5265 results about "Power substation" patented technology

The invention discloses a box-type substation state monitoring and early warning method based on artificial intelligence, relates to the technical field of intelligent power grids, and aims to solve the problems of missing report, false report and response lag caused by the fact that an existing static threshold ignores multi-physical coupling and a depth model highly depends on scarce fault samples. According to the scheme, sliding window kernel density estimation is carried out on a multi-channel time sequence signal, a dynamic coupling matrix is constructed through recursion Copula decomposition, a three-level threshold surface is generated through time-varying quantile regression, abnormal samples and graph attention network extraction state representation are generated in combination with a conditional variation auto-encoder, lightweight recursion pruning is carried out, and the dynamic coupling matrix is obtained. An abnormal score is generated through a multilayer Bayesian network and particle filtering, a multi-step risk trend is discriminated through a Gaussian kernel derivative slope, and finally unscented Kalman filtering is used for smoothing and online threshold correction; according to the method, the detection sensitivity and the early warning recall rate of the box-type substation to the transient coupling fault are remarkably improved, the response speed is improved, and the false alarm frequency is effectively reduced.

The invention discloses an intelligent substation safety measure checking method and system, and the method comprises the steps: obtaining a secondary system topological structure, equipment information and historical safety measure ticket data of a substation, and constructing a quaternary knowledge graph; according to the quaternary knowledge graph, extracting multi-modal features of the maintenance task and identifying the type of a maintenance scene by combining a memory guide reflection decision reasoning mechanism, optimizing a rule reasoning process through a distributed guide local search algorithm, and generating an optimal safety measure operation set for a specific maintenance scene; automatically identifying a correlation loop and determining a minimum safety isolation range through a memory guide decision reasoning mechanism, dynamically generating a minimum safety operation set according to the real-time state of the equipment, and adaptively adjusting operation steps; and an operation dependency relationship model is constructed in combination with unwrapping variational multi-graph representation learning and a distributed guide local search algorithm, and operation sequence compliance verification, risk level assessment and dynamic visual early warning are realized. According to the invention, accurate formulation, dynamic adjustment and risk early warning of safety measures of the intelligent substation are realized.

The invention discloses a substation equipment rare defect simulation and identification method and system and a storage medium. The method comprises the following steps: constructing an equipment reference feature library; marking dynamic features of rare defects in historical inspection according to a spatial-temporal feature enhancement algorithm, generating a knowledge graph, and constructing a dynamic defect learning library; the method comprises the following steps: learning space association and environmental factor influence of defects and equipment through a bimodal generation network, and generating initial defect data matched with a weak area of the equipment; generating high-credibility defect fusion data through physical constraint-intelligent detection double screening; constructing a three-dimensional mixed data set, and screening high-quality training samples through a dynamic defect evolution algorithm and hierarchical cognitive evaluation; and constructing a multi-algorithm collaborative fine tuning network by using a federated learning framework, simulating and labeling defect information, and outputting a multi-dimensional identification prediction report. The method aims at solving the problems that the model is insufficient in rare defect recognition precision and lack of evolution prediction ability, and high-quality simulation of rare defect samples and high-precision recognition of the model are achieved.

The invention discloses an intelligent self-monitoring temperature management system for a box-type substation, and relates to the technical field of intelligent power grid equipment monitoring. The problems that an existing system is large in temperature measurement deviation, low in reliability, delayed in early warning, inaccurate in hot spot positioning and extensive in heat dissipation control are solved. According to the scheme, multi-source signals are acquired in parallel through a data acquisition module, and a temperature time sequence is extracted; a boundary calibration module is adopted to fuse data to generate a three-dimensional boundary condition; the multi-physical field solving module obtains an internal temperature / stress field; the physical information prediction module is fused with a heat transfer physical constraint training graph neural network to predict a hotspot migration trend; the hierarchical scheduling module is used for solving a fan and oil pump collaborative optimization instruction in real time based on model predictive control; according to the invention, the accuracy of internal temperature monitoring of the box transformer substation, the reliability of hot spot prediction and the accuracy of heat dissipation control are remarkably improved, the insulation life of equipment is effectively prolonged, and the operation safety and reliability of the system are improved.

The invention relates to the technical field of smart power grids and artificial intelligence, in particular to a substation autonomous intelligent inspection method and system based on multi-modal data, and the method comprises the steps: obtaining inspection data of multiple modals, and generating fusion features; identifying system alarm information; performing intention recognition and task classification to generate an executable task sequence; generating a multi-device cooperative scheduling scheme; executing the multi-device cooperative scheduling scheme; iterative optimization is carried out; according to the intelligent inspection method provided by the invention, more accurate and more robust multi-mode perception and diagnosis are realized, and deep understanding of complex instructions and safe and efficient cooperation of multiple devices are also realized; and meanwhile, through dynamic re-planning and a verification type feedback learning mechanism, high real-time performance and robustness are ensured, and meanwhile, the system is endowed with the capability of iterative optimization.

The invention provides an electric power inspection path planning method and device and electronic equipment, and relates to the technical field of unmanned aerial vehicle electric power inspection. The method comprises the following steps: acquiring obstacle information of an electric power facility environment, wherein the obstacle information comprises the type and position of an obstacle; based on the type of the obstacle and a preset safety distance coefficient, determining a differentiated safety distance, the type of the obstacle including a power transmission line, a transformer substation, a tower and other obstacles; based on the obstacle information and the differentiated safety distance, obtaining an initial global path through a path search algorithm; based on a preset multi-objective optimization function, the initial global path is optimized, a Pareto optimal path set is generated, and the multi-objective optimization function comprises a path length objective, a safety margin objective and an electromagnetic safety objective; and determining a target global path from the Pareto optimal path set based on a preset inspection task mode. According to the invention, the inspection efficiency and adaptability can be improved while the safety is guaranteed.

The invention relates to the technical field of transformer substation intelligent management, in particular to a modular prefabricated cabin transformer substation intelligent comprehensive management system with a multi-source data integration and collaborative management function. Comprising a multi-source data acquisition unit; the edge intelligent processing unit is used for carrying out local preprocessing, feature extraction and abnormal early warning on the acquired multi-source heterogeneous data of the transformer substation, and realizing data noise reduction, abnormal recognition and equipment health degree evaluation through a lightweight AI algorithm and a hybrid communication protocol carried by the edge computing terminal module; and a data integration collaboration unit. According to the invention, through an adaptive sampling strategy and a multi-modal feature fusion mechanism of the multi-source data acquisition unit, multi-dimensional data such as electric power parameters, power environment, fire protection and security protection and the like are incorporated into a monitoring system, feature layer association analysis is realized by depending on a Jousseme distance and a Dempster synthesis rule, the assessment limitation of single equipment and single-dimensional data in a traditional scheme is broken through, and the method has the advantages of high reliability and high reliability. And a more comprehensive state basis is provided for equipment health degree research and judgment.

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

The invention provides a defect identification method and device for substation equipment and electronic equipment, and relates to the field of image identification. According to the method, an infrared image, an electric field leakage map and a visible light image are obtained through a multi-channel imaging system deployed in a substation site, and a multi-channel image tensor is generated and input into a multi-channel recognition model to extract fusion features. And fusing the features, inputting the fused features into a YOLOv8 backbone network, constructing a joint attention domain in combination with an equipment prior structure, generating a high-confidence candidate box, and performing non-maximum suppression to obtain a detection result. And constructing an inter-frame residual tensor for a detection result to perform time sequence modeling, thereby improving the detection effect. And for equipment with complex shielding, complementing a structure contour through an edge prediction path, and finally outputting target boundary and defect positioning information. By implementing the technical scheme provided by the invention, defect identification of the substation equipment is facilitated.

The invention relates to a substation operation risk identification method based on a multi-view video and high-precision positioning. Acquiring video data of a working site through a plurality of cameras with fixed visual angles and mobile video acquisition equipment; a high-precision positioning system is used for obtaining three-dimensional space coordinates of operators and equipment in real time; establishing a three-dimensional digital twinborn model of the substation equipment, and performing dynamic scene reconstruction based on the multi-view video stream to generate a real-time three-dimensional scene of the operation site; fusing the positioning data and the three-dimensional scene by adopting a space-time fusion algorithm to generate a dynamic digital portrait of the operator; and carrying out real-time analysis on behaviors and positions of operators by using a risk assessment algorithm based on a preset risk rule, calculating to obtain a risk assessment value, and setting a feedback mechanism to continuously optimize positioning and scene reconstruction precision. According to the invention, efficient, accurate and real-time identification and early warning of the operation risk of the transformer substation are realized, and the safety management level of an operation site is effectively improved.

The invention discloses an integrated verification and test system and method for complex rules of a substation monitoring system, and the system comprises a rule editing module which provides a visual interface to support a user to edit the complex rules containing various types of variables and time sequence constraints; the analysis and verification module is used for performing grammar, variable conflict and business compliance multi-level verification on the rules; the intelligent test case generation module is used for automatically generating test cases covering normal, boundary and abnormal conditions based on the rule tree structure; the simulation test interface module converts the use case into a standard test script, submits the standard test script to a simulation tool for execution, and outputs the standard test script to a monitoring system through protocol conversion; the result analysis module is used for comparing an execution result with an expected result and generating a structured test report; according to the invention, full-process automation of rule verification and testing is realized, the verification efficiency is remarkably improved, and the test requirements of various transformer substation business scenes are met.

An offshore wind power plant current collection system optimization method considering cable type selection comprises the following steps: S1, acquiring wind power plant basic data including fan and transformer substation coordinates, fan rated power, multi-specification cable electrical and cost parameters and the like; s2, on the basis of the basic data, generating a candidate cable set by using clustering partitions and a distance threshold value, completing candidate set reduction, and synchronously generating a cable crossing avoidance constraint pair set; s3, an optimization model with the minimum total cost of the whole life cycle is established, the double-side ring path does not need scene variables, and the composite ring path needs to integrate scene driving variables; s4, carrying out path accelerated solution, carrying out hot start on a double-sided ring, carrying out cross constraint simplification, and carrying out additional fault set dimension reduction on a composite ring to obtain an initial scheme; and S5, performing full fault set N-1 verification on the initial scheme, bringing a violation scene into a fault set for resolution until the scheme meets an N-1 criterion, and outputting a final result. According to the method, topology and cable type selection integrated optimization is realized, and the solving quality and efficiency are considered.

The invention provides a remote intelligent operation monitoring method and system for an intelligent substation, relates to the technical field of intelligent operation and maintenance of substations, and relates to multi-source heterogeneous sensing, depth feature modeling, fault prediction evaluation and model self-optimization. According to the method, electrical, environmental and meteorological data are collected through heterogeneous sensors, a structured original data set is constructed, time sequence prediction is carried out in combination with a convolution-LSTM model, a Transform fusion network is utilized to output a fault probability and a confidence interval, online early warning and response control are realized, and the method has a federated learning driven adaptive updating capability.

The invention relates to the technical field of power system automation, in particular to a monitoring and maintenance system and method for a transformer substation. The system comprises a quantitative classification module, a difference analysis module and a grading adjustment module. Equipment in a transformer substation is classified through the quantitative classification module, correction parameters such as aging and load loss are introduced for single-equipment monitoring through the difference analysis module, misjudgment and missed judgment caused by equipment state changes are reduced by combining multi-level threshold values, associated equipment is modeled through a GNN model, neighbor states are aggregated, the equipment health degree is output, and the equipment quality is improved. The method comprises the following steps of: quantifying a fault propagation probability, pre-warning cascading fault risks in advance, and adjusting a threshold value of a single device in a linkage manner while a hierarchical adjustment module takes risk response measures, so that single device monitoring is adaptively matched with an associated device risk state, full-coverage accurate monitoring is realized, and local and global risks are considered.

The invention relates to the technical field of substation equipment fault diagnosis, in particular to a substation equipment thermal fault diagnosis method and system based on an infrared image. The invention discloses a substation equipment thermal fault diagnosis method based on an infrared image. The method comprises the following steps: acquiring an equipment temperature distribution image through an infrared thermal imager; carrying out denoising, contrast enhancement and normalization preprocessing on the image; extracting features such as temperature anomaly, temperature gradient and hot spot areas; a YOLOv13 model is adopted to identify the equipment type; inputting the features into a deep learning model for fault classification; and implementing multi-level alarm according to the classification result confidence. According to the method, temperature gradient analysis and a regional dynamic contrast enhancement technology are creatively fused, so that the fault detection precision and the early warning capability are remarkably improved, and intelligent diagnosis and graded early warning of the thermal fault of the substation equipment are realized.

The invention relates to an LLM-Agent-based transformer substation TSN intelligent scheduling method and system. The system comprises an RAG-based TSN knowledge base management module, a TSN network global situation awareness module, an LLM-RAG-based TSN intelligent scheduling decision module, an LLM-based reflection module and a dynamic TSN scheduling simulation verification module. The RAG-based TSN knowledge base management module is used for constructing a knowledge base comprising a TSN scheduling algorithm, a TSN scheduling rule, a TSN protocol and a TSN network topology structure; the TSN global situation awareness module provides real-time data support for intelligent scheduling decision making; the LLM-RAG-based TSN intelligent scheduling decision-making module is used for carrying out intelligent scheduling decision-making by utilizing an LLM-RAG technology; the LLM-based reflection module ensures the rationality and effectiveness of a scheduling result; and the dynamic TSN scheduling simulation verification module performs simulation verification on the TSN scheduling result optimized by the LLM-based reflection module, so that intelligent scheduling of the transformer substation TSN network is realized, manual intervention is greatly reduced, and manpower cost investment is effectively reduced.

The invention belongs to the field of bird repelling and equipment state monitoring, and particularly discloses a bird inhabitation multi-mode repelling and state monitoring device and method. The device is combined with a photosensitive device for day and night environment discrimination, is integrated with an intelligent hardware device of a multi-mode driving unit and a state monitoring module, and is suitable for long-term bird prevention operation in unattended scenes such as power transmission lines and transformer substations; further, accurate detection of bird approaching is realized by fusing various sensors, efficient repelling is performed by combining a multi-channel interference means of touch, hearing and vision, meanwhile, the running state of the anti-bird structure is monitored in real time, and data is remotely transmitted back, so that long-term, stable, protection and monitoring integration in an unattended scene is realized, and the safety and reliability of the system are improved. The threat of bird damage to safe operation of a power system is effectively reduced.

The invention provides an intelligent inspection risk assessment method and system based on multi-sensor fusion, and the method comprises the steps: obtaining original multi-source data of a transformer substation, the original multi-source data comprises a binocular vision image, infrared thermal image data and environment sensor data, and carrying out the time-space calibration and preprocessing of the original multi-source data, obtaining a multi-source sensor data stream; carrying out feature extraction on the multi-source sensor data stream to obtain a multi-modal feature set, generating a refined semantic mask based on the multi-modal feature set, constructing an initial scene relation graph, calculating a risk level based on a multi-layer perceptron classifier, and generating a risk level evaluation result and a risk distribution graph; outputting a safety distance violation warning and a risk area identifier; and generating comprehensive risk early warning information based on the risk level assessment result, the risk distribution diagram, the safety distance violation warning and the risk area identifier.

The invention provides an image processing method and device for substation equipment and electronic equipment, and relates to the field of image recognition. In the method, a fusion image sequence is generated through multi-source disturbance space-time tensor modeling and nonlinear image attenuation correction, features are extracted in combination with a pulse modal attention mechanism, a refined segmentation mask is generated, illumination compensation and texture reduction are completed in a strong reflection material scene, and geometric return is further performed by using electrical topology and a BIM model. And finally, outputting a return image sequence and an identification result under a standard view angle. By implementing the technical scheme provided by the invention, the problems of image processing degradation and distortion of the substation equipment can be solved conveniently, so that the accuracy of defect identification and classification is improved.

The invention provides a transformer substation operation and maintenance-based lock control terminal work log query method and system, and the method comprises the steps: collecting an original operation log stream generated by a transformer substation lock control terminal in a preset time period, serializing the original operation log stream to obtain a log data sequence, and then carrying out the context correlation analysis and recognition of an entity interaction relationship and behavior attribute description in a log text; a structured log metadata set is obtained after standardized integration; constructing a spatial topological index based on the physical layout and the electrical connection relationship of the substation equipment, mapping the equipment associated information in the spatial topological index to a corresponding node position of the spatial topological index, and generating a spatial enhanced log data set; receiving a natural language query request, generating a structured query expression, performing multi-dimensional retrieval matching on the spatial enhancement log data set, and outputting a preliminary query result set; and carrying out time sequence correlation analysis and operation influence evaluation on the log records to generate a query report. According to the invention, efficient and accurate query of the work log of the transformer substation lock control terminal can be realized.

The invention belongs to the technical field of transformer substation fault diagnosis, and relates to a transformer substation equipment health diagnosis and operation and maintenance suggestion generation method, in the invention, a dynamic portrait of an equipment state is constructed through acquisition and preprocessing of multi-source heterogeneous data, comprehensive extraction and fusion modeling of equipment operation characteristics are realized, and on the basis, the equipment health diagnosis and operation and maintenance suggestion generation method is provided. A dynamic equipment association diagram is introduced, the health state of equipment can be comprehensively evaluated and a risk propagation path can be identified by describing an association relationship between the equipment and combining a risk propagation model, and finally, an interpretable operation and maintenance suggestion is automatically generated based on a case reasoning and strategy optimization method, so that the problems of insufficient data integration capability and high risk propagation efficiency of a traditional method are effectively solved. The risk assessment dimension is single; and the operation and maintenance suggestion generation lacks intelligent support.

The invention discloses a micro-grid dispatching optimization method and system based on multi-modal information, and relates to the technical field of micro-grid dispatching optimization systems. The method comprises the following steps: performing space-time alignment on power data, energy storage charge state data and power grid operation data to generate a space-time associated multi-modal state matrix; solving the multi-target dynamic optimization model according to the multi-modal state matrix to obtain a hierarchical control instruction and an energy storage charging and discharging plan; decomposing the hierarchical control instruction to obtain and execute a substation-level global optimization task, a feeder-level power mutual aid task and a court-level in-situ balance task of a prediction time domain; correcting the energy storage charging and discharging plan in a short period, or updating the load priority list in a long period to obtain an execution result; and updating the data acquisition rule according to the execution result, and restarting data acquisition according to the updated data acquisition rule. According to the invention, the operation performance of the micro-grid can be improved.

The invention discloses a substation switching misoperation risk intelligent control method and system, and relates to the technical field of power system safety production, and the method comprises the steps: collecting multi-dimensional real-time risk factor data, building a dynamic probability dependency relationship model based on the risk factor data, quantifying the correlation influence between different dimensions, and updating the model parameters, feature information of a current operation scene is converted into a standardized vector, similarity matching is carried out on the standardized vector and a scene template, an adaptive basic strategy is screened, the basic strategy is dynamically adjusted according to a real-time risk assessment result, and a scenarized risk management and control strategy is generated; and implementing a scenarized risk management and control strategy, collecting multi-dimensional feedback data in an execution process, generating a new generation of optimization strategy based on the feedback data, and updating the strategy library. The risk management and control strategy is generated and adjusted according to the actual situation, the strategy is continuously optimized, the safety and reliability of switching operation of the transformer substation are improved, accidents caused by misoperation are reduced, and stable operation of a power grid is guaranteed.

The present invention provides a system and method for generating proposals for infrastructure modalities, such as electrical substations, using advanced artificial intelligence. It includes an input interface for data collection, a lightweight generative or rendering pipeline for creating preliminary 2D designs, and a generative model selected from diffusion, transformer-based, GAN or other architectures for refining these into detailed 3D models and generating preliminary designs. The system evaluates designs against predefined criteria to ensure compliance and feasibility. Supported by a cloud-based infrastructure for robust data processing and integration with third-party services, this system enhances the efficiency, accuracy, and compliance of modality planning and proposal generation.

The invention discloses a transformer substation intelligent patrol recognition algorithm optimization method based on multi-modal fusion, and belongs to the technical field of transformer substation patrol, and the method comprises the following steps: synchronously obtaining a visible light image, an infrared thermal imaging spectrum, an ultrasonic partial discharge signal and environmental parameters, and building a four-dimensional space-time calibration matrix; a dual-channel attention mechanism is adopted to realize multi-source data fusion, a spatial alignment channel and a feature enhancement channel are included, the spatial alignment channel realizes pixel-level registration of visible light and infrared images through affine transformation, and the feature enhancement channel utilizes a graph neural network to construct topological correlation mapping of acoustic, thermal and optical features; constructing a three-stage training architecture containing noise injection; and adding an adversarial generative network in a preprocessing layer to simulate rain fog and electromagnetic interference environments. According to the method, the identification threshold is updated online by deploying a sliding time window mechanism in an output layer, and the problems of poor feature extraction robustness and high false alarm rate of a traditional algorithm in complex environments such as strong electromagnetic fields and rain and fog are solved.

The invention discloses a transformer substation detection report automatic generation method based on artificial intelligence, and the method comprises the following steps: obtaining primary detection data through multi-modal data collection and preprocessing; performing standard verification on the primary detection data by using a rule engine, completing trend analysis and anomaly detection, and generating structured detection data; inputting the structured detection data into an automatic summarization and suggestion generation module to obtain an analysis abstract and a professional suggestion; filling the structured detection data, the analysis abstract and the suggestion into a standardized detection report template for automatic matching; a standard detection report is generated by adopting automatic typesetting and is pushed to a multi-role cooperation module, and the standard detection report is audited by related auditing personnel; and after checking and confirming, filing the detection report, and distributing the detection report to a related management system or a specified terminal according to a set rule. According to the invention, intelligent acquisition, anomaly detection, automatic report generation and whole-process closed-loop management of multi-modal data are realized, and the detection efficiency, accuracy and report standardization level are greatly improved.

The invention discloses a substation dispatching data network security communication method based on quantum tunnel encryption, relates to the technical field of large-scale power grid security assurance, and aims to solve the problems of endpoint security loss, boundary solidification, weak quantum threat resistance and inflexible deployment of a dispatching data network. According to the method, the establishment of a secure session and the encapsulation, encryption and restoration of data are realized by integrating quantum key distribution and a post quantum cryptographic operation module through quantum secure communication terminals deployed in a dispatching master station and a transformer substation. According to the method, application layer encryption and signature are carried out on telecontrol protocol data, a double-layer encryption tunnel is constructed, and transmission of various physical network media is supported. By means of the scheme, end-to-end protection is achieved, quantum attacks are effectively resisted, double encryption is provided, and networking flexibility and smooth evolution are improved.

The invention provides a transformer substation unmanned aerial vehicle three-dimensional route real-time planning and autonomous obstacle avoidance system, and relates to the technical field of non-electrical variable control or adjustment systems. Through cooperative work of a navigation mode switching module and a near-field navigation control module, an electric field of equipment is used as a natural navigation beacon; and high-precision autonomous flight is realized in a strong electromagnetic interference area with RTK signal failure, and the dependence on external positioning is eliminated, so that the inspection blind area is eliminated. Meanwhile, a fusion environment map containing multi-physics field information is constructed, the value of observation points is scientifically quantified according to waypoint inspection efficiency indexes, and a path planning algorithm aims at optimizing a comprehensive target fusing energy consumption, risks and inspection efficiency. The design not only ensures that the unmanned aerial vehicle can safely get close to the core equipment, but also enables the planned route to be the most valuable path rather than the shortest path, and finally fundamentally improves the safety, the operation efficiency and the data validity of substation inspection.

The invention relates to the technical field of digital twinning, discloses a digital twinning-driven transformer substation state artificial intelligence evaluation system, and aims to solve the problems that the diagnosis precision is reduced and the model adaptability is insufficient when a traditional digital twinning model is inconsistent with a physical entity state. The system constructs a closed-loop architecture comprising a state perception and deviation quantification module, a systematic risk and cognitive focus distribution module, a focused causal diagnosis and introspection verification module and a cognitive twin adaptive evolution module. The method can actively sense the state deviation of a physical entity, intelligently distributes diagnosis resources based on the evaluation of systematic risks, and achieves the efficient diagnosis of a fault source. The system determines the real state of a physical entity through introspection verification, and performs closed-loop updating on a reference twin model by using the result, so that the reference twin model can be continuously evolved, and the accuracy, the automation level and the full life cycle adaptability of state monitoring and fault diagnosis of a complex industrial system are remarkably improved.

The invention relates to the technical field of cable positioning, in particular to a one-way grounding fault positioning method based on a signal injection method. The method comprises the following steps: installing an impedance detection unit at a grounding knife switch of a transformer substation, and injecting a controllable AC signal into a power transmission line through a built-in injection coil to enable the line to be in an open circuit state; collecting the injected current signal and performing frequency enhancement to extract the amplitude and phase of a target frequency component; zero-sequence impedance is calculated based on the injection voltage and current, and the zero-sequence impedance is compared with a preset impedance model to judge single-phase grounding; and when a fault is detected, determining a fault distance, a tower position and a grounding phase according to an impedance difference matching result, thereby realizing accurate positioning. According to the invention, by injecting the controllable AC signal into the power transmission line and combining frequency enhancement, phase self-calibration and impedance difference matching analysis, high-sensitivity detection, high-stability signal extraction and high-precision positioning of the single-phase earth fault are realized.