6963 results about "Transmission line" patented technology

The invention discloses a power transmission line multi-mode warning system and a power transmission line multi-mode expelling method, belongs to the technical field of power transmission line safety monitoring, and aims at solving the problems that power transmission line invasion target monitoring is not accurate, and the warning and expelling effect is poor. An environment image is acquired through an acquisition unit, and redundancy compensation is carried out on a fault unit. In the aspect of image splicing, a database is constructed by using structural feature points of a power transmission line, rapid projection transformation of a fixed area is realized, incremental feature matching is adopted for a dynamic area, and an environment panoramic image is generated. The method comprises the following steps: establishing a background coordinate system based on an environment panoramic image by aligning a fixed structure region, extracting multi-modal features of an intrusion target, constructing a dynamic trajectory parameter set, completing species classification and behavior recognition, constructing a multi-dimensional evaluation index system, dividing threat levels, and generating a thermodynamic diagram. And finally, according to data such as threat levels, a multi-mode grading warning system is constructed, warning equipment is dynamically adjusted, a target track is tracked, a warning effect is evaluated, and accurate and efficient invasion target expelling is realized.

The invention discloses a power transmission line connection fitting fault detection method based on acoustics and electromagnetic induction, and belongs to the technical field of intelligent inspection and nondestructive detection of power transmission lines. A multi-mode sensor is carried by an unmanned aerial vehicle, ultrasonic wave, vibration, images and power frequency electromagnetic field data are synchronously collected, and a multi-dimensional original data set is constructed. Signal quality is improved by adopting wavelet noise reduction, beam forming and sound image fusion technologies, and flight vibration and electromagnetic interference are effectively suppressed by combining an adaptive filtering algorithm and a physical shielding structure. And acoustic, image and electromagnetic characteristics are extracted and normalized and fused, a dynamic threshold reference library is established, and intelligent grading discrimination of abnormity, defects and faults is realized through multi-stage early warning logic. A detection result automatically generates a report and is mapped to a three-dimensional line model, and operation and maintenance system linkage is supported. According to the method, synchronous identification of surface and internal defects is realized, the anti-interference capability is high, the detection accuracy is high, the inspection efficiency and safety are remarkably improved, and the method is suitable for intelligent operation and maintenance of the high-voltage transmission line.

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.

A system for breakage early warning of high-voltage transmission lines based on acoustic prints, including a data acquisition unit, an edge processing unit, a cloud server, a warning execution unit, and a communication unit. The data acquisition unit is configured to collect environmental information of a high-voltage transmission tower in real time through a multimodal sensing device. The edge processing unit includes an adaptive filtering module configured to eliminate wind noise interference in the environmental information and compensate for frequency response deviation. The cloud server can generate a warning instruction based on the environmental information. The warning execution unit can perform warning and alarming actions based on the warning instruction. The communication unit is configured to enable data and information transmission among the units. A method implemented by such system is also provided.

The invention discloses a power transmission line inspection unmanned aerial vehicle obstacle intelligent detection and obstacle avoidance method. The method comprises the steps that multi-source sensing data is acquired, and alignment is completed through calibration and timestamp matching; heterogeneous data preprocessing and feature enhancement; constructing a high-precision environment fusing a geometric structure and a semantic tag, mapping a two-dimensional target detection result output by the recognition network to a three-dimensional coordinate system through spatial transformation, and fusing the two-dimensional target detection result with a point cloud structure to construct a semantic occupation grid map; performing preliminary route planning according to a preset power grid topological structure and task coverage requirements, and generating a barrier-free flight path covering the whole inspection area; reinforcing learning of a dynamic obstacle avoidance strategy; track dynamic reconstruction and energy consumption optimization scheduling are carried out; the technical problems that an existing technical system has defects in the aspects of obstacle recognition accuracy, complex environment adaptability, data fusion capacity and obstacle avoidance strategy intelligence, and the requirements for high-reliability, low-energy-consumption and high-efficiency unmanned aerial vehicle power transmission line inspection are difficult to meet are solved.

The invention discloses a power transmission channel risk early warning evaluation method and system, and relates to the technical field of power transmission channel abnormal condition early warning, and the method comprises the steps: analyzing the current waveform distortion degree caused by a nonlinear load in a power transmission line through monitoring the voltage harmonic distortion rate, and evaluating the influence of harmonic waves on the insulation aging and heating loss of the line and equipment; the real-time gradient of the temperature of the wire is analyzed, abnormal heating conditions of different sections of the wire are judged, and the abnormal rising degree of the temperature of the wire and potential thermal damage accumulation caused by overload and poor contact factors are evaluated; by monitoring and analyzing multi-dimensional indexes such as the voltage harmonic distortion rate, the lead temperature real-time gradient, the sag dynamic deviation rate and the corona discharge intensity, the defect of a traditional monitoring means in the aspect of abnormal heating risk assessment is overcome.

The invention provides an online monitoring method and system based on a power transmission line, and relates to the technical field of power transmission line monitoring. According to the method, the heterogeneous sensing terminal, edge calculation, the graph neural network and Bayesian reasoning are combined, multi-source data acquisition, state identification and risk prediction are realized, the fault diagnosis accuracy and the risk early warning capability are improved, and the intelligent level and the safety guarantee capability of power transmission line operation are enhanced; the monitoring data is analyzed in real time through an edge calculation unit to generate a state label, a potential fault mode is recognized by combining graph neural network modeling space-time relevance, a risk factor library is further constructed, and a real-time fault probability graph is generated based on a Bayesian network. And dynamic identification and early warning of risk types such as wire strand breakage, icing overrun and mechanical fatigue can be realized.

The invention is suitable for the technical field of power transmission line maintenance, and provides a line early warning judgment method and system based on multi-source data fusion, and the method comprises the steps: constructing a multi-dimensional knowledge graph, and building a semantic association network; collecting meteorological and equipment state data in real time, fusing the data into a knowledge graph space through a mapping function, identifying an abnormal meteorological-equipment combination mode by using fuzzy reasoning, and calculating a thickness change rate in combination with an icing growth prediction model; the icing thickness and the galloping probability are calculated, and a composite early warning level is generated; and dynamically generating a risk thermodynamic diagram on the GIS map, and visually displaying a risk propagation path. Through semantic fusion of multi-source data, composite risk modeling and spatialization decision making, the system breaks through single parameter criterion limitation of traditional early warning, realizes intelligent identification and dynamic early warning of meteorological-equipment coupling risks, and significantly improves accuracy of power transmission line disaster early warning and operation and maintenance decision making efficiency.

The invention provides an unmanned aerial vehicle high-precision autonomous navigation method and system based on GNSS and IMU deep fusion, and aims to solve the problems of insufficient navigation precision and poor robustness in a complex electromagnetic environment. Through a tight coupling architecture, GNSS original observed quantity and IMU pre-integration results are jointly modeled in an observation layer, and multi-source constraints are introduced in combination with factor graph optimization, so that the positioning precision and consistency in weak signal and shielding scenes are remarkably improved. For abnormal observation, a robust kernel function is adopted to dynamically adjust the weight, and the influence of electromagnetic interference and a multipath effect is effectively inhibited. Meanwhile, navigation calculation and model prediction control MPC are combined, and sub-meter hovering and high-precision trajectory tracking are achieved. According to the method, in high-voltage transmission line inspection, dependence on a high-cost sensor is reduced, the engineering application value is high, the method can be widely applied to the fields of electric power inspection, disaster emergency, infrastructure monitoring and the like, and reliable technical support is provided for high-precision autonomous navigation of the unmanned aerial vehicle in a complex environment.

The invention relates to the technical field of power detection, and discloses a vegetation risk hidden danger detection method and system based on sparse point cloud segmentation, and the method comprises the steps: collecting image data of a monitoring region at different angles, generating sparse point cloud data, and carrying out the preprocessing of the sparse point cloud data; and performing point cloud segmentation on the sparse point cloud data through a neural network, dividing the monitoring region according to region types, analyzing a spatial relationship between vegetation and power equipment in a target region, performing risk assessment, and generating an early warning signal in combination with multi-modal data. According to the method, the sparse point cloud data is precisely segmented, the three-dimensional space characteristics of the tree are extracted, and the potential risk of the tree and the power transmission facility can be dynamically monitored in real time by precisely calculating the space relation between the tree and the power transmission facility; according to the multi-modal fusion data, multi-level early warning information is generated, so that the efficiency and precision of power transmission line inspection are greatly improved, and the pre-judgment and timely treatment of hidden dangers are realized.

The invention provides an edge fusion diagnosis system and method for multi-source heterogeneous power transmission monitoring data, and belongs to the technical field of power transmission line monitoring. The multi-source heterogeneous power transmission monitoring data edge fusion diagnosis system comprises a multi-source heterogeneous data access module, a data preprocessing and standardization module, an edge computing node, an edge node collaborative diagnosis module, a data compression and feature extraction module and a cloud fusion analysis platform. The method comprises the following steps: accessing multi-source heterogeneous data, preprocessing and standardizing, locally diagnosing by an edge computing node, triggering multi-node collaborative diagnosis when the credibility is insufficient, and uploading key feature data to a cloud for global analysis and model optimization. According to the method, comprehensive integration and efficient processing of multi-source data are realized, the real-time performance, accuracy and comprehensiveness of power transmission monitoring diagnosis are improved, and the efficient monitoring requirement of a complex power transmission network can be met.

The invention discloses an automatic three-dimensional reconstruction method and system based on power transmission corridor point cloud. The method comprises the following steps: acquiring data and exporting point cloud data; noise points, outliers and ground points are removed from the acquired point cloud data of the power transmission corridor, and down-sampling is performed on the point cloud with a large data volume; performing clustering segmentation on the preprocessed point cloud of the power transmission corridor, positioning the position of a tower, determining a point cloud range of a power line, and removing point clouds of other objects on the ground; aiming at the structural characteristics of a power line and an insulator in the power transmission line, extracting point clouds of the power line, the insulator and a tower by adopting a geometric constraint principal component analysis (PCA) algorithm; performing automatic geometric model reconstruction on the tower, insulator and power line point clouds according to different reconstruction rules to generate each module grid model; and transforming the grid model of each module, restoring the grid model to the original position of the point cloud, and generating a power transmission line digital twin model conforming to the real size. According to the method, automatic segmentation, classification and real-time reconstruction can be accurately carried out on the point cloud of the power transmission corridor.

The invention discloses an efficient denoising power transmission line point cloud processing method and system, and relates to the technical field of three-dimensional point cloud data processing and intelligent analysis, and the method comprises the steps: carrying out the segmentation processing of original power transmission line point cloud data, dividing the point cloud into power equipment parts, carrying out the local density analysis of the segmented point cloud, and generating a density distribution diagram; based on the density distribution diagram and the local geometric features of the point clouds, multi-stage filtering is carried out, denoising model parameters are optimized, a statistical filtering method is combined to remove noise points, curvature features and normal vector consistency features of the point clouds are extracted, classification feature vectors are generated, and based on the classification feature vectors, fine classification is carried out on the point clouds. Through data preprocessing, multi-stage filtering, self-supervised learning, feature extraction and refined classification, the technical defects of insufficient multi-modal feature fusion efficiency and poor equipment feature adaptability in power transmission line point cloud denoising are effectively solved.

The invention provides a power transmission line mountain fire disaster risk assessment method and system based on space-time event driving, and the method comprises the steps: obtaining mountain fire monitoring data of a to-be-assessed region, and generating a space-time event according to the mountain fire monitoring data; according to an event association rule, the space-time event is associated with a power transmission line tower through a directional edge, and then a power transmission line mountain fire disaster hidden danger map is constructed; performing similarity matching on the time-space events generated in real time and the time-space events in the power transmission line mountain fire disaster hidden danger map, and searching the corresponding time-space events in the power transmission line mountain fire disaster hidden danger map by adopting a map traversal algorithm; and for the searched towers influenced by the space-time events, quantitatively calculating the risk score of each tower based on the data values of the real-time space-time events, and dividing the risk grades according to the risk scores. According to the method, the association precision of power transmission line risk assessment and the precision and efficiency of power transmission line forest fire prevention and control can be improved.

The invention relates to the technical field of power transmission line monitoring, in particular to a multi-modal data fusion power transmission line digital twinning real-time monitoring system and method, and the system comprises a data collection module which is responsible for collecting optical images, environment characteristic data, current waveform data and point cloud data, and transmitting the data to a digital twinning module; the digital twinborn module constructs a three-dimensional digital twinborn body of the power transmission line, fuses the multi-modal data through a graph neural network and an attention mechanism, generates multi-modal fusion features, establishes a dynamic correlation model of conductor temperature-sag-current-carrying capacity based on the features, and transmits the dynamic correlation model to the online monitoring module; the on-line monitoring module fuses real-time collected data and historical data, analyzes the line state, generates multi-level early warning information and outputs a line state analysis result and fault prediction information, the system adopts an innovative data fusion method, the sensitivity and accuracy of fault detection are improved, and the operation safety and efficiency of the power transmission line are improved.

The invention relates to a multi-element microphone array sound source localization method based on multistage signal preprocessing and subspace spectrum optimization, and belongs to the technical field of acoustic detection. Aiming at the problems of poor noise immunity, weak multi-sound-source resolution capability and low calculation efficiency of the existing sound source positioning technology, a triple signal preprocessing and subspace collaborative optimization scheme is provided; firstly, incoherent noise is suppressed through phase coherent filtering, a signal is reconstructed through principal component analysis, and phase deviation is calibrated through fundamental frequency; then constructing a guiding matrix and decomposing a noise subspace, and extracting a coarse positioning result; and finally, high-precision angle optimization is realized based on a chaos initialization differential evolution algorithm, and the efficiency is improved by combining a dynamic search range and an early stop mechanism. According to the method, the anti-interference capability in a low signal-to-noise ratio environment is remarkably enhanced, the problems of missing detection and false detection during dense distribution of multiple sound sources are effectively solved, meanwhile, the positioning precision and the real-time performance are considered, and the method is suitable for acoustic fault detection of complex scenes such as power transmission line inspection.

The invention provides an ultra-short beam homogenization transmission line system and method based on a four-eight-pole composite magnet. The system comprises the four-eight-pole composite magnet, a system, a linear beam homogenization structure, a bent beam homogenization structure and secondary iron used for a beam homogenization transmission line. The system further comprises an ultra-short linear beam homogenization transport line, an ultra-short bent beam homogenization transport line, an ultra-short linear bent composite beam homogenization transport line and an ultra-short symmetrical bent beam homogenization transport line based on the homogenization structure and / or the dipolar iron. The method comprises an exciting current debugging method based on the four-pole and eight-pole composite iron and a linear type and bending type beam debugging method. According to the invention, the problem that the maximum enveloping part on the transmission line is occupied and the effect of octopole iron cannot be fully exerted is solved; the problems that the beam envelope of the position where the octopole iron is located is small, and the requirement for the field intensity of the octopole magnet is high are solved. The problem that the civil engineering shielded around the transmission line is more expensive because the transmission line is too long is solved.

The invention is suitable for the technical field of fault diagnosis, and provides a distributed power transmission line fault diagnosis method based on ground potential end transient information perception, and the method comprises the steps: collecting a broadband transient signal of each node ground potential end of a power transmission line; synchronously acquiring an electromagnetic transient signal and an environmental parameter through the composite sensing unit; performing dynamic filtering correction on the broadband transient signal according to a preset transmission line impedance matching model; a deep learning model is adopted to perform hybrid processing on the corrected broadband transient signals and electromagnetic transient signals, traveling wave head time-frequency features and power frequency abrupt change features are extracted, and fault probability and type classification results are output; verifying a fault positioning result through a multi-stage verification mechanism; the extracted characteristic quantity is preprocessed at the edge side of the detection terminal, and the processed data is transmitted to a master station system through an encryption protocol, so that the fault diagnosis accuracy of the distributed power transmission line is effectively improved.

The invention belongs to the technical field of fault detection, and relates to a high-voltage transmission line fault detection and identification method. The method comprises the following steps of: dividing candidate fault sections according to a topological structure by synchronously acquiring steady-state and transient-state traveling wave signals of key nodes of a power grid; identifying a fault by using a multi-feature fusion criterion and extracting traveling wave features; executing mode recognition-based steady-state section judgment and multi-terminal ranging and polarity verification-based transient positioning in parallel to generate two types of positioning result sets; and finally, carrying out intelligent fusion judgment on the two types of results under topological constraints. According to the method, the problems that a traditional single-point signal analysis method cannot determine a fault section, lacks space positioning capability and is insufficient in adaptability are solved, section-level accurate positioning of the fault is realized, and the positioning accuracy, robustness and fault processing efficiency are remarkably improved.

The invention relates to the technical field of target detection, in particular to a light-weight YOLO-based power transmission line fault inspection method for an unmanned aerial vehicle, which introduces RFCACConv convolution, the convolution introduces a receptive field optimization mechanism, provides a K3RFCA structure, effectively fuses shallow feature information and deep feature information, and provides a K3RFCA structure in a feature fusion stage. An improved KBiFPN is adopted to enhance the fusion capacity of distinguishing features, meanwhile, the module is further combined with K3Star, the adaptive capacity of the KBiFPN to objects with different scales is remarkably enhanced, the invention provides a novel multi-scale multi-dimensional cooperative attention module MMCA, the module fuses multi-scale convolution and space-channel multi-dimensional information, and the algorithm has the advantages that the algorithm is simple, the algorithm is easy to implement, and the algorithm is easy to popularize and use. According to the light-weight RKM-YOLO power transmission line fault inspection method, the detection precision reaches 90.7%, compared with an initial YOLOv8n model, the parameter quantity is reduced by 18.27%, meanwhile, the recall rate and mAP50 are both improved by 3.1%, and the detection speed meets the inspection requirement of an unmanned aerial vehicle.

The invention discloses a ground wire multi-machine synchronous traction pay-off scheduling method and system, relates to the technical field of high-voltage transmission line construction, and is used for solving the problem of insufficient multi-machine real-time tension synchronization precision in the existing method. A tension sensor and a speed sensor which are arranged on each traction device collect a tension value, a traction speed value and position coordinate data of a ground wire in real time, and transmit the data to a central scheduling controller; establishing a multi-machine unified time synchronization reference and tension reference model, calibrating a timestamp through a GPS module, and initializing a threshold value based on historical parameters; comparing tension values one by one to calculate a deviation vector sequence; activating a compensation algorithm to execute a speed fine adjustment and tension compensation instruction; iteratively calculating the feedback adjustment value until the deviation is stable; and integrating the synchronous data to the overall paying-off scheduling model to dynamically update the path planning and speed curve. According to the method and the system, the problem of insufficient multi-machine real-time tension synchronization precision in the prior art is effectively solved.

The invention discloses a linear beam homogenization transmission line debugging method based on a four-and-eight-pole composite magnet, and the method comprises the steps: adjusting the current of four-pole iron, and enabling the X-direction beam near the four-and-eight-pole composite magnet 1 to form a waist; the current of the quadrupole iron of the quadrupole-octupole composite iron 1 is adjusted, so that the Y-direction beam near the quadrupole-octupole composite iron 2 forms a waist; when the octupole field current of the fourth-and-eighth-level composite iron 1 is adjusted, the current of the intersection point is obtained for the second time; when the octupole field current of the fourth-and-eight-level composite iron 2 is adjusted, a method of combining intersection point current and quadrupole iron current increase is adopted. According to the invention, through a method of forming a waist in an X-direction beam near the four-eight-pole composite iron 1, forming a waist in a Y-direction beam near the four-eight-pole composite iron 2, and combining dynamic intersection points and adjustment of current of the four-pole iron, the problem that a transmission line based on discrete components of the four-pole iron and the eight-pole iron in the prior art is poor in transmission efficiency is solved. And the beam homogenization debugging of octopole iron is difficult to homogenize the Gaussian distribution beam.

The invention discloses a zero-sequence pilot-frequency excitation method for a double-circuit line in an incomplete power failure environment, which comprises the following steps of: applying zero-sequence pilot-frequency signal excitation on a power failure line by using a pilot-frequency power supply as an excitation signal source of a zero-sequence network of the double-circuit line, different-frequency induced current and voltage are measured at secondary sides of a current transformer and a voltage transformer at two sides of the operation line, and different-frequency zero-sequence voltage and different-frequency zero-sequence current in the double-circuit line zero-sequence network are obtained; simultaneously solving a zero-sequence network equation, and calculating mutual impedance between double circuits; performing frequency conversion to obtain the power frequency mutual impedance of the line; and finally, carrying out simulation verification by constructing a non-full-stop double-circuit line zero-sequence mutual inductance impedance measurement system. According to the method, the multi-path coupling caused by the existing power frequency interference method and the accuracy problem caused by the signal measurement error of the zero sequence increment method are solved, the test calculation can be accurately completed under the complex interference working condition, and the safety risk caused by the single-phase reclosing operation is avoided.

The invention discloses a linear polarization in-band scattering reconfigurable radiation time-sharing regulation array antenna, which belongs to the technical field of antenna engineering, and comprises an antenna unit and a one-to-sixteen power divider, the antenna unit is connected with the one-to-sixteen power divider, and the antenna unit is connected with the one-to-sixteen power divider. The antenna unit comprises a first radiation patch, a first dielectric substrate, a second radiation patch, a second dielectric substrate, a feed coupling branch knot, a feed metal column, a third dielectric substrate, a perforated metal floor and a fourth dielectric substrate, and a first PIN tube, a second PIN tube, a feed transmission line, a direct current bias device, a reflective phase shifter and a low-pass filter are arranged at the bottom of the fourth dielectric substrate; the direct-current bias device is connected with the reflection type phase shifter and the feed transmission line through the low-pass filter, and the feed metal column sequentially penetrates through the third dielectric substrate, the perforated metal floor and the fourth dielectric substrate and is connected with the feed transmission line. According to the invention, the normal radiation efficiency of the antenna can be ensured, and the reconfigurability of antenna scattering can be effectively improved.

The invention discloses a control and positioning method of an active variable-angle laser radar unmanned aerial vehicle, which comprises a variable-angle laser radar dynamic control module, a two-stage external parameter calibration module, a radar point cloud distortion removal module and a fusion positioning algorithm module. The optimization frequency of iterative Kalman filtering is dynamically adjusted, and the positioning precision under extreme working conditions such as complex electromagnetic interference and dense vegetation shielding is remarkably improved. In combination with a robust estimation strategy and a covariance convergence threshold, the system can quickly suppress sensor noise and environment dynamic interference, ensure small horizontal positioning error and elevation error values, and avoid convergence oscillation caused by overfitting. According to the mechanism, point cloud dispersion and rasterization density mapping are counted through a sliding window, dynamic balance of computing resources and positioning precision is achieved, and the mechanism is suitable for stable operation requirements of heterogeneous scenes such as power transmission line towers and tree canopies.

The invention discloses a power transmission line icing state detection method and system, and the method comprises the steps: obtaining an original icing image of a power transmission line, and calculating the equivalent icing thickness based on the original icing image; converting the meteorological data into a parameterized expression, and performing iterative optimization on parameters in the expression to obtain a composite correction factor; performing correction optimization on the equivalent icing thickness based on a composite correction factor to obtain a corrected icing thickness; according to the method, the preliminary equivalent icing thickness is corrected and optimized by constructing the environmental meteorological element composite correction factor, so that the icing state detection precision can be ensured in a complex environment.

The invention relates to the technical field of electromagnetic environment influence prediction, and particularly discloses an alternating current transmission line electromagnetic environment influence prediction and evaluation method. The method comprises the following steps: constructing a spatial region model; modeling and predicting the electric field intensity based on the spatial region model; based on the spatial region model, modeling and predicting magnetic induction intensity; determining a plurality of basic typical working conditions, and performing electromagnetic influence classification prediction; performing limit value comparison and space risk evaluation on the electric field distribution prediction data, the magnetic induction distribution prediction data and the classification prediction data; and carrying out prediction centralized induction and report output. According to the method, the phase balance charge distribution network is established, the mirror image charge is dynamically calibrated by combining actual measurement data of the ground sensor, and the adaptive charge safety envelope line is generated based on the road section type, so that the problem of electric field prediction deviation caused by neglecting space phase difference in the prior art is solved.

The invention discloses a power transmission line insulator multi-modal multi-type defect detection method based on an MEWL-YOLO model. The method comprises the following steps: acquiring a multi-modal power transmission line data set; constructing an insulator multi-type defect detection model of the MEWL-YOLO model; the method specifically comprises the steps that on the basis of a YOLOv10n network framework, a multi-head attention mechanism and a gated convolution feedforward network are combined, a multi-head gated convolution feedforward network MGDB module is designed, and a C2K layer is optimized; a multi-scale efficient attention mechanism (EMA) is inserted into the Neck part of the YOLOv10n network; an Inner-Wise-MPDIOU loss function is adopted to replace a traditional loss function; a detection head in a YOLOv10n network is replaced by an LSCD lightweight detection head, and defect detection is carried out on the power transmission line insulator based on an insulator multi-type defect detection model of a constructed MEWL-YOLO model. According to the method, the MEWL-YOLO model is provided to realize defect detection of the multi-modal power transmission line insulator image, the detection effect of multi-modal and multi-type defects is improved, and the method has good practical application value.

The invention discloses a power transmission line fault positioning method and system based on ground potential end transient information perception, and relates to the technical field of line fault positioning, and the method comprises the steps: obtaining a voltage traveling wave signal and a current abrupt change rate signal detected by a trunk line and branch node sensor array in a power transmission line; processing the voltage traveling wave signal and the current abrupt change rate signal to obtain traveling wave components and polarity-amplitude calibration results of different modes; according to traveling wave components and polarity-amplitude calibration results of different modes, in combination with topological parameters of the power transmission line and spatial distribution of a ground potential end sensor, adjusting edge weights or connection relationships in the graph structure to obtain a composite feature matrix and an edge weight matrix of each node; and inputting the composite feature matrix and the edge weight matrix of each node into a graph neural network positioning model, and outputting a power transmission line fault positioning result. According to the invention, through ground potential end information fusion and the dynamic graph neural network, accurate fault positioning under a complex power transmission topology is realized.

The invention relates to the technical field of power supply system health assessment, in particular to a health assessment method and system of a power supply system based on artificial intelligence. The method comprises the following steps: acquiring data of a power supply system, estimating initial performance parameters of the power supply system, evaluating an initial running state of the power supply system, and acquiring initial state data of the power supply system; performing power supply equipment load operation simulation based on the initial state data, and predicting a fault evolution trajectory; the transmission quality attenuation degree of the power supply system is estimated by analyzing the fault evolution trajectory of the power supply system, the loss increase trend of a transmission line is detected, and the performance decrease trend is evaluated in combination with the attenuation degree; evaluating health attenuation data of the power supply system according to the performance reduction trend; transmitting the data to an artificial intelligence model for deep learning training to obtain accurate power supply system health state evaluation data; according to the invention, health analysis is carried out on the power supply system, so that the power supply system is safer and more stable.