717 results about "Electric parameter" patented technology

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 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 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 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 relates to the field of data processing, in particular to an electric energy meter real-time electrical parameter processing method and device based on cloud computing, and the method comprises the steps: carrying out the sliding of a target current sequence through a preset sliding step length, obtaining a plurality of windows, constructing an amplitude sequence, and calculating the difference significance degree of target harmonic waves; according to the waveform factor sequence, calculating the in-phase time domain anomaly of the current sequence of the to-be-detected electric energy meter; based on the difference significance degree and the in-phase time domain anomaly degree, calculating the in-phase total anomaly degree of the current sequence of the to-be-detected electric energy meter, updating the window size, traversing to obtain the in-phase total anomaly degree of the current sequence of the to-be-detected electric energy meter under each window size, calculating the comprehensive anomaly degree of the to-be-detected electric energy meter, and obtaining the in-phase anomaly degree of the current sequence of the to-be-detected electric energy meter. And completing electric energy meter electrical parameter processing according to the comprehensive abnormality. According to the technical scheme, the precision and efficiency of the electric energy meter electric parameter processing result can be improved.

The invention relates to the technical field of electrical parameter measurement, in particular to an SOC prediction method and system applied to a lithium battery. The method comprises the following steps: collecting an electrical variable parameter set of the lithium battery in a current operation period, and determining an electric quantity change value and a change trend of terminal voltage; dividing a voltage response state of the lithium battery in each operation stage to determine an operation state mapping relation; predicting the state of charge of the lithium battery in the current operation period by using the operation state mapping relation; in response to the first change of the operation condition of the lithium battery, collecting a current electrical variable parameter set of the lithium battery to correct the operation state mapping relation; and predicting the state of charge of the lithium battery after the first change of the operation condition according to the corrected operation state mapping relation. The method is beneficial for dynamically adapting to unsteady state behaviors of the lithium battery in different working states, SOC prediction lag and fluctuation caused by working condition changes are effectively reduced, and the dynamic response capability and reliability of prediction are improved.

The invention provides a robust optimization method and system for a flexible power distribution network, computer equipment and a storage medium, and belongs to the field of power grid planning, and the method comprises the steps: obtaining a network topology structure and line electrical parameters of a to-be-optimized flexible power distribution network, and constructing a line model corresponding to the to-be-optimized flexible power distribution network according to the network topology structure and the line electrical parameters; acquiring historical operation data of each node in the line model; performing clustering analysis on the historical operation data of each node based on a fuzzy C-means clustering algorithm to obtain a typical scene of the to-be-optimized flexible power distribution network; constructing a polyhedral uncertainty set according to the typical model; taking the limited extreme scene as input, and minimizing the operation cost as an optimization target to construct a main problem; and taking a decision scheme for solving the main problem and the polyhedral uncertainty set as input, taking a scene with the maximum search operation cost as a target to construct a sub-problem, and iterating to obtain a robust optimization result of the to-be-optimized flexible power distribution network. According to the method, reliable optimization of the flexible power distribution network in a real uncertain environment is realized.

The invention discloses a power distribution network cluster local control method and device, equipment and a medium. The method adopts a cloud edge end three-level collaborative architecture, and comprises the following steps: acquiring electrical parameters through a terminal equipment layer; constructing a graph model based on an electrical distance at the edge calculation layer; performing initial cluster division and issuing on the cloud computing layer; dynamically optimizing cluster division in an edge calculation layer through a lightweight deep reinforcement learning model; local control strategies such as voltage reactive power cooperative control, frequency active power balance control and fault rapid isolation and self-healing are executed based on the final division. The system comprises a terminal device layer, an edge computing layer and a cloud computing layer. According to the method, decoupling of global optimization and local real-time control is realized through cloud-side cooperation, the problems of high data processing pressure, high communication delay, expanded local fault influence range and the like of centralized control are effectively solved, and the real-time performance, reliability and disaster resistance of the power distribution network are remarkably improved.

The application relates to the technical field of motor life prediction, and provides a stepping motor life prediction method and system based on multi-physical field coupling, electrical parameters of a stepping motor, multi-physical field data in a running state and a multi-dimensional feature vector corresponding to the multi-physical field data are acquired, a physical damage index corresponding to the stepping motor is determined according to the electrical parameters, the multi-physical field data and a preset physical damage index calculation formula, the multi-dimensional feature vector is input into a trained data-driven model, a data-driven damage index corresponding to the stepping motor is acquired, and a life prediction result of the stepping motor is acquired according to the physical damage index, the data-driven damage index and a preset life prediction formula. In this way, the influence of the multi-physical field data on the life of the stepping motor can be comprehensively considered, the limitation of a single mode in predicting the life of the stepping motor is overcome by fusing the physical damage index and the data-driven damage index, and the accuracy of the life prediction result of the stepping motor is improved.

The invention relates to the technical field of power distribution equipment maintenance, in particular to a power equipment fault maintenance debugging system and method, and the system comprises a multi-modal data fusion acquisition module, a digital twin fault simulation positioning unit, a self-adaptive debugging execution module, a maintenance safety linkage control unit and a full-life-cycle data closed-loop management module. The method comprises the steps of multi-dimensional data acquisition, digital twin simulation positioning, adaptive strategy debugging, safety linkage monitoring and data closed-loop optimization. The problem of single traditional data is solved through multi-modal data fusion, 1: 1 simulation positioning of a fault scene is realized by combining digital twinning, the adaptive module can automatically match various fault debugging strategies, and the safety linkage unit interacts with a power grid dispatching system in real time through an IEC61850 protocol to realize automatic early warning / power failure when electrical parameters of a maintenance area exceed a threshold value. The data closed-loop module optimizes a strategy library based on historical overhaul data, so that the overhaul efficiency is improved, and the debugging period is shortened.

The invention discloses a complex wave-based permanent magnet synchronous motor all-electrical parameter online identification method and device. The method comprises the following steps: analyzing the influence of a voltage error caused by dead zone time on parameter identification, and performing corresponding dead zone compensation; a bias complex wave with a certain amplitude and frequency is injected into the d-axis current, so that the d-axis current meets a parameter identification continuous excitation condition; constructing three groups of effective current slopes through four groups of different input data stored in the steady state of the motor, and identifying three groups of parameters of resistance, direct-axis inductance and quadrature-axis inductance by referring to a d-axis voltage equation and adopting a least square method with a forgetting factor; and finally, updating the identified three groups of parameters as known quantities in a q-axis voltage equation in real time, storing two groups of different input data to construct a group of effective current slopes, and identifying flux linkage parameters by adopting a least square method with a forgetting factor. According to the method, the under-rank problem of all-electrical parameter identification of the motor is solved, the identification precision is high, and the identification error of each parameter is about 3%.

The invention discloses a workstation electrical parameter real-time monitoring and fault early warning method, which belongs to the technical field of electrical equipment monitoring, and comprises the following steps: acquiring voltage parameter data, current parameter data and temperature parameter data through a multi-source sensor deployed in a workstation, and performing timestamp alignment processing; initial electrical parameter data are obtained and preprocessed, and preprocessed electrical parameter data are generated; historical electrical parameter data are acquired and analyzed and calculated in combination with the preprocessed electrical parameter data, and a dynamic early warning threshold value is generated; extracting multi-parameter characteristics based on the preprocessed electrical parameter data, calculating a correlation coefficient, performing collaborative analysis in combination with a dynamic early warning threshold, and generating a fault prediction result; and performing fault diagnosis according to the fault prediction result, and generating and outputting an early warning instruction. According to the invention, a mode of generating the dynamic early warning threshold and carrying out multi-parameter collaborative analysis is adopted, so that accurate prediction and intelligent graded early warning of the electrical fault of the workstation can be realized.

The invention discloses an intelligent diagnosis method for analyzing line hidden dangers based on dynamic electrical fingerprint characteristics, and belongs to the technical field of power line state monitoring and fault prediction. According to the method, a dynamic electrical fingerprint is constructed through high-frequency acquisition of line voltage and current instantaneous value signals and extraction of multi-dimensional features such as a time domain, a frequency domain and a time-frequency domain; establishing an electrical fingerprint database and a health model of different load working conditions in a line health state; during on-line monitoring, through three-level analysis of deviation comparison, state classification and trend prediction, graded early warning of line hidden dangers is realized, and abnormal feature items are output for auxiliary diagnosis. According to the invention, subtle changes of electrical parameters can be captured, the problems of low sensitivity, weak anti-interference capability and incapability of early warning in the prior art are solved, early recognition and advanced prediction of latent hidden dangers are realized, and the safety and reliability of a power system are significantly improved.

The invention relates to the technical field of electrical parameter measurement and power distribution automation monitoring, and discloses a method for verifying the consistency of the opening and closing states of a measurement switch. The method is used for solving the problem that a traditional method lacks an automatic identification and explainable checking mechanism for an electric parameter evidence validity condition. The method comprises the following steps: firstly, obtaining a measurement frame stream, an event frame stream and a topology configuration snapshot, and establishing and locking a parameter version verification session; voltage, current, zero-sequence measurement and action event time marks are aligned at the session time window, and the sampling quality grade, the event quality grade and missing information are solidified; obtaining an electric parameter evidence boundary and an access grade according to the measurement point position, the parallel shunting, the load type and the disturbance section; and checking the causal time sequence of the key event, processing the contradiction by judging the priority, outputting a structured verification record, writing back a parameter version according to an amplitude limiting rule, and completing the verification of the consistency of the switching-on and switching-off states under the complex working condition.

The invention relates to the field of electric power measurement, and provides a voltage transformer error evaluation method and device, electronic equipment and a storage medium, and the method comprises the steps: collecting the voltage phasor information of a measurement node in a preset time window, and obtaining the reference technical parameters of a voltage transformer corresponding to the measurement node; feature extraction based on a sliding window is carried out, sequence diagram structural features are obtained in combination with an adjacent matrix, and the adjacent matrix is constructed based on network topology and line electrical parameters of a power system; error estimation is carried out through a trained error estimation model based on the time sequence diagram structure features, an error estimation value is obtained, and the error estimation model is obtained by training a graph neural network model based on historical voltage phasor information and corresponding error labels; and performing error state evaluation based on the error estimation value. According to the method, the device, the electronic equipment and the storage medium provided by the invention, online continuous evaluation of the voltage transformer error is realized, and the limitations of long period and high cost of a traditional offline verification method are broken through.

The invention relates to the technical field of power system security, and particularly discloses a ground wire management operation system based on ground wire cabinet monitoring, which comprises a physical sensing layer, an edge computing layer, a block chain consensus layer and an application service layer. The physical sensing layer collects the identity, the in-place state and the electrical parameters of the grounding wire; the edge calculation layer carries out local data processing and event judgment; the block chain consensus layer realizes data credible evidence storage and cross-domain sharing through an intelligent contract and a distributed account book; and the application service layer provides asset panoramic display, cooperative verification and risk early warning services. Through the above architecture, unified management and safety collaboration of data of the whole life cycle of the grounding wire are realized, and the safety operation and maintenance level of a power grid is improved.

The invention discloses a power transmission line lightning stroke current waveform inversion method and device, terminal equipment and a storage medium, and belongs to the field of electric pulses. The method comprises the steps of obtaining electrical parameters of a to-be-processed power transmission line and fault waveform data of a plurality of detection points; constructing a lossy transmission line equivalent circuit according to the electrical parameters and determining a telegraph equation; replacing a space differential in the telegraph equation with a central difference, and replacing a time differential in the telegraph equation with a forward difference to determine a lightning wave propagation model; inputting the fault waveform data into a lightning wave propagation model, and traversing each detection point according to a preset time step to obtain intermediate waveform data; the fault waveform data comprises a first voltage parameter and a first current parameter of the head end; the intermediate waveform data comprises a second voltage parameter and a second current parameter of the tail end; and determining the original waveform of the lightning stroke current based on the intermediate waveform data and a preset shunting coefficient. By implementing the invention, the problem that the real lightning waveform during the lightning stroke fault is difficult to accurately obtain in the prior art can be solved.

The invention relates to the technical field of transformer substation monitoring, and discloses a power supply and distribution safety monitoring method and system for a transformer substation. The method comprises the following steps: acquiring various data in a transformer substation, and performing time synchronization calibration on the three types of data; dividing a power supply and distribution operation mode into a steady-state mode, a transient mode and an overload mode based on the calibrated data, extracting a harmonic distortion rate, a dielectric loss angle tangent value and a vibration spectrum entropy value of a current waveform in each mode, and generating an operation characteristic matrix; detecting an operation mode switching event, and calculating a characteristic offset between modes; decomposing a degradation characteristic component in the operation characteristic matrix according to a matching degree between an insulation aging stage and current dielectric loss data in a historical fault case library; coupling compensation correction is performed on the degradation characteristic component in combination with the cross influence relationship between the electrical parameters and the mechanical parameters of the equipment; and reversely tracing the abnormal signal source equipment node along the topological structure of the power supply and distribution network based on the corrected degradation characteristic component, and outputting an equipment maintenance sequence.

The invention relates to a cable insulation aging state evaluation method based on operation data analysis. The method comprises the following steps: S1, acquiring cable operation data in real time through a heterogeneous sensor array; s2, adopting a layered preprocessing strategy on the edge side; s3, constructing a hybrid deep learning model; s4, outputting by adopting an improved evidence theory fusion model; s5, outputting a fourth-level aging state label and a confidence evidence theory fusion model; electrical parameters and non-electrical parameters of the cable are collected in real time through the heterogeneous sensor array, and multi-dimensional data such as partial discharge signals, three-phase current harmonic distortion rate, distributed optical fiber temperature, mechanical vibration acceleration and environment dew point temperature are covered. The multi-source data fusion mode can comprehensively reflect the operation state of cable insulation, the limitation of single parameter evaluation is avoided, and the accuracy of the evaluation result is greatly improved.

The invention, which belongs to the technical field of the energy storage converter, discloses an electrical parameter sensing-based service life monitoring system for a capacitor of a network-forming type energy storage converter, comprising the following steps of: synchronously acquiring a direct-current voltage signal and a three-phase alternating-current signal of a direct-current bus capacitor through an electrical parameter acquisition module to form a multi-dimensional feature vector; a state observer built in the ESR estimation module is used for analyzing the signal, a target capacitor ESR value is obtained, and a capacitor core temperature value is obtained through back-stepping through an electric-thermal coupling field analysis model of a core temperature back-stepping module in combination with preset capacitor surface temperature data; based on the ESR value and the capacitor core temperature value, a life prediction module calculates the residual life and health degree index of the capacitor; and the power scheduling module executes capacitor health degree-power output joint scheduling according to the health degree index, adjusts the power fluctuation range of the converter, and feeds back real-time working condition data to the electrical parameter acquisition module, thereby realizing accurate monitoring of the capacitor state and intelligent scheduling of the converter power.

The invention discloses a method and a system for automatically testing electrical parameters of a fan, relates to a technology for testing the electrical parameters of the fan, and aims to automatically test the electrical parameters of the fan by automatically acquiring identification information, dynamically constructing a performance parameter mapping model and executing a self-adaptive test cycle. The technical problems of low efficiency, incomplete data coverage, significant personal error and poor model adaptability in the traditional test are effectively solved, the efficiency and accuracy of the automatic test of the electrical parameters of the fan can be improved, the data comprehensiveness is enhanced, and the model adaptability is improved.

The invention discloses an intelligent sensing socket based on equipment feature recognition and an energy consumption management method thereof. The method comprises the steps that a sensing layer collects various feature parameters and environment data of connecting equipment in an omnibearing mode; the control layer is used for deeply processing and analyzing data acquired by the sensing layer and executing equipment identification, strategy management and communication coordination; the platform layer provides far-end service support and comprises an equipment management module, a data analysis module, a user interaction module, an energy consumption management module and a core algorithm module; the original data collected by the sensing layer is subjected to feature extraction and identification analysis of the control layer, and the result is uploaded to the platform layer. According to the invention, through a multi-modal feature fusion identification technology, extremely high identification accuracy can be maintained in various use scenes; the potential risk can be identified before the fault occurs through the trend analysis of the electrical parameters and the temperature change.

The invention discloses a primary and secondary fusion pole-mounted switch fault studying and judging method based on edge computing, and relates to the technical field of power system fault detection and diagnosis. Primary side electric parameters and secondary side state data are synchronously collected under the unified time scale of edge computing nodes, a primary and secondary fusion time sequence data set is constructed, and the primary and secondary fusion pole-mounted switch fault studying and judging method based on edge computing is achieved; the problem that fine time sequence correlation analysis cannot be carried out due to asynchronous data time marks in a traditional method is solved fundamentally, and a reliable data foundation is laid for follow-up accurate analysis. The change trend of the primary response time difference sequence and the secondary response time difference sequence in multiple cycles is extracted and analyzed, progressive defects such as slow drifting of action time caused by aging and jamming of a switching mechanism or a control loop can be effectively recognized, early warning of latent faults is achieved, and the limitation that judgment is prone to missing in single-cycle threshold judgment is broken through.

The invention provides a power switch fault detection method, a photovoltaic power optimizer and a photovoltaic power generation system. The method comprises the steps that first electrical parameters of a circuit are collected, the circuit comprises a first power switch device and a second power switch device, the first power switch device is connected between the input end and the output end of the circuit in series, and the second power switch device is connected to the input end or the output end of the circuit in parallel; when the circuit works normally, the second power switch device is switched off when the first power switch device is switched on, and the second power switch device is switched on when the first power switch device is switched off; when the first electrical parameter meets a first condition, the duty ratio of the first power switch device is controlled to change; acquiring a second electrical parameter of the circuit in the duty ratio change process of the first power switch device; and determining a fault of the first power switching device or the second power switching device according to the change of the acquired value of the second electrical parameter.

The invention relates to the technical field of power system analysis, in particular to a distribution network grounding fault information research and judgment method and system, computer equipment and a medium. The method comprises the following steps: acquiring multi-source fault data in a distribution network system and preprocessing the multi-source fault data to obtain a standardized fault data set; extracting fault feature information from the standardized fault data set through a time sequence pattern recognition algorithm; based on the fault feature information, performing clustering analysis based on a topological relation on the fault feature information through a multi-source data fusion research and judgment algorithm, and generating a fault section positioning result; and generating distribution network grounding fault information according to the topological relation identifier and the electrical parameter characteristics in the fault section positioning result. By means of the mode, the technical problem that an existing ground fault research and judgment technology is insufficient in positioning precision in a power distribution network with the high reliability requirement is solved, and the automation and intelligence level of fault research and judgment and the accuracy and usability of a research and judgment conclusion are improved.

The invention discloses an oil well intelligent monitoring and remote control system, which belongs to the technical field of oil well monitoring and control, and is characterized by comprising the following steps: acquiring oil well production data, and obtaining a real-time indicator diagram and indicator diagram characteristics according to a preset electrical parameter inverse demonstration indicator diagram model; sending a preset security perturbation instruction sequence and obtaining response data corresponding to the security perturbation instruction sequence; obtaining core mechanism parameters of the oil pumping unit system based on the response data and a preset safety perturbation instruction sequence; obtaining a digital shadow model according to the core mechanism parameters and the indicator diagram characteristics; according to the method, core mechanism parameters are identified through anti-demonstration indicator diagram characteristics of an electric parameter anti-demonstration indicator diagram model and a safety perturbation instruction, and closed-loop intelligent control from oil well state monitoring to liquid production flow regulation and control is formed through the digital shadow model. And the accuracy of oil well flow control, the safety of system operation and the energy efficiency management level are improved.

The invention provides a measurement completion method, device and equipment serving panoramic transparency of a power distribution network, and the method comprises the steps: constructing a power distribution network graph model integrating a power grid topological structure, electrical parameters and measurement configuration information, carrying out bad data identification and cleaning on the original measurement data uploaded by the acquisition terminal to obtain a real-time measurement data set; for lines, buses, distribution transformers and circuit breakers in a power distribution network, missing electrical measurement values are obtained through classification certainty deduction calculation by using electrical network topology constraints, conservation laws and forward-back substitution load flow calculation so as to realize measurement completion; and performing quality evaluation on the complemented measurement data, and performing closed-loop feedback optimization based on an evaluation result. According to the scheme, high-precision complementation of the missing electrical quantity can be realized by depending on the measurement data acquired in real time, and the data storage and computing resource overhead is reduced.

The invention relates to a heat dissipation analysis and optimization method for an IGBT module, and the method comprises the steps: obtaining the electrical parameters and working conditions of the IGBT module, and calculating the total power loss during the operation of the IGBT module based on the electrical parameters and working conditions. Based on the total power loss and the physical structure of the IGBT module, a thermal resistance network model of a semiconductor junction region and a cooling environment and a Cauer type thermal impedance model based on dynamic characteristics are established. Inputting the total power loss into the thermal resistance network model and the Cauer type thermal impedance model, calling a finite element simulation algorithm to solve a temperature distribution field of the IGBT chip, and determining the total equivalent thermal resistance. And when the junction temperature or the total equivalent thermal resistance of the IGBT chip does not meet the set index, positioning the heat dissipation bottleneck and generating a heat dissipation optimization strategy. And executing a heat dissipation optimization strategy, and updating the thermal resistance network model and the Cauer type thermal impedance model after executing the heat dissipation optimization strategy so as to update the temperature distribution field and the total equivalent thermal resistance.

The invention belongs to the technical field of power distribution equipment monitoring, and relates to an intelligent power distribution equipment monitoring and fault early warning system and method, and the system comprises an acquisition unit which is used for obtaining an electric parameter, an environment parameter and an operation state quantity of power distribution equipment and forming time synchronization feature data; the topology unit is used for generating a local topology model according to the relation between the cabinet, the loop and the busbar and outputting topology attributes; the evaluation unit is used for generating an operation state evaluation result based on the feature data and the topological attributes and updating an early warning threshold value; the governance unit is used for performing deduplication, suppression and credibility calculation on the alarm to form a disposal level; the interlocking control unit is used for checking and outputting a control action when the disposal level meets an interlocking condition; and the operation and maintenance unit is used for generating a preventive test plan and updating parameters based on a test result. According to the technical scheme of the invention, topological association and threshold update are introduced into monitoring, evaluation, disposal and control links, so that state recognition and control execution are kept consistent.

The invention provides an oil well load indicator diagram prediction method and system, and the method comprises the steps: synchronously collecting an initial electrical parameter data set, a load data set, an accelerometer data set and a barometer data set of an oil well, the initial electrical parameter data set comprising an active power data unit, and then obtaining an active power data segment and a load data segment; obtaining a time lag based on the two, and further obtaining a final electrical parameter data segment; acquiring a reference displacement sequence through the accelerometer data set and the barometer data set; obtaining a dynamic stroke length based on the reference displacement sequence and the load data segment, and further obtaining a final displacement sequence; and constructing a prediction neural network, and obtaining an oil well load indicator diagram based on the prediction neural network and the real-time electrical parameter data segment. According to the technical scheme, the indicator diagram is obtained through continuous and high-frequency real-time electric parameter data segments, motion period information loss caused by low-frequency data collection is avoided, accumulative errors are avoided, and the accuracy of the indicator diagram is ensured.