597 results about "Power-system automation" patented technology

The invention discloses a power grid equipment state sensing driving dynamic response method based on the Internet of Things technology, and relates to the technical field of power system automation and informatization, and the method comprises the following steps: S001, collecting original multi-dimensional state signals of a plurality of sensors deployed in a power grid equipment state sensing channel, constructing an electromagnetic disturbance recognition model, and carrying out the recognition of the original multi-dimensional state signals; frequency domain and time domain feature extraction is carried out on the signals, and a feature comparison parameter set used for distinguishing electromagnetic interference and real faults is generated. Frequency domain and time domain features are extracted through an electromagnetic disturbance recognition model, dynamic threshold judgment and response strategy adjustment are achieved in combination with multi-source sensing data, interference and faults can be accurately distinguished, early warning and protection logic can be corrected in real time, protection actions can be accurately triggered, closed-loop control is achieved, the delayed fault tolerance and multi-source verification capacity is achieved, and the method is suitable for large-scale popularization and application. The false operation rate and the false stop risk are effectively reduced, and the intelligence, the safety and the stability of power grid operation are improved.

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 discloses a power distribution network fault identification driving accurate isolation method based on an edge computing architecture, and relates to the technical field of power system automation, and the method comprises the following steps: S001, collecting voltage and current signals at each edge computing node of a power distribution network, analyzing the signal fluctuation amplitude and propagation time delay of different nodes based on the same fault event, and obtaining a fault event; and calculating a fault identification judgment difference degree between the nodes. According to the method, the identification credibility is quantified and abnormal node judgment is corrected through fault identification judgment difference analysis and topological constraint modeling; self-adaptive identification logic optimization is realized in combination with historical and real-time data; control priority and pre-simulation analysis are introduced, so that the feasibility and safety of the isolation action are improved; and finally, a closed-loop mechanism integrating recognition, simulation and feedback is constructed, and the fault response accuracy, coordination and operation stability of the power distribution network under a distributed architecture are remarkably improved.

The invention relates to the technical field of power system automation, in particular to a climbing-frequency modulation coordinated optimization method and system considering energy storage capacity uncertainty, and the method comprises the steps: building an opportunity constraint model of an energy storage system based on the capacity uncertainty, and enabling the opportunity constraint model to be used for describing a reliable feasible region under the deviation of energy storage electric quantity; establishing an energy storage system linearization model considering a climbing-frequency modulation power unified constraint, and realizing power coupling and sharing constraint description of two types of adjustment services; aiming at the nonlinear characteristic of the gradeability of the thermal power generating unit, establishing a piecewise linearization model of the gradeability by adopting a piecewise linearization method; and establishing a thermal power-energy storage combined climbing-frequency modulation coordinated optimization model, and solving the optimization model by adopting a quadratic programming method to obtain the climbing power and frequency modulation reserve capacity of the thermal power generating unit and energy storage. According to the method, the actual available range of the energy storage system capacity can be dynamically reflected, the electric quantity out-of-limit risk caused by the uncertainty of the energy storage capacity is avoided, and the reliability of the scheduling result is improved.

The invention relates to the technical field of automation and intelligent operation and maintenance of a power system, in particular to a knowledge graph-fused reinforcement learning switching operation anti-error verification method, which systematically extracts a multi-dimensional anti-error rule covering an operation sequence, an equipment state and an electrical safety distance by constructing an operation ticket knowledge graph, and improves the accuracy of the operation ticket knowledge graph. The defect that a traditional single-station anti-error system is incomplete in rule coverage is overcome, meanwhile, in combination with deep mining of a reinforcement learning model on historical operation data, implicit anti-error rules can be automatically extracted, illegal scenes which are not covered by a traditional rule base are supplemented, overall-process and multi-level accurate verification of switching operation is achieved, and the verification efficiency is improved. And the risks of misoperation and missing detection are greatly reduced.

The present invention belongs to the field of smart power grids and power system automation control. Disclosed is a load emergency control method based on a dynamic event triggering mechanism, which method comprises: on the basis of an indicator of safe operation of a system, performing region division on a distribution network; proposing an adaptive load level division method based on user response willingness, and by taking the minimization of the system operation cost of the distribution network as an objective, establishing a precise load control model of the distribution network; and using a dynamic event triggering mechanism to reduce communication between substations and a master station of the distribution network, so as to realize effective exchange and precise execution of load control policy information. The method of the present invention can precisely realize precise control of a load, and can effectively alleviate communication transmission pressure of a distribution network, thereby ensuring the stability of a power grid and the continuous supply of critical loads.

The invention provides a circuit breaker action coil current characteristic diagnosis and analysis system and method. Belongs to the technical field of power system automation and substation state monitoring. The method comprises the following steps: carrying out synchronous wave recording on current of a plurality of circuit breaker actuating coils on a direct current feeder screen side to generate multi-channel synchronous wave recording data; meanwhile, SOE message information is collected, a circuit breaker with specific action is automatically matched and positioned by utilizing a timestamp and action identification information in an SOE message, and action circuit breaker positioning data is generated; the current waveform data of the circuit breaker actuating coil are collected in real time, and a high-precision algorithm is used for feature extraction and analysis, so that abnormal fluctuation in current change can be accurately captured, such as overlarge starting current and unstable holding current, the risk of misoperation or rejection of operation of the circuit breaker caused by abnormal current characteristics is reduced, and the service life of the circuit breaker is prolonged. Electric power system accidents caused by equipment faults are avoided, and the operation reliability of the circuit breaker is improved.

The invention discloses a power distribution network layered optimization method oriented to source-network-load interaction, and relates to the technical field of power system automation. The method comprises the following steps: constructing a dynamic adaptive collaborative architecture, and dynamically generating a three-layer logic structure of global coordination, regional autonomy and local execution according to the real-time state of the power distribution network; operating a three-layer mixed game mechanism, and determining an optimal strategy of each main body through master-slave, cooperation and bilateral interactive games; executing an evaluation-planning-operation three-level closed-loop optimization process, and taking a key performance index fed back by an operation layer as a basis for scheme correction of a planning layer; and a mixed intelligent strategy combining multi-agent deep reinforcement learning and an improved particle swarm optimization algorithm is adopted for solving. According to the method, the problems of hierarchical solidification, benefit coordination imbalance and planning operation splitting in the prior art are effectively solved, and the adaptive capacity, the economical efficiency and the flexibility of the power distribution network in the high-permeability source-network-load interaction scene are 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 system automation, and particularly discloses a cross-domain heterogeneous computing power real-time calling and unified scheduling system for the power industry, which comprises a computing power resource global sensing unit, a business demand dynamic modeling unit, a cross-domain unified scheduling decision unit and a task execution and feedback control unit, according to the method, heterogeneous computing power states are collected in real time, task requirements are dynamically modeled, a scheduling scheme is generated based on multi-objective optimization, and task execution monitoring and load balancing are realized by means of closed-loop feedback, so that the utilization efficiency of global computing power resources is improved, and the real-time performance and reliability of power business are guaranteed.

The embodiment of the invention discloses an automatic acceptance method and system for relay protection equipment, and relates to the technical field of power system automation, and the method comprises the steps: obtaining a preset acceptance outline template and a configuration description file to which target relay protection equipment belongs; analyzing the configuration description file to extract actual signal information related to the target relay protection equipment; automatically associating the actual signal information with a preset signal in the acceptance outline template to generate an instantiated acceptance outline; in response to the acceptance instruction, automatically interacting with the target relay protection equipment according to the instantiated acceptance outline to execute the test item; automatically acquiring a response result of the target relay protection equipment in the process of executing the test item; comparing the response result with an expected result preset for the test item in the instantiated acceptance outline, judging a test conclusion and generating a structured test report; automation of the acceptance process of the relay protection equipment can be realized, and the accuracy, reliability and efficiency of testing are improved.

The invention discloses a primary and secondary fusion pole-mounted circuit breaker state monitoring system and method, and relates to the technical field of power system automation. The system comprises a native signal acquisition module which is used for multiplexing an inherent electrical loop of a circuit breaker and acquiring a current signal of an opening and closing coil and a capacitive reactance change signal of a parasitic capacitor between a high-voltage electrode and a grounding end; and the edge analysis module is used for processing the signal acquired by the original signal acquisition module, including generating a mechanical health degree through feature extraction and state mapping, calculating an insulation aging index based on capacitive reactance change, and dynamically correcting a monitoring reference in combination with a digital twin model and a filtering algorithm. Non-auxiliary element monitoring is realized by multiplexing the inherent electrical loop of the circuit breaker, so that the service life of the monitoring system and the service life of primary equipment are completely synchronous, and elements do not need to be replaced in the whole life cycle; errors introduced by additional sensors are avoided by using the original electrical signals, and the mechanical fault recognition and insulation state evaluation precision is remarkably improved.

The invention relates to the technical field of automatic operation and maintenance of a power system, in particular to an automatic operation and maintenance method and system for plant-station equipment based on a knowledge graph, and the method comprises the steps: collecting static ledger data, real-time operation data and historical operation and maintenance data of the plant-station equipment, constructing a multi-dimensional association knowledge graph, creating a digital twinborn body for target equipment in a plant station, and carrying out the operation and maintenance of the plant-station equipment. Establishing a bidirectional data channel between the digital twin and a corresponding equipment entity node in the knowledge graph; and based on a state evaluation rule and a fault propagation path model preset in the knowledge graph, performing real-time health state evaluation on the target equipment by using the state evaluation rule and the fault propagation path model to generate a health index, and when an abnormality is monitored, tracing along the topological relation between the electrical connection and the equipment in the knowledge graph, the fault source is positioned, the confidence probability is calculated, risk level evaluation and processing priority ranking are carried out according to the confidence probability, an operation and maintenance diagnosis work order containing an execution time sequence is obtained, and the fault reason can be quickly traced and positioned.

The invention discloses an intelligent power grid load prediction and dynamic response coordinated scheduling method, and relates to the technical field of power system automation, and the method comprises the steps: accessing intelligent ammeters, distributed power controllers and other devices of Modbus, IEC61850 and DL / T645 protocols through a multi-protocol adaptive gateway, and achieving data standardization; time stamps are calibrated by means of Beidou time service and an IEEE1588PTP protocol, and it is ensured that multi-source data synchronization errors are controllable; deploying an edge computing node cluster, distributing high-priority tasks to low-load nodes through an edge coordinator in combination with a load fluctuation level and a greedy algorithm, and ensuring real-time processing efficiency; the edge nodes generate short-term load prediction, and the cloud platform outputs medium and long-term prediction based on a historical data training model; and finally, the coordinated scheduling decision module fuses the two types of prediction results and the real-time parameters of the power grid, and generates a dynamic instruction to control the output of the adjustable load and the distributed power supply.

The invention discloses a direct current B code wave recording alarm analysis device and method based on an FPGA, and relates to the technical field of power system automation and signal analysis, and the device comprises an FPGA collection module which is used for collecting a TTL level signal of a direct current B code in real time at a preset sampling frequency, recording a high-precision timestamp of each sampling point, and carrying out the subsequent pulse parameter calculation; the pulse width measurement module is connected with the FPGA acquisition module, is used for calculating the pulse width of the current pulse based on the rising edge timestamp and the falling edge timestamp of the signal, triggering a wave recording module, and is used for starting wave recording operation when the pulse width is detected to exceed a preset threshold range or abnormity occurs for a plurality of times continuously; collecting complete waveform data before and after the fault; the DC B code wave recording alarm analysis device and method based on the FPGA improve the intelligent level, response speed and fault visualization capability of a B code monitoring system, and are especially suitable for high-reliability monitoring and management scenes of key synchronization signals in a DC system.

The invention relates to the technical field of power system automation, in particular to a node fault monitoring method and system for a direct current power distribution network, electronic equipment and a medium, and the method comprises the steps: injecting a difference frequency band signal into each measurement node on the direct current power distribution network, and obtaining an initial signal propagation feature set from each measurement node; distinguishing from the initial signal propagation characteristic set according to the shunting effect intensity of the distributed capacitance on the signal under each difference frequency band to obtain a high shunting frequency band subset and a low shunting frequency band subset; and a far-end fault frequency band subset is extracted from the high-shunting frequency band subset and the low-shunting frequency band subset, so that the fault position and type in the direct-current power distribution network can be calculated. By means of the mode, the technical problem that in the prior art, the fault positioning precision and the detection range of the direct-current power distribution network conflict with each other is solved, dynamic monitoring of the insulation defect propagation characteristics is achieved, and the fault positioning accuracy and the system reliability are improved.

The invention relates to the technical field of power system automation and artificial intelligence, and particularly discloses a power network risk alarm event analysis method, which comprises the following steps of: firstly, carrying out standardization processing and space-time slicing on a multi-source heterogeneous original alarm flow, and constructing a dynamic fault propagation graph in combination with power grid physical topology; then, multi-dimensional indexes such as topology centrality, time sequence attenuation and semantic rare degree are comprehensively calculated, and the causal significance of each alarm node is quantified; on this basis, adaptive sparse sampling based on maximum interpretation gain is implemented, and high-score core events are precisely preserved and public precursor nodes with global collective power are forcibly recalled by evaluating common-mode interpretation utility and space-time coupling strength of nodes to downstream alarms on the premise of strictly complying with the limitation of context tokens of a large model. And finally, inputting the simplified high-value evidence chain into a large language model to carry out thinking chain reasoning, thereby realizing accurate positioning of complex fault root causes and automatic generation of a disposal strategy.

The invention relates to a flexible regulation and control load remote control terminal and method, and belongs to the technical field of power system automation and demand side management.The control terminal comprises an intelligent communication adaptation module, a coupling modeling module, a multi-scene self-adaption regulation and control module and a user interaction feedback module which are interconnected through an internal data bus; real-time interaction and cooperative work of data are realized; according to the invention, the breakthrough of the regulation and control mode from static presetting to dynamic optimization is realized, full-protocol compatibility and high-reliability transmission are realized, the real-time performance and interaction depth of user side response are improved, and the refinement and precision of control are improved.

The invention discloses an AGC instruction anti-error method and system based on digital twinning and multi-risk look-ahead, and belongs to the technical field of automation and control of power systems. The method comprises the following steps: constructing a digital twinborn body which operates synchronously with a physical power system AGC control loop; acquiring operation state data; receiving an original control instruction from a superior AGC system; performing look-ahead simulation on the execution effect of the original control instruction to obtain a system state track prediction value; calculating five-dimension risk indexes by using a risk quantitative evaluator; judging whether any risk index of the five-dimension risk indexes exceeds a preset threshold value or not; if yes, triggering the instruction safety correction module, and executing the next step; if not, directly issuing the original control instruction to a station physical execution unit; an instruction safety correction module is started, rolling optimization is carried out on the original control instruction, and a safety instruction is generated; and issuing the security instruction to a physical execution unit.

The invention discloses a new energy power grid subsynchronous oscillation source positioning method, system and device and a medium, and belongs to the technical field of power system automation and new energy grid-connected operation control, and the method comprises the steps: carrying out AVMD-SVD combined noise reduction and T-MTF spatial-temporal feature conversion based on AVMD-SVD and T-MTF; the high-dimensional spatial-temporal feature image after T-MTF conversion is used as structured input, and a spatial-temporal Transform oscillation source positioning model is constructed; and carrying out subsynchronous oscillation source positioning of the new energy power grid based on AVMD-SVD and space-time Transform fusion. According to the method, the signal processing precision in a strong noise environment is improved, the feature identification problem caused by multi-oscillation-source coupling and low observability is solved, the space-time propagation law of the energy flow is captured, the positioning performance in a complex scene is improved, and an effective solution with noise resistance, robustness and adaptability is provided for real-time positioning of the subsynchronous oscillation source of the new energy power grid.

The invention discloses a power transformation equipment defect processing method and system based on edge-cloud collaboration, and belongs to the technical field of power system automation, the power transformation equipment defect processing method based on edge-cloud collaboration comprises the following steps: synchronously acquiring and preprocessing a visible light image and an infrared image of power transformation equipment; the edge processing layer performs equipment part positioning on the preprocessed image, extracts structural features in the visible light image and temperature features in the infrared image according to a positioning result, preliminarily screens out a suspicious area meeting a suspected defect condition, and uploads the result to the cloud analysis layer; after the cloud analysis layer receives the data, visible light features and infrared features of the visible light image and the infrared image are extracted respectively, and cross-modal feature fusion is carried out by adopting an attention mechanism. Through the edge-cloud collaborative cross-modal recognition and dynamic optimization mechanism, the problems that efficiency and precision are difficult to consider at the same time, the single-modal omission ratio is high, and the model adaptability is poor in the prior art are effectively solved.

The invention provides a method for improving source network load storage integrated adjustment capability, and belongs to the technical field of power system automation and energy management. Multi-side data acquisition terminals are deployed to acquire power grid operation parameters in real time and perform adaptive extended Kalman filtering noise elimination; a source-load-storage dynamic coupling matrix is constructed based on historical data, adjustment weight coefficients of all links are calculated, and an adaptive prediction fusion model is used for energy storage state estimation and new energy output prediction. An energy storage power compensation instruction, an adjustable load starting instruction and a source load storage scheduling instruction are generated in a grading manner according to the magnitude of power grid frequency deviation by adopting a hierarchical cooperative regulation and control strategy, and an adjustment weight coefficient and a control instruction are iteratively optimized according to an adjustment effect, so that the technical problem of insufficient source network load storage multi-link cooperative adjustment capability is solved.

The invention discloses a power distribution network small current grounding fault diagnosis positioning method and system based on edge calculation, and belongs to the field of power system automation, and the method comprises the steps: extracting and recognizing transient features, if a suspected grounding fault is detected, carrying out the multi-source feature fusion analysis through a regional main node, and constructing a transient response time sequence map; a weighted fuzzy clustering algorithm is combined with a multi-channel criterion to determine a fault channel, and a specific fault branch is further positioned through a topology tracking algorithm; introducing a multi-scale wavelet packet energy analysis method into the regional main node, performing energy inversion calculation on the transient current signal under multiple frequency bands, and identifying an energy concentration region near the grounding point through an energy distribution gradient; and constructing a robustness data redundancy model based on the historical data of the edge nodes and the topological relation, and identifying and eliminating abnormal feature data containing noise or distortion. According to the method, transient characteristics are rapidly extracted and preliminary judgment is made after a fault occurs, so that the real-time performance of fault response is greatly improved.

The invention relates to a transformer substation switch state monitoring system based on centralized monitoring, and relates to the technical field of power system automation, and the system comprises a data collection module which is used for integrating multi-source heterogeneous data; the state judgment module outputs the unique real-time comprehensive state of each switch according to the communication state, the alarm condition function and the multi-stage judgment logic of the original remote signaling; the outage diagnosis module is used for classifying events when the switch trips and automatically screening event sequence records (SOE) to position core diagnosis information; the influence domain pre-judgment module is used for determining all potential influenced switch sets through a graph traversal algorithm on the basis of a power grid topological structure when accident tripping occurs; and an interface generation module. According to the invention, through automatic integration and visual linkage of the operation state, the fault reason and the influence range of the switch, the monitoring intuition, the diagnosis accuracy and the emergency disposal decision-making efficiency are significantly improved.

The invention discloses an LCC-HVDC operation range optimization method and system considering transient overvoltage constraint, and belongs to the field of power system automation. The method comprises the following steps: calculating the capacity of a matched capacitor based on the power system topology and the LCC-HVDC transmission power; carrying out load flow calculation in combination with a capacitor scheme, initializing equipment parameters, establishing a quasi-steady-state model of each equipment, and solving a quasi-steady state at the moment of maximum reactive power consumption during commutation failure; based on the model and the result, solving the quasi-steady state at the initial moment of the commutation failure recovery stage; and calculating the node voltage of the rectifier station, optimizing the LCC-HVDC power and equipment parameters along the gradient if the node voltage exceeds the limit, and iterating until the transient voltage safety is met, otherwise, determining that the current scheme is an operation scheme. The system comprises a corresponding function module and a data storage module. The method can be applied to an actual hybrid system, the transient overvoltage is accurately evaluated, the LCC-HVDC safety power range is determined, the power grid safety is guaranteed, and the new energy consumption efficiency is improved.

The invention discloses an electric power knowledge graph construction method and system based on multi-head attention and a medium, and belongs to the technical field of electric power system automation. The method comprises the following steps: constructing graph structure data including power equipment, parts, defects and environment entities, and encoding node features; secondly, inputting graph data into a graph attention module, reducing storage overhead through a low-rank compression key value vector, independently performing decoupled rotation position coding on a query vector to inject hierarchy and time position information, respectively modeling an equipment hierarchy relationship and a dynamic time sequence relationship through a division-of-labor multi-head attention mechanism, and performing multi-head attention mechanism modeling on the equipment hierarchy relationship and the dynamic time sequence relationship; obtaining a node vector representation; and finally, performing hierarchical processing and dynamic updating on the node representation, and constructing and continuously updating the electric power knowledge graph through knowledge reasoning after fusion. The problems that a traditional method is low in storage efficiency, insufficient in space-time relation modeling and difficult in dynamic updating are solved, and the efficiency, precision and real-time performance of graph construction are remarkably improved.

The invention relates to the technical field of power system automation, and discloses a distributed photovoltaic spatio-temporal data recovery and unsupervised clustering method and system.The distributed photovoltaic spatio-temporal data recovery and unsupervised clustering method comprises the steps that observation data of a distributed photovoltaic system are collected, and the observation data comprise historical distributed photovoltaic output time sequences; the collected historical distributed photovoltaic output time sequence is used as an input signal to be transmitted to each module of the GRAIL framework so as to drive a network to jointly learn time sequence dynamic characteristics and a clustering structure; wherein the GRAIL framework is a closed-loop iterative structure, and comprises a clustering sensing data generation module, a latent feature representation module and a self-adaptive clustering and refining module. The method can be widely applied to distributed photovoltaic station operation monitoring and abnormity identification scenes, and intelligent regional state recovery and mode grouping are realized.

The invention belongs to the technical field of power system automation control, and particularly relates to a low-voltage power distribution cabinet intelligent control system applied to an intelligent power grid, which comprises a multi-dimensional sensing module, an edge calculation and decision center and an execution and communication interface module. A multi-time-scale adaptive control mechanism adopted by the system separates protection, optimization and reconstruction decisions in different periods, the problems of response lag and target conflict of a single control loop are effectively solved, and the quick response capability and the adaptive adjustment capability of the system in a dynamic load and distributed energy access scene are remarkably improved. And the operation and maintenance mode is converted into predictive maintenance from post-maintenance through a deep fusion equipment health state evaluation and prediction function, so that the risk of unplanned shutdown is greatly reduced.

The invention provides a power distribution network voltage quality online evaluation method and system supporting new energy group access, and belongs to the field of power system automation. Comprising the following steps: generating a node dynamic feature vector through multi-source data fusion and feature extraction; constructing a dynamic causal association diagram, and synchronously outputting a voltage quality multi-dimensional evaluation index and disturbance source contribution degree analysis by using a gating space-time diffusion mechanism; and generating graded early warning based on evaluation and traceability results, and generating auxiliary decision-making information of recommended control measures through strategy matching, effect simulation and multi-target optimization. The system comprises a data acquisition and fusion module, a dynamic causal association diagram construction module, a causal time sequence diagram diffusion evaluation module, an endogenous traceability analysis module, a grading early warning and decision auxiliary module and the like. According to the invention, real-time evaluation, accurate traceability and intelligent decision support of the voltage quality problem of the new energy power distribution network are realized.

The invention provides an industrial vision-based power line repair operation safety monitoring method, which relates to the technical field of power system automation, and comprises the following steps: collecting multi-source visual data of a repair operation site, and carrying out space-time alignment and feature level fusion on the multi-source visual data; based on the fused visual features, through a personnel safety equipment detection model and an operation behavior identification model, identifying the safety equipment wearing state and the operation behavior compliance of the operator in real time; the position, the motion track and the distance between the operator and the danger source are tracked in real time, and potential risks in a few seconds in the future are predicted through a rehearsal algorithm; and generating a safety operation report based on the monitoring data of the whole process, and feeding back key data in the report to the personnel safety equipment detection model and the operation behavior recognition model for parameter optimization. According to the method, interference of severe weather, shielding, illumination change and the like is effectively overcome, and accurate and robust recognition of the safety equipment wearing standard degree and various operation behaviors is achieved.