338 results about "Multiple fault" patented technology

The invention discloses an optical cable fault positioning method based on an OTDR (Optical Time Domain Reflectometer), and relates to the technical field of optical cable fault positioning. Periodic waveform data of an optical cable is acquired through OTDR equipment, an echo spectrum is generated, a dynamic threshold algorithm is introduced according to the echo spectrum to carry out fault event identification, and a dynamic threshold value is generated; the processing end judges whether a fault point area exists in the echo based on the dynamic threshold value, if yes, the distance between the fault point and the OTDR equipment is calculated through propagation time delay, the spatial position of the fault point is determined, and after multiple fault points are recognized, fault types are classified according to reflection intensity. According to the positioning method, a dynamic threshold algorithm is introduced, so that the system has adaptive capacity to background environment change, the robustness of the system under long-distance, high-loss optical cable or severe noise conditions is remarkably enhanced, and weak reflection fault or hidden fault events neglected by a traditional method can be effectively identified.

The invention discloses an automatic control method of a molded case circuit breaker operating mechanism, and particularly relates to the technical field of molded case circuit breakers. Electrical parameters are monitored in real time and transmitted to a control system, and comparative analysis is performed based on a preset threshold value and a fault model, so that faults are detected in time and a control instruction is generated, and the system can identify and sort multiple fault scenes, automatically execute switching-on or switching-off actions and ensure the reliability and stability of a power system; a judgment mechanism based on the circuit recovery condition is introduced, after the circuit is disconnected, whether recovery operation is carried out or not is reasonably judged according to the state of the circuit breaker and the recovery condition of the power system, the self-recovery capacity and the fault diagnosis capacity of the power system are effectively improved, the method can adapt to the influences of high loads, complex environments and power grid fluctuation, and the reliability of the power system is improved. And the reliability and the precision of an automatic control system are obviously improved.

The invention discloses an intelligent power grid fault prediction method, system and device, and relates to the technical field of power grid fault prediction, and the method comprises the steps: carrying out the real-time collection of the multi-source operation data of a target intelligent power grid, and obtaining a multi-dimensional power grid operation feature set; operation fault analysis is carried out, a fault feature vector group is generated, transfer learning is carried out, and a fault probability distribution matrix is generated; performing fault risk analysis according to the fault probability distribution matrix, and dividing a plurality of risk levels; and performing propagation path simulation of the target smart power grid on the plurality of fault early warning signals to generate a fault prediction report. According to the invention, the technical problems of low prediction precision and poor timeliness caused by fault prediction lagging and inaccurate fault propagation path simulation of the smart grid in the prior art are solved, and the technical effects of accurate prediction and risk early warning of the smart grid fault and improvement of the timeliness and accuracy of fault pre-judgment are realized.

The invention discloses a decision boundary adaptive open set fault diagnosis method based on a domain adversarial neural network, and the method comprises the steps: S1, obtaining a rolling bearing fault data set under multiple working conditions and multiple fault states, and dividing the rolling bearing fault data set into source domain data and target domain data; s2, establishing a bearing fault diagnosis model, wherein the bearing fault diagnosis model can determine a decision boundary of a known fault state; performing iterative training on the bearing fault diagnosis model, and when the total loss function is converged, obtaining a trained bearing fault diagnosis model; the total loss function comprises a Bhatcharyya coefficient loss function, and the Bhatcharyya coefficient loss function is utilized to reduce the feature overlapping degree of different types of faults; and S3, based on the trained bearing fault diagnosis model, carrying out fault diagnosis on unknown faults in the actually collected bearing operation signals. According to the method, cross-working-condition open-set fault diagnosis can be realized, and the fault diagnosis capability is remarkably improved by ensuring accurate identification of unknown faults.

The invention discloses a self-adaptive dynamic fusion method and system for heterogeneous data, and relates to the technical field of data processing, and the method comprises the steps: obtaining multi-source heterogeneous data of power equipment; detecting the state of the equipment and judging whether the equipment is in a fault mode, and recording a fault feature vector if the equipment is in the fault mode; inputting the fault feature vectors into a fault resistance evaluation model, evaluating the fault resistance of each data source, and outputting a plurality of fault resistance indexes; setting a preset fault resistance index, and dividing the data into a first type and a second type of heterogeneous data; and configuring fusion weights for the two types of data, performing data fusion according to the weights, and outputting a fusion result. The technical problem that in the prior art, the reliability difference of multi-source heterogeneous data cannot be effectively recognized in the power equipment fault state, and consequently the fault diagnosis accuracy is poor is solved, and the purposes of dynamically optimizing data fusion, improving the data reliability and improving the fault diagnosis accuracy are achieved through fault resistance evaluation and a self-adaptive sampling strategy. Therefore, the fault diagnosis accuracy is improved.

The invention discloses a server fault processing method and device and a medium, and relates to the technical field of computers. And the server fault analysis problem is converted to obtain vector information, and the vector information is input to the pre-training model for reasoning analysis, so that accurate fault positioning is realized, and the search accuracy of fault analysis is improved. The fault analysis problem comprises a potential fault analysis problem, so that unknown faults are predicted and reasoned, and the fault processing delay is reduced. In inference analysis of a pre-training model, corresponding target inference strategies are matched based on priorities of different fault modes and association degrees of multiple faults instead of being directly processed one by one according to the priorities of the faults, and the association degrees of the multiple faults and whether the corresponding inference strategies are conflicted or not are concerned firstly; according to the method, the situation that secondary faults are caused by contradictory occurrence of heavy faults under the condition that the faults with low priorities are processed in a delayed mode is avoided, the fault maintenance progress is further accelerated, and the reliability of the server is improved.

The invention provides a rotor-bearing system fault diagnosis method and system based on virtual-real fusion, and relates to the technical field of signal detection, and the method comprises the steps: deducing a mixed eccentric unbalanced magnetic pull expression, and building a rotor-bearing system twinborn model in combination with a Hertz contact theory; introducing rotor eccentricity and bearing inner / outer ring faults, and constructing a multi-fault working condition twinborn model; performing multi-parameter identification on the model through an improved genetic algorithm (introducing a feature sensitivity evaluation factor, a self-adaptive crossover mutation probability and simulated annealing) to obtain a corrected twin model; generating a twin fault sample based on the correction model, and training by using a one-dimensional cyclic generative adversarial network (designing a comprehensive loss function) with a classifier to generate a sample close to real distribution; and virtual and real fusion samples form a balanced data set, and fault classification is realized through one-dimensional convolutional neural network training. According to the method, the problems of fault data shortage, large difference between a twin model and real data and the like are solved, and the fault diagnosis precision and reliability are improved.

The invention discloses an abnormal operation state detection method and system for waste gas purification equipment, and relates to the field of fault detection. The method is applied to the detection equipment and comprises the following steps: acquiring multi-source sensor data of the purification equipment; calculating a fluctuation index of the multi-source sensor data, and updating the fluctuation index to a preset fault influence relation graph; determining a plurality of abnormal equipment nodes according to the fluctuation indexes of the plurality of equipment nodes; traversing the fault influence relation graph by taking the plurality of abnormal equipment nodes as starting points to obtain a plurality of fault chains; calculating chain responsibility degrees of the plurality of fault chains, and determining the fault chain with the maximum chain responsibility degree as a root cause result; and the root cause result is output to an equipment management interface, so that troubleshooting personnel can rapidly troubleshoot abnormal causes. By implementing the technical scheme provided by the invention, the problem of low troubleshooting efficiency caused by various and complex fault types of the waste gas purification equipment is solved.

The invention discloses an air pre-heater cold end multi-fault diagnosis method and device based on a panorama and a dynamic intelligent model, and relates to the technical field of thermal power plant auxiliary engine fault diagnosis, and the method comprises the steps: constructing a panorama monitoring image of an air pre-heater rotor, and dividing the panorama monitoring image into a plurality of independent monitoring regions; extracting color channel information of each monitoring area, and dynamically updating a threshold range of each color channel based on a multi-trigger condition; obtaining morphological edge contour features and color channel deviation features, and jointly inputting the morphological edge contour features and the color channel deviation features into a pre-trained multi-fault classification model; the fault type recognition result, the confidence coefficient and the preset fault type weight are fused, the comprehensive degradation index of each monitoring area is calculated, a three-dimensional diagnosis result is output in combination with the spatial position information of the monitoring areas, and accurate positioning diagnosis, degradation trend quantification and multi-fault intelligent distinguishing of the cold end area of the air preheater are achieved. The method is suitable for full-life-cycle online monitoring and maintenance decision making of the rotary air pre-heater.

The invention discloses a photovoltaic power station fault diagnosis method based on digital twinning and transfer learning, and belongs to the technical field of photovoltaic power generation fault detection. According to the method, a photovoltaic power station digital twin model based on a physical mechanism is constructed, operation behaviors in normal and multiple fault states are simulated, and a simulation data set with an accurate label is generated; in combination with the acquired real operation data, a fault diagnosis model is trained by adopting a transfer learning framework containing a domain adaptation loss item, so that the distribution difference between simulation data and real data is effectively reduced; a physical consistency loss item is innovatively introduced, and a physical rule is used as a constraint embedded model, so that the reliability and interpretability of a diagnosis result are improved; and finally, performing accurate fault diagnosis on the real-time operation data by using the trained model. According to the method, the model training problem caused by scarcity of real fault data is solved, the diagnosis precision and generalization ability are remarkably improved, and the method is suitable for intelligent operation and maintenance of the photovoltaic power station.

The invention provides a vehicle fault diagnosis method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a fault code sequence of a vehicle, determining a target fault diagnosis path corresponding to the fault code sequence according to a target fault diagnosis map, determining a root cause fault part causing a target fault according to the target fault diagnosis path, and carrying out the fault diagnosis of the target fault. In addition, a fault diagnosis path in the target fault diagnosis atlas comprises a plurality of fault component nodes, a plurality of fault code nodes, a first logic type sub-path and a second logic type sub-path, and the first logic type sub-path and the second logic type sub-path are alternately connected in series. The first logic type sub-path is a path from a fault component node to a fault code node, and the second logic type sub-path is a path from the fault code node to the fault component node, so that fault diagnosis is carried out based on the target fault diagnosis map and the fault code sequence; accurate positioning of vehicle fault components in a multi-fault code scene can be realized.

The invention relates to the technical field of fluid sealing, in particular to a PINN-based mechanical sealing fault diagnosis method and device, and the method comprises the steps: embedding a multi-physical field coupling simulation model, meeting a preset condition, of a target mechanical sealing system into a loss function of a physical information neural network (PINN), so as to construct an initial PINN fault diagnosis model, and inputting actual monitoring data of a plurality of sensors of the target mechanical sealing system into the initial PINN fault diagnosis model to output a plurality of fault parameters to be identified, further updating the initial PINN fault diagnosis model, generating a target PINN fault diagnosis model, and diagnosing the sealing state of the target mechanical sealing system according to the target PINN fault diagnosis model. And generating a fault diagnosis result. Therefore, the problems that in the prior art, in the face of complex working conditions and variable environments, the sealing performance cannot be comprehensively evaluated in real time, and consequently the accuracy and timeliness of mechanical sealing fault diagnosis are insufficient are solved.

The invention discloses a fault analysis method, device and equipment for a rotary drilling rig and a medium, and belongs to the technical field of engineering machinery technologies. The fault analysis method for the rotary drilling rig comprises the following steps: acquiring equipment parameters of the rotary drilling rig, and respectively establishing corresponding mathematical models for a plurality of functional systems of the rotary drilling rig according to the equipment parameters; wherein the functional system comprises a mechanical system, a hydraulic system, a control system and a power system; establishing a model library by utilizing the mathematical model; the simulation models corresponding to all the function systems are connected based on the model library, and a digital prototype of the rotary drilling rig is obtained; the digital prototype is controlled to operate according to multiple fault working conditions, and the component energy loss of the digital prototype under each fault working condition is obtained; and establishing a mapping relationship between the energy loss of the component and the fault type of the fault working condition, and performing fault analysis on the rotary drilling rig according to the mapping relationship. The fault analysis precision and efficiency of the rotary drilling rig can be improved.

The invention discloses a flexible AC / DC hybrid power distribution network fault recovery optimization method and system based on multi-agent deep reinforcement learning, a computer device and a non-transient computer readable storage medium, and the method comprises the steps: firstly constructing an AC / DC power distribution network model considering the operation characteristics of a flexible switch and a voltage source converter; then, flexible equipment is modeled as an independent agent, a multi-agent training architecture is established, a composite reward function fusing a load recovery rate, voltage stability and operation safety is designed, and a mechanism combining centralized training and distributed execution is adopted to realize multi-point cooperative control between the flexible equipment; and finally, designing a recovery optimization process for rapid decision and control execution after the fault. The method can realize higher load recovery rate and better voltage control performance in various fault scenes, has the advantages of strong decision real-time performance, good system expandability, strong strategy generalization ability and the like, and is suitable for autonomous recovery and intelligent regulation and control of the novel flexible AC / DC hybrid power distribution network.

The invention provides a multi-path fault recovery method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining state data of storage equipment associated with a plurality of fault paths in a target storage system, and detecting whether the plurality of fault paths are recovered to a normal state or not based on the state data; when it is detected that the path state of the target fault path is recovered, performing multi-stage verification processing on the target fault path; if the target fault path does not pass the multi-stage verification, performing path repair operation on the target fault path which does not pass the multi-stage verification based on the non-passed verification type; and registering the repaired target fault path and the target fault path passing the multi-stage verification to an active path pool. Compared with the prior art, when it is detected that the path state of the target fault path is recovered, multi-stage verification processing is performed on the target fault path, and whether the recovered path has potential problems or not can be comprehensively and meticulously checked.

The invention provides a crown block operation fault detection method and system, electronic equipment and a storage medium, and belongs to the technical field of fault detection, and the method comprises the steps: detecting a crown block based on a first detection frequency, and obtaining target state detection data; and if the target state detection data does not meet the first condition, detecting operation data of the crown block based on the target state detection data to obtain crown block operation detection data. And inputting the crown block operation detection data and the target state detection data into the crown block fault detection model to generate a detection result. The crown block fault detection model is obtained by training crown block fault sample data and comprises a first correlation coefficient and a second correlation coefficient. The first correlation coefficient represents the correlation between each fault feature in the crown block fault sample data and the crown block fault type, and the second correlation coefficient represents the correlation among a plurality of fault features in the crown block fault sample data. According to the invention, the accuracy of crown block fault detection can be improved.

The invention discloses an elevator electromechanical fault diagnosis system based on a dynamic focusing network and an automatic trimming capsule network, and the system comprises a multi-mode data collection module which is provided with a vibration sensor, a temperature sensor and a displacement sensor, and collects the operation signals of an elevator traction machine, a brake and a steel wire rope in real time; the dynamic focusing network module is used for carrying out adaptive frequency band selection on the input signal and inhibiting noise interference; the automatic trimming capsule network module is used for realizing decoupling and classification of multiple fault features through a dynamic routing and capsule trimming mechanism; the federal learning coordination module is used for aggregating local model parameters of a plurality of elevator manufacturers and generating a global diagnosis model; and the diagnosis output module is used for outputting the fault type, the confidence coefficient and the residual service life prediction result, and triggering a maintenance alarm. Dynamic spectrum focusing is achieved, a self-adaptive frequency band attention mechanism is introduced into elevator fault diagnosis for the first time, and the noise suppression effect is improved by 42% compared with traditional band-pass filtering; the capsule is dynamically trimmed, the intensity threshold is activated to dynamically optimize the network structure, the multi-fault classification accuracy is improved to 96.5%, and the problem of cross-manufacturer data islands is solved.

The invention discloses a nonlinear system robust residual error multi-fault detection and reconstruction method based on a sliding-mode observer, and solves the problems that an actuator fault and unknown input disturbance in a servo driving system are closely related and mutually coupled, and a fault detection algorithm is insensitive to identification of a first-degree fault, so that fault report failure or misjudgment is caused. According to the method, a general nonlinear system is transformed into two subsystems with independent fault influence conditions based on global differential homeomorphic transformation for the first time. Equivalently transforming reverse channel disturbance to forward channel disturbance by using a filter, designing a sliding mode state observer according to different actuator fault types, and respectively extracting fault information and unknown input disturbance information of a servo driving system in the two subsystems, thereby realizing decoupling of the fault and the unknown input disturbance. And the sensitivity of fault signal detection and the accuracy and robustness of fault diagnosis are effectively improved.

The invention relates to the technical field of photovoltaic equipment diagnosis, in particular to a distributed photovoltaic diagnosis method and system based on multi-modal time sequence data. The invention discloses a distributed photovoltaic diagnosis system based on multi-modal time sequence data. The distributed photovoltaic diagnosis system comprises a multi-modal time sequence data acquisition module, an electrical feature vector extraction module, a single fault source judgment module and a fault source combination judgment module. According to the method, the electrical feature vector and the electrical feature fingerprint database are matched, the similarity is calculated, and the threshold value is set, so that the fault source can be directly judged under the condition of a single fault source, linear splitting is performed through a group optimization algorithm under the condition of multiple fault sources, and the fault source can be determined in combination with the contribution component and the occurrence frequency of the fault source. The occurrence probability of the fault sources is calculated and analyzed with a set fault source combination probability threshold value, it is ensured that fault source combination information is output only when contribution of the multiple fault sources reaches a certain threshold value, and then the diagnosis precision of the fault sources is optimized.

The invention relates to the technical field of smart power grids, in particular to a smart power grid fault real-time diagnosis system based on digital twinning. The system comprises a multi-point detection arrangement unit, a multi-fault feedback unit and a virtual digital twinning unit. Through cooperation of multi-point detection, dual fault feedback and digital twinborn mapping, high-precision real-time diagnosis of smart power grid faults is realized, a microscopic clustering and macroscopic verification combined mechanism is adopted, true and false faults are effectively distinguished, and islanding effect misjudgment is inhibited; reverse reasoning and pseudo-cluster filtering are introduced, so that the robustness of the system is improved; the fault situation is dynamically rendered based on BIM + GIS, visual operation and maintenance are supported, the problems that in traditional diagnosis, single-point misinformation is difficult to eliminate, and large-range positioning is delayed are solved, and the fault recognition accuracy and the power grid operation reliability are remarkably improved.

The invention provides a steer-by-wire vehicle fault processing method, device and equipment and a medium, and belongs to the technical field of vehicle fault processing, and the method comprises the steps: dividing a plurality of fault categories based on corresponding fault causes and fault influences when a redundant steer-by-wire system breaks down; respectively determining the function state of each sub-function module in the system when the redundant steer-by-wire system is subjected to different fault types; and respectively formulating a corresponding fault processing strategy for each degraded sub-function module based on the current fault category and the function state of each sub-function module. Through the technical scheme in the embodiment of the invention, corresponding fault-tolerant processing can be designed according to different function degradation conditions.

The invention discloses a cascade H-bridge frequency converter multi-fault source identification method. The method comprises the following steps: acquiring real-time time sequence data of a frequency converter on line; generating a real-time system residual function representing system deviation by using a physical information health system operator which performs dynamic weight modulation through the working condition state vector; and a three-layer cascade operator decomposition architecture considering physical topology constraints is adopted to decompose the residual function so as to identify a single fault and a coupling effect. The method can adapt to dynamic working conditions, effectively suppress pseudo residual errors, accurately decouple concurrent faults, and significantly improve the accuracy and robustness of diagnosis.

The invention discloses a multi-feature combined double three-phase permanent magnet synchronous motor turn-to-turn short circuit fault diagnosis method, which belongs to the technical field of motor fault diagnosis and comprises the following steps of: establishing a turn-to-turn short circuit fault mathematical model of a double three-phase permanent magnet synchronous motor under a rotating coordinate system; extracting a plurality of fault features according to the obtained mathematical model, jointly reconstructing a fault index, and introducing a rotating speed into the fault index to obtain a new fault index; and comparing the new fault index with a set threshold value, and judging whether the motor has a fault or not. According to the method, the turn-to-turn short circuit fault mathematical model of the dual three-phase permanent magnet synchronous motor under the rotating coordinate system is set, and a plurality of fault features are adopted to construct the fault indexes, so that the problems of high error / missed diagnosis rate, difficulty in early fault recognition and the like in fault diagnosis under a single feature are solved, and the influence of the rotating speed on the fault diagnosis is reduced.

A method for detecting faults in a building management system (BMS) is shown. The method includes receiving time series data characterizing an operating performance of one or more BMS devices. The method further includes processing the time series data using multiple different fault detection methods to generate multiple fault detection results. The method includes providing the multiple fault detection results as outputs from the multiple different fault detection methods. The method includes applying the multiple fault detection results as inputs to a neural network that determines whether the multiple fault detection results are indicative of a fault condition in the BMS.

The invention discloses a substation high-voltage switchgear multi-dimensional data fault diagnosis system, which comprises a multi-dimensional data acquisition module, a data processing module and a diagnosis decision module, and is characterized in that the multi-dimensional data acquisition module is used for acquiring multi-dimensional data information of substation high-voltage switchgear; the data processing module is used for preprocessing the collected multi-dimensional data information, and the diagnosis decision module comprises a historical data-based fault diagnosis unit, a data mining-based fault diagnosis unit, a machine learning-based fault diagnosis unit and a high-voltage switchgear-based fault diagnosis unit. The multi-dimensional data information of the switchgear is collected, information of different sources is integrated and analyzed, and support is provided for comprehensive and accurate diagnosis results. And meanwhile, fault diagnosis is performed on information of different sources in a targeted manner by adopting multiple fault diagnosis modes, so that the accuracy, effectiveness and reliability of fault diagnosis are improved, and clear guidance and requirements are provided for research of a fault diagnosis technology.

The invention discloses a single-multiple fault combined diagnosis method and system for textile logistics equipment, and the method comprises the steps: obtaining equipment operation data, carrying out the preprocessing and feature extraction, and forming a standardized feature data set; performing single-fault diagnosis on the standardized feature data set, outputting a confidence result, completing confidence judgment according to a preset threshold value, and selectively outputting a single-fault conclusion or entering multi-fault combined diagnosis; in the multi-fault combined diagnosis stage, a fault relevance matrix is constructed through data relevance analysis, a causal relation graph is generated, the coupling relation between different faults is recognized, combined reasoning is completed, and a multi-fault combined diagnosis result is obtained. And finally, carrying out visual display on the diagnosis conclusion, and generating alarm grading and operation and maintenance auxiliary decision-making information. According to the method, self-adaptive switching between single-fault diagnosis and multi-fault combined diagnosis can be achieved, and the fault recognition precision and diagnosis reliability under the complex working condition are improved by combining correlation analysis and causal reasoning.

The invention provides an extension value intelligent monitoring bolt and a real-time monitoring and fault early warning method. A miniature piezoelectric sensing intelligent bolt is integrated, and extension is sensed through integrated packaging; dual-mode wired / wireless transmission is achieved, and remote real-time monitoring is achieved; aI threshold early warning is carried out, a comparison curve is automatically generated, looseness, overload, bolt breakage and signal abnormity are intelligently recognized, and full-life-cycle safe operation and maintenance are achieved. According to the invention, real-time continuous monitoring and multi-fault-mode intelligent early warning of the bolt elongation value can be realized, and the safety and the operation and maintenance efficiency of the bolt connection structure are effectively improved.

The invention discloses a single-phase earth fault comprehensive discrimination method based on multiple fault criteria and weight dynamic calculation, and the method comprises the steps: collecting phase voltage and zero sequence voltage characteristic quantities before and after a power distribution network fault, and discriminating the type of a single-phase earth fault according to the change rule of the characteristic quantities; the method comprises the following diagnosis steps: a, adopting a new zero-sequence current method, calculating the zero-sequence current break variable of each feeder switch based on a zero-sequence current reference value, and achieving the positioning of a fault feeder and a section; b, a new phase current method is adopted, a phase current fault characteristic quantity is defined based on the sum of three-phase current difference values before and after a fault, and positioning of a fault feeder line and a section is achieved; constructing an evaluation model by adopting a fuzzy analytic hierarchy process according to the fault category judged by the parallel diagnosis, and dynamically determining a weight coefficient; and performing weighted fusion on the confidence coefficient according to the weight coefficient to obtain the comprehensive fault probability of each feeder line, selecting the feeder line with the maximum comprehensive fault probability as a fault feeder line, and judging a fault interval.

The invention relates to the technical field of sensors, and discloses a signal optimization method based on a diagnosability evaluation result, and the method comprises the following steps: S1, obtaining monitoring signal data collected by a plurality of candidate sensors in a normal state and a plurality of fault states of equipment, and S2, carrying out the optimization of the signals based on the monitoring signal data, according to the method, a fault detectability model is constructed, the fault detectability model is used for quantifying the difference between a fault state and a normal state on sensor signals, and when sensor signal optimization is carried out, the fault detectability model and the fault isolability model are constructed in a unified manner; and a detectability evaluation index and an isolability evaluation index are calculated based on a joint mean-variance difference value, so that comprehensive quantitative evaluation of the signal fault detection capability and the fault isolation capability is realized, and it is ensured that performance requirements of different dimensions of fault diagnosis in a signal optimization process are balanced. And the comprehensiveness and accuracy of fault diagnosis are improved.

The invention relates to the technical field of new energy power generation and power grid modeling, in particular to a multi-working-condition matching model parameter identification method for new energy actual measurement modeling. According to the technical scheme, the multi-working-condition matching model parameter identification method for new energy actual measurement modeling comprises the following steps that actual measurement data of a new energy converter under multiple fault working conditions are obtained, and a fault parameter matrix containing current control values during symmetric faults and asymmetric faults is constructed; calculating a current limiting value parameter based on the fault parameter matrix; an initial identification matrix is constructed, an intermediate matrix is generated according to the current limiting value parameters, and the intermediate matrix is used for marking elements reflecting real control characteristics in the identification matrix. Multi-working-condition parameter intelligent optimization and model verification are achieved, repeated simulation and comparison calculation of a large number of working conditions are avoided, and a modeling scene with the large number of working conditions, the large deviation item calculation amount and the complex model parameters can be efficiently completed.