8765results about "Circuit arrangements" 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 a power equipment fault diagnosis method and system based on a dynamic knowledge graph and large model collaborative reasoning, and the method comprises the steps: achieving the automatic extraction of an entity relationship through a weak supervision entity relationship extraction mechanism in combination with a power field dictionary and a remote supervision technology, and obtaining a weak supervision entity relationship; constructing a time sequence knowledge graph to capture a dynamic evolution rule of the fault propagation chain; structured knowledge graph embedded representation is fused with a large model input layer through a knowledge injection layer, a two-stage reasoning process is generated by adopting a graph retrieval enlarged model, and finally a diagnosis conclusion containing a structured evidence chain is generated. According to the method, the fusion of weak supervised learning and sequential relation modeling is realized, and the automatic extraction and dynamic updating capability of the knowledge in the electric power field is remarkably improved; through a knowledge injection layer and a two-stage joint reasoning mechanism, the structured reasoning advantage of a knowledge graph and the semantic generation capability of a large model are effectively combined, and the diagnosis accuracy, the time sequence reasoning capability and the interpretability are greatly enhanced.

The invention provides a Beidou electric power data intelligent acquisition and dynamic communication scheduling method and system. The method comprises the steps that a master station constructs a database and trains a data priority classification model, a channel quality prediction model and an intelligent compression model; a substation collects power data through an edge calculation unit, constructs a skyline candidate set to perform data screening, and performs intelligent classification marking by using a data priority classification model; performing reliability evaluation on the data stream by using a Gaussian mixture model, selecting a compression strategy according to a reliability score, a data type and a priority, and packaging into a data frame; determining a transmission strategy in combination with a channel quality prediction result and a context-aware intelligent switching protocol, and sending a data frame; the master station receives the data frame, performs integrity verification, decompresses and reconstructs the data frame, and feeds back a communication state for model updating; and monitoring the operation state, performing early warning based on the anomaly detection model, and triggering a self-healing strategy. According to the invention, the Beidou communication resource utilization rate and the system reliability are improved.

The invention discloses a power distribution room anomaly detection system based on cloud side-end cooperation, and belongs to the technical field of intelligent power grids. In order to solve the problems of high network bandwidth pressure, insufficient edge computing capability, low anomaly detection accuracy, difficulty in multi-source data fusion and the like caused by the adoption of an end-cloud direct connection architecture in an existing power distribution room monitoring system, the system comprises: a data acquisition layer configured with various heterogeneous sensors to acquire operating parameters and environmental data in real time; the edge storage and calculation layer carries out local real-time processing, anomaly detection, model training and visual display, an anomaly detection module of the edge storage and calculation layer carries out research and judgment on real-time data to generate early warning information, and a prediction and detection linkage module monitors an anomaly probability trend and adjusts a sampling frequency; the edge gateway realizes protocol conversion and data forwarding; and the cloud decision-making layer aggregates multi-edge node data, optimizes a global model through federal learning, and issues and updates a local model. The system is used for improving the accuracy, real-time performance and reliability of anomaly detection of the power distribution room, reducing the operation and maintenance cost and realizing intelligent operation and maintenance.

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 relates to the technical field of photovoltaic power generation, in particular to a flexible photovoltaic intelligent monitoring and management system and method based on the Internet of Things, multi-source heterogeneous data are comprehensively collected through deployed multiple types of Internet of Things sensor nodes, the data are uploaded to a cloud platform after being cleaned and standardized through edge nodes, a big data processing architecture integrated with flow and batch is adopted, and the intelligent monitoring and management system and method based on the Internet of Things are established. The method comprises the following steps: performing real-time analysis and state judgment on a real-time data stream, performing deep batch processing and feature mining on historical data, extracting high-order features such as a performance attenuation trend and an abnormal mode, fusing real-time and historical features, and realizing comprehensive scoring of a health state of a component and accurate prediction of residual life by utilizing a machine learning model. And based on an evaluation result and a preset knowledge base, automatically generating a differentiated precise operation and maintenance instruction, and issuing and executing the differentiated precise operation and maintenance instruction to form closed-loop management. According to the invention, the monitoring depth and breadth of the flexible photovoltaic system are effectively improved, the conversion from passive alarm to active predictive maintenance is realized, and the operation reliability of the system is significantly enhanced.

The invention discloses an intelligent electric equipment monitoring and optimizing method, and relates to the technical field of electric power, and the method comprises the steps: collecting high-dimensional voltage-current time sequence data and transient event marks, calculating topological invariant features, inputting the topological invariant features to a lightweight neural network model, and recognizing the features of all electric equipment; performing abnormal attribution and anti-fact energy efficiency prediction by using causal reasoning and dynamic regularization regression based on the identified characteristics of each electric device, and constructing a multi-objective optimization function through the abnormal attribution and anti-fact energy efficiency prediction; and based on the multi-objective optimization function, generating an equipment operation scheduling strategy through a deep reinforcement learning agent, based on the operation scheduling strategy, sending a control instruction to the electric equipment, and collecting an operation result feedback in real time for optimization and updating. According to the method, through fusion of topological features, causal reasoning and safety reinforcement learning, the precision, robustness and safety of monitoring and optimization of the intelligent electric equipment are improved.

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 discloses a power distribution equipment on-line monitoring system based on multi-modal data fusion, and relates to the field of power distribution equipment management, and the system comprises a sensing module which is used for collecting an electrical signal, a thermal infrared signal, a mechanical vibration signal and an acoustic signal generated in the operation process of power distribution equipment through a multi-type sensing channel, converting the collected various signals into processable equipment state original data to form an equipment operation state original data set; according to the invention, through accurate acquisition of multi-dimensional signals, combination of space mapping and time delay compensation, data quality is optimized, the reliability of original information is ensured, key features are extracted according to working conditions, subtle state changes are captured by means of temperature field analysis and a variable-resolution spectrum technology, the comprehensiveness, accuracy and response timeliness of equipment operation monitoring are effectively improved, and the real-time performance of equipment operation monitoring is improved. Misjudgment and missed judgment are reduced, and fault risks are avoided in advance.

The invention discloses a fault diagnosis and remote monitoring system and method for a solar power supply system, and relates to the technical field of fault diagnosis of a solar system, and the system comprises a heterogeneous multi-mode sensing module which collects the multi-dimensional information of an assembly through a plurality of sensors; the memristor storage and calculation integrated unit is used for realizing data filtering and feature extraction; the multi-scale causal diagnosis engine is used for diagnosing faults by fusing deep learning and causal diagrams; a self-adaptive topology communication network ensures data transmission; a digital twinborn monitoring platform and visual operation and maintenance are adopted, and in addition, an intelligent evolution decision and self-reconfiguration sensor network module is further arranged, so that the intelligence and reliability of the system are improved. Through cooperation of multiple modules, accurate fault diagnosis and positioning are realized, the diagnosis time is shortened, stable data transmission is ensured, self-repairing and autonomous learning capabilities are provided, the operation and maintenance cost can be reduced, the power generation efficiency can be improved, and the reliability and economic benefits of a solar power supply system can be enhanced.

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 provides a power distribution network operation risk intelligent identification and early warning method, which comprises the steps of identifying an equipment contact failure probability through an actual wear state, and when the equipment contact failure probability exceeds a safe operation requirement, determining an equipment fault early warning signal through historical fault statistical data, identifying potential equipment failure risk points and extracting risk distribution characteristics; identifying a high-risk equipment node through the equipment fault early warning signal, evaluating whether a cascading fault of adjacent equipment overload is caused after power flow redistribution of a power grid according to the identified high-risk node, extracting a fault propagation path, and determining a system risk level distribution diagram; and carrying out risk area division on the system risk level distribution diagram, identifying key equipment nodes in a high-risk area, extracting a load transfer scheme of the high-risk area, and determining a load distribution path and a power transmission direction.

The invention discloses a photovoltaic power station multi-mode fault early warning and collaborative repairing system and method. The photovoltaic power station multi-mode fault early warning and collaborative repairing system comprises a multi-mode sensing layer and a digital twinborn platform. According to the photovoltaic power station multi-mode fault early warning and collaborative repair system, the fault symptom identification sensitivity is improved, the missed judgment risk of a single monitoring means is effectively avoided, a physical entity high-precision mapping model constructed by a digital twin platform is combined with the dynamic analysis capability of a space-time diagram neural network on a component-level topological relation, and the fault early warning and collaborative repair efficiency is improved. Equipment health state evaluation breaks through the limitation of traditional threshold comparison, a double verification mechanism based on a physical mechanism and data driving is realized, the fault early warning accuracy is essentially improved, the positioning precision of potential fault points is improved to a component level through a space-time coupling analysis mode, a key response period is strived for operation and maintenance decision, and the fault early warning accuracy is improved. A prediction-verification-execution full-link optimization system is formed, and the operation mode of traditional photovoltaic power station operation and maintenance is fundamentally changed.

The invention relates to the technical field of power equipment state monitoring, in particular to an AI fusion processing method, system and equipment for power transmission and transformation equipment state monitoring data and a storage medium. Collecting multi-source monitoring data of the power transmission and transformation equipment, constructing an equipment health portrait, and identifying key feature parameters and a coupling relationship among the parameters through association mining; performing multi-dimensional consistency check on the key feature parameters through interactive verification to obtain an initial health degree evaluation value, extracting a historical feature reference and performing deviation degree calculation, and combining the initial health degree evaluation value to obtain a comprehensive health degree and a corresponding threshold value; and based on the time sequence change of the comprehensive health degree, identifying a degradation evolution trajectory, determining a degradation type and a degradation rate, predicting the future state evolution trend of the equipment, and generating graded early warning information. Fusion and multi-dimensional accurate evaluation of multi-source heterogeneous data are obtained, and static evaluation is converted into dynamic prediction.

The invention discloses an electric power measurement data management system based on cloud edge collaboration and an electric quantity distributed acquisition method, and relates to the technical field of electric power information acquisition and processing. The system comprises an intelligent electric meter terminal layer, an edge computing node layer, a regional collaborative gateway layer and a cloud data center. The intelligent terminal can dynamically adjust the sampling frequency according to the load change rate and the historical volatility, and identifies abnormity through an LSTM-ATT model; the edge nodes realize prediction caching based on Kalman filtering and RMSE judgment; the regional gateway executes dynamic time warping and consistency arbitration of topological weighting; and the cloud end completes digital twinborn modeling and exception evidence storage. According to the method, the real-time performance and accuracy of electric power data acquisition are improved, the communication bandwidth occupation is reduced, the system abnormity sensing capability and the operation stability are enhanced, and the method is suitable for an intelligent power distribution network and a distributed energy metering scene.

The invention belongs to the technical field of railway inspection, and particularly relates to a space-air-ground integrated railway power transformation and distribution intelligent inspection method and system which comprises a sensing layer, a platform layer, a data and intelligent center, an application layer and a user and presentation layer. The sensing layer is composed of an aerial inspection layer, a ground inspection layer and a fixed monitoring layer; the platform layer is based on a cloud computing center and edge computing node collaborative architecture, combines data and an intelligent center, integrates multi-source heterogeneous data through a big data platform, carries an AI algorithm engine to realize automatic identification of equipment defects, analyzes images, synchronously predicts the images, and realizes automatic identification of the equipment defects. And the application layer is combined to complete the functions of automatic generation of an inspection report, automatic distribution of a maintenance work order, equipment health state evaluation and the like. According to the method, functions of railway power place inspection, power supply line inspection, power supply and distribution facility operation and maintenance emergency and the like can be realized, deep fusion analysis of multi-dimensional data is realized, and meanwhile, the problems of insufficient training samples, sparse defect types and the like of an existing AI model are solved.

The embodiment of the invention provides an energy storage system fault processing method and device, equipment, a medium and a product, and relates to the field of photovoltaic power generation, the method solves the problem of low fault processing efficiency in the prior art by constructing a cloud-side-end cooperative fault processing architecture, changes the traditional static threshold judgment logic, and improves the fault processing efficiency. A decision-making mode combining multi-source information fusion and dynamic prediction is adopted, firstly, preliminary diagnosis of real-time data is achieved through edge calculation, the instantaneity of response is ensured, and then historical trend analysis, equipment health prediction and multi-dimensional real-time state evaluation are fused through a cloud platform. According to the method, the dynamic fault evaluation capable of accurately reflecting the actual severity and development trend of the fault is generated, so that the system can be adaptive to equipment aging and environment change, and a grading response strategy accurately matched with the fault grade is triggered, thereby realizing the crossing from passive alarm to active and accurate fault management and control, and improving the fault processing efficiency.

The invention relates to the technical field of wireless charging, and provides a foreign matter detection method and system for a wireless charging system and a storage medium. The method comprises the following steps: firstly, calibrating at multiple temperature points and establishing a temperature-parameter comparison table under the condition of no foreign matters; in a no-load standby state, the inversion module sends out detection drive and collects voltage, current and temperature; calculating a real-time quality factor and resonant frequency according to sampling, obtaining a compensation reference according to a temperature look-up table, and solving the variation of the quality factor and the resonant frequency; and performing fusion judgment by adopting dual-condition locking or weighted scoring, and triggering fault protection when a foreign matter is confirmed. Due to the fact that equivalent inductance and resistance changes are caused by metal foreign matters, quality factors are remarkably reduced along with resonant frequency deviation, temperature excursion and foreign matter effects are distinguished through temperature compensation, and dual-condition locking or weighted scoring fusion judgment is combined, the interference immunity and accuracy of detection are improved, false alarms and missing alarms are remarkably reduced, and the detection accuracy is improved. The method is suitable for high-power wireless charging scenes.

The invention discloses a power distribution switchgear remote monitoring and maintenance method based on the Internet of Things, relates to the technical field of power system operation and maintenance, and solves the problems that in an existing power distribution system, the edge anomaly recognition capability is weak, a control feedback chain is not closed, and multi-terminal control conflicts occur frequently. According to the scheme, a structured time sequence index of multi-source data is constructed on edge equipment, and equipment state drift is judged based on residual function included angle change; a control command management mechanism of a unique transaction identifier is introduced, a dual-channel feedback structure and a control mutual exclusion stack are combined, and a remote command consistency control chain is established; a hidden danger propagation chain is constructed on the cloud based on the abnormal Boolean bitmap and the topological relation, and a multi-node state linkage analysis and risk priority intervention strategy is achieved; according to the invention, the abnormity identification accuracy, the remote control stability and the system-level operation and maintenance response efficiency of the power distribution switch equipment are obviously improved.

The invention relates to the technical field of data processing, and discloses a secondary equipment hidden danger mining method and system based on a wave recording file and monitoring data. The method comprises the steps of calculating a conditional probability and intervention probability difference identification causal relationship by analyzing a recording file in combination with equipment topology and protection logic to construct a correlation graph, and performing Fourier transform and wavelet decomposition on a sampling sequence to extract multi-scale features; small signal test excitation is injected, a system identification estimation transfer function is fused with monitoring data to form a health feature vector to judge a health state, and a correlation map and the health state are input into a map neural network to calculate weighted attention coefficients and aggregate neighbor information to predict a fault propagation path; and establishing a degradation model, correcting the failure rate, predicting the residual life and generating a graded early warning report. According to the method and the device, the transformation from passive post analysis to active predictive maintenance is realized, and the timeliness, accuracy and systematicness of hidden danger identification of the secondary equipment are improved.

The invention belongs to the technical field of permanent magnet synchronous generator demagnetization fault detection, and particularly discloses a permanent magnet motor fault early warning system based on big data, which comprises a high-frequency injection and grid-connected control module, a fault early warning module and a fault early warning module, actively injecting a high-frequency disturbance signal of which the frequency is higher than the fundamental frequency into a control loop of the permanent magnet synchronous generator; the multi-source synchronous acquisition module is used for synchronously acquiring multi-dimensional operation parameters of the permanent magnet synchronous generator and performing time domain alignment and frequency domain separation on the multi-dimensional operation parameters to obtain synchronous acquisition signals; the composite feature extraction module is used for extracting a multi-dimensional demagnetization criterion of the permanent magnet synchronous generator based on the synchronous acquisition signal; the self-adaptive threshold regression prediction module is used for performing fusion evaluation on the multi-dimensional demagnetization criteria by using the demagnetization health index model to generate a demagnetization judgment result of the demagnetization degree of the permanent magnet synchronous generator; the method has the advantages that the demagnetization recognition accuracy is improved, and early warning is achieved.

A system and method are disclosed for AI-orchestrated multi-source renewable energy harvesting and decentralized trading. A plurality of energy harvesting nodes convert renewable resources, including solar irradiance, wind, hydrodynamic flow, salinity gradients, geothermal heat, and waste-heat streams, into electrical power. Each node includes a sensor array that measures operating and environmental parameters and an edge AI processing unit that predicts performance degradation and computes control actions to maximize net power output subject to stress constraints. A power conversion subsystem conditions the generated power for delivery to storage, microgrids, or utility grids. A blockchain-based orchestration layer receives validated energy summaries, tokenizes discrete energy quanta as digital energy tokens with provenance metadata, and executes smart contracts for peer-to-peer trading, dynamic pricing, and programmable revenue distribution. A fleet-level AI coordination module performs federated learning across nodes and coordinates dispatch and curtailment among heterogeneous modalities, improving portfolio efficiency, grid stability, and environmental accounting.

The invention discloses an intelligent control and optimization system and method for the whole process of a photovoltaic power station. The method comprises the following steps: constructing a component-level high-fidelity digital twin through multi-source heterogeneous data fusion and an attention enhancement deep learning model; injecting annotated historical fault data, carrying out joint training by combining a feature embedding layer and an adversarial generative network, and generating a digital mapping body with fault evolution deduction capability; a three-layer architecture is introduced, reinforcement learning and a multi-objective optimization algorithm are fused, and autonomous decisions of power station level power output maximization, equipment loss minimization and maintenance cost optimization are realized in a virtual simulation environment constructed by a digital mapping body. According to the method, accurate dynamic mapping of the physical entity and the virtual space, fault pre-judgment and deduction and closed-loop control of global autonomous optimization are realized, the intelligent level and the full-life-cycle economy of the photovoltaic power station are improved, and the problems of insufficient fidelity, lack of fault deduction capability and low global optimization efficiency in the prior art are solved.

The invention discloses a cable branch box multi-mode cooperative control system and method, and relates to the technical field of power equipment intelligent control, and the method comprises the following steps: injecting a low-amplitude synchronous detection pulse in an operation environment of a cable branch box, collecting a feedback response signal of the detection pulse, inverting an arrival sequence of an interference signal according to the feedback response signal, and carrying out the inversion of the arrival sequence of the interference signal; and constructing a micro time slot interference portrait, and generating a time sequence baseline for an interference identification reference. According to the method, the time sequence base line is established through micro time slot detection, and accurate identification of microsecond interference signals is realized in combination with phase consistency analysis, multi-channel sampling and a cross-cycle evolution model. The false triggering probability is reduced by adopting a phase-locked suppression and delay cache mechanism, and the safe release of peak energy is realized through a phase turn-back amplitude limiting and energy guiding mode, so that the stability and the anti-interference capability of the cable branch box in a complex electromagnetic environment are remarkably improved.

The invention discloses an electric power communication analysis system and method based on big data, and the method comprises the steps: collecting multi-source heterogeneous data, carrying out the sensitivity grading, and generating labels; gathering the data to a big data platform, and completing storage, partitioning and index construction; an asynchronous main control chain mechanism is constructed, high-optimal data is dispatched for rapid anomaly judgment, and other data is processed in parallel; performing desensitization mapping on the high-sensitivity data to generate a short-time mapping bill and performing desensitization analysis; issuing a local task for the low-confidence exception, collecting backflow data, and verifying and determining a high-risk event; restoring the sensitive data in the authorization window, and destroying the bill and the secret key after abnormity analysis is completed; and triggering network regulation based on an analysis result, and recording the whole process to an audit log. By introducing an asynchronous scheduling and reversible desensitization mechanism, high-efficiency anomaly analysis and whole-process traceability of the electric power communication big data on the premise of ensuring safety and compliance are realized.

The invention relates to the technical field of state monitoring, in particular to an intelligent fuse comprising a monitoring module, and the fuse comprises a data analysis module, a steady state recognition module, a temperature analysis module, a coupling calculation module and a state calibration module. According to the method, time consistency verification and corresponding matching are carried out on temperature and current sampling, abnormal interference is eliminated in a stable current-carrying interval, a continuous sequence is formed, and a definite time relation is established between thermal response and a current-carrying state by combining analysis of accumulated deviation and change turning of the temperature along with time; the abnormal trend of slow evolution is amplified and presented, correction is carried out according to the direction consistency of multi-segment time difference change, progressive features before fusing are recognized in advance, early warning continuity and operation decision reliability are improved, misjudgment is reduced, continuous updating of state evaluation is supported, and stability of a control closed loop is improved.

The invention relates to the technical field of intelligent power distribution and electrical equipment state monitoring, in particular to an intelligent power distribution cabinet data acquisition control system and method, and the method comprises the steps: presetting a temperature sensor and a Hall sensor at the lap joint of a circuit breaker contact and a copper bar of an intelligent power distribution cabinet; calibrating inherent parameters of the system; in response to starting operation of the intelligent power distribution cabinet, the following steps are triggered: obtaining real-time load current, real-time environment temperature and actual temperature through filtering processing; generating a theoretical healthy temperature through a thermoelectric coupling evolution equation; calculating aging resistance increment through dynamic impedance differential evolution processing; generating an aging resistance estimated value through contact state multi-dimensional evaluation; generating a dynamic current limit value through an intelligent current limiting control model; outputting a control signal to adjust the operation state of the intelligent power distribution cabinet; according to the invention, the problem that a traditional single threshold scheme cannot accurately identify a real health state is solved, and the sensitivity and accuracy of contact aging detection are remarkably improved.

The invention provides a mountain power station current collection system state online monitoring method and device, and relates to the field of data processing. According to the method, distributed optical fiber data, environmental meteorological data and electrical measurement data are acquired to construct a multi-mode time sequence matrix, an operation state diagram is formed through graph structure representation and graph signal processing, a working condition mode cluster is obtained by combining dynamic clustering, a constraint diagram optimization framework is input, thermal, electrical and mechanical constraints are introduced, and an operation state track is generated. And constructing and fusing a non-stationary degradation path library and environment exposure history to form a dynamic degradation evolution model, and finally outputting in-service reliability and predicting residual life by a risk inference engine to realize online monitoring of the state of the current collection system of the mountain power station. By implementing the technical scheme provided by the invention, the accuracy of online monitoring of the state of the current collection system of the mountain power station is improved.

The invention relates to the technical field of power system automation, discloses a remote operation and maintenance method, system and equipment for wide-area substation automation equipment, and aims to solve the problems of data islanding, inaccurate diagnosis, strategy stiffness, low safety cooperation efficiency and the like in remote operation and maintenance of the wide-area substation automation equipment. According to the method and the system, a comprehensive platform is constructed, and multi-source heterogeneous data acquisition, intelligent fusion analysis, fault diagnosis and prediction, adaptive strategy generation, security remote control and block chain evidence storage and collaborative operation and maintenance are integrated. The method can solve the problem of data islands, improve the diagnosis and prediction precision and timeliness, optimize the operation and maintenance strategy, prolong the service life of equipment, and enhance the remote operation and maintenance safety and cooperation efficiency.

The invention relates to the technical field of power optical cable monitoring, and discloses a power optical cable operation situation sensing method, which comprises the steps of collecting temperature, strain, vibration and scattering signal data along an optical cable in real time through a distributed optical fiber sensor, and constructing a multi-source heterogeneous data set in combination with power grid operation data and environment monitoring data; carrying out denoising, abnormal value elimination and missing value filling processing on the data by adopting a time sequence synchronization algorithm, and extracting a temperature rise rate and a strain fluctuation frequency to generate a multi-dimensional feature matrix; by dynamically fusing multi-source data, situation awareness indexes such as a thermal stability score and an overload risk index are generated; an LSTM algorithm and a dynamic threshold model are combined to predict the operation risk level of the optical cable in the next 24 hours, a power grid dispatching system is linked through visual means such as a situation map and three-dimensional simulation, and intelligent alarm and operation and maintenance suggestions are automatically issued. The fault identification precision can be improved, the operation and maintenance cost can be reduced, and preventive maintenance can be realized.