used for high-frequency collection of multi-parameter environment data, and equipment operation safety is guaranteed; a plurality of communication protocols are supported by means of a multi-time scale control module, and distributed energy equipment is seamlessly connected; based on three core units of a data processing center module, a scene simulation unit and a cooperative control center module, second-to-hour-level electric parameter data generation, complex scene simulation verification and intelligent generation, execution and optimization of a control strategy are realized; and finally, under the complex working condition of high-proportion new energy access, the control precision, the operation stability and the comprehensive benefits of the power system are remarkably improved.

The invention discloses a multi-mode self-learning diagnosis and early warning method and system for a power distribution system, and relates to the technical field of intelligent operation and maintenance of power systems. According to the method, a visible light image, an infrared thermal image, a sound signal, an environmental parameter and an electrical parameter of the power distribution equipment are collected through a multi-mode sensor, and time synchronization and feature extraction are carried out; multi-modal features are constructed into a hypergraph structure, and cross-modal feature fusion is realized by using a graph attention mechanism; adopting a strategy of combining self-supervised pre-training and supervised fine tuning to carry out multi-objective joint optimization on the fused features to obtain a self-learning diagnosis model; the system inputs multi-modal data in real time during operation, outputs an equipment operation state and a health degree index, and realizes early warning and fault alarm through threshold comparison. According to the method, deep fusion and adaptive learning of multi-source information can be realized, high-precision diagnosis, real-time early warning and online updating capabilities are realized, and the intelligence and safety of a power distribution system are remarkably improved.

The invention discloses a photovoltaic power generation prediction method and device based on digital twinning and a medium, and relates to the technical field of new energy, and the method comprises the steps: constructing a three-dimensional geometric model of a photovoltaic power station; acquiring electrical parameters and environmental data of each component in the photovoltaic power station, and inputting the electrical parameters and environmental data into the three-dimensional geometric model according to the three-dimensional coordinates corresponding to the components to form a digital twinborn model; outputting a future environment data sequence in a preset time period based on the environment data through a trained generative model, and inputting the future environment data sequence into a digital twinborn model; performing power generation simulation through a digital twinborn model to obtain a power prediction value in a preset time period; and adjusting model parameters of the digital twinborn model according to the power predicted value and the actual power. And in combination with a high-resolution future environment sequence output by the generative model, power generation simulation of the digital twin model is fully fused with physical position characteristics and meteorological evolution laws.

The invention discloses a cooperative autonomous synchronous networking method for a multi-networking type energy storage converter without communication interconnection. The method comprises the following steps: S1, state perception and estimation based on local measurement: acquiring local electrical parameters in real time by using a sensor, and performing state estimation through an improved Kalman filtering algorithm and a local dynamic model; s2, a cooperation mechanism of the virtual synchronous machine and droop control: combining the virtual synchronous machine with the droop control mechanism, and realizing automatic power adjustment and cooperation work by setting a virtual inertia, a damping coefficient and a droop coefficient; s3, self-adaptive synchronization algorithm: based on the local state estimation and the output of the virtual synchronous machine, adjusting the phase and frequency of the output voltage, and dynamically adjusting synchronization parameters according to the state of the power grid; and S4, detecting a fault by monitoring local electrical parameters and a self state in real time, and automatically adjusting a control strategy and realizing system fault tolerance when the fault occurs.

The invention discloses an electrical safety test data analysis method and system for an electrical cabinet, and relates to the technical field of electrical measurement and test. The method is used for solving the problems of single test dimension, poor anti-interference capability and inaccurate fault positioning in the safety test of the electrical cabinet. Firstly, multi-channel electrical parameter synchronous acquisition is triggered based on a working condition event, real discharge and interference signals are distinguished through time-frequency analysis, and a time sequence incidence matrix of discharge pulses and load currents is established; then, analyzing phase distribution characteristics of partial discharge, calculating a correlation coefficient between a discharge repetition rate and a load current, and dynamically adjusting a weight to generate an insulation state comprehensive test index; further, insulation degradation characteristics are extracted through signal decomposition, and an insulation degradation trend and a fault probability are predicted in combination with energy entropy analysis and discharge mode recognition; and finally, the position of a fault component is analyzed and positioned based on the impedance network, and a maintenance priority sequence is generated according to the fault probability and the degradation rate.

The invention discloses a fault monitoring method and system for power supply equipment, and the method comprises the following steps: 1, collecting the time-space correlation electric parameters of the power supply equipment in real time through an edge calculation node disposed at the side of the power supply equipment; the time-space correlation electric parameters comprise a steady state quantity, a transient state quantity and a zero sequence component, and marking microsecond-level precision timestamps on all the collected data; step 2, carrying out real-time analysis on a transient state quantity in the time-space correlation electric parameter to generate a transient state characteristic value; and step 3, performing real-time comparison on the steady-state quantity in the time-space correlation electric parameter and a preset dynamic threshold value, and generating a first-level early warning signal when the data exceed the threshold value. Through the implementation of the invention, the full-spectrum monitoring of the power supply equipment from the steady state abnormity to the transient state fault is realized. According to the scheme, the fault detection precision is remarkably improved, full-life-cycle health state monitoring from emergency limit exceeding to early trend abnormity is covered, and false alarm and missing alarm are effectively reduced.

The invention relates to the technical field of power distribution fault positioning, in particular to a power distribution single-phase earth fault positioning method and device based on multi-source information fusion. The device comprises a data acquisition module, a fault line selection module, a grading emergency module, a fault positioning module and a parameter optimization module. According to the method, the zero-sequence current is monitored in real time, the dynamic change curve is generated, the fault line is accurately identified in combination with amplitude and phase characteristics, electrical parameters, environmental factors and load importance are fused to carry out comprehensive risk assessment and start a hierarchical response mechanism, and the fault point of the fault line is accurately positioned by adopting a voltage drop method. And the monitoring frequency and the decision threshold value are adaptively adjusted according to the fault characteristics and historical data, so that response lag and insufficient precision of a traditional fault positioning method are avoided, full-flow intelligent management from fault detection, accurate positioning to parameter self-optimization is realized, the accuracy and efficiency of fault processing are improved, and safe and stable operation of a power grid is ensured.

The invention discloses a distribution box operation state monitoring method and system, and relates to the field of distribution boxes, and the system comprises an acquisition module which is used for collecting current and voltage signals of each loop in a distribution box, surface temperature distribution of a box body and opening and closing state signals of a cabinet door, and converting the collected analog signals into a digital signal sequence; the extraction module is used for receiving the digital signal sequence and extracting a signal fluctuation feature, a temperature field distribution feature and a state signal change feature to generate a three-dimensional feature set; the system can obtain loop electrical parameters, box body temperature and cabinet door state in real time, accurately analyze operation characteristics to deduce state trend, quickly position defective loop components and influence range in case of abnormity, scientifically evaluate health level, timely give an early warning, synchronously disconnect power supply of associated equipment in case of severity, effectively reduce fault shutdown, and improve work efficiency. The power utilization safety and the maintenance efficiency are improved, and stable operation of the distribution box is guaranteed.

The invention discloses a distributed intelligent power distribution method and system for a power network, and belongs to the technical field of power distribution control, and the method comprises the steps: collecting multi-dimensional electrical parameters of a power generation node in real time, and carrying out the dynamic power monitoring at a feeder terminal of a transformer substation through an edge calculation node; executing line loss calculation and load prediction in the power distribution unit, generating a demand feedback instruction to a feeder terminal node, and triggering an adaptive power distribution strategy; and on the basis of a digital twin system of a regional dispatching center, credibility verification is carried out on operation decisions of all edge nodes, and global optimization compensation control is executed. The method has the remarkable advantages in the aspects of optimizing power distribution, reducing line loss, improving system stability and safety and achieving efficient energy utilization, and intelligent management of a power network becomes possible.

The invention provides a control method and system for an energy storage PCS with an AC / DC circuit breaker and a storage medium, and the method comprises the steps: firstly, synchronously collecting the electrical parameters of a DC side and an AC side, and carrying out the preprocessing of the collected data, so as to form a standardized electrical matrix; then, all parameters in the electrical matrix are cyclically compared with corresponding early warning thresholds, first-level thresholds, second-level thresholds and fusing thresholds in a multi-level threshold mapping table; if the pre-warning threshold value is exceeded, a log is recorded, if the pre-warning threshold value is exceeded, self-healing operation is executed, if the pre-warning threshold value is exceeded, turn-off operation is executed, and if the pre-warning threshold value is reached, physical fusing is triggered; meanwhile, all operation logs are recorded, and the threshold mapping table is dynamically optimized by analyzing log data; according to the method, the fault response speed and the protection precision are improved through multi-level threshold serial comparison and a cooperative protection mechanism, the stability and the reliability under different working conditions are enhanced through the adaptive learning capability, and meanwhile, the whole life cycle cost is reduced.