32316 results about "Transformer" patented technology

The invention provides an intelligent power distribution load prediction and adaptive scheduling method, and aims to improve the response capability and operation efficiency of a power distribution system to load fluctuation. According to the method, historical load data, real-time meteorological information and user power consumption behavior data of a target power distribution area are collected, a multi-dimensional input data set is established, a short-term load prediction model is constructed based on the data set, and prediction of future load levels of multiple feeder lines and key nodes is achieved. According to a prediction result, potential voltage deviation, load imbalance and equipment overload risks are identified, and a dynamic scheduling control strategy is generated by adopting a rolling horizon optimization method in combination with a power grid topological structure and operation constraints; the strategy comprises control measures such as transformer tap position adjustment and capacitor bank switching; through issuing a scheduling strategy to an equipment control unit, active adjustment is completed before load change, voltage stabilization, load balancing and energy optimization are realized, and intelligent and adaptive operation guarantee is provided for a power distribution system.

The present application relates to the technical field of load prediction, and provides an electrical load prediction method and device based on spatial-temporal correlation. The method comprises: performing spatial-temporal modeling on the basis of historical electrical load data and historical environment data to obtain a multi-layer spatial-temporal model, wherein each layer model is a directed graph and represents spatial causality in the fixed time, and a directed edge between the layer models represents temporal causality; on the basis of an interpretable spatial-temporal attention transformer, performing feature extraction and priori knowledge fusion on the multi-layer spatial-temporal model to obtain spatial-temporal characteristics of an electrical load, wherein the interpretable spatial-temporal attention transformer comprises a spatial causal attention network, a temporal attention network, and a spatial dependence comparison module; and on the basis of the spatial-temporal characteristics of the electrical load, and the real-time electrical load data and the real-time environment data of a target electrical node, predicting by using a trained STEF-DHNet model to obtain the predicted electrical load data of the target electrical node. The present application can improve the prediction accuracy and the model applicability.

The invention discloses an electric energy metering box fault prediction method and system based on big data analysis, relates to the technical field of smart power grids, and solves the problems of progressive aging missing detection and instantaneous interference misjudgment caused by dependence on single parameter threshold alarm and fault positioning misalignment caused by multi-source data isolated analysis in the prior art. According to the scheme, electrical, environment and equipment state parameters are collected in real time through a multi-dimensional sensing network; the error drift of the mutual inductor is dynamically predicted based on an LSTM-Kalman filtering model, and core breakdown early warning is realized in combination with wavelet transform; outputting a corrected resistance value and a fault mark by using a BP neural network; predicting the life of the piezoresistor by adopting a gradient boosting decision tree and fusing lightning overvoltage characteristics; the transient interference is suppressed through the combination of a Transform self-attention mechanism and dynamic time warping; according to the method, the aging detection precision and the complex environment adaptability are remarkably improved, the misjudgment rate is reduced, and the multi-fault associated positioning and active defense capability is realized.

The invention discloses a predictive maintenance strategy optimization method for transformer area equipment maintenance, relates to the technical field of transformer area equipment maintenance, and is used for solving the problem of poor maintenance effect of transformer equipment. Sensors are installed in a transformer area to collect current, voltage, temperature and other key data, preprocessing, frequency alignment and fusion are carried out to generate the same equipment state data, and when data fluctuation exceeds a threshold value, re-collection and calibration are triggered, a health state model is constructed, and a health index is calculated to evaluate the equipment state. The method comprises the following steps: predicting a future health state through an LSTM model, triggering early warning when the future health state is lower than a baseline, dividing risk levels according to health indexes and risk factors, setting maintenance priorities based on health assessment and fault prediction, applying a multi-objective optimization algorithm in combination with an emergency degree and equipment importance to assess resource requirements, and dynamically monitoring resource use conditions. The prediction precision is improved, and the equipment resource allocation efficiency and the maintenance response speed are improved.

Electric heat for generating smoke from tobacco or the like in a hookah type smoking pipe. Electric heat is obtained from an electrically powered heating element which may be placed proximate the tobacco. The heating element may be contained within a housing which in turn may be placed above the smoking chamber of the hookah. The housing may have adjustably damped holes disposed to pass air over the heating element. Electrical circuitry serving the heating element may comprise a step down transformer and a voltage adjusting switch. The heating element may be integral with the hookah, may take the form of a separate component which is mountable over the smoking chamber of the hookah, or may comprise a free standing assembly which may be placed to stand adjacent to the hookah.

The invention discloses a transformer substation electric power parameter real-time monitoring and analysis platform, and relates to the technical field of electric power monitoring. The monitoring analysis platform is in communication connection with a real-time monitoring module, a data acquisition and processing module, a data storage and management module, a fault feature extraction module, an event early warning module, a fault diagnosis module and a data visualization module. The transformer station electric power parameter real-time monitoring and analysis platform timely discovers abnormal conditions of electric power equipment by collecting and analyzing key parameters of voltage, current, power, temperature and humidity in real time, so that potential faults and accidents are prevented, and the real-time monitoring and analysis of the electric power parameters of the transformer station is realized by learning historical fault data and identifying modes. The platform can predict and identify an abnormal trend which may cause a fault, gives out an early warning in advance, remarkably improves the reliability and safety of a power system, reduces the risks of accidental power failure and equipment damage, and guarantees the continuity and stability of power supply.

The invention relates to the technical field of monitoring and early warning of power equipment, in particular to a monitoring and early warning method and system for substation power equipment. Comprising a data acquisition module, an intelligent diagnosis module, a risk assessment module, a collaborative early warning module and an interaction control module. The data acquisition module acquires equipment operation data including current, voltage, temperature and humidity and vibration signals. And the intelligent diagnosis module identifies equipment abnormity through the generative adversarial network and the graph neural network. The risk assessment module generates a risk assessment curve based on a fractional order differential model and a reinforcement learning algorithm. And the collaborative early warning module generates a graded early warning scheme by utilizing edge calculation. And the interaction control module displays a state and optimizes a control strategy through a human-computer interface. The intelligent monitoring and management level of the power equipment of the transformer substation is improved, the service life of the equipment is prolonged, and the operation and maintenance cost is reduced.

The invention discloses a fault detection method and device for secondary equipment of a transformer substation. The method comprises the following steps: acquiring real-time operation parameters of the secondary equipment of the transformer substation, inputting the real-time operation parameters into a pre-trained graph neural network model to obtain equipment operation characteristics, and determining an equipment state vector according to a plurality of equipment operation characteristics in a continuous time window and a message passing mechanism; determining an equipment health degree score according to the equipment state vector and a knowledge graph, and triggering the intelligent inspection robot to execute a deep detection task under the condition that the health degree is lower than a preset health degree threshold value so as to collect target test data; determining a target fault type according to the target test data, the knowledge graph and the inference rule base, obtaining a fault processing scheme corresponding to a historical fault case, and generating corresponding maintenance guide information according to the fault processing scheme, thereby achieving the real-time detection, precise fault diagnosis and automatic maintenance of the secondary equipment of the transformer substation. And the operation safety and the maintenance efficiency of the power system are improved.

The invention discloses a self-adaptive frequency spectrum monitoring and interference suppression method for a railway power transformer. The method comprises the following steps: S1, collecting original multi-source signal data; s2, performing high-order filtering and Z-score normalization processing on the original multi-source signal; s3, inputting the original multi-source signal into a multi-scale residual fusion time-frequency transformation network, and extracting a time-frequency feature tensor; s4, inputting the time-frequency feature tensor into the interference identification network fused with the attention mechanism; s5, dynamically activating an interference suppression module according to an identification result; s6, constructing a multi-dimensional tensor data structure, extracting sparse dictionary morphological features and spectral domain statistics, and generating a composite feature vector set; s7, inputting the composite feature vector into a health state evaluation module; and S8, uploading the diagnosis result to a remote monitoring platform through the embedded communication module. According to the invention, multi-dimensional perception and adaptive modeling are fused, and intelligent identification and remote monitoring of railway transformer faults are realized.

The invention relates to the technical field of transformer harmonic monitoring, in particular to a harmonic detection and early warning system for an intelligent transformer, which obtains voltage and current signals of the transformer in real time, performs frequency domain decomposition by using Fourier transform, extracts multi-dimensional harmonic characteristics such as amplitude, phase, frequency and the like, and performs harmonic detection and early warning on the intelligent transformer. And inputting into a deep learning model to train and generate a dynamic harmonic characteristic model. On the basis, a harmonic wave anomaly classification module analyzes harmonic wave signals in real time, carries out type judgment and position association on abnormal harmonic waves, and generates a report containing anomaly types and positions. Through a dynamic early warning module, a power grid load coefficient is calculated based on power grid load state parameters collected in real time, and a harmonic detection threshold is dynamically adjusted in combination with an initially set amplitude and frequency threshold. The application of the dynamic threshold enhances the adaptability of the system to the load state change, and significantly improves the sensitivity and accuracy of harmonic detection and early warning.

The invention relates to the technical field of power system fault monitoring, and discloses a 10kV voltage transformer ground fault real-time monitoring method and system, and the system comprises a ground fault data collection module, a waveform distortion analysis module, a transient signal detection module and the like. Three-phase current, zero-sequence current and voltage waveform data are collected, a current harmonic component proportion, a zero-sequence current high-frequency transient energy value and the like are analyzed, a fault type is determined by combining a phase difference, and a fault position is positioned according to topological parameters and line impedance. And the temperature of the grounding down lead can be monitored, and the fault probability is evaluated and the threshold value is dynamically adjusted by machine learning. According to the invention, real-time accurate monitoring can be realized, fault types and positions can be accurately diagnosed, potential hidden dangers can be comprehensively monitored, the intelligence of a monitoring system is improved, the operation and maintenance cost and risks are effectively reduced, and safe and stable operation of the 10kV power distribution network is ensured.

The invention discloses a low-voltage transformer area examination meter practical training device and a fault simulation control method, and relates to the technical field of electric power practical training. The device aims at the problems of single scene, extensive evaluation and low skill improvement efficiency in traditional electric power practical training. The meter simulation module is compatible with various meters and is matched with a dual-communication protocol to realize efficient data transmission; the intelligent interaction module provides high-quality interaction experience through a high-definition touch screen and low-delay transmission; the data generation module can quickly switch customized scenes, and the fault simulation module can accurately inject various faults; the training management module depends on advanced technologies of knowledge maps, deep learning and fuzzy comprehensive evaluation to realize full-process closed-loop management from project design and operation guidance to evaluation feedback and intelligent optimization; all the modules cooperate efficiently, and the professionality, the accuracy and the efficiency of power failure troubleshooting and operation and maintenance skill training of trainees are remarkably improved.

The invention discloses a power transformer partial discharge positioning method based on multi-sensor array fusion, and the method comprises the following steps: S1, selecting a sensor installation point, and laying a multi-sensor array structure; s2, partial discharge signals of the three types of sensors are collected, and primary signal processing is carried out; s3, calculating propagation time differences between the reference channel and other channels by adopting a generalized cross-correlation weighting algorithm, and generating a time difference matrix; s4, constructing a TDOA model in combination with the layout coordinates and the time difference matrix, and solving three-dimensional initial coordinates of a power supply; s5, establishing a structure correction model, compensating the path deviation, and outputting corrected positioning coordinates; s6, calculating an error and generating a confidence score; s7, mapping a positioning result to the three-dimensional model and generating an image; and S8, writing the positioning information into a database for filing management. According to the method, the multi-frequency sensor and the path correction model are fused, and high-precision three-dimensional positioning of partial discharge of the transformer is realized.

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

The invention provides a power supply energy analysis method and system for a medium-voltage power distribution network. The method comprises the following steps: collecting multi-modal data of the medium-voltage power distribution network; constructing a health state evaluation model, and predicting a health score and a fault risk of the transformer of the medium-voltage power distribution network according to the fusion features after dimension reduction; constructing an optimization target of the power electronic equipment of the medium-voltage distribution network according to the health score and the fault probability of the transformer; constructing a multi-modal load prediction model to predict the load change of the power electronic equipment in the future; constructing a multi-objective optimization function, and generating an optimal scheduling strategy; and executing the optimal scheduling strategy to actually control the medium-voltage power distribution network equipment. According to the method, the defects of a traditional method in the aspects of equipment monitoring, load prediction and scheduling optimization are overcome, and the overall management level and coping capacity of a power grid are improved by introducing a leading-edge artificial intelligence algorithm and a data fusion technology.

The invention discloses a deep learning-combined power transformer partial discharge signal extraction and diagnosis method. The method comprises the following steps of S1, setting a multi-channel synchronous acquisition system in a power transformer body area to acquire a multi-dimensional original partial discharge data set; s2, preprocessing the acquired multi-dimensional original partial discharge data set; s3, performing time alignment and amplitude matching on the processed signal, and dividing the processed signal into a sliding time window to construct a standard input tensor; s4, constructing an attention enhancement model fused by the convolutional neural network and the bidirectional gating circulation unit; s5, performing supervised training on the attention enhancement model by using the labeled sample; s6, inputting the real-time signal into the training model, and outputting a discharge type label; s7, risk grade evaluation is carried out in combination with statistical characteristics; and S8, generating a structured diagnosis report and uploading the structured diagnosis report to a monitoring platform. According to the invention, multi-source signals and a depth model are fused, and intelligent diagnosis and risk assessment of transformer partial discharge are realized.

Disclosed are techniques for combining data using transformer-based machine learning models. In some examples, a first transformer is used to combine a first dataset with a second dataset. The results are then combined with a third dataset, using a second transformer. Each dataset can represent data from a different sensor modality. The transformers compute scores based on queries and apply the scores to values. The first dataset can be used to generate queries for the first transformer, and the values for the first transformer can be derived from the second dataset. Similarly, the third dataset can be used to generate queries for the second transformer, and the values for the second transformer can be derived from the output of the first transformer. The output of the second transformer is therefore a combination of all three datasets and can be used for object detection, for example, determining three-dimensional boundaries of objects.

The invention relates to the technical field of abnormity early warning, in particular to a transformer load abnormity early warning method and system, and the method comprises the following steps: obtaining an electrical parameter sequence and correcting a phase, constructing a current and power change rate sequence, extracting a load fluctuation trend factor, calculating a disturbance growth rate and judging whether the disturbance growth rate crosses a boundary, and recognizing a power gradient abrupt change feature. And judging whether periodic overlapping exists or not, and outputting a combined abnormity early warning identification sequence. According to the invention, by introducing phase difference and time base offset calculation, constructing a phase-aligned electrical parameter sequence group, and analyzing a periodic rate mean value and an integral quantity, disturbance trend evaluation is realized, a power gradient sequence is used to identify a section mutation interference signal, and a joint anomaly identification mechanism is constructed on two dimensions of a period and a section. The method achieves the composite judgment of the load disturbance trend and sudden change interference, effectively improves the accuracy and response time efficiency of load abnormity early warning, and reduces the recognition error risk caused by hidden fluctuation or local sudden change.

A transformer fault diagnosis method based on vibration mechanisms of an iron core and a winding. The method comprises: performing signal processing and feature extraction by means of calling simulation results of an iron core and a winding; and establishing a transformer fault diagnosis model. Thus, the accurate diagnosis of a transformer fault is realized. A conventional fault diagnosis method is improved, and chaotic mapping and an opposition-based learning strategy are introduced to optimize the diagnosis model, thereby improving the accuracy of diagnosis. Compared with the conventional method, a fault type and a fault degree of a transformer can be more accurately determined, so as to facilitate an engineering technician in maintenance, thereby improving the operation reliability of the transformer. In addition, a transformer fault diagnosis system based on vibration mechanisms of an iron core and a winding, and a computing device and a computer-readable storage medium are further provided.

The invention relates to the field of transformer parameter regulation and control, in particular to a parameter optimization method and device for a coupling type transformer. The method comprises the following steps: acquiring multi-dimensional real-time working monitoring parameters and real-time working environment state parameters of the coupling type transformer; performing nonlinear dynamic analysis and global state sensing modeling on the multi-dimensional real-time working monitoring parameters so as to construct a transformer global state sensing model; performing multi-parameter combination definition on the multi-dimensional real-time working monitoring parameters, performing dynamic operation simulation based on a transformer global state sensing model, and collecting operation simulation response data of a plurality of schemes; performing multi-target dynamic balance analysis on the operation simulation response data of the plurality of schemes to obtain an optimal transformer working parameter combination; and carrying out environment dynamic parameter change analysis on the real-time working environment state parameters, and constructing a real-time environment condition distribution field. According to the invention, efficient and self-adaptive adjustment transformer parameter optimization is realized.

The invention relates to the technical field of transformer monitoring, and discloses an explosion-proof intelligent monitoring and early warning device for a transformer. The device comprises a multi-source sensing module used for collecting multi-dimensional heterogeneous data of transformer operation; the feature extraction module is used for fusing data cross-domain features to generate various feature representations; the anomaly detection module is used for generating an abnormal signal space-time incidence matrix based on a dynamic causal network construction model; the risk early warning module outputs a risk level and an early warning instruction through a multi-task decision-making mechanism; and the self-adaptive regulation and control module is used for optimizing monitoring parameters and hardware resource allocation according to instructions. The device also can carry out critical state identification and emergency intervention, and constructs an insulation degradation prediction model to correct an early warning threshold value. According to the device, omnibearing monitoring, accurate early warning and intelligent regulation and control of the transformer are realized, the operation safety and reliability of the transformer are effectively improved, and the fault risk and loss are reduced.

The invention relates to the technical field of intelligent control of transformer substations, in particular to a standardized intelligent control system of a transformer substation, which realizes real-time response to the running state of equipment by acquiring the current, voltage, power factor, temperature and humidity, operation state, insulation state and environment temperature and humidity of a power transformation unit in real time. A gradient boosting decision tree is combined with a variational auto-encoder and a self-attention mechanism, state vector features are extracted, complex abnormal states are accurately recognized, potential faults are detected in an early stage, and the monitoring efficiency and the early warning precision are greatly improved. The future power demand is accurately predicted by combining the long-short-term memory network model with historical and real-time load data, and the limitation of traditional static load management is overcome. Based on the predicted power demand, the reinforcement learning model is adopted to dynamically adjust the operation number of the power transformation units, optimal configuration of resources is achieved while the demand is met, and the overall operation efficiency of the transformer substation is remarkably improved.

The invention relates to the technical field of power transformer discharge fault diagnosis in a power supply system, in particular to a power transformer discharge fault diagnosis method based on a neural network, and the method comprises the steps: collecting a partial discharge signal, characteristic gas data in oil and power supply system operation parameters in real time through a sensor, and unifying timestamps; statistical aggregation is performed on the partial discharge signals, and standardization processing is performed on the electrical quantity parameters and the discharge characteristic parameters to generate fusion characteristic vectors; constructing an expansion mapping relation library containing laboratory simulation data and actual operation data; training a diagnosis model by adopting a mixed structure of a convolutional neural network and a long-short-term memory network; and deploying the model in an edge computing device, and dynamically verifying and optimizing the performance of the model through online learning. According to the method, the problems of low multi-source data fusion efficiency and insufficient generalization ability of a neural network model to a complex discharge mode are solved, and the operation stability of a power supply system is improved.

The invention discloses a collapse geological disaster vulnerability model and risk assessment method and system, and relates to the technical field of geological disaster risk assessment, and the method comprises the steps: collecting and preprocessing target rock mass information; collecting monitoring data and long-term rainfall data of the three-axis acceleration sensor; the acceleration fluctuation characteristics are analyzed, and the risk of rock mass instability is recognized in combination with rainfall data; the instability risk of the rock mass is quantified based on a Monte Carlo method; predicting a motion path and a stacking range of the collapsed rockfall by utilizing numerical simulation, and evaluating threats of the collapsed rockfall to surrounding electric power facilities; and performing level-to-level management on the surrounding area of the transformer substation through the risk level model. Real-time monitoring, probability calculation and numerical simulation technologies are adopted, and the problem that in the prior art, power facilities lack dynamic monitoring, accurate evaluation and graded protection is solved. According to the overall scheme, real-time and automatic monitoring and risk grading of collapse geological disasters are achieved, and the safety and reliability of electric power facilities under the disaster condition are effectively improved.

The invention relates to the technical field of industrial component detection, in particular to a transformer iron core detection method based on computer vision, which comprises the following steps of: acquiring an iron core image, extracting key pixel characteristics, screening a directional scattering abnormal region to generate an interference map, extracting a consistent gradient region correction image to generate a reconstruction map, and positioning a symmetric disturbance generation structure map by integral gray difference. And analyzing an overlapping relation by a superposition structure graph to generate an abnormal component graph, and evaluating a risk level by matching a reference index to generate an early warning graph layer. Interference reflection and structural features can be distinguished through linkage analysis of the pixel direction vector and the brightness change frequency, correction of a distorted area in an image is realized based on a gray statistical stable value, and the distortion of the image is corrected by constructing a symmetric point map and analyzing the change trend of a gradient difference value sequence. And the structural overlapping relation is quantitatively judged by combining a component mapping profile diagram, so that the relevance between an abnormal region and a key component is clearly expressed, and the grading evaluation capability of various fault risks in the iron core is improved.

The invention relates to a low-rank fine-tuning transformer fault diagnosis method based on adaptive attention guidance, and belongs to the technical field of artificial intelligence. An attention scoring mechanism, an adaptive attention scoring mechanism, a dynamic rank allocation strategy and a hierarchical learning rate adjustment mechanism are introduced, and a context-aware dynamic updating strategy is further fused, so that the low-rank fine-tuning transformer fault diagnosis method based on adaptive attention guidance is realized. According to updating of real-time performance, loss and gradient dynamic intelligent triggering key parameters in the model training process, a large-model lightweight adaptation frame suitable for a transformer fault diagnosis task is constructed, and on the premise that diagnosis accuracy is ensured, model fine adjustment and deployment cost is remarkably reduced.

The invention relates to a transformer area self-healing optimization control method and system based on flexible interconnection. The method comprises the following steps: S1, constructing an operation monitoring network based on equipment ledgers in a transformer area; s2, deploying flexible interconnection equipment at key nodes of a transformer area to realize fault isolation and power failure transfer, dynamic load transfer and voltage regulation, and flexible cooperative control of a distributed power supply and energy storage equipment; s3, introducing an equipment state evaluation model based on the transformer area operation data obtained by the operation monitoring network, and obtaining state evaluation and risk early warning of each equipment in the transformer area; s4, constructing a flexible interconnection dynamic topology management mechanism between the stations, and dynamically adjusting the interconnection topology between the stations according to the load transmission demand and the power supply condition; and S5, based on state evaluation and risk early warning of each device, performing load regulation and control and fault recovery according to a dynamic topology management mechanism of flexible interconnection. According to the invention, efficient and intelligent transformer area operation and power supply reliability improvement are realized.

The invention discloses a multi-dimensional information integrated transformer health state monitoring method, which relates to the technical field of power systems, and comprises the following steps: deploying a multi-source sensor, collecting various signals of a transformer, and carrying out signal conditioning and analog-to-digital conversion on analog signals collected by the sensor; performing timestamp alignment on the received multi-source heterogeneous data, and eliminating data noise by adopting a filtering algorithm; key features which are sensitive to the health state of the transformer and complement each other are selected from the extracted feature values to form a health state evaluation index set; setting a sliding time window, obtaining historical data in the window, performing normalization processing on the data, calculating the information entropy and the variation coefficient of each index, and fusing the information entropy and the variation coefficient to obtain the dynamic weight of each index; obtaining a health state index of the transformer according to the normalized value and the dynamic weight; and displaying the health state index and the change trend thereof in real time, and setting an early warning threshold value and an alarm threshold value of the health state index.

The invention discloses a transformer partial discharge detection method and system based on multi-modal feature combination, and relates to the technical field of transformer detection, and the key points of the technical scheme are that the method comprises the steps: collecting an electromagnetic wave signal, a pulse current signal and a sound wave signal of a target transformer; respectively extracting an electromagnetic wave time-frequency matrix, a pulse current waveform parameter and a sound wave space coordinate; performing multi-modal feature fusion on the electromagnetic wave time-frequency matrix, the pulse current waveform parameters and the sound wave space coordinates based on a dynamic weighting strategy to generate a joint feature tensor; and inputting the joint feature tensor into a pre-constructed neural network model, identifying a discharge type and optimizing a positioning coordinate. According to the method, multi-modal features are subjected to tensor fusion, cross-modal correlation learning is realized through the neural network model, information islands can be eliminated, and the misjudgment rate is effectively reduced; and electromagnetic wave-current-sound wave signal adaptive fusion is realized through a dynamic weight coefficient, and the signal-to-noise ratio is obviously improved through characteristic complementation in an oil paper insulation discharge scene.

The present invention relates to the technical field of secondary device monitoring. Disclosed are an online monitoring method for an operating state of a secondary current loop neutral line, and a system. The method comprises: by using a high-precision sensor for collection and a composite filtering algorithm for processing, obtaining a secondary current fundamental component of each neutral line of a transformer substation which is not influenced by harmonic waves and transient states, and uploading the secondary current fundamental component to a protection information master station; retrieving originally configured access protection information master station information to obtain three-phase secondary current loop information of each bay of the transformer substation, summarizing secondary current information of all monitoring ranges, and performing grouping and classification marking on said information by transformer substation, bay and winding; and using the collection information for operation analysis, automatically identifying and confirming an operating state of the line, and on the basis of the operating state, calculating the outlier deviation linearity and the continuous dynamic trend deviation degree of each winding of each bay. The present invention solves the problem that intermittent faults in secondary current loop neutral lines are concealed and are not easily found, realizes comprehensive monitoring for the whole system, and can quickly and accurately position a specific winding and issue an abnormal early warning.