62 results about "Fault severity" patented technology

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

The invention relates to the technical field of vehicle drive-by-wire function safety, and discloses a brake-by-wire and steering-by-wire fault reconstruction system and a vehicle redundancy architecture wherein a fusion diagnosis module is used for obtaining sensor parameters in a brake subsystem and a steering subsystem in real time through a plurality of sensors; the fault reconstruction module is used for performing multi-level fault reconstruction on the braking subsystem based on a three-level response mechanism according to the fault type and the fault severity level; performing redundancy control link dynamic switching on the steering subsystem; and the resource scheduling module is used for determining the energy distribution proportion of brake locking and steering differential motion according to the voltage of the shared emergency power supply. According to the embodiment of the invention, the global composite fault criterion library is constructed through the fusion diagnosis module, the multi-dimensional and cross-system combined diagnosis of the braking subsystem and the steering subsystem is realized, the complex fault mode can be effectively covered, the recognition and response capability of the system to multiple concurrent faults is improved, and the safety degree of the vehicle is further improved.

The invention provides a DC Buck converter voltage and current sensor fault-tolerant control system, which comprises the following steps: establishing a time-varying state space model of a DC Buck converter, and dynamically reconstructing a parameter matrix by the time-varying state space model according to the real-time change of a duty ratio; the time-varying augmentation fault estimator is used for monitoring faults of a voltage sensor and a current sensor in real time, the fault estimator is implemented through a model reference adaptive filter, a Buck converter is adopted to construct a reference model in a healthy state, and efficient real-time diagnosis and correction can be achieved through the faults of the voltage sensor and the current sensor of the Buck converter. And various types of sensor faults such as a jump fault, an offset fault, a stuck fault, a high-frequency fault and a gap fault can be effectively estimated. For measurement distortion of the voltage sensor and the current sensor, the fault estimator can provide comprehensive evaluation information of fault types and fault severity.

The invention discloses an electric power system fault protection system and method based on multi-modal data fusion, and relates to the technical field of electric power systems.The method comprises the steps that an electric power equipment fault analysis model is constructed, and historical electric power fault data is collected to train the electric power equipment fault analysis model; inputting the electrical measurement abnormal condition, the equipment state abnormal condition and the environment meteorological abnormal condition into the power equipment fault analysis model to judge abnormal equipment, positioning a fault point of the abnormal equipment, analyzing the abnormal condition of a module corresponding to the fault point based on the electrical measurement data of the fault point of the abnormal equipment, and determining the abnormal condition of the module. Meanwhile, the influence degree of equipment outage caused by the fault of the fault point is evaluated, the equipment fault severity degree is analyzed based on the abnormal condition of the module corresponding to the fault point and the influence degree of equipment outage caused by the fault of the fault point, and an equipment first-aid repair sequence is generated based on the equipment fault severity degree; the accuracy of power failure degree judgment and the emergency response capability of a power system are improved.

The invention is suitable for the technical field of expressways, and provides an intelligent scheduling method for operation and maintenance resources of electromechanical equipment of an expressway, and the method comprises the steps: collecting the road network structure data of the expressway, and constructing an equipment network topological graph; based on the equipment network topological graph, obtaining equipment operation parameters, meteorological data and traffic flow data, and preprocessing the data to obtain a multi-source data set; based on the multi-source data set, the fault severity of the electromechanical equipment of the highway is quantized, and a severity quantized value is obtained; based on the equipment network topological graph, combining with historical fault records, analyzing fault association influence, carrying out fault evolution, and calculating a fault influence score of the equipment; based on the severity quantized value and the fault influence score, the operation and maintenance priority of the equipment is determined in combination with the environmental factors and the traffic factors; and on the basis of the operation and maintenance priorities, operation and maintenance personnel information and material inventory information are combined, a scheduling scheme is generated by adopting a multi-objective optimization algorithm, and efficient operation and maintenance of all electromechanical devices on the expressway are realized.

The invention discloses a railway auxiliary positioning method and system for a signal fault scene, and the method comprises the following steps: a train auxiliary positioning system obtains a train positioning quality parameter and a train Beidou positioning signal, carries out the normalization processing of the train positioning quality parameter, and calculates a fault diagnosis coefficient; setting a sliding window threshold according to the fault diagnosis coefficient; designing a train auxiliary fitting algorithm to obtain a final fitting position of the train; and finally, jointly calculating the train position according to the train inertial navigation position and the final fitting position of the train. According to the method, the fault diagnosis coefficient is calculated according to the normalized train positioning quality parameters, then the fault severity of the positioning signal is judged, the richness and the calculation efficiency of positioning signal quality judgment are improved, the auxiliary fitting algorithm is designed, the fitting parameters of the train auxiliary fitting algorithm are solved continuously in an iteration mode, and the fault diagnosis accuracy is improved. The train position is calculated in combination with the train inertial navigation position, and the accuracy of the scheme is further improved.

The invention relates to the technical field of electronic device test, in particular to a high-frequency high-speed switch test system based on diversified fusion analysis, which comprises a diversified test center, a test execution unit, an acquisition response unit, a progressive test unit, an area positioning unit, a degree division and label acquisition unit and a test feedback unit, according to the method, preliminary analysis is performed from the perspective of test preconditions, interference of test preparation work of the high-frequency high-speed switch on subsequent tests is reduced, and the performance condition of the high-frequency high-speed switch is evaluated in an information progressive and comprehensive quantification mode so as to judge whether the performance of the high-frequency high-speed switch is abnormal or not. According to the method, multi-dimensional collaborative testing of electrical performance, thermal characteristics and environmental adaptability is achieved, so that the test coverage rate is increased, the reliability of performance test results is improved, analysis is carried out from the perspective of feature extraction, fault positions and fault types are judged, and a test feedback label of the whole high-frequency high-speed switch is obtained through fault comprehensive severity analysis.

The invention discloses a rolling bearing fault detection method and system based on mechanism and data fusion, and relates to the technical field of equipment state monitoring and fault diagnosis, and the method comprises the steps: collecting a multi-source operation state signal and an operation condition parameter of a rolling bearing in real time; constructing and solving oscillatory differential equations under different fault types based on the signals and the parameters in combination with a rolling bearing dynamical model, and generating a mechanism feature library; denoising vibration signals in the multi-source operation state signals, and performing data reconstruction on the denoised vibration signals based on a gamma hybrid model; generating a high-dimensional data feature set according to the reconstructed vibration signal, and performing dimension reduction on the high-dimensional data feature set by adopting a matrix t-SNE algorithm to obtain low-dimensional data features; constructing a joint feature set according to the low-dimensional data features and the mechanism features, and calculating a health index; and the severity and position of the fault are judged based on the joint feature set and the health index, so that the precise detection requirement of the early-stage tiny fault of the rolling bearing under the complex working condition can be met.

This invention provides a method and system for handling redundant communication faults in a steer-by-wire system, relating to the field of steer-by-wire technology. The method includes: acquiring the original communication status parameters of independently configured first and second communication channels; the first and second communication channels are two independent communication channels between the main control unit and the redundant control unit in the steer-by-wire system; verifying the original communication status parameters based on a preset multi-dimensional verification strategy, and determining the current fault level of the system by combining preset fault level judgment rules and a dynamically adjustable fault judgment cycle, wherein the fault level is based on the communication channel status combination and fault severity; and executing matching fault handling, signal switching, and current regulation strategies based on the fault level, combining hierarchical signal control logic and a motor current gradient limiting mechanism to achieve precise switching between system signals and motor current.

This application proposes a method and apparatus for detecting phase loss in a power plant starting transformer, relating to the field of power fault detection technology. The method includes: acquiring the signal energy distribution of the starting transformer; constructing an energy feature vector reflecting the relative energy relationship between the three-phase current signals and inputting it into a phase loss identification model to obtain the initial probability of phase loss faults in each phase; performing state inversion calculations by combining real-time calculated zero-sequence and negative-sequence current amplitudes to infer the changes in the equivalent circuit parameters of the transformer's internal windings and leads before and after the fault, thereby correcting the initial probability to obtain a confidence level, and finally generating a phase loss detection report including the fault phase and fault severity. This application comprehensively characterizes the energy correlation characteristics of the three-phase currents through energy feature vectors and corrects the initial probability of the model using changes in equivalent circuit parameters, eliminating random biases in the output of a single model, achieving accurate matching between the fault determination result and the actual fault state of the transformer, and improving the accuracy of fault phase identification.

The application discloses a primary and secondary fusion intelligent circuit breaker on a column, which comprises a fault detection module, a protection action module, a reclosing module and an information reporting module. The fault detection module collects multi-dimensional electrical quantities through current and voltage transformers, judges the fault and identifies the type in combination with dynamic normal range and duration threshold; the protection action module calculates the fault distance based on the corrected impedance, determines whether to act in combination with the current direction and the protection range, and selects the protection level according to the fault severity; the reclosing module dynamically sets the observation time window, distinguishes the transient or permanent fault through the retest verification mechanism, and adaptively adjusts the reclosing strategy; the information reporting module uploads the fault related information. The application solves the problems of the traditional circuit breaker, such as high misjudgment rate, poor protection selectivity, rigid reclosing logic and the like, and significantly improves the accuracy, speed and self-healing ability of the power distribution network fault processing.

This invention relates to the field of power distribution network monitoring technology, and is a digital panoramic monitoring method and system for power distribution networks based on multi-dimensional data. The specific method includes: collecting working data from the working surfaces of high-voltage fuse contacts in the power distribution network; analyzing the working data, outputting abnormal feature identification tags and fault feature screening tags, filtering the abnormal feature identification tags based on the fault feature screening tags, quantifying the severity of faults on the working surfaces of the contacts using a fuzzy comprehensive evaluation model, obtaining a quantitative value of the fault severity, and simultaneously generating maintenance strategy suggestions. This invention solves the problems of low fault detection accuracy and the inability to timely and accurately identify and handle complex faults in existing technologies.

The invention discloses a permanent magnet synchronous motor turn-to-turn short circuit fault multivariate state monitoring method based on counter electromotive force, which comprises the following steps of: obtaining dq-axis voltage and dq-axis current according to direct current bus voltage, three-phase current and electrical angle of a permanent magnet synchronous motor, and further obtaining dq-axis counter electromotive force and fault component thereof; performing negative sequence rotation transformation on the fault component of the dq-axis counter electromotive force to obtain a dq-axis counter electromotive force negative sequence component, and extracting a normalized dq-axis counter electromotive force negative sequence direct current component to obtain the amplitude and phase of the counter electromotive force negative sequence component; judging a motor fault state according to the amplitude to obtain a fault indication, and obtaining a fault phase according to the phase; the counter electromotive force, the fault indication and the fault phase are used as input, and the fault severity is estimated through an extended Kalman filtering algorithm. According to the method, extra hardware is not needed, the turn-to-turn short circuit fault detection, positioning and fault severity estimation of the permanent magnet synchronous motor servo system can be realized, and the method has relatively high robustness for the operation condition of the motor.

This application provides a fault handling method and a vehicle control system, comprising: starting an electric swing system and acquiring fault monitoring data of the electric swing system, the fault monitoring data including a target fault code; querying a fault data table to determine the fault severity level and fault handling strategy corresponding to the target fault indicated by the target fault code, the fault data table describing the mapping relationship between faults, fault severity levels, and fault handling strategies, the fault handling strategy describing the fault alarm method and fault handling rules under the corresponding fault severity level, the fault severity level being pre-classified according to the degree of impact of the fault on the electric swing system; and alarming and handling the target fault generated by the electric swing system according to the fault severity level and fault handling strategy corresponding to the target fault. This application reduces driver reliance on experience and improves equipment operation safety by providing a handling method matching the fault severity level when a fault occurs.

The invention discloses a permanent magnet synchronous motor turn-to-turn short circuit fault diagnosis and positioning method. According to the method, extra hardware is not needed, whether the turn-to-turn short circuit fault occurs in the motor can be accurately diagnosed only based on the voltage and current signals of the two-phase stator coordinate system (alpha-beta coordinate system), the severity of the fault is quantitatively represented, and the fault phase is accurately positioned. The method does not need an accurate model or a large amount of data, is not affected by environmental interference, is accurate in positioning, is simple to implement, and is high in real-time performance.

The invention relates to the technical field of power systems and flexible direct current power transmission, in particular to a VSC-HVDC self-adaptive fault ride-through control method, system and device considering the influence of near-field new energy. The method comprises the following steps: firstly, according to real-time fault equivalent parameters, solving the maximum fault allowable time of a receiving-end converter station considering the influence of the near-region new energy, and taking the maximum fault allowable time as an important basis for the severity of the fault and the subsequent formulation of a control strategy; according to different possible fault durations, a control strategy is flexibly adjusted, whether unloading needs to be started or control parameters need to be adjusted is determined, the fault ride-through capacity of the system is improved, reasonable cooperation of unloading is achieved, it is ensured that dependence on unloading input is reduced, and system energy loss is reduced, so that the efficiency and adaptability of fault ride-through are improved, and the fault ride-through reliability is improved. And a powerful technical guarantee is provided for stable operation of the flexible direct-current power transmission system in a new energy access scene.

This invention relates to the field of fault diagnosis technology, specifically to an intelligent fault diagnosis method and system for tunnel electromechanical equipment based on big data. It includes: acquiring power consumption data, pollutant concentration, and natural wind speed data; assessing the air supply pressure at each detection point within the tunnel; calculating the effective air supply output of the jet fan at any given detection point; calculating the energy efficiency index and energy efficiency deviation of the jet fan at that detection point; identifying faulty jet fans and calculating their fault severity, thereby performing fault diagnosis for each faulty jet fan. This invention can significantly improve the accuracy and maintenance efficiency of fault diagnosis for electromechanical equipment within tunnels.

The invention discloses a bogie fault identification method based on an agent model and significance sorting, and the method comprises the steps: carrying out the preprocessing of the collected original state information in the operation process of a bogie, and obtaining the processed information; performing factor calibration on the processed information to obtain a fault factor vector; establishing a mapping relation between the fault factor and an actual physical fault state; the fault factor vector is used as an input variable, the fault severity quantitative index is used as an output variable, and a fault function meeting monotonicity or segmented monotonicity constraint is constructed; and constructing an improved Kriging model, fitting a nonlinear relationship between the fault factors and the fault function, and quantifying the proportion of the main effect and the interaction effect of each fault factor. According to the method, multi-factor significance sorting and high-precision prediction capability can be considered, and technical support is provided for intelligent fault diagnosis of the railway vehicle bogie.

According to an embodiment of the present disclosure, a device and a method for diagnosing faults in an electronic brake system based on a deep learning model, which may be provided, which collect and preprocess vehicle state data including sensor data and actuator state data through an in-vehicle communication network, input the preprocessed vehicle state data into a deep learning anomaly detection model to determine a normal state or a fault state of the electronic brake system, input the preprocessed vehicle state data into a deep learning detailed fault diagnosis model to determine a fault type and fault severity of the electronic brake system, if the electronic brake system is determined to be in the fault state, and independently control the electronic brake system based on the fault type and fault severity.

The invention provides a marine brushless motor control method and device and a computer readable storage medium, and the method comprises the steps: monitoring the state of a marine brushless motor in real time, and recognizing the fault type of the marine brushless motor; if the fault of the marine brushless motor is identified to belong to an open-phase fault, evaluating an open-phase fault severity index, calculating a compensation current according to the evaluated open-phase fault severity index, and adjusting the torque of the marine brushless motor according to the compensation current to realize torque balance of the marine brushless motor; and if it is identified that the fault of the marine brushless motor belongs to an open-circuit fault, evaluating an open-circuit fault severity index, calculating an adaptive adjustment excitation voltage of a fault phase according to the evaluated open-circuit fault severity index, and adjusting a voltage waveform of a residual phase based on the adaptive adjustment excitation voltage to recover torque performance. According to the method and the corresponding system, safe and efficient operation of the brushless motor for the ship in a complex environment is ensured through a comprehensive fault identification and response mechanism.

The present application relates to the technical field of electrical measurement, and discloses a track construction cable fault online diagnosis and data intelligent analysis method and system, the method comprising: multi-dimensionally fusing multi-source operation data of the track construction cable to obtain fused data of the track construction cable; extracting fault features in the fused data to obtain abnormal characteristic values of the track construction cable; multi-level comparing the abnormal characteristic values with a historical fault threshold library to obtain a graded comparison result of the track construction cable; quantitatively scoring the graded comparison result to obtain abnormal indication information of the track construction cable; based on the abnormal indication information, dividing the abnormal state of the track construction cable to obtain a fault severity level of the track construction cable; and generating a diagnosis report of the track construction cable according to the fault severity level; the present application can improve the accuracy of track construction cable fault online diagnosis and data intelligent analysis.

The invention discloses an equipment maintenance method and device, equipment, a medium and a product. The method comprises the following steps: acquiring at least one type of equipment data of target equipment; inputting the equipment data into a pre-trained fault diagnosis model to obtain at least one fault type currently possessed by the target equipment; any fault type is used as a target fault, and a fault severity index corresponding to the target fault is determined according to the equipment data; according to the at least one fault severity index corresponding to the target equipment and the equipment data, determining an overhaul priority index of the target equipment; and overhauling the target equipment according to the overhauling priority index. According to the technical scheme provided by the embodiment of the invention, the equipment maintenance accuracy is improved, and the equipment maintenance efficiency is improved.

The invention discloses a wind power converter open-circuit fault diagnosis method based on a digital twinning technology and current characteristic analysis, and the method comprises the steps: building a digital twinning model based on a deep neural network, taking a wind speed, a rotating speed, a DC bus voltage and a pitch angle as model input variables, and outputting a reference value of a three-phase current space vector amplitude; a three-phase current signal is collected in real time, a measured value of a current space vector amplitude is calculated, a fault severity coefficient is calculated by using the measured value and a reference value, when the fault severity coefficient exceeds a threshold value, a double-IGBT open-circuit fault is determined, and otherwise, a single-IGBT open-circuit fault is determined; performing effective current interval proportion analysis on the three-phase current signal; positioning a fault phase by combining a sampling point proportion and a fault type; and analyzing the normalized current average value of the fault phase current, and judging a fault bridge arm. According to the method, the digital twinborn technology and current characteristic analysis are combined, rapid and accurate diagnosis of the open-circuit fault of the wind power converter is achieved, and the method has the advantages of being high in diagnosis precision and good in real-time performance.

The application provides a power plant starting transformer open-phase detection method and device, and relates to the technical field of power failure detection. The method comprises the following steps: acquiring signal energy distribution of the starting transformer, constructing an energy feature vector reflecting the relative energy relationship among three-phase current signals and inputting the energy feature vector into an open-phase identification model to obtain initial probabilities of each-phase open-phase failure; combining real-time calculated zero sequence and negative sequence current amplitudes to perform state inversion calculation, inferring the change amount of equivalent circuit parameters of transformer internal windings and lead wires before and after the failure, correcting the initial probabilities to obtain confidence, and further generating an open-phase detection report containing fault phase and fault severity. The application completely depicts the three-phase current energy correlation characteristics through the energy feature vector, corrects the initial probabilities of the model by using the change amount of the equivalent circuit parameters, eliminates the random deviation of the single model output, realizes accurate matching between the fault determination result and the actual fault state of the transformer, and improves the fault phase identification accuracy.

The invention discloses a multilevel systematic protection method and system for a modular multilevel converter, and relates to the technical field of power system protection, and the method comprises the steps: protection starting: judging whether to trigger the protection starting or not through monitoring the capacitor voltage peak-to-peak values of all sub-modules of a single-phase single bridge arm in real time; fault severity classification: taking the bridge arm current after protection starting as an input feature, inputting the input feature into a shared classification head model based on a LightGBM classifier, and carrying out preliminary classification on faults; fault identification: according to a preliminary classification result, taking a capacitor voltage peak-to-peak value, a bridge arm current and a bridge arm reactor voltage drop of a sub-module after protection starting as characteristic quantities, respectively inputting the characteristic quantities into different models, and identifying a specific fault type; and protection response: adopting a differential protection response strategy according to a specific fault type. The invention provides a multi-level systematic protection method and system which can quickly and accurately identify MMC multi-level faults and take corresponding protection measures.

The application particularly relates to a dynamic reactive power reserve coordination optimization method based on transient partition and predictive control, which comprises the following steps: establishing a corresponding transient voltage stability index (TVSI) according to the transient bus voltage characteristics; evaluating the fault severity based on the TVSI, and then partitioning the power grid; solving the reactive power optimization problem through the scanning of the expected fault set, the sensitivity analysis of the TVSI and the optimization of the reactive power reserve based on the result of the fault partition; optimizing the solution result of the reactive power optimization problem, replacing the reactive power output through the slow dynamic reactive power compensation device, generating a control optimization model of the dynamic reactive power reserve optimization, and realizing the coordinated control of the corresponding equipment. The dynamic reactive power reserve coordination optimization method can reduce the damage degree of the fault or disturbance at a faster speed and with a smaller control cost, and then ensure the safe and stable operation of the whole system, so that the stability of the power system operation control can be improved.

This invention relates to the field of grid-connected control of new energy power generation and power system fault recovery, and discloses a collaborative fault recovery method for energy storage and AC / DC hybrid transformers in a wind-solar-storage complementary system. This method identifies abnormal operating conditions and constructs a fault severity index by monitoring the voltage, frequency, and power status of the point of common coupling (PCC). During a fault, the energy storage system provides rapid active power support, and when the energy storage response is limited, the AC / DC hybrid transformer provides compensation and adjustment. After the fault is cleared, the collaborative optimization weights are adaptively updated based on power balance constraints and the grid stability index, completing the full-process coordinated control of the energy storage system and the AC / DC hybrid transformer, thereby improving the system's fault ride-through capability, recovery smoothness, and operational stability.

The embodiment of the invention discloses a fault diagnosis method based on a hybrid capacitor electrochemical model, and relates to the technical field of hybrid capacitor health management, and the method comprises the steps: obtaining the experimental data of a hybrid capacitor in a plurality of degradation stages; establishing an electrochemical model, and selecting a plurality of mechanism parameters as a to-be-identified set; performing parameter identification on the to-be-identified set by taking an experimental voltage curve as a reference and adopting an optimization strategy taking a terminal voltage error as a target function to obtain a mechanism parameter sequence evolved along with a degradation stage; extracting offset characteristics of the mechanism parameter sequence; establishing a mapping relationship between the offset feature and a preset typical fault mode to identify a fault type; a weighted degradation index is constructed based on the offset features, and the fault severity is quantified; the method can realize interpretable diagnosis of the degradation mechanism of the hybrid capacitor, has the advantages of high precision, strong generalization, irrelevance with working conditions and the like, and can be used for health management and safety early warning of the hybrid capacitor and an energy storage system.

The application discloses a continuously adjustable airplane fault model library construction method and belongs to the technical field of system simulation verification. The application is aimed at key accessories of an airplane take-off and landing system, establishes a mechanism mapping of fault modes and model variables according to FMEA and physical failure analysis, forms fault models containing output pressure deviation, coil short circuit or open circuit, valve core sticking, signal drift, internal leakage or external leakage and the like, and defines three-layer standardized interface structures, namely, a fault enable control interface, a fault control interface and a model physical coupling interface. Finally, a standardized fault model library is constructed through structured storage. The application overcomes the limitations of discrete preset of fault severity, model invasive replacement and lack of multi-accessory coupling simulation in the prior art, and provides support for high-confidence and full-coverage virtual verification of the airplane take-off and landing system.

The application relates to a method and device for scanning a predicted fault applied to an element of a power system, a computer device, a storage medium and a computer program product, and relates to the technical field of power grid safety monitoring. The application can shorten the time of detailed power flow calculation and improve the efficiency of the predicted fault scanning. The method comprises the following steps: obtaining a node admittance matrix and an active power flow total increment of an element to be detected under a predicted fault condition; obtaining a voltage phase angle increment corresponding to each bus in the element to be detected according to the node admittance matrix and the active power flow total increment; obtaining an active power flow increment of a current branch according to a first voltage phase angle increment, a second voltage phase angle increment and a current reactance value of the current branch for each branch in the element to be detected; determining a weight coefficient and a power flow limit value of the current branch; and inputting the active power flow, the weight coefficient and the power flow limit value into a preset severity index operation model to obtain a predicted fault severity index when the current branch is disconnected.