9411 results about "Power substation" patented technology

The invention discloses a transformer substation electric power parameter real-time monitoring and analysis platform, and relates to the technical field of electric power monitoring. The monitoring analysis platform is in communication connection with a real-time monitoring module, a data acquisition and processing module, a data storage and management module, a fault feature extraction module, an event early warning module, a fault diagnosis module and a data visualization module. The transformer station electric power parameter real-time monitoring and analysis platform timely discovers abnormal conditions of electric power equipment by collecting and analyzing key parameters of voltage, current, power, temperature and humidity in real time, so that potential faults and accidents are prevented, and the real-time monitoring and analysis of the electric power parameters of the transformer station is realized by learning historical fault data and identifying modes. The platform can predict and identify an abnormal trend which may cause a fault, gives out an early warning in advance, remarkably improves the reliability and safety of a power system, reduces the risks of accidental power failure and equipment damage, and guarantees the continuity and stability of power supply.

The invention relates to the technical field of monitoring and early warning of power equipment, in particular to a monitoring and early warning method and system for substation power equipment. Comprising a data acquisition module, an intelligent diagnosis module, a risk assessment module, a collaborative early warning module and an interaction control module. The data acquisition module acquires equipment operation data including current, voltage, temperature and humidity and vibration signals. And the intelligent diagnosis module identifies equipment abnormity through the generative adversarial network and the graph neural network. The risk assessment module generates a risk assessment curve based on a fractional order differential model and a reinforcement learning algorithm. And the collaborative early warning module generates a graded early warning scheme by utilizing edge calculation. And the interaction control module displays a state and optimizes a control strategy through a human-computer interface. The intelligent monitoring and management level of the power equipment of the transformer substation is improved, the service life of the equipment is prolonged, and the operation and maintenance cost is reduced.

The invention discloses a fault detection method and device for secondary equipment of a transformer substation. The method comprises the following steps: acquiring real-time operation parameters of the secondary equipment of the transformer substation, inputting the real-time operation parameters into a pre-trained graph neural network model to obtain equipment operation characteristics, and determining an equipment state vector according to a plurality of equipment operation characteristics in a continuous time window and a message passing mechanism; determining an equipment health degree score according to the equipment state vector and a knowledge graph, and triggering the intelligent inspection robot to execute a deep detection task under the condition that the health degree is lower than a preset health degree threshold value so as to collect target test data; determining a target fault type according to the target test data, the knowledge graph and the inference rule base, obtaining a fault processing scheme corresponding to a historical fault case, and generating corresponding maintenance guide information according to the fault processing scheme, thereby achieving the real-time detection, precise fault diagnosis and automatic maintenance of the secondary equipment of the transformer substation. And the operation safety and the maintenance efficiency of the power system are improved.

The invention discloses a transformer substation engineering construction drawing intelligent analysis and evaluation system based on image recognition, and relates to the technical field of transformer substation engineering construction informatization. The data processing center is in communication connection with a data acquisition layer, an image preprocessing layer, a feature extraction layer, an intelligent analysis layer, an evaluation decision-making layer and an application display layer, and all layers of architectures are in electric signal connection. According to the method, the improved convolutional neural network model is adopted, and an image preprocessing algorithm specially aiming at engineering drawing feature optimization is combined, so that the drawing recognition accuracy is remarkably improved compared with a traditional OCR technology, the manual proofreading workload is greatly reduced, a drawing element correlation analysis system based on a knowledge graph is constructed, and the engineering drawing recognition accuracy is improved. Key information such as equipment arrangement, line connection and size marking in the drawing can be automatically extracted and associated, and structural storage and intelligent retrieval of drawing content are achieved.

The invention relates to the field of electric power monitoring, and discloses an electric power monitoring system and method fusing image recognition and data analysis, and the method comprises the steps: carrying out the visual angle coverage modeling of a target equipment group through a multi-type visual collection unit disposed at a transformer substation and a power distribution terminal; performing cross-frame fine-grained texture differential analysis on the equipment state image sequence, and constructing an image event time window in combination with synchronous disturbance characteristics of multi-source monitoring parameters; based on the high-vigilance candidate frame set, fusing the image structure variability index and the operation data multi-dimensional deviation vector by using a feature encoder, and constructing a multi-modal state coupling feature tensor; map mapping is carried out on the potential fault evolution trend, and semantic association is established between structural nodes with abnormal attributes in the image and frequently fluctuating parameter indexes in the monitoring data; and combining a node interference path in the local fault association subgraph with fault precursor distribution induced in a historical accident sample. The method has the advantage that the operation safety is improved.

The invention discloses a power failure monitoring system, and relates to the technical field of failure monitoring. During operation of the system, multi-dimensional data acquisition is carried out on key equipment of a transformer substation and a power distribution room, the operation state of the equipment is comprehensively covered through real-time acquisition of data of various sensors, and data acquired by a data acquisition module is preprocessed; the intelligent gateway equipment is deployed, real-time data analysis and preliminary fault diagnosis are carried out locally, the running state of the equipment is monitored in real time through set rules and threshold values, potential faults are detected, preliminary alarm is given out, and the system automatically judges the severity of the faults and decides whether to take automatic control measures including load switching or equipment power failure or not. Meanwhile, alarm information is generated and sent to operation and maintenance personnel, data, fault diagnosis results and automatic control measures in system operation are fed back, and a deep learning model and an edge calculation strategy are periodically updated through online learning and model updating.

The invention discloses an intelligent substation communication link fault self-healing regulation and control method and system, and relates to the technical field of fault self-healing regulation and control, and the method comprises the following steps: constructing a link disturbance intensity sequence reflecting link quality fluctuation; performing trend fitting and mutation identification on the link disturbance intensity sequence based on a sliding window mechanism, and generating a link state transaction index; when the link state transaction index exceeds a first threshold value, identifying an affected key easy-to-disturb service set according to the service path topological data of the abnormal link; constructing a context-aware path migration cost model based on the key easy-to-disturb service set, and generating an optimal switching path sequence; and implementing hierarchical link regulation and control according to the path switching optimal sequence. According to the method, trend fitting and mutation detection are carried out by adopting an overlapped sliding window mechanism and a mutation scoring function, so that intermittent or hidden link degradation abnormity which is difficult to capture can be effectively identified, and a link state transaction index has higher judgment precision.

The invention discloses a substation equipment state monitoring and intelligent fault early warning method based on deep learning. The method comprises the following steps: S1, obtaining a preprocessed multi-source state data set; s2, generating a high-dimensional equipment state feature matrix; s3, a fault sensitive deep belief network model is adopted to form a preliminary fault state recognition result; s4, obtaining an optimized sensitive depth belief network model; s5, performing online analysis on the multi-source state data acquired in real time by using the optimized sensitive deep belief network model, generating a real-time fault prediction result of the equipment state, and classifying and grading fault risks; and S6, according to a real-time fault prediction result, triggering a remote fault early warning mechanism, and sending fault early warning information including a fault risk level, an early warning signal and an emergency processing suggestion to a substation operation and maintenance center. According to the invention, intelligent alarm linkage and hierarchical control of the scheduling system are effectively supported.

The invention relates to the technical field of geological monitoring, in particular to a method for intelligently monitoring geological disasters based on satellite remote sensing, which comprises the following steps of: obtaining corresponding earth surface coverage information through image registration and ortho-rectification processing; identifying terrain boundaries, geological structure features and geological attribute information through the land surface coverage information; recognizing surrounding hidden danger points through a topographic feature extraction method; an image fusion enhancement method is matched with a permanent scatterer interference method, a stable scatterer is extracted, a deformation monitoring network is constructed, and a phase difference value in a high-risk area is obtained through calculation; carrying out unwrapping processing through the phase difference value, and carrying out inversion to obtain earth surface three-dimensional deformation field information in the monitoring area; establishing a geological disaster risk assessment model through space-time clustering analysis; by establishing a risk assessment model, the geological disaster risk in the monitoring area is quantitatively assessed. The beneficial effect of the invention is that comprehensive monitoring and accurate early warning of geological hazard hidden dangers around the transformer substation are realized.

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 relates to the technical field of power system automation, in particular to a real-time monitoring and fault positioning system for a vehicle-mounted mobile substation. The system comprises a signal acquisition and processing unit which monitors and acquires line parameters and environmental parameters of a transformer substation and traveling wave signals generated when a fault occurs in real time; the fault type identification unit calculates the confidence coefficient of each fault type based on the traveling wave signal so as to judge the fault type generated by the traveling wave signal, and marks the arrival time of the traveling wave head; an algorithm fusion positioning unit preliminarily positions a fault point according to the fault type and the arrival time of a traveling wave head, and then constructs a fitness function through a particle swarm optimization algorithm in combination with line topology and environmental parameters to correct a preliminary positioning error; according to the system, high-precision fault positioning and rapid isolation recovery are realized, the accuracy of fault section division in the complex power distribution network is ensured by modeling switch state and branch change, and misjudgment caused by topological change or equipment overload is effectively avoided.

The invention discloses a city expansion area transformer substation layout method and system based on an artificial intelligence algorithm. The method comprises the following steps: predicting future electrical load demand data of an area by mining an association rule between geographic space data and historical load data of the city expansion area; analyzing the prediction data by using a large language model, partitioning the city expansion area according to an analysis result, obtaining each sub-area and load difference data thereof, combining the data with historical power distribution network grid structure data of the city expansion area, and processing the data through the large language model to obtain an initial layout scheme of the transformer substation; by simulating the power supply capacity of each sub-region at different load growth rates, the initial layout scheme of the transformer substation is optimized, an optimization scheme is obtained and executed, and the optimal layout of the transformer substation is obtained by comprehensively applying artificial intelligence algorithms such as a large language model and a machine learning algorithm dynamic planning algorithm. Accurate prediction of the electrical load demand of the urban expansion area and effective optimization of the transformer substation layout scheme are realized.

The invention relates to the technical field of mode recognition, in particular to a transformer substation equipment temperature measurement method and system based on visible light guidance. Firstly, a visible light image, an infrared thermogram and vibration spectrum data are synchronously collected, a dynamic compensation coefficient matrix is constructed through illumination intensity analysis, the dynamic compensation coefficient matrix is applied to the infrared thermogram, and illumination interference compensation is achieved. And then, inputting the corrected infrared thermogram and visible light image into a pre-trained heterogeneous atlas matching model, and identifying the category and three-dimensional coordinates of the target equipment. And based on the equipment category information, calling a standard temperature field template in the digital twin model library, establishing a heat conduction path model in combination with the vibration spectrum data, and generating a temperature field prediction reference map of the equipment. And finally, through pixel-by-pixel deviation analysis, inputting the temperature deviation value, the image features and the vibration data into the probability graphic model, and outputting equipment fault information. The method can realize high-precision temperature measurement and fault diagnosis.

The invention relates to the technical field of power system automation, in particular to a substation management system and method based on the Internet of Things technology, and the system comprises a ubiquitous Internet of Things sensing matrix, an electric power intelligence brain evolution center, a space-time fusion twin module, a nonlinear optimization decision module, and a multi-level vibration risk management and control model. Wherein the ubiquitous internet-of-things sensing matrix acquires substation equipment, environment and power grid data in real time; the electric power intelligence brain evolution center carries out health assessment, life prediction and abnormity positioning on the equipment; the space-time fusion twinborn module constructs a digital twinborn body, fuses equipment space-time data and historical data, and performs operation and maintenance simulation and fault reproduction; the nonlinear optimization decision-making module is used for generating a response regulation and control strategy aiming at the nonlinear and uncertain problems in the operation of the power grid; and the multi-level vibration risk management and control model carries out dynamic assessment and strategy optimization on equipment faults, power grid safety and environmental risks. Therefore, the problems of low inspection efficiency, high false detection risk, poor transmission stability and the like in the prior art are solved.

The invention relates to the technical field of electrical fault monitoring, in particular to an intelligent mobile substation electrical fault monitoring method, which comprises the following steps of: synchronously acquiring an electrical monitoring signal, a multi-dimensional environment noise signal and a dynamic working condition parameter of a mobile substation through a multi-source sensor; the method comprises the following steps: constructing an environmental noise floor vector group by adopting multi-scale spectrum mode decomposition, filtering an environmental noise interference component from an electrical monitoring signal through orthogonal projection filtering, and outputting a baseline correction signal; a working condition disturbance response field matrix is constructed based on time domain and frequency domain correlation analysis of dynamic working condition parameters, gradient sensitivity coefficients are calculated, and components strongly related to working condition disturbance and residual components weakly related to equipment faults are separated out; reconstructing the residual component into a pure fault feature vector; and finally, fault type diagnosis and risk early warning are carried out on the basis. The method effectively solves the problem of fault feature annihilation caused by noise pollution in a complex environment and the problem of false alarm and missing alarm caused by confusion of working condition disturbance and real fault signals.

The invention relates to a substation equipment fault early warning method and system, and belongs to the technical field of intelligent substations, and the method comprises the steps: obtaining the target operation state data of substation equipment, the target operation state data comprising target sensor data and target image data obtained by an unmanned plane which carries out the field inspection of a substation; inputting the target operation state data into a completely trained fault diagnosis model, extracting spatial features of target image data by the completely trained fault diagnosis model through a long-short-term memory network, extracting time features of target sensor data through a convolutional neural network, and splicing and fusing the spatial features and the time features to obtain a fault diagnosis result; outputting a fault prediction result; and performing early warning based on the fault prediction result. According to the invention, the fault diagnosis model is combined with the spatial features of the image data and the time sequence features of the sensor data to carry out early warning on the substation equipment fault, and multi-modal data fusion and fault prediction of the substation equipment are realized.

The invention relates to the technical field of energy scheduling, and discloses a smart city energy dynamic scheduling system and method based on big data analysis, and the method comprises the following steps: the operation state of a transformer substation, the basic parameters of a charging pile and regional load prediction data are collected in real time, data cleaning and abnormal value filtering are carried out; constructing a complete power grid-charging facility dynamic information base; calculating the power supply margin of each region based on the capacity loss of the faulty transformer substation, establishing a weight scoring system of charging pile power adjustment in combination with the charging demand urgency, and determining the reduction or recovery priority of each charging load; and generating a charging pile power adjustment instruction through a multi-target optimization model, and iteratively correcting a power distribution scheme and generating a final scheduling instruction set by taking minimization of user satisfaction loss as a target while meeting the power grid capacity. According to the invention, by constructing a dynamic response mechanism and a multi-target collaborative optimization model, accurate and rapid regulation and control of the traffic load are realized in the scene of sudden power shortage of the power grid.

The invention discloses a transformer substation scene three-dimensional semantic segmentation method based on point cloud enhancement and attention guidance, and the method specifically comprises the steps: carrying out the region division and category labeling of three-dimensional point cloud data in a transformer substation scene, setting a semantic tag based on an equipment type, and constructing a semantic segmentation point cloud data set adaptive to a power scene; a three-dimensional semantic segmentation model is constructed, and collaborative modeling of local details and global context is realized through a point cloud enhancement module and an attention guidance coding module; a weighted loss function and a parameter optimizer training model are adopted to solve the problem of unbalanced equipment category distribution in a substation scene; and performing semantic segmentation on the substation scene point cloud data based on the trained model, and outputting a high-precision equipment classification result. According to the method provided by the invention, through collaborative design of point cloud enhancement and attention guidance, the accuracy and robustness of three-dimensional semantic segmentation in a complex scene are effectively improved, and the method is particularly suitable for a scene with dense substation equipment and a fine structure.

The invention relates to the technical field of intelligent control of transformer substations, in particular to a standardized intelligent control system of a transformer substation, which realizes real-time response to the running state of equipment by acquiring the current, voltage, power factor, temperature and humidity, operation state, insulation state and environment temperature and humidity of a power transformation unit in real time. A gradient boosting decision tree is combined with a variational auto-encoder and a self-attention mechanism, state vector features are extracted, complex abnormal states are accurately recognized, potential faults are detected in an early stage, and the monitoring efficiency and the early warning precision are greatly improved. The future power demand is accurately predicted by combining the long-short-term memory network model with historical and real-time load data, and the limitation of traditional static load management is overcome. Based on the predicted power demand, the reinforcement learning model is adopted to dynamically adjust the operation number of the power transformation units, optimal configuration of resources is achieved while the demand is met, and the overall operation efficiency of the transformer substation is remarkably improved.

The invention discloses a collapse geological disaster vulnerability model and risk assessment method and system, and relates to the technical field of geological disaster risk assessment, and the method comprises the steps: collecting and preprocessing target rock mass information; collecting monitoring data and long-term rainfall data of the three-axis acceleration sensor; the acceleration fluctuation characteristics are analyzed, and the risk of rock mass instability is recognized in combination with rainfall data; the instability risk of the rock mass is quantified based on a Monte Carlo method; predicting a motion path and a stacking range of the collapsed rockfall by utilizing numerical simulation, and evaluating threats of the collapsed rockfall to surrounding electric power facilities; and performing level-to-level management on the surrounding area of the transformer substation through the risk level model. Real-time monitoring, probability calculation and numerical simulation technologies are adopted, and the problem that in the prior art, power facilities lack dynamic monitoring, accurate evaluation and graded protection is solved. According to the overall scheme, real-time and automatic monitoring and risk grading of collapse geological disasters are achieved, and the safety and reliability of electric power facilities under the disaster condition are effectively improved.

The invention discloses a three-dimensional automatic modeling method for a high-precision transformer substation. The method comprises the following steps: collecting laser point cloud data and multi-view pictures of the transformer substation; dense point cloud data registration, multi-view image camera parameter reconstruction, multi-view image projection and adaptive point cloud density control are carried out; approximating the point cloud data by using three-dimensional Gaussian distribution functions, summing all the three-dimensional Gaussian distribution functions to obtain a Gaussian distribution geometric field represented by the three-dimensional Gaussian distribution point cloud, and reconstructing and rendering a three-dimensional grid to obtain a three-dimensional grid model; setting constraint conditions of equipment on the basis of the three-dimensional grid model, and generating a three-dimensional equipment model, equipment coordinates and equipment classification names by using a volume segmentation algorithm and a three-dimensional marking algorithm; and converting the equipment model into a GIM model file, generating a PMS file from the equipment coordinate and the equipment classification name, and synchronizing the PMS file to a GIM and PMS 3.0 system. According to the method, the problems of low efficiency and inconsistent quality in the traditional manual modeling process can be effectively solved.

The invention discloses a substation autonomous inspection and foreign matter cleaning cooperative control system and method, and belongs to the technical field of intelligent inspection and foreign matter cleaning. The system comprises a multi-modal detection module used for acquiring images, point clouds and multi-source sensing data and identifying foreign matter coordinates and visual features; the space positioning module is used for executing space registration to obtain a three-dimensional coordinate of the foreign body; the risk decision module fuses the multi-dimensional information and evaluates the foreign matter risk level; the cleaning execution module generates a cleaning path and execution parameters and issues the cleaning path and the execution parameters; and the inspection execution module plans inspection paths and actions based on the state and constraint conditions, and completes subsequent equipment inspection tasks. According to the scheme, the system realizes clear division of labor and accurate control in multiple stages of foreign matter identification, space positioning, risk assessment, cleaning instruction generation, polling after cleaning and the like, and the expansibility and the overall response efficiency of the system are improved, so that efficient, safe and stable execution of autonomous polling and foreign matter cleaning tasks of the transformer substation is guaranteed.

The invention discloses a power equipment fault prediction method based on multi-modal data and related equipment, and belongs to the technical field of intelligent monitoring and fault prediction of power equipment, and the method comprises the steps: collecting and preprocessing the multi-modal operation data of the power equipment; extracting features of the preprocessed multi-modal operation data and performing global feature fusion to obtain multi-modal features; inputting the multi-modal features into a pre-trained fault prediction model to obtain a fault category and an occurrence probability of the power equipment; the fault prediction model is obtained by inputting the multi-modal features into a hybrid neural network for training. Multi-modal operation data of power equipment is collected, key features are extracted by using a deep learning model, multi-modal data fusion is performed, and a fault prediction model is constructed based on a hybrid neural network. Real-time fault early warning and maintenance suggestions are provided, the operation reliability of power equipment is improved, the unplanned shutdown risk is reduced, and the method is suitable for health management of transformer substations, high-voltage power transmission equipment and wind generating sets.

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 present invention relates to the technical field of secondary device monitoring. Disclosed are an online monitoring method for an operating state of a secondary current loop neutral line, and a system. The method comprises: by using a high-precision sensor for collection and a composite filtering algorithm for processing, obtaining a secondary current fundamental component of each neutral line of a transformer substation which is not influenced by harmonic waves and transient states, and uploading the secondary current fundamental component to a protection information master station; retrieving originally configured access protection information master station information to obtain three-phase secondary current loop information of each bay of the transformer substation, summarizing secondary current information of all monitoring ranges, and performing grouping and classification marking on said information by transformer substation, bay and winding; and using the collection information for operation analysis, automatically identifying and confirming an operating state of the line, and on the basis of the operating state, calculating the outlier deviation linearity and the continuous dynamic trend deviation degree of each winding of each bay. The present invention solves the problem that intermittent faults in secondary current loop neutral lines are concealed and are not easily found, realizes comprehensive monitoring for the whole system, and can quickly and accurately position a specific winding and issue an abnormal early warning.

The invention relates to the technical field of knowledge graph construction, and discloses a transformer substation knowledge graph construction and optimization method based on multi-view learning, which comprises the following steps of: processing transformer substation multi-source data through a heterogeneous model; multi-source heterogeneous data of operation and maintenance texts, monitoring data, regulations and rules and infrared images of substation equipment are mapped to a unified feature space through linear projection, a multi-mode positive and negative sample pair of the same equipment is constructed, the distance of related equipment features is shortened by adopting comparative learning, projection matrixes of various data are dynamically optimized, and the multi-mode heterogeneous data of the substation equipment is obtained. And jointly detecting the power transformation equipment entity boundary in the operation and maintenance text and the equipment monitoring data, fusing the multi-modal equipment characteristics through an attention mechanism, and reasoning the relationship type between the equipment. According to the method, the multi-source heterogeneous data of the transformer substation and expert experience are deeply fused, so that the fragmentation and staticization problems of a traditional knowledge management system are effectively solved, and the accuracy of state perception and fault diagnosis of the power equipment is remarkably improved.

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 a standardized centralized DTU multi-level fault diagnosis and self-healing method and system, and relates to the technical field of intelligent power grid fault diagnosis, and the method comprises the steps: building a dynamic topological graph according to a unified coding rule, extracting spatial-temporal characteristics through a graph convolution network, and obtaining a spatial-temporal characteristic power grid topological graph; building a power grid digital twin environment based on a multi-level standardized fault diagnosis result, generating a plurality of sets of self-healing strategy candidate sets by adopting a reinforcement learning algorithm, screening an optimal self-healing strategy, and converting the optimal self-healing strategy into a control instruction conforming to a substation automation standard protocol; the control instruction is sent to the DTU and analyzed, and the intelligent power switch cabinet is controlled to execute self-healing operation. By automatically correcting the abnormal diagnosis result, false alarm and missing alarm are reduced, and the accuracy of fault diagnosis is improved. The intelligent level of power grid management is obviously improved, the maintenance cost is reduced, and the operation efficiency and the service quality of a power system are optimized.

The invention discloses a transformer substation on-line monitoring system based on big data analysis, and relates to the technical field of power system state monitoring, the system obtains data through a data acquisition module, and after the data is processed by a signal preprocessing and feature window extraction module, a system response entropy calculation module calculates a multi-scale permutation entropy, a multi-scale fuzzy entropy and a transfer entropy; the method comprises the following steps of: constructing a composite entropy feature vector, establishing a working condition self-adaptive health entropy baseline by an entropy feature baseline management module by utilizing a machine learning algorithm, comparing a current entropy feature with the health baseline by a degradation evaluation and critical early warning module, performing analysis by combining various abnormal judgment rules and indexes based on a critical moderation theory, and outputting an evaluation and early warning result; according to the method, the functional out-of-order of the equipment can be sensed in advance, the dynamic interaction health degree is evaluated in a non-intrusive mode, effective early warning is provided for critical transformation such as system instability, and the reliability and safety of operation of the transformer substation are remarkably improved.

The invention discloses a transformer substation inspection unmanned aerial vehicle path planning method based on a fusion A *-grey wolf algorithm, and aims to solve the problem of unmanned aerial vehicle path planning in a transformer substation inspection scene, and the method fully considers coupling of task point traversal and obstacle avoidance, thereby ensuring that an inspection path allocated for unmanned aerial vehicle inspection accords with a real transformer substation scene. A transformer substation virtual environment model is established through a grid map of an A * algorithm, and path implementation of the algorithm is simplified; a population is initialized by improving Tent chaotic mapping, a grey wolf algorithm is improved by introducing an adaptive position updating strategy and an elite grey wolf optimization strategy, and an initial traversal path is generated; and refining through an A * algorithm dynamically weighted by an evaluation function to obtain a final path. Simulation results prove that the feasibility and effectiveness of the improved fusion algorithm in obstacle avoidance and path planning in a substation simulation scene, and the improved algorithm is more suitable for path planning in a complex environment with multiple task targets and multiple obstacles.