15397results about "Current/voltage measurement" patented technology

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

The invention relates to the technical field of electric energy storage systems, in particular to an energy storage power station operation monitoring management system and method. The method comprises the following steps: obtaining voltage data of each energy storage node in a standing state, and carrying out periodic voltage sampling within a preset standing time, thereby obtaining voltage springback time sequence data; performing temperature correction processing on the voltage springback time sequence data so as to obtain standardized voltage springback characteristic data; acquiring charge state data of each energy storage node before and after operation; calculating a charge-discharge depth change value of each node according to the charge state data; and constructing a node health state evaluation model according to the standardized voltage rebound characteristic data and the charge and discharge depth change value, and obtaining node aging differentiation parameters. According to the method, the node charging and discharging power allocation model is constructed based on the node aging differentiation parameters, differentiation management of energy storage nodes in different states is achieved, and the problem that the aging degree between the nodes is further expanded in a traditional method is solved.

The invention provides a method for calculating the accuracy of charging voltage and current frequency data of a charging pile through a standard deviation, and the method comprises the steps: extracting continuous and non-zero voltage and current data segments from original time sequence data collected from the charging pile, and obtaining an effective charging data sequence; for the effective charging data sequence, a time sequence segmentation method is adopted, charging starting time and charging ending time are taken as boundaries, and a complete charging process data segment is divided; under the determined time granularity, the standard deviation of the voltage and current data is calculated, and a data fluctuation index is obtained; for the zero charging data, a rule-based classification algorithm is adopted, and in combination with an equipment operation log and an environment parameter, the reason category of the zero charging data is judged; and inputting the power comparison reference, the data fluctuation index and the fault judgment result into a preset comprehensive evaluation model, and calculating the operating state score of the charging pile.

The invention discloses a system for monitoring line loss rate abnormity of a power distribution network station area, and relates to the technical field of power distribution network monitoring. The line loss rate abnormity monitoring system for the power distribution network station area comprises a data acquisition module, a data processing module, a line loss rate calculation module, an abnormity identification and alarm module and an on-site troubleshooting and diagnosis module. Through integrating functions of intelligent electric meter data acquisition, power grid data collection and area division, data processing, line loss rate calculation, abnormity identification and alarm, on-site troubleshooting and diagnosis and the like, comprehensive and real-time monitoring of the line loss rate of the power distribution network station area is realized. And a regional line loss calculation model is established according to the characteristics of the power distribution network of each region, and the line loss rate can be monitored in real time, dynamically calculated and predicted, so that the efficiency and accuracy of line loss management are greatly improved. And the abnormity identification and alarm module can timely discover and give an alarm of line loss abnormity, provides probability analysis of abnormity reasons, and is helpful for operation and maintenance personnel to quickly respond and process.

The invention discloses an emergency power supply quick access method based on scene recognition, which relates to the technical field of emergency power supplies, and comprises the following steps: in an emergency power supply system, firstly obtaining electrical input parameter information through a preset detection frequency; by introducing a load change evaluation mechanism based on machine learning, the system can identify load fluctuation in real time and dynamically adjust the detection frequency, and the response speed to the rapid change of the load is improved. Compared with a traditional detection method, the load state is intelligently evaluated and accurately divided, it is ensured that the power factor is adjusted in time when the load fluctuates violently, and power loss and waste are reduced. Correction is carried out according to the traditional frequency during stable load fluctuation, the frequency is dynamically adjusted during rapid fluctuation, the electric energy use is optimized, the electric power quality is improved, and harmonic distortion and overload risks are reduced.

The invention discloses a booster station monitoring operation and maintenance system, and relates to the technical field of data management. A plurality of heterogeneous intelligent sensors are deployed in a booster station to monitor the operation state of equipment in real time, an electromagnetic field sensor is utilized to sense the electromagnetic interference intensity, and when abnormal high-frequency pulses are detected, a data anomaly detection mode is triggered to detect the abnormal high-frequency pulses; calculating an error compensation value according to the abnormal degree, correcting abnormal data, ensuring the accuracy of the data, performing equipment state evaluation according to the recovered data, and if potential safety hazards are found, sending intelligent early warning to operation and maintenance personnel and providing suggestions for adjusting monitoring strategies or optimizing shielding measures; according to the invention, the anti-interference capability and accuracy of booster station monitoring data are effectively improved, the risk of operation and maintenance misjudgment caused by electromagnetic interference is reduced, the reliability of intelligent monitoring in an ultrahigh-voltage power transmission scene is enhanced, and a powerful guarantee is provided for safe and stable operation of a power system.

The invention discloses a multi-parameter measurement system and method for a power frequency non-partial discharge test transformer, and relates to the technical field of state monitoring of high-voltage test equipment and power equipment, and the system comprises a synchronous trigger unit which is connected to a power frequency voltage signal collection end and is used for detecting a zero crossing point of a power frequency voltage signal and generating a synchronous trigger signal, a power frequency period is divided into a plurality of sub-windows with equal phase angles. According to the multi-parameter measurement system and method for the power frequency non-partial discharge test transformer, the problem of phase mismatch of a power frequency signal and a high-frequency partial discharge signal in traditional multi-parameter measurement is effectively solved through a power frequency phase locked synchronous trigger mechanism and a dynamic noise suppression technology; and the positioning precision of the partial discharge source and the insulation defect diagnosis reliability are improved. The nanosecond-level time sequence error control is realized, and the power frequency coupling interference is inhibited while the details of the high-frequency pulse are kept by combining a phase correlation dynamic filtering strategy, so that the signal-to-noise ratio of the weak discharge signal is improved.

The invention relates to the technical field of industrial safety, and discloses an electrolytic aluminum short circuit port operation safety early warning system based on multi-parameter collaborative awareness and intelligent diagnosis, and the system comprises a parameter collaborative awareness module, a dynamic diagnosis module, an early warning decision module, and an execution feedback module. By constructing a multi-dimensional parameter collaborative sensing mechanism, fusing temperature field distribution, current balance degree and insulation state multi-source data in real time and dynamically capturing early abnormal symptoms of a short circuit port, the hysteresis problem of traditional single-parameter threshold monitoring is solved, conversion from passive response to active defense is achieved, and the comprehensiveness and timeliness of operation state monitoring are improved; and meanwhile, based on a historical fault database and a real-time evolution model, a health index is generated and a fault path is predicted, so that maintenance personnel can pre-judge a development trend and a time window of potential risks in advance, and sudden equipment accidents are avoided.

The invention relates to the technical field of power distribution network fault monitoring, in particular to a power distribution network fault monitoring system and method. Performing primary processing and feature extraction on multi-source data by edge calculation; the distributed computing node further fuses and analyzes the data by using a multi-source data fusion technology to generate a fault feature vector; summarizing analysis results of the distributed computing nodes, performing comprehensive analysis on the fault feature vector, and determining a fault position; comparing and verifying the fault position result with historical data, and if uncertainty exists, starting a secondary diagnosis process; and according to a fault position result, fault early warning information is automatically sent to operation and maintenance personnel. By collecting multi-source data such as traveling wave signals, current and voltage and combining the data fusion technology of edge calculation and distributed calculation nodes, single data sources are effectively reduced, and the accuracy of fault positioning is improved.

The invention discloses an intelligent diagnosis system of a power distribution network fault self-judgment switch device, and relates to the technical field of power system automation and intelligent power grids, and the intelligent diagnosis system comprises an intelligent diagnosis system interconnection hub which is in communication connection with a power parameter monitoring module, an intelligent fault detection module and a visual decision support module; the electric power parameter monitoring module monitors the state of a power distribution network in real time through a mutual inductor, voltage, current, power factors and harmonic data are collected through an intelligent electric meter, the intelligent fault detection module achieves fault recognition, trend prediction and anomaly detection through an LSTM neural network, and the decision support module provides a graphical interface and automatically generates a fault response strategy. According to the method, autonomous judgment and quick response of the power distribution network fault are achieved, the accuracy and efficiency of fault detection are remarkably improved, the false alarm rate and the missing report rate are reduced, meanwhile, the decision support module of the graphical interface simplifies the fault processing flow, and the stability and safety of power grid operation are improved.

The invention provides a power line fault distance measurement system and method, and belongs to the technical field of power line detection. The system comprises monitoring terminals deployed at two ends of a line and a background processing system, and each monitoring terminal comprises a sensor module used for detecting a traveling wave current signal related to a power line fault in the line; the data acquisition module is used for extracting the arrival time of a traveling wave head according to the detected traveling wave current signal; the GPS time synchronization module is used for adjusting the local clock of the data acquisition module, reducing the time synchronization error of the two monitoring terminals and correcting the arrival time of the traveling wave head; and the background processing system is used for calculating a traveling wave time difference and a junction wave velocity normalization processing result according to the corrected arrival time at the two ends of the line, and determining a fault position coordinate and a fault type. According to the invention, collaborative optimization of fault positioning precision and type identification in a hybrid power line environment is realized.

The invention discloses a power distribution system and method for a modular power supply, and aims to optimize power distribution through an intelligent means, improve the operation efficiency and stability of the system, and control a unit to check the states of all power supply modules, and evaluate the required total power output according to the load demand. Then, based on the capability of each module and a redundancy planning principle, an appropriate module combination is selected to meet the load requirement, and necessary voltage or current adjustment is carried out to ensure stable power supply. In the operation process, the control system continuously monitors the state and the overall performance of each module, and once a fault or an abnormal condition is detected, measures such as automatic switching to a standby module are taken immediately, so that the continuity and the safety of the system are ensured. In addition, through analysis and learning of historical data, the system can continuously optimize a power distribution strategy, and efficiency and reliability are further improved. And the ability of the system to deal with emergency situations is also enhanced.

The invention relates to an optically controllable switching device having a first and second line section, between which an optically controllable switch (T2) is inserted, and having a sensor element (SE). The optically controllable switch (T2) disconnects or connects depending on an optical switching signal (SB), whether the switch is "on" or "off". The sensor element (SE) comprises a carrier material (TM) with crystals comprising one or more paramagnetic centers (NV). When irradiated with pump radiation (LB), the sensor element (SE) emits fluorescent radiation (FL) which depends on the magnetic flux density B at the location of the sensor element (SE). A line current (I LTG) in the first line section of the line (LTG) generates a magnetic field with a flux density B which, with sufficient current strength, influences the fluorescent radiation (FL) from one or more paramagnetic centers (NV). The optically controllable switching device has first means (LWL1, PL1, LIV) for irradiating the one or more paramagnetic centers (NV) with pump radiation (LB) and second means (LWL2, F1, PD, LIV) for detecting and separating the fluorescent radiation (FL) and for detecting a measured value signal which depends on the intensity of the fluorescent radiation (FL) and / or on the phase shift of the modulation signal of the temporal profile of the intensity of the fluorescent radiation (FL). The optically controllable switching device has third means (LIV) for generating an optical switching signal (SB) and for controlling the state of the optically controllable switch (T2) using this optical switching signal (SB).The optical switching signal (SB) depends on the intensity of the fluorescence radiation (FL) and / or on the phase shift of the modulation signal of the temporal course of the intensity of the fluorescence radiation (FL).

The invention discloses a leakage current monitoring and early warning system based on current monitoring of a new energy photovoltaic inverter, and belongs to the technical field of leakage current monitoring. The invention discloses a leakage current monitoring and early warning system based on current monitoring of a new energy photovoltaic inverter, and the system comprises a sensing module, a data collection and preprocessing module, a central processing module, a multi-stage dynamic threshold module, a complex mode recognition module, and a multi-stage early warning module. And real-time early warning cannot be realized, and a complex leakage current mode cannot be identified. According to the environment parameters monitored in real time and the working state of the inverter, the threshold value of the leakage current is dynamically adjusted, the complex leakage current mode is recognized by adopting a machine learning and mode recognition algorithm in combination with the comprehensive analysis result of the zero-sequence current, the phase and the frequency parameters, and the reliability of the inverter is improved. The multi-level early warning module can give alarms of different levels according to the degree of the leakage current.

The invention discloses a power grid visual diagnosis system based on multi-source data fusion, and relates to the field of power grid diagnosis systems. The system comprises an information acquisition module, an electrical fault alarm module, a non-electrical quantity fault alarm module, a fault discrimination module, a communication module and a fault recording module. Firstly, information of a power grid is collected through the information collection module, then the information is transmitted to the electrical fault alarm module and the non-electrical fault alarm module for monitoring, corresponding actions are made when abnormity occurs, then the information is transmitted to the fault judgment module, input information is analyzed, and the fault type is obtained; meanwhile, the fault recording module records and broadcasts the power grid; the communication module collects information of other modules in the system and exchanges information with the monitoring system. According to the method, the abnormal information of the power grid is monitored and alarmed in combination with the multi-source data, and the fault type is judged through the data model, so that the omnibearing monitoring of the power grid is realized, and data support is provided for a subsequent maintenance plan.

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

The invention relates to the technical field of power equipment state monitoring, in particular to a lightning arrester state monitoring method, system and equipment based on multi-physics field coupling and a storage medium. Acquiring current-voltage characteristic parameters, temperature distribution data and mechanical stress data of the lightning arrester, performing electro-thermal-mechanical coupling analysis based on multi-physical field monitoring data, identifying overlapping positions of an electric field distortion area, a temperature abnormal area and a stress concentration area, and determining the overlapping positions as a degradation key area; establishing a correlation response relationship between the leakage current and the temperature and a transfer response relationship between the temperature and the mechanical stress for the deteriorated key area; on the basis of the leakage current change trend of the degradation key area and the coupling influence of the superimposed temperature field and stress field, a degradation feature fusion factor is constructed, the evolution law of the degradation feature fusion factor in the time sequence is analyzed, and the degradation threshold value under the multi-field synergistic effect is determined in combination with the electric-thermal coupling acceleration effect and the thermal-mechanical coupling weakening effect.

The invention relates to the technical field of resistance measurement, in particular to a constructional engineering electrical grounding resistance detection system which comprises a path monitoring module, a delay judgment module, a path switching module, an interval regulation and control module and a data correction module. According to the method, the voltage stability is judged by collecting the path node voltage change value and the timestamp and calculating the voltage difference, precision screening is completed in cooperation with path state information, the abnormal fluctuation recognition capability is improved, the delay value change trend is combined with a period difference value comparison mode, and path stability evaluation is assisted; the path switching behavior is combined with feedback response and relay state recognition, the accuracy of channel switching is improved, the sampling interval is dynamically regulated and controlled according to delay change, clearer response characteristics are obtained in combination with waveform displacement mapping operation, the accuracy of data alignment is enhanced, and the accuracy of channel switching is improved. The whole process realizes a closed-loop processing structure of path identification, delay judgment, switching confirmation and waveform correction, and effectively improves the response consistency and time precision of ground resistance measurement.

The invention discloses a power battery health state on-line monitoring and early warning method and system based on multi-data fusion. The method comprises the following steps: synchronously acquiring multi-dimensional heterogeneous data in a battery operation process through a multi-sensor data acquisition module; carrying out standardization processing and preprocessing on the multi-dimensional heterogeneous data, extracting a comprehensive feature vector and carrying out multi-sensor data fusion; a multi-sensor data fusion algorithm is adopted to carry out weight distribution on the fused comprehensive feature vector, a dynamic weight coefficient is calculated according to the sensitivity and reliability of each parameter to the health state of the battery, and a fused battery state feature descriptor is obtained; and performing mode recognition and state classification on the fused battery state feature descriptors by using a pre-established evaluation model to realize quantitative evaluation of the health degree of the battery. According to the invention, real-time monitoring, evaluation and early warning of the health state of the battery in the whole life cycle are realized, and a reliable basis is provided for safe operation and maintenance decision of a battery system.

The present invention relates to the field of cable fault early warning technology, and in particular, to a cable fault early warning method and system based on GIS. The present invention solves the problem of how to use a GIS system to accurately identify potential faulty cables in a stacked line and improve the accuracy of early warning. To solve the above problem, the present invention provides an early warning method, including: obtaining a location image and working information of a cable line according to a GIS system; determining a historical fluctuating voltage within a historical monitoring period according to the working information, and predicting a theoretical fluctuating voltage according to future meteorological data and the historical fluctuating voltage; recording a historical maximum voltage within a historical monitoring period as a voltage fluctuation threshold; correcting the voltage fluctuation threshold according to the operating conditions and location image of the cable line to obtain a corrected threshold; judging whether the cable line meets early warning conditions according to the rated voltage, the theoretical fluctuating voltage and the corrected threshold; and modifying the early warning conditions according to the corrected voltage and the corrected threshold.

The invention discloses a high-current accurate measurement method, which relates to the technical field of current measurement, and comprises the following steps: obtaining multi-dimensional measurement parameters, carrying out preprocessing, constructing an error model to obtain a comprehensive error, and carrying out calculation based on the comprehensive error to obtain a comprehensive feedback value; based on the comprehensive feedback value, utilizing a closed-loop feedback mechanism to dynamically optimize a compensation parameter, and further utilizing the compensation parameter and the comprehensive feedback value to carry out error correction on the measurement current; real-time measurement parameters are obtained through a built-in sensor and a factory calibration reference model, an error model is constructed, the dynamic closed-loop self-calibration function is achieved, dependence on external calibration equipment is not needed, the problems that a traditional measurement method is high in complexity and high in cost are solved, and the method is suitable for integrated application of a high-precision electric energy meter and industrial measurement equipment and has wide application prospects. And meanwhile, the real-time performance and the convenience of measurement are remarkably improved.

The invention discloses an ultrasonic transducer characteristic parameter test system, which belongs to the technical field of ultrasonic detection and comprises an excitation signal generation module, an impedance matching adjustment module, a resonance characteristic extraction module, a signal acquisition and processing module, a temperature control loading module, a physical vibration isolation module and a frequency spectrum calibration module. The excitation signal generation module is combined with an off-chip frequency locking feedback loop through an on-chip oscillator to generate a multi-frequency-point sweep frequency pulse array, the multi-frequency-point sweep frequency pulse array is connected to the resonance characteristic extraction module after passing through the impedance matching adjustment module, the multi-frequency-point sweep frequency pulse array and the frequency spectrum calibration module construct a bidirectional feedback path and share harmonic node synchronous calibration logic, and coupling offset compensation is achieved; the frequency spectrum calibration module performs bidirectional real-time error correction on the excitation output frequency and the resonance input phase; and the transducer is arranged in the thermal convection temperature equalizing cabin of the temperature control loading module and is overlapped with the physical vibration isolation module. The method has the advantages that high-precision, low-interference and full-frequency-domain dynamic testing of characteristic parameters of the ultrasonic transducer is achieved, and testing stability is improved.

The invention relates to the technical field of fault diagnosis, in particular to a big data-based bus duct fault diagnosis system, which comprises a data synchronization module, an anomaly identification module, an amplitude judgment module, a traceability positioning module and a risk classification module. According to the invention, automatic synchronization of multi-node data is realized through a time correlation mode, layered discrimination is carried out on an abnormal working condition state by means of continuous comparison of data streams, dynamic identification of node parameter fluctuation is realized through interval adjustment and trend aggregation, and abnormal signals are positioned and traced in a link topology. The node operation risk is comprehensively researched and judged in combination with historical and real-time parameters, linkage analysis and active classification of multi-dimensional information are formed, efficient connection of links such as monitoring, recognition, tracing and classification is promoted, operation and maintenance decision and data full-process response are supported, node difference judgment and risk prompt in a complex scene are facilitated, and the method is suitable for popularization and application. And intelligent operation and maintenance of power supply system data driving are improved.

The invention provides a power grid cable temperature monitoring and alarming method, device and equipment and a storage medium. The method comprises the following steps: acquiring cable outer sheath temperature data through a temperature sensor and acquiring cable current load data and health condition indexes; calculating the temperature of the conductor according to the temperature data of the outer sheath, the current load data and the environmental parameter data by using a superposition principle calculation model based on time attenuation coefficient correction; according to the cable health condition index, calculating a grading early warning threshold value, and comparing the conductor temperature with the threshold value to determine an early warning grade; and when the early warning level reaches a preset condition, matching the sheath temperature data with a template in an abnormal mode library, identifying an abnormal reason through multiple algorithms, and generating a disposal suggestion in combination with geographic information system data. According to the invention, the conductor temperature can be quickly and accurately calculated, the early warning threshold value is dynamically adjusted, the abnormal reason is intelligently identified, and the accuracy and effectiveness of cable temperature monitoring and early warning are greatly improved.

The invention relates to the technical field of monitoring, in particular to a cable aging monitoring and early warning method and system. The method comprises the following steps: acquiring cable monitoring data, and performing electrical characteristic analysis to obtain electrical characteristic data; performing conductor oxidation analysis based on the electrical characteristic data to obtain conductor oxidation data; cable image feature extraction is carried out according to the cable monitoring data, cable morphology type division is carried out, and curve segment cable data and winding segment cable data are obtained; performing bending fatigue analysis on the curve section cable data to obtain bending fatigue data; performing winding stress analysis on the winding section cable data to obtain winding stress data; performing material damage evaluation according to the bending fatigue data and the winding stress data to obtain material damage data; and carrying out damage position identification according to the material damage data to obtain damage position data. The cable aging monitoring accuracy and the early warning response rate are improved based on the monitoring technology.

The invention relates to the field of cable monitoring, in particular to a high-voltage cable running state comprehensive monitoring method based on a TMR magnetic sensor. According to the method, firstly, a TMR magnetic sensor is adopted to collect sheath current and partial discharge pulse signals of a cable joint grounding wire, then amplification, filtering and AD change are carried out, Fourier transform is carried out, and low-frequency signals and high-frequency signals are separated out; carrying out frequency spectrum analysis on the high-frequency signal, preliminarily judging whether a discharge defect exists or not, carrying out Fourier inversion on the low-frequency signal, evaluating a grounding state and an insulation health state of a cable sheath, carrying out Fourier inversion on the high-frequency signal, and carrying out partial discharge fault type analysis in combination with a time domain partial discharge pulse signal and a power frequency current phase; and finally, comprehensively evaluating the operation state of the high-voltage cable by analyzing the grounding current of the sheath and the related parameters of partial discharge. According to the invention, the hardware cost is reduced, the system design and installation complexity is simplified, and the accuracy of partial discharge fault type identification is improved.

The invention discloses a method for diagnosing an IGBT open-circuit fault and a current sensor fault of a T-type three-level inverter, belongs to the technical field of inverter fault diagnosis, and is used for solving the problems of diagnosis and positioning when a three-level T-type inverter IGBT switch tube and a sensor have faults. Comprising the following steps: classifying and marking and numbering fault types of an open-circuit fault of a T-type three-level inverter IGBT and a fault of a current sensor; fault samples are collected and divided into a training set and a test set according to the proportion of 7: 3; a TCN-LSTM-SelfAction fault diagnosis model is constructed, and model training is carried out by using the training set; the trained model is used for evaluating the accuracy of fault type diagnosis of the training set and the test set. According to the diagnosis method, the fault types and the fault positions of the IGBT open-circuit fault and the current sensor fault can be diagnosed at the same time, the TCN and the LSTM are selected to be combined, local information and global relation of fault features can be captured at the same time, and meanwhile, the performance of a model is improved by combining a self-attention mechanism in the LSTM, and the accuracy is improved.

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

The invention discloses a self-adaptive switching system and method for power supply and distribution, and the system comprises a data collection module, an analysis module, a modeling module, a decision module, an execution module and an early warning module, and the data collection module generates a real-time monitoring signal; the analysis module performs dynamic weighted evaluation according to the real-time monitoring signal and generates an evaluation signal; the modeling module constructs a virtual model of the power supply system and generates an analog signal; the decision signal generates a control signal containing a path selection instruction; the execution module controls the semiconductor device to switch the physical connection state of the power supply according to the control signal and generates a switch feedback signal; and the early warning module forms an equipment health assessment result according to the switch feedback signal and an abnormal arc signal when the execution module switches the connection state and transmits the result to the decision module. The self-adaptive switching system for power supply and distribution can solve the problem that a traditional power supply and distribution switching system depends on a single threshold to judge the state of a power supply and cannot comprehensively evaluate the quality of electric energy.

The invention relates to the technical field of three-phase current data processing, in particular to an electric energy quality intelligent monitoring method and system for a four-fusion integrated terminal. The method comprises the following steps: acquiring a current sequence and a monitoring phase corresponding to each phase line; calculating the suspected abnormal degree of the current monitoring point in the current sequence of the monitoring phase, and judging whether the current monitoring point is an abnormal monitoring point or not; if the current monitoring point is an abnormal monitoring point, acquiring a continuation curve; calculating a continuation distribution characteristic value of the abnormal monitoring point according to each data point in the continuation curve; correcting the continuation distribution characteristic value based on the distance between trough points of the crest of the continuation curve and the height of the crest to obtain a corrected continuation distribution characteristic value; obtaining the current abnormal degree of the abnormal monitoring point based on the suspected abnormal degree of the abnormal monitoring point and the corrected continued distribution characteristic value; and judging whether the electric energy quality is abnormal according to the current abnormal degree. According to the invention, the electric energy quality of the four-fusible integrated terminal can be accurately monitored.