13886 results about "Power equipment" patented technology

The invention relates to the technical field of power equipment, and discloses a digital twin operation monitoring system for power equipment, which comprises a data sensing and acquisition system for acquiring key operation parameters of temperature, current, voltage, partial discharge, vibration and humidity of the power equipment in real time, and performing multi-dimensional data acquisition through a sensor and a data transmission module; the state evaluation and prediction system is used for performing equipment health evaluation and residual life prediction by using a prediction model LSTM based on the collected data, and updating a prediction result in real time; provided is a digital twin modeling system. Through the combination of edge calculation, an LSTM model and a digital twinning technology, the precision and real-time performance of health management of power equipment are improved, data quality is optimized through edge calculation, the LSTM model captures an equipment degradation trend, virtual-real fusion is realized through digital twinning, and accurate monitoring and early warning of the health state of the equipment are ensured, so that intelligent operation and maintenance decisions are optimized, the failure rate is reduced, and the safety of power equipment health management is improved. The equipment life is prolonged.

The invention discloses a multi-modal AI-based power equipment anomaly detection method and system, and the method comprises the steps: synchronously collecting electrical, mechanical and thermal modal data of power equipment through an edge computing node, carrying out the load adaptive dynamic preprocessing, and uploading the data to a cloud end; the cloud constructs a multi-modal feature extraction network based on a structural causal model, analyzes a causal path between modals through a Bayesian network and performs weighted fusion on feature vectors; capturing device state mutation by using a gating attention mechanism, and updating the feature vector; executing time-space consistency verification of the equipment group to identify regional group abnormality and suppress single-point misinformation; generating an interpretable report containing an abnormal root cause analysis and priority ranking maintenance strategy; and establishing a closed-loop feedback mechanism to correct the cause and effect probability distribution of the Bayesian network model. The system comprises a multi-modal sensor array, an edge computing node and a cloud analysis platform, wherein the cloud analysis platform is integrated with a causal reasoning engine, a space-time consistency verification module and the like. According to the invention, by analyzing the multi-modal deep causal association, the method adapts to the dynamic change of the equipment, reduces the false alarm rate, generates an interpretable report, supports closed-loop self-optimization, and improves the anomaly detection accuracy and operation and maintenance decision efficiency of the power equipment.

The invention belongs to but not limited to the technical field of fault diagnosis and maintenance of power equipment, and discloses an intelligent diagnosis and maintenance system and method for power equipment based on a knowledge graph. A data acquisition and preprocessing module acquires real-time operation data and historical data of the power equipment; the knowledge graph construction module associates various kinds of information of the power equipment to construct a knowledge graph; the intelligent diagnosis module performs association analysis on power equipment faults based on a knowledge graph and a machine learning algorithm, identifies fault modes and potential faults, and predicts possible faults of the equipment; the maintenance suggestion module automatically generates a detailed maintenance scheme according to the diagnosis result; the operation and maintenance visualization module provides visual fault diagnosis results and maintenance suggestions, supports multi-level data display, and helps operation and maintenance personnel to clearly understand the equipment state. The operation safety of the power equipment is improved, the operation and maintenance cost is remarkably reduced, and the overall efficiency of equipment management in an intelligent power grid environment is improved.

The invention provides a multi-source data fusion diagnosis method and system for new energy station equipment, and relates to the technical field of intelligent operation and maintenance of power equipment, and the method comprises the steps: collecting equipment operation data, and carrying out the preprocessing to obtain standardized data; transient features and drift features are extracted, a transient fault mode and a degradation fault mode are identified, and a dual-frequency-domain fault feature library is established; calculating a power transmission fluctuation value between devices, analyzing energy transmission efficiency, positioning a fault source device and identifying a fault propagation path; establishing a fault diagnosis evaluation index for grading diagnosis, and evaluating a fault development trend; and generating and executing a maintenance decision scheme. According to the invention, through multi-source data fusion analysis, accurate diagnosis and early warning of equipment faults of the new energy station are realized, and the equipment operation and maintenance efficiency is improved.

The invention discloses a power equipment fault cross-domain collaborative analysis system and method, and relates to the technical field of power grid dispatching, and the method comprises the steps: obtaining preprocessed multi-source heterogeneous data of power equipment, constructing a cross-domain knowledge graph based on the topological relation of the preprocessed data and historical fault data, and marking a fault propagation path. And a graph neural network is adopted to carry out embedded representation. Designing a space-time multi-branch network, respectively extracting space, time sequence and modal interaction features by using the space-time multi-branch network, and performing fusion in a feature fusion layer to obtain fusion features and branch weights; according to the method, mapping knowledge domain embedded representation is combined, a collaborative reasoning model is constructed by utilizing a Bayesian network, reasoning decision is performed on fusion features, finally, a cross-domain collaborative analysis result of the power equipment fault is obtained, and fusion and efficient reasoning of multi-source heterogeneous data are realized through combination of the mapping knowledge domain and a space-time multi-branch network. And the accuracy and efficiency of fault diagnosis are improved.

The invention provides a thermal power equipment real-time monitoring method and system based on edge computing, and relates to the technical field of thermal power equipment real-time monitoring, and the method comprises the steps: deploying an edge computing node array to collect multi-source heterogeneous data of thermal power equipment, and carrying out the preprocessing through data screening, multi-scale adaptive filtering and wavelet packet decomposition, a conditional variation auto-encoder is used to extract features, a hierarchical attention mechanism and a deep feature fusion network are combined to generate mixed feature representation, refined distribution estimation and abnormal mode recognition are performed on equipment states, cooperative monitoring modeling is performed based on a multi-scale spatial-temporal feature fusion network and a hierarchical depth deterministic policy gradient network, and a multi-scale spatial-temporal feature fusion network is established. The real-time monitoring accuracy and efficiency of the thermal power equipment can be effectively improved, the equipment failure rate is reduced, and safe and stable operation of a thermal power plant is guaranteed.

The invention relates to the field of power equipment data processing, in particular to a biomass power generation combustion parameter deep learning method and system, and aims to solve the problem of phase mismatch caused by sampling frequency difference and clock reference offset of multi-source heterogeneous time sequence data. A parallel multi-scale convolution and bidirectional long-short-term memory network hybrid model is constructed, transient fluctuation and long-period trend features are extracted, and combustion stage feature weights are dynamically distributed through a gating attention mechanism. The optimization control module generates a multi-target constraint condition, an operation instruction is output in combination with a fuzzy inference engine, and a digital twin platform simulates an extreme working condition to enhance model robustness. A closed-loop feedback mechanism dynamically adjusts model parameters through combustion efficiency monitoring data and simulation results, and a two-stage fault-tolerant strategy realizes sensor abnormity compensation and historical control strategy backtracking. The problem of asynchronous data stream feature misalignment is effectively solved, and the combustion efficiency prediction precision and the control decision reliability are improved.

The invention discloses a power grid intelligent monitoring and dynamic scheduling system and method based on digital twinning, and belongs to the technical field of power grid scheduling. The system comprises a power grid topology generation module for generating a power grid topology and a full-life-cycle file of power equipment; the modeling module and the knowledge graph establishing module are used for acquiring power grid operation situation data, and establishing a digital twin model and a knowledge graph according to the power grid operation situation data, power grid topology and power equipment full life cycle archives; the mapping module is used for establishing a mapping model to obtain a mapping data set; the risk analysis module performs multi-risk analysis through the mapping data set to generate a comprehensive risk analysis report; and the dispatching module is used for obtaining a power grid dispatching strategy by adopting a multi-target adaptive optimization algorithm and a load balance algorithm according to the comprehensive risk analysis report. The method effectively improves the anti-risk capability of the scheduling strategy, and improves the accuracy of the scheduling strategy through the comprehensive application of the digital twin model and the knowledge graph.

The invention relates to the technical field of multi-modal data processing, and discloses a multi-modal sensor fusion inspection method and system, and the method comprises the steps: collecting the multi-modal original data of power equipment through a multi-modal sensor in an inspection robot, and constructing a feature vector set; performing adaptive weight calculation on the multi-modal sensor according to the feature vector set to obtain a sensor weight set; carrying out conflict identification and resolution on the multi-modal original data to obtain a fusion data set; performing abnormal feature extraction on the power equipment based on the fused data set to obtain an abnormal feature set; and carrying out routing inspection trajectory optimization based on the abnormal feature set to obtain a target routing inspection path sequence, and carrying out equipment state joint prediction in combination with historical equipment routing inspection data to obtain an equipment fault prediction result. And thus, more accurate equipment state joint prediction is realized.

The invention relates to a high-voltage equipment defect detection method based on deep learning and multispectral image fusion, and relates to the technical field of electric power high-voltage equipment state detection. The method comprises the following steps: acquiring an ultraviolet image, an infrared image and a visible light image of the surface of the high-voltage equipment; carrying out image pixel feature-based fusion processing on the ultraviolet image, the infrared image and the visible light image through an image fusion method; establishing a high-voltage equipment defect detection model, and training the high-voltage equipment defect detection model by using the fused image data to obtain a high-voltage equipment defect identification model based on the YOLO-STrans multispectral fusion network; and inputting the ultraviolet image, the infrared image and the visible light image of the outer surface of the power high-voltage equipment into a high-voltage equipment defect identification model to obtain a fault identification result of the to-be-detected power high-voltage equipment. The method can improve the recognition precision of the extremely early insulation degradation and temperature anomaly defects of the surface of the high-voltage power equipment.

The invention relates to the field of power equipment data processing, in particular to a steam turbine vibration fault diagnosis system fused with deep learning. Comprising a dynamic knowledge base construction module, a working condition adaptive data synchronization module, a knowledge-guided heterogeneous feature fusion module, a dynamic structure neural network module, an online self-optimization weight distribution module and a knowledge-enhanced coupling fault reasoning module. The dynamic knowledge base construction module updates the multi-modal knowledge graph through an incremental knowledge distillation mechanism, and generates an interpolation strategy template and a frequency band sensitivity matrix; the working condition self-adaptive data synchronization module dynamically calls an interpolation algorithm based on a rotating speed fluctuation mode to realize time sequence alignment optimization of multi-source sensor data; the system effectively solves the problems of time scale asynchronism and feature heterogeneity in multi-source heterogeneous data fusion through a knowledge-driven and data-driven closed-loop interaction mechanism, and realizes accurate diagnosis and early warning of steam turbine vibration faults under complex working conditions.

The invention relates to the field of power equipment control data processing, in particular to a boiler combustion optimization control method based on data driving, which comprises the following steps of: acquiring multi-source data such as temperature field distribution, air and smoke pressure, smoke components and coal quality characteristics, and eliminating noise interference by adopting sliding window mean filtering; generating a standardized feature matrix in combination with principal component analysis and a dynamic time warping algorithm; constructing a dynamic coupling model fusing a gradient boosting decision tree and a long short-term memory network, analyzing a nonlinear relationship between pulverized coal particle size distribution and a wind-coal ratio, and predicting combustion efficiency, pollutant concentration and temperature field uniformity; and model parameter self-correction and weight dynamic adjustment are triggered through actual combustion data feedback, and a closed-loop control link is formed. According to the method, accurate modeling of the multi-physical field coupling characteristic of the combustion system is achieved, the time sequence generalization ability under the dynamic working condition is improved, and the purposes of heat efficiency improvement and pollutant emission reduction are effectively balanced.

The invention relates to the technical field of power systems, provides an association rule analysis-based power load prediction method and system, and aims to solve the problem of hidden fault response lag caused by lack of an equipment health state and load fluctuation dynamic coupling mechanism in the prior art. And the problem of load prediction compensation deviation caused by insufficient weight quantization of the fault propagation path is solved. The method comprises the following steps: generating equipment state data according to a vibration spectrum and an insulation aging index of power equipment; performing fusion analysis, generating an equipment health degree evaluation index, and establishing a dynamic influence model of the equipment abnormal event on the power grid load fluctuation according to the association rule; identifying potential abnormal equipment, performing logic mapping, and generating a fault propagation path weight; and dynamically adjusting according to the weight to obtain an adjusted power load predicted value. According to the technical scheme provided by the invention, equipment vibration and insulation aging data are fused, a health assessment and fault propagation model is constructed, and load prediction is dynamically corrected to prevent and control power grid risks.

The invention relates to a power equipment fault prediction method based on multi-source heterogeneous data and a twinborn model, and the method comprises the following steps: S1, obtaining multi-modal data related to the operation of power equipment, constructing a joint feature encoder through an attention mechanism in deep learning, and carrying out the joint modeling of multi-modal information, and obtaining a joint feature representation; s2, based on joint feature representation, adopting a dynamic twin model of self-supervised learning and fusion reinforcement learning, and dynamically updating the equipment health state; s3, constructing a complete-cycle prediction framework based on Transform, capturing long-term dependency and dynamic change characteristics in equipment operation, and identifying trigger factors of faults in combination with causal relationship analysis; and S4, based on the equipment health state and the fault prediction result, optimizing equipment operation parameters by adopting an intelligent operation and maintenance optimization engine, and dynamically generating an optimal operation and maintenance plan in combination with a genetic algorithm. The reliability and the operation efficiency of the power equipment are remarkably improved.

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 relates to the technical field of power fault diagnosis, in particular to a multi-modal data fusion-based power fault diagnosis method and system, which comprehensively collects and analyzes electrical, environmental and mechanical parameters of power equipment through a multi-modal data fusion technology, constructs a multi-modal parameter feature matrix, and performs multi-modal data fusion on the basis of the multi-modal parameter feature matrix. Dynamic anomaly detection and personalized monitoring strategy formulation are realized, the accuracy and timeliness of fault diagnosis are improved, false alarm and missing alarm are reduced, operation and maintenance resource allocation is optimized, and stable operation and efficient power supply of a power system are ensured; the method comprises the following steps: respectively acquiring multi-modal data information of all same power equipment in a transformer area; feature extraction is carried out on the multi-modal data information of each power device to obtain a multi-modal parameter set; and carrying out data integration on all the multi-modal parameter sets which are collected at the same time to obtain a multi-modal parameter characteristic matrix which can represent the operation states of all the power equipment of the same model in the transformer area.

The invention discloses a multi-modal-based power equipment health state monitoring method, and relates to the technical field of power equipment detection.The power equipment monitoring method is based on multi-modal data and knowledge graph fusion, cross check, expert rule cleaning and label correction are implemented by collecting sensing data such as chromatography, temperature, current and vibration in oil, and the detection result is obtained. Outputting high-credibility data; a graph model is constructed based on equipment topology by adopting self-encoder dimension reduction fusion, early anomaly detection is realized by utilizing a graph neural network, and an anomaly alarm is generated; mechanism matching and consistency evaluation are carried out based on the fault mechanism knowledge graph, and interpretable diagnosis is output; and when the diagnosis result is significantly deviated from the actual operation and maintenance conclusion, triggering an online increment and transfer learning updating model and expanding the knowledge graph to form a closed-loop self-learning mechanism. According to the method, the fault detection accuracy is remarkably improved, false alarms and missing alarms are reduced, the operation and maintenance decision-making efficiency is improved, and meanwhile operation and maintenance intelligence and real-time alarm are enhanced.

The invention discloses an automatic control method and system for secondary granulation of a high-voltage zinc oxide resistor disc, relates to the technical field of intelligent manufacturing of power equipment, and solves the problems of out-of-control particle morphology caused by dynamic coupling parameter identification lag and control instability caused by multi-physical field parameter coupling in an existing method. According to the invention, a dynamic physical property parameter matrix is generated in real time based on multi-band dielectric relaxation spectrum analysis and terahertz wave tomography; predicting a fluidized phase change threshold value and an energy gathering area through multi-physics field coupling modeling; a time sequence attention deep reinforcement learning algorithm is adopted to generate a multi-field cooperative adjustment instruction; positioning a parameter conflict source and triggering decoupling compensation by combining a high-frequency vibration and acoustic emission combined monitoring module; performing closed-loop correction on the control network weight based on the laser spectrum data and a partial least squares regression model; the real-time performance of fluidization parameter identification, the stability of multi-field coupling control and the recovery efficiency of abnormal working conditions are remarkably improved, and meanwhile the batch consistency of the electrical performance of the resistor discs is guaranteed.

The invention discloses a power distribution cabinet fault detection method and system, and relates to the technical field of power equipment state monitoring, and the method comprises the steps: laying a multi-modal sensor array, constructing a multi-dimensional data collection network, collecting various types of sensing data in real time, and carrying out the data preprocessing; establishing a multi-modal data fusion model by using a deep learning algorithm, and forming a fault feature mapping relation through feature extraction, data correlation analysis and cross validation to realize collaborative perception of a data level; and constructing a multi-dimensional fault diagnosis decision tree based on a collaborative perception result, accurately positioning a specific device position where a fault occurs in combination with weight distribution and confidence evaluation of data of each sensor, and giving out evaluation results of a fault type and severity. The system effectively reduces the equipment failure rate, prolongs the service life, reduces the maintenance cost, improves the power supply reliability, promotes the development of an electric power system towards the intelligent operation and maintenance direction, and is of great significance to the improvement of the operation reliability and maintenance efficiency of a power distribution system.

The invention provides a power anomaly detection method and system based on multi-mode special collaborative causal thinking chain reasoning, and relates to the technical field of power anomaly detection. Comprising the following steps: acquiring an embedded representation of multi-modal data in power equipment, inputting the embedded representation into a general model, and generating a framework of a causal chain; the power exclusive model is based on a framework of a causal chain, each node of the causal chain is refined layer by layer, detail information is supplemented, key factors are filled, and a causal graph is obtained; inputting the causal graph into a hypergraph neural network, mining high-order association in data through multi-layer convolution operation, and updating embedded representation of nodes in the causal graph to obtain an updated causal graph; and on the basis of the embedded representation of the nodes in the updated causal graph, calculating the potential risk influence of the nodes, and determining abnormal nodes. According to the method, a general model and an electric power exclusive model are combined, efficient and accurate abnormal causal chain reasoning is realized by integrating multi-modal data, and the accuracy and effectiveness of electric power anomaly detection are improved.

The invention discloses a self-adaptive multi-dimensional adjustment metering box based on the Internet of Things, and relates to the technical field of intelligent monitoring of power equipment. The problem of measurement distortion caused by high fixed threshold false triggering rate, weak anti-aliasing capability and multi-source data asynchronization in a high-dynamic industrial scene in the prior art is solved. Multi-parameter acquisition is realized through the sensing acquisition module and the annular buffer area; a reconfigurable FIR filtering module is adopted to dynamically switch a low-pass / band elimination mode to suppress aliasing interference; noise features are extracted by combining spectral kurtosis analysis and concept drift detection, and a dynamic threshold value is generated by using exponentially weighted moving average and a sliding window standard deviation; a multi-channel time sequence is aligned through an IEEE 1588 protocol, and cloud parameter closed-loop optimization is realized based on extended Kalman filtering and a particle swarm algorithm; according to the method, the threshold fault tolerance, the high-frequency transient signal capturing precision and the multi-source heterogeneous data fusion reliability in a high-noise environment are remarkably improved.

The invention relates to the technical field of intelligent operation and maintenance of power equipment, in particular to a cable system full life cycle health management method based on digital twinning, which comprises the following steps: step 1, constructing a cable digital twinning body fusing electric-thermal-mechanical-chemical multi-physical fields; 2, dynamically fusing multi-source monitoring data, and collecting a cable skin continuous temperature sequence, a partial discharge pulse waveform and soil environment parameters; 3, solving a coupling equation of conductivity-thermal conductivity-stress tensor-ion diffusivity through iteration to realize multi-physical field linkage simulation; 4, generating a self-adaptive maintenance decision, inputting the insulation aging factor into an LSTM predictor to output a residual life prediction value, and generating a maintenance work order when the residual life is lower than a threshold value; and 5, carrying out closed-loop correction on the model, and updating the insulation aging factor calculation model and the physical attributes of the three-dimensional grid according to actual maintenance data. The method improves the safety, reliability and operation and maintenance efficiency of the cable system, and has important industrial application prospects.

The invention discloses an unmanned aerial vehicle electric power inspection image intelligent analysis method and system based on deep learning and multi-modal fusion and a medium thereof, and relates to the technical field of electric power equipment detection. The method comprises the following steps: planning an optimal inspection path by adopting an A * algorithm to realize multi-sensor synchronous data acquisition; adaptive histogram equalization and defogging processing are carried out on the visible light image, non-uniformity correction and temperature calibration are carried out on the infrared image, and filtering and registration are carried out on point cloud data; constructing a multi-scale feature fusion network based on improved VGGNet-16, and introducing deformable convolution and a cross-modal attention mechanism to realize multi-source data fusion; defect detection is carried out based on a three-level template library and a feature map cross-correlation algorithm, and the precision is improved in combination with non-maximum suppression and sub-pixel positioning; and finally generating a detection report containing defect types, positions and maintenance suggestions. According to the invention, the automation level and the detection precision of power inspection are obviously improved.

The invention discloses a power equipment operation state prediction and anomaly simulation method based on digital twinning, and relates to the technical field of power grid operation. According to the method, through establishment of a digital twinborn model of the power equipment and real-time data analysis, prediction and anomaly simulation of the operation state of the power equipment are realized. The method comprises the following steps of: analyzing a physical structure, operation characteristics and load distribution of power equipment, constructing a digital twin model, performing multi-dimensional analysis on real-time data by utilizing edge calculation, extracting electrical characteristics and behavior patterns, generating an equipment state data set, and constructing an abnormal causal chain model based on the model and the data set. The method comprises the steps of identifying a fault mode caused by uneven load or overload, evaluating the influence of an abnormal state on the stability and efficiency of a power system through virtual simulation and fault propagation analysis, and obtaining an abnormal influence index of power equipment. The accuracy of power equipment fault prediction is improved, the stability and efficiency of a power system are optimized, and support is provided for intelligent operation and maintenance and fault early warning.

The invention discloses an electric power inspection system and method based on an unmanned aerial vehicle, which are used for solving the problem of how to realize autonomous path planning of the unmanned aerial vehicle in a complex three-dimensional environment so as to reduce redundant flight time and improve inspection efficiency. The electric power inspection system comprises a path planning unit, an obstacle avoidance positioning unit and a dynamic obstacle avoidance unit which are connected in sequence. Three-dimensional map data and real-time environment information of a to-be-inspected power line are obtained through a path planning unit, real-time path planning is carried out based on the three-dimensional map data and the real-time environment information, and an optimal flight path of the unmanned aerial vehicle is dynamically generated; obstacle avoidance positioning is carried out on the power equipment on the optimal flight path and the surrounding environment thereof through an obstacle avoidance positioning unit, and real-time positioning information of an obstacle is generated; and a dynamic obstacle avoidance unit constructs a dynamic obstacle map based on the real-time positioning information, and automatically plans an obstacle avoidance strategy according to the dynamic obstacle map, so that the unmanned aerial vehicle realizes dynamic obstacle avoidance based on the obstacle avoidance strategy.

The invention discloses a power distribution operation and maintenance inspection track intelligent planning and optimizing method, and relates to the technical field of power distribution network intelligent operation and maintenance, and the method comprises the steps: collecting the topological data and equipment state information of a power distribution network, and constructing a power equipment association relationship model through an intelligent analysis model; on the basis of the incidence relation model, the inspection importance degree of the power equipment is calculated, and an initial inspection sequence is generated according to the inspection importance degree; and in combination with the initial inspection sequence and the real-time road condition information, constructing a dynamic inspection track network, and generating a final inspection path through an intelligent optimization algorithm. The problems of inaccurate equipment state evaluation, unreasonable routing inspection task planning, insufficient routing inspection path optimization and the like in the prior art are solved. The method has the main beneficial effects that the utilization efficiency of inspection resources is improved, the adaptability of an inspection strategy is enhanced, and the scientificity of an inspection path is optimized.

The invention discloses an electric power knowledge system construction and intelligent question and answer method based on a lightweight large model, and relates to the technical field of artificial intelligence, and the method comprises the steps: constructing an electric power field knowledge system, and building a reasoning logic classification system to carry out the classification marking of a knowledge base in the electric power field knowledge system; based on the power domain knowledge system, a multi-dimensional similarity matching strategy is adopted to process a problem input by a user; generating a reasoning link based on a matching result of the multi-dimensional similarity matching strategy; the reasoning link is used for extracting and combining answers from the power domain knowledge system, and the generation process of the reasoning link comprises question decomposition and answer integration. According to the invention, systematic management of electric power professional knowledge is realized, and the matching accuracy of professional problems is improved; by generating the self-adaptive multi-hop reasoning link based on the question type and combining the question decomposition and answer integration technology, the intelligent question and answer ability of the system under the scenes of power equipment fault diagnosis, maintenance scheme recommendation, risk assessment and the like is improved.

The invention discloses a power system Internet of Things intelligent detection method, and relates to the technical field of power detection, and the system comprises the following components: S1, data collection and preprocessing, S2, big data analysis and feature extraction, S3, equipment life prediction model construction and training, S4, fault prediction and evaluation, S5, predictive maintenance strategy making, and S6, adaptive adjustment. According to the method, the equipment life prediction model of deep neural network self-learning modeling is adopted, the quantum optimization algorithm is used for optimization, accurate prediction of the residual life and the fault occurrence probability of the power equipment is achieved, and meanwhile, the power equipment state comprehensive evaluation model based on the combination of fuzzy logic and the evidence theory is combined, so that the power equipment state comprehensive evaluation efficiency is improved. The equipment health state grade can be comprehensively evaluated, a scientific basis is provided for formulating a predictive maintenance strategy, a maintenance plan can be reasonably arranged in advance, the maintenance cost is reduced, the service life of equipment is prolonged, and economic benefits and social benefits of a power system are improved.

The invention discloses a fault detection method for high-resolution infrared thermal imaging display, and relates to the technical field of power equipment monitoring. According to the fault detection method for the high-resolution infrared thermal imaging display, a thermal imaging image of the surface of power equipment is obtained through high-resolution infrared thermal imaging equipment, dynamic correction is carried out on a temperature field in combination with environmental parameters and load current, and an interference-free temperature matrix is generated. The method comprises the following steps: modeling a heat conduction relationship between equipment by using a graph neural network, detecting a temperature abnormal region through a U-Net segmentation network, and generating an abnormal region mask and temperature space features; the time sequence correlation between temperature fluctuation and electrical parameters is analyzed through a dynamic time warping algorithm, and continuous faults and instantaneous interference are distinguished. And based on small sample transfer learning, in combination with a pre-training feature extractor and an attention mechanism, fault type classification is carried out, and fault types and confidence coefficients are output. Therefore, efficient and accurate detection of power equipment faults is realized.

The invention relates to the technical field of power equipment state monitoring, in particular to an oil-immersed transformer distributed temperature measurement method based on a fluorescent optical fiber sensor, and the method comprises the steps: arranging the fluorescent optical fiber sensor in a key temperature rise region in a transformer in a partitioned and layered three-dimensional topological structure, and forming a distributed temperature measurement network; exciting light is injected through a pulse laser to excite a fluorescence signal; a dual-channel phase-locked amplification technology is used to collect signals, and a dual-weight adaptive attenuation model and an oil flow coupling compensation function are combined to demodulate the temperature; reconstructing a dynamic temperature field based on a three-dimensional thermodynamic inversion algorithm, and marking high-gradient hot spots; and temperature, load current and oil flow velocity data are fused to realize graded alarm. According to the invention, the limitation of traditional single-point monitoring is broken through, the strong electromagnetic interference resistance is excellent, a global temperature field can be accurately reconstructed, dynamic early warning is realized, and the operation safety and the operation and maintenance efficiency of the transformer are remarkably improved.