584 results about "Power system security" patented technology

The invention belongs to the technical field of power system safety, and discloses a high-voltage transmission line safety assessment method based on an intelligent algorithm. A database is constructed through multi-source data acquisition and fusion processing, a deep learning model is utilized to analyze relevance between meteorological conditions and an icing formation mechanism, an evolution law of the meteorological conditions and the icing formation mechanism is mined, multi-scene simulation and sensitivity analysis are performed in combination with physical characteristics and landform information of a power transmission line, and a line icing risk partition assessment system is established. According to the method, a critical value of line mechanical strength and icing thickness is calculated based on a mechanical model, a grading early warning threshold standard is formulated, a time sequence prediction algorithm and a risk propagation model are constructed to realize intelligent early warning, and a prevention and control strategy is formed through optimal configuration of anti-icing resources and self-adaptive generation of a deicing scheme. According to the invention, the capability of resisting icing disasters of the high-voltage transmission line is effectively improved, and the safe and stable operation level of a power grid is enhanced.

The invention relates to the technical field of smart power grids, in particular to a hybrid game-based virtual power plant optimization operation method under multiple markets, and the method comprises the steps: building an electricity-certificate-carbon multi-market coupling transaction framework, deeply analyzing a master-slave game relationship between a virtual power plant operation platform and a VPP alliance, and building a price guidance-based double-layer interaction mechanism; according to the method, research is carried out aiming at the condition that a virtual power plant belongs to different interest subjects, a resource complementation-based alliance cooperation model is proposed, and a VPP alliance cooperation optimization framework considering demand side load elasticity is established by introducing a refined model of a flexible load; and establishing a virtual power plant operation platform and VPP alliance mixed game optimization model. Solving a master-slave game stage by adopting a double-layer iterative algorithm; and solving a cooperative game stage in combination with an alternating direction multiplier method. The virtual power plant is guided to participate in multi-market cooperative regulation and control through a mixed game mechanism, safe and stable operation of the power system is ensured, and the operation economy and environmental protection property are improved.

The invention relates to the technical field of electric energy metering and electric power system safety, and discloses an electricity larceny prevention method and system for multi-source data fusion of an electric energy meter, and the method comprises the steps: obtaining and preprocessing current and voltage waveform data and user electricity consumption behavior data; generating multi-dimensional power consumption characteristic parameters, performing screening to form a characteristic vector set, and constructing an abnormal power consumption detection reference set; dynamically verifying power utilization data based on the reference set, and analyzing a phase relation and a mismatch state of current and voltage signals; and generating an electricity consumption behavior deviation index to judge an electricity stealing behavior. The system comprises a multi-source data acquisition module, a feature modeling analysis module, an electrical relation verification module and an electricity stealing judgment module. According to the invention, through multi-source data fusion and multi-dimensional feature analysis, accurate detection of electricity larceny behaviors is realized, accuracy and reliability of electricity larceny prevention are improved, and the method is suitable for power utilization safety management of a smart power grid.

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

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

The invention discloses an electric power system safety early warning method and system based on multi-modal cooperation, and relates to the technical field of electric power system safety early warning, and the method comprises the steps: collecting multi-source operation data, carrying out the preprocessing, carrying out the multi-modal feature extraction and fusion based on the preprocessed data, and carrying out the multi-modal feature extraction and fusion. Inputting an edge detection model and outputting an abnormal confidence score in combination with an attention mechanism; and performing alarm grading according to the abnormal confidence score, constructing a causal diagram for alarms with high risk levels in combination with associated security events, and performing future attack path prediction by adopting a time sequence diagram neural network. According to the method, multi-scale convolution and a channel attention mechanism are fused, the extraction capability of the multi-source data time sequence features of the power system is enhanced, the anomaly detection precision is improved, dynamic attack path prediction is realized in combination with RMTPP and causal atlas topological constraints, sequence modeling is enhanced through self-attention and position coding, and the detection accuracy is improved. And the perspectiveness and the reliability of the safety early warning of the power system are obviously enhanced.

The invention relates to the technical field of fault equipment detection, and discloses a power equipment fault detection method and system based on high-precision temperature measurement, and the method comprises the steps: scanning the surface temperature of power equipment through a high-precision temperature measurement device, obtaining a temperature distribution image, processing the temperature distribution image, and obtaining a temperature distribution matrix; carrying out gradient analysis and temperature difference identification on the temperature distribution matrix to obtain a hot spot candidate area; performing clustering analysis on the hot spot candidate region to obtain an initial hot spot region, and generating a static boundary by adopting a boundary tracking algorithm; correcting the static boundary according to the temperature change of each boundary point to obtain a hot spot area; and performing fault risk assessment according to the load data of the power equipment and the temperature data of the hot spot area. According to the invention, through temperature data analysis and region boundary correction, the hot spot region can be accurately identified, and in combination with a machine learning algorithm, the accuracy of power equipment fault risk assessment is improved, and safe and stable operation of a power system is ensured.

The invention relates to the technical field of power system safety early warning, in particular to a power grid risk intelligent early warning and analysis method, system and equipment, and the method comprises the steps: obtaining multi-source heterogeneous data of a power grid, constructing an association relationship and an index through an association key, and constructing a directed graph knowledge base; constructing a risk knowledge graph based on the directed graph knowledge base, and setting a risk rule, so as to carry out risk event and risk level early warning when the target equipment is overhauled; and for the maintenance of the target equipment, determining a comprehensive risk representation index from the equipment risk, the user influence and the load transfer capability, and determining a risk early warning level and generating an early warning list based on the comprehensive risk representation index. Thus, through fusion of multi-source data, an intelligent risk level determination model is established, and efficient, accurate and intelligent risk early warning is realized.

The invention relates to the technical field of relay protection loop detection, and discloses a relay protection loop fault detection method and system, comprising data acquisition, data processing, feature extraction, fault diagnosis and early warning and alarming. Multi-dimensional state data of current, voltage, temperature and vibration are collected in real time, wavelet filtering, mean or median filtering and normalization preprocessing are performed, comprehensive characteristic parameters are extracted in combination with time domain and frequency domain analysis and wavelet packet decomposition, a fault is accurately diagnosed and positioned through a fault diagnosis model, early warning and alarming are achieved based on graded thresholds, and the fault diagnosis accuracy is improved. The fault key information is synchronously transmitted to the operation and maintenance terminal, database storage and display terminal display are matched, the problems of poor real-time performance, one-sided feature extraction, no graded early warning and the like of traditional detection are effectively solved, the fault detection efficiency, the diagnosis accuracy and the operation and maintenance response speed are greatly improved, the operation risk of a power system is remarkably reduced, and the safety and stability of the power system are guaranteed.

The invention discloses a switch equipment performance aging intelligent diagnosis method and device based on multi-parameter fusion, relates to the technical field of power equipment abnormal defect diagnosis, and solves the problem of low precision of aging judgment of switch equipment depending on a single temperature and humidity threshold value or a single gas parameter in the prior art. The method comprises the following steps: acquiring internal and external humiture and internal gas characteristic parameters of equipment; constructing multi-channel standardized monitoring data through time synchronization, filtering and de-noising and exception elimination processing; calculating an absolute humidity difference inside and outside the cabinet, constructing a humidity expectation model, and obtaining a standardized residual error; performing offset detection and trend smoothing processing on the standardized residual error to obtain a cumulant and a smoothing trend quantity, and fusing the cumulant and the smoothing trend quantity to calculate an offset index; outputting a moisture seepage risk score, and triggering early warning when the moisture seepage risk score and the characteristic gas concentration exceed a threshold value. According to the invention, real-time high-precision evaluation of the aging state of the equipment is realized, early hidden dangers are effectively identified, and the safety and reliability of a power system are improved.

The invention relates to the technical field of electrical equipment detection, and discloses a terahertz time-domain spectroscopy-based GIS insulating material nondestructive testing method, which comprises the following steps: acquiring GIS insulating material time-domain signals by using a reflection-type terahertz time-domain spectroscopy (THz-TDS), and separating and reconstructing defect echoes based on electromagnetic simulation modeling in combination with wavelet transform, STFT and a transmission matrix method; multi-dimensional features are extracted, SVM and RBFNN are trained after dimensionality reduction, and a classification and quantitative regression model fusing physical constraints is constructed; and inputting a signal of a sample to be detected into the model to realize rapid, non-contact and accurate detection on the type, size and depth of the defect. The method overcomes the defects that traditional detection is low in efficiency, has damage or is difficult to quantify, the signal-to-noise ratio, the sensitivity and the automation level are remarkably improved, the method can be widely applied to GIS production quality control, installation acceptance, operation and maintenance monitoring, health assessment and life prediction, and powerful support is provided for safe and reliable operation of an electric power system.

The invention relates to the technical field of power system safety monitoring equipment, in particular to an intelligent monitoring and early warning method, system and equipment for the icing thickness of a distribution line and a medium, and synchronous acquisition and preprocessing of multi-dimensional parameters are realized by deploying an ice-resistant packaging multi-parameter sensing array; dynamically optimizing a modal number and a penalty factor of variational modal decomposition by using a genetic algorithm to realize multi-scale feature decoupling of the icing signal; a gating circulation unit is adopted to extract time sequence features, the time sequence features are input into a Kolmophilov-Arnod network to execute nonlinear mapping, and dual-path output is fused through residual connection; deploying the trained hybrid model to an industrial-grade edge computing unit, and completing local real-time reasoning by using edge computing power; triggering incremental updating of the sliding window based on a prediction error threshold value, and dynamically adjusting the weight of an output layer to maintain the adaptability of the model; through hardware-algorithm collaborative innovation, reliable operation is guaranteed in an extremely low-temperature environment, the early warning response time is remarkably shortened, and the prediction precision is improved.

The invention relates to the technical field of power system safety monitoring, in particular to a power transmission line online safety monitoring method, system and device based on quantum communication and a storage medium. The multi-modal fusion sensor synchronously collects multi-parameter electric signal data such as current, voltage and temperature of the power transmission line, and performs quantum encryption instantaneous recording on the data; based on quantum encryption instantaneous record extraction data, a deep learning adaptive Kalman filtering algorithm is combined with a neural network to predict the operation state of the power transmission line; generating a new Manchester code according to the multi-parameter electric signal data and the operation state predicted value, and encrypting by adopting quantum key distribution to generate a ciphertext; and transmitting the ciphertext to a client based on a terahertz communication channel, decrypting and recovering the data through the quantum key, and carrying out safety early warning. Theoretically uncracked data security assurance is realized by using the unclonable characteristic of the quantum state, high-rate data transmission is realized by terahertz communication, and the transformation from passive monitoring to active early warning is realized by a prediction algorithm.

The invention discloses a virtual power plant optimization scheduling method and device for power system regulation capability improvement, and relates to the technical field of virtual power plants. The method comprises the following steps: acquiring basic data of virtual power plant aggregation resources; constructing a resource operation constraint model under multiple scenes based on the basic data; embedding a conditional value-at-risk (CVaR) risk control mechanism in the operation constraint model, and establishing a multi-scene random optimization scheduling model; and solving the model by adopting a CVX-SeDuMi algorithm and a particle swarm optimization (PSO) hybrid algorithm to obtain an optimal scheduling scheme considering adjustment capacity maximization and risk management and control. On the premise of ensuring safe and stable operation of the power system, the regulation potential of the virtual power plant can be fully mined, meanwhile, the scheduling risk in an extreme scene is effectively avoided, and a scientific optimization scheduling method is provided for the virtual power plant to participate in power system regulation.

The invention relates to the technical field of power system safety analysis, in particular to a source-grid-load-storage integrated n-1 scene voltage safety analysis method, which comprises the following steps of: acquiring and preprocessing n-1 scene data; constructing a static voltage safety analysis model based on the gradient boosting regression tree; constructing a dynamic voltage safety analysis model based on a long short-term memory network; analyzing safety collaboration of the static voltage and the dynamic voltage; and verifying and optimizing the static voltage safety analysis model and the dynamic voltage safety analysis model. According to the method, the static voltage stability margin and the dynamic response characteristic are fused, so that the comprehensive risk identification accuracy is improved; a gradient boosting regression tree and a long-short-term memory network are adopted to capture nonlinear characteristics, and the voltage prediction error of an n-1 scene is remarkably reduced; characteristics and weights are customized for different source network load storage integrated project types such as industrial and incremental power distribution networks, and the pertinence and engineering practicability of analysis are improved.

The invention discloses an anti-misoperation method and system suitable for a novel power system. The anti-misoperation method comprises the steps of intelligent anti-misoperation terminal automatic forced locking, remote control forced locking and mechanical interlocking forced locking. Electrical misoperation is prevented mainly by monitoring the states of primary and secondary equipment of an electric power system in real time, establishing communication connection, setting forced locking and the like. Basic subsystems including Internet of Things anti-misoperation control, intelligent electronic two-ticket operation management and control, intelligent tool management, intelligent lock control, maintenance isolation locking, field operation management and control and the like are included, and the safety of power equipment and the operation normalization of personnel are ensured. And meanwhile, advanced systems such as digital twin error prevention, intelligent personnel management and control and a remote intelligent control subsystem are covered, and the monitoring efficiency and the emergency response capability are improved by utilizing technologies such as 5G and AI. Misoperation of the power system can be prevented, the efficiency and safety of switching, overhauling and operation and maintenance operation are improved, and safe and stable operation of the novel power system is ensured.

The invention discloses a power load prediction method, and the method comprises the steps: firstly solving an extreme event data sparsity problem through a generative adversarial network, and constructing an event time sequence library through a time sequence anomaly detection algorithm; then analyzing the causal relationship between the event and the load by applying a causal discovery algorithm, and converting prediction output into probability distribution by adopting a Bayesian neural network to quantify uncertainty; constructing a prediction model triggered by an event, and generating a multi-time scale probability prediction interval; and finally, generating a multi-scene prediction result through Monte Carlo simulation, quantifying the system recovery capability in combination with a toughness index, and integrating the system recovery capability to a decision support system to generate a risk response scheme. According to the method, the accuracy and robustness of load prediction under the extreme climate are remarkably improved, full-chain risk insight from early warning to recovery is realized, and prospective decision support is provided for safe operation of a power system.

The invention relates to the technical field of safe operation of a power system, in particular to a power distribution network information physical system fault collaborative recovery method, which comprises the following steps of: acquiring basic operation data of a communication layer and a physical layer, and respectively constructing fault recovery models of the communication layer and the physical layer; the communication layer takes optimal link performance as a target and applies constraints such as link capacity and connectivity, the physical layer takes multiple parameters such as load recovery as an optimization target and applies constraints such as network topology and power flow, and a double-layer optimization model, an upper layer optimization communication recovery strategy and a lower layer optimization physical recovery strategy are constructed for concurrent faults; and solving by using a double-layer optimization objective function to realize power distribution network fault collaborative recovery. According to the method, the interaction between the communication layer and the physical layer is comprehensively considered, so that the fault point can be more accurately positioned, the fault area can be quickly identified, the optimal recovery strategy is formulated, the power supply reliability is improved, and the healthy development of the smart power grid is promoted.

The invention relates to a power transmission line fault risk grading evaluation method based on multistage early warning, and belongs to the field of power system safety. Comprising the following steps: acquiring an insulator surface pollutant data generation matrix; extracting spatial distribution characteristics of pollutants; leakage current waveforms under different humidity are calculated; extracting a leakage current harmonic component and pulse characteristics; dividing pollution flashover risk grades; matching the maintenance strategy and generating an equipment scheduling instruction; and performing fault risk grading evaluation to generate a report. According to the method, through multi-source data fusion and dynamic modeling, accurate evaluation and preventive maintenance of the pollution flashover risk of the insulator are realized, and the safety and reliability of a power system are improved.

The invention relates to the technical field of power systems and automation thereof, in particular to a generative adversarial network-based source-load joint scene generation method, which comprises the following steps of: constructing a multi-source time sequence database and extracting weather, time, space and historical state driving factors; establishing a joint probability distribution model based on a vine connection function; taking a numerical weather forecast path and a date type as conditional input, constructing a generative adversarial network embedded with a physical constraint microloss function of the power system, and forming a physical information generator; performing dependent structure fidelity verification on the generated scene by using the joint probability distribution model; generator parameters are fixed, potential space vectors are optimized through a gradient ascending method to maximize power grid risk indexes, and a high-risk source-load joint scene set is generated. According to the technical scheme, accurate generation of the source-load joint scene which is physically feasible and reasonable in statistics and focuses on the high-risk working condition is realized, and the safe operation toughness and the risk early warning capability of the novel power system are remarkably improved.

Disclosed in the present application are a method and apparatus for quickly evaluating the safety and stability of a power system, and a terminal. The method comprises: pixelating a steady power characteristic variable acquired by means of measurement or generated by means of simulation, so as to generate an offline three-channel pixelated image, and constructing a sample set; using the constructed sample set to train a fusion model based on a deep residual network and a transformer, wherein the trained fusion model is used for quickly evaluating the safety and stability of a power system; and pixelating a steady power characteristic variable measured in real time, so as to generate an online three-channel pixelated image, and using the trained fusion model to process the generated online three-channel pixelated image, so as to generate a transient stability state evaluation conclusion for the power system. Thus, a fusion model integrating ResNet and a transformer can meet the requirements for the timeliness and accuracy of online safety and stability evaluation of a power system.

The invention relates to the technical field of power system safe operation safety and intelligent monitoring, and discloses a vegetation intrusion risk assessment method and system for a power transmission corridor and electronic equipment, and the method comprises the steps: obtaining multi-source remote sensing data and meteorological data of vegetation in a preset range of a target power transmission corridor; performing data fusion on the multi-source remote sensing data to obtain fused data; extracting a vegetation height change sequence based on the fused data, and performing prediction based on the vegetation height change sequence and the meteorological data to obtain a future time sequence height change curve; obtaining engineering parameters of a target power transmission corridor, and calculating a vegetation invasion risk value based on the engineering parameters and a future time sequence height change curve; and performing risk assessment based on the vegetation invasion risk value. The method breaks through the limitation of traditional static monitoring, integrates InSAR time sequence analysis and a machine learning algorithm, realizes vegetation growth dynamic modeling and future trend prediction, realizes risk level quantitative evaluation in combination with power transmission line structure parameters, and can provide technical support for power grid risk early warning.

The invention relates to the technical field of power system safety and stability control, and discloses a new energy field station broadband oscillation risk early warning method based on voltage dynamic indications, which comprises the following steps: acquiring instantaneous value data in a new energy electric field station to obtain electric quantity time sequence data; processing the electrical quantity time sequence data according to the target sliding window and outputting risk characteristic quantity; performing nonlinear normalization processing on the risk characteristic quantity by using a weighted fusion technology, and generating a single broadband oscillation risk index reflecting the broadband oscillation level of the new energy station according to a normalization processing result; and carrying out difference comparison on the single broadband oscillation risk index and a dynamic risk early warning threshold, and triggering an oscillation risk early warning signal and implementing oscillation risk early warning when a difference result meets a target warning requirement. According to the method, the defense gateway is moved forward by capturing the dynamic symptoms before oscillation occurs, so that the fundamental conversion from post-event to beforehand is realized, and a precious decision window period is won for operation adjustment.

The invention discloses a power system abnormal data clustering and vulnerability detection method based on a fuzzy mean value algorithm, and belongs to the field of power system security. Aiming at the defects of a traditional method in the aspects of data fuzziness, multi-dimensional association and dynamic adaptability, a'data preprocessing-dynamic fuzzy clustering-vulnerability diagnosis' closed-loop process is constructed. 12-dimensional feature vectors including electrical quantities, equipment states and environmental parameters are constructed, and Z-score standardization is combined with a sliding window updating mechanism to carry out data preprocessing; a feature weighted fuzzy C-means algorithm with entropy regularization is used, and a time decay factor and space-time correlation modeling are fused to realize dynamic fuzzy clustering; abnormal clusters are screened through double thresholds, and vulnerability diagnosis and scoring are completed in combination with an association rule base and a three-level vulnerability scoring model. According to the method, through verification of power grids with different voltage levels, the anomaly detection recall rate reaches 95% or above, the operation and maintenance efficiency is improved by 30%, and the accuracy and real-time performance of anomaly detection of the power system are effectively improved.

The invention discloses an electric power economic dispatching model solving method and device, a storage medium, a program product and computer equipment, and the method comprises the steps: constructing a safety constraint mathematical model based on an overload generator tripping type electric power system safety and stability control device, and determining an original feasible region of an overload generator tripping type condition section according to the safety constraint mathematical model; reconstructing the original feasible region through the introduced cut plane to obtain a reconstructed feasible region; solving is carried out based on power monitoring and data acquisition SCADA real-time acquisition data, market clearing data and a reconstructed feasible region to generate a new cut plane through iteration, so that on the premise of guaranteeing precision, power market intra-day real-time rolling clearing can be rapidly solved, and the efficiency is improved. The method effectively guarantees the speed and precision requirements of power market construction for safety constraint economic dispatching, ensures that the market clearing result has the actual performability of a power grid, and especially solves the problem that the real-time clearing of the power market is difficult due to overload generator tripping type condition sections.

The invention discloses a transformer substation safety risk prediction and alarm system based on edge calculation, and belongs to the technical field of power system safety monitoring. The system comprises a sensing layer equipment group, an edge computing device, a cloud edge collaborative management platform and an intelligent alarm linkage system. The sensing layer equipment group acquires multi-source data in real time through a broadband current transformer, an ultrahigh frequency partial discharge detection array and a micro-meteorological monitoring module; the edge computing device performs local risk prediction by adopting a lightweight model generated by knowledge distillation, and realizes millisecond response in combination with FPGA hardware acceleration; the cloud edge collaboration platform realizes model dynamic optimization through federated learning and incremental updating; and the intelligent alarm system triggers a light stripe coding alarm and priority instruction sequence based on the multi-source fusion confidence coefficient. The problems that a traditional system is lagged in response, slow in model updating, difficult in multi-source data fusion, weak in privacy protection and the like are solved, and real-time prediction and accurate disposal of the safety risk of the transformer substation are achieved.

The invention provides a power monitoring system safety operation and maintenance service and inspection management optimization method and system, and relates to the field of power system safety operation and maintenance, and the method comprises the steps: collecting equipment operation information to construct a feature vector, generating an operation track, analyzing an abnormal interval, and calculating the equipment state correlation degree to form a fault propagation link diagram; recognizing a fault source node, determining a sweep device, and establishing a routing inspection sequence and path; constructing a security isolation domain and a communication matrix deployment protection strategy; polling is executed, and feature matching is carried out to position potential safety hazards. According to the invention, accurate positioning and prevention of faults of the power monitoring system are realized, and the operation and maintenance efficiency and the system safety are improved.

The invention provides a converter valve key component burning defect identification method based on a neural network, and belongs to the technical field of power electronic equipment fault diagnosis, and the method comprises the steps: collecting multi-modal data through an infrared thermal imager and a plurality of sensors, and inputting the pre-processed multi-modal data into a specially designed neural network architecture; the framework comprises an image feature extraction module, a time sequence feature extraction module, a feature fusion module and a classification positioning module. According to image processing, improved ResNet50 is combined with an attention mechanism, time sequence features are extracted through a bidirectional long-short-term memory network and a time convolution network, and effective feature combination is achieved through a dynamic weight fusion mechanism. Meanwhile, a double-phase heat conduction model is introduced to analyze temperature distribution, accurate identification of burning defects of key components of the converter valve in a complex environment is realized through large-scale data set training and a two-stage optimization strategy, and key technical support is provided for safe operation of a power system.

The invention relates to the technical field of power system security, and particularly discloses a ground wire management operation system based on ground wire cabinet monitoring, which comprises a physical sensing layer, an edge computing layer, a block chain consensus layer and an application service layer. The physical sensing layer collects the identity, the in-place state and the electrical parameters of the grounding wire; the edge calculation layer carries out local data processing and event judgment; the block chain consensus layer realizes data credible evidence storage and cross-domain sharing through an intelligent contract and a distributed account book; and the application service layer provides asset panoramic display, cooperative verification and risk early warning services. Through the above architecture, unified management and safety collaboration of data of the whole life cycle of the grounding wire are realized, and the safety operation and maintenance level of a power grid is improved.

The invention provides a transformer substation operator dynamic authority management and control system based on AI vision, and belongs to the technical field of power system safety management. According to the system, identity living body verification is achieved through infrared imaging and visible light videos, a real-time data stream is formed by combining positioning data and environment sensing data, and analysis is conducted through a behavior intention recognition model. When the four conditions of the identity, the position, the action and the environment are all compliant, the system generates a dynamic authority token and authorizes an operator to execute operation within a short time limit; and if any condition is not compliant, the dynamic permission token immediately loses efficacy and triggers blocking feedback, and the permission is cancelled and the operation is stopped within microsecond-level time delay. According to the system, through a multi-parameter binding and parallel judgment mechanism, the dynamic and fine management and control of the operation authority are realized, the unauthorized operation is effectively avoided, and the safety and the real-time performance of the operation of the transformer substation are improved.