3925results about "Fault location" patented technology

The invention discloses a distribution line fault intelligent diagnosis method based on multi-source information fusion, and belongs to the technical field of power system fault detection and diagnosis, and the method comprises the steps: obtaining distribution line data and meteorological data; superposing the distribution line data with the meteorological data to generate a space-time fusion feature matrix based on the wire space displacement; dynamically adjusting a preset initial threshold value by using the space-time fusion feature matrix; and performing multi-round verification on the abnormal fluctuation of the electrical parameters of the line according to the fault judgment threshold, generating a fault positioning instruction, performing real-time matching with the topological structure of the line, correcting the priority queue of the fault section based on the switching action feedback data of the feeder terminal, and triggering the fault removal action matched with the priority. According to the method, spatial-temporal feature fusion of current, voltage, weather and spatial displacement data and a dynamic threshold adjustment mechanism are adopted, the fault type can be accurately identified, the fault section can be positioned in real time, and the diagnosis precision and the response speed in a complex environment are remarkably improved.

The invention provides a power distribution network optical fiber composite fault intelligent positioning and diagnosis method based on hybrid sensing, and relates to the technical field of power distribution networks, and the method comprises the steps: obtaining and preprocessing optical fiber and electrical sensing data, extracting time-frequency domain features through a multi-channel convolutional neural network, calculating a dynamic weight coefficient through an attention mechanism, and carrying out the feature fusion, a dynamic fault propagation graph is constructed, spatial-temporal feature extraction is performed by combining graph convolution and causal convolution, and fault positioning and type identification are realized. The method can make full use of multi-source heterogeneous data, improves the fault positioning precision and diagnosis reliability, and is suitable for fault diagnosis of a complex power distribution network.

The invention relates to the technical field of cable operation state detection, and particularly discloses a multi-source data driven cable operation state comprehensive evaluation method, which comprises the following steps of S1, adopting a layered distributed sensing network architecture, and deploying three types of core sensors at key nodes of a cable, through space-time calibration of the multi-source heterogeneous sensor, data consistency is improved, fusion deviation is eliminated, the problem of data islands of a traditional system is solved, and a precise evaluation foundation is laid; noise suppression and dynamic correlation modeling are adopted, environmental interference is stripped, a vibration and displacement coupling relation is quantified, limitation of a single parameter is broken through, heterogeneous fault features are captured, and evaluation comprehensiveness and sensitivity are improved; a self-adaptive threshold mechanism is constructed based on environment weight and historical data, the bottleneck of a fixed threshold is broken through, an evaluation standard is corrected along with equipment aging and environment change, misjudgment is avoided, and diagnosis robustness in different scenes is enhanced.

The invention provides a power cable joint insulation state intelligent monitoring method based on a complex environment. The method belongs to the technical field of power system monitoring, and comprises the steps that related data are collected through multiple means, and the related data comprise environment temperature data, partial discharge signals and temperature distribution images; collected environment parameters and partial discharge signals are input to an intelligent analysis system, preprocessing and feature extraction are carried out, an insulation state prediction model is established based on a machine learning algorithm, and the aging degree and potential fault points of a cable joint insulation layer are predicted according to historical data and real-time monitoring data. Environment temperature data, partial discharge signals and temperature distribution images are collected through multiple means, and an intelligent analysis system and a machine learning algorithm are combined, so that the aging degree and potential fault points of the cable joint insulating layer can be predicted more accurately.

The invention provides a power grid fault waveform identification and intelligent relay protection rapid control method and system, and relates to the technical field of electric power, and the method comprises the steps: collecting the voltage and current waveform data of a power grid, carrying out the digital filtering and noise elimination processing, carrying out the Fourier transform and wavelet transform, extracting the time domain and frequency domain features, and constructing a combined feature vector. And a deep learning model including a convolutional neural network and a long-short-term memory network is used to identify and evaluate the type, position and amplitude of the power grid fault based on the feature vector database. And then, a multi-target particle swarm optimization algorithm for constructing a target weight matrix based on historical fault data is adopted to optimize fault isolation time, a power supply recovery rate and system stability to obtain a control strategy, and a relay protection device setting value is adjusted according to the control strategy to execute fault isolation and power grid topology reconstruction.

The invention discloses a power system Internet of Things intelligent detection method, and relates to the technical field of power detection, and the system comprises the following components: S1, data collection and preprocessing, S2, big data analysis and feature extraction, S3, equipment life prediction model construction and training, S4, fault prediction and evaluation, S5, predictive maintenance strategy making, and S6, adaptive adjustment. According to the method, the equipment life prediction model of deep neural network self-learning modeling is adopted, the quantum optimization algorithm is used for optimization, accurate prediction of the residual life and the fault occurrence probability of the power equipment is achieved, and meanwhile, the power equipment state comprehensive evaluation model based on the combination of fuzzy logic and the evidence theory is combined, so that the power equipment state comprehensive evaluation efficiency is improved. The equipment health state grade can be comprehensively evaluated, a scientific basis is provided for formulating a predictive maintenance strategy, a maintenance plan can be reasonably arranged in advance, the maintenance cost is reduced, the service life of equipment is prolonged, and economic benefits and social benefits of a power system are improved.

The invention relates to the technical field of data processing, and discloses a comprehensive evaluation method and system for the health degree of a distribution network cable. The method comprises the following steps: collecting cable joint multi-source monitoring signals, normalizing the monitoring signals to obtain a degradation degree feature vector, correcting multi-physics field coupling model parameters, obtaining a recessive degradation index through finite element calculation to obtain an enhanced feature vector, and performing time-frequency domain decomposition to extract multi-scale feature parameters to obtain a comprehensive feature matrix; a double attention mechanism calculates a feature weight and a time sequence correlation degree to obtain a deterioration trend prediction value, and fuzzy integral is fused with a multi-classifier output probability to obtain a health degree evaluation grade and an early warning result. According to the invention, the early defect identification accuracy and the degradation trend prediction precision are improved.

The invention relates to the technical field of power distribution protection cabinet fault diagnosis, and discloses an intelligent fault diagnosis and rapid isolation method for a power distribution protection cabinet, and the method comprises the steps: synchronously collecting electrical parameters, environment states and equipment motion signals in the cabinet as multi-source monitoring data when the power distribution protection cabinet operates; parameter fluctuation, state change and action time sequence features are extracted after preprocessing, cross-data association analysis is carried out through a multi-dimensional association model to generate an association feature set, a fault diagnosis information set is formed based on the identified abnormal types, fault positions and development trends, an isolation strategy is formulated according to severity levels and influence ranges, and a fault diagnosis result is obtained. And generating a rapid isolation instruction and dynamically adjusting along with fault evolution. According to the method, multi-source data fusion analysis is realized, faults can be accurately diagnosed and rapidly isolated, the operation safety and reliability of the power distribution protection cabinet are improved, and the method is suitable for fault processing of the power distribution protection cabinet in a power system.

The invention relates to the technical field of fault analysis, in particular to a power grid fault intelligent diagnosis and analysis system which comprises a fault signal detection module, a propagation path analysis module, a fault positioning calculation module, a fault type analysis module and a fault influence analysis module. According to the invention, by monitoring the voltage transient data of the plurality of nodes of the power grid in real time, the instantaneous offset and the time sequence difference of the voltage can be accurately calculated, so that the voltage mutation exceeding the preset threshold value can be quickly identified in the continuous time window, the early identification of the power grid fault is quicker, the risk of false alarm is reduced, and the accuracy of the early identification is improved. By analyzing the propagation path and speed of the sudden change quantity along the power grid, the fault positioning precision is improved, rapid and accurate fault source tracing is realized, and by careful analysis of the current waveform of a fault point and combination of real-time tripping current data, the fault type can be identified more accurately, the fault handling strategy can be optimized, and the fault handling efficiency is improved. And the power grid fault processing efficiency and safety are improved.

The invention relates to the technical field of fault diagnosis, in particular to an AI-based power system fault diagnosis and early warning system, which comprises a data acquisition module, a data processing module, a fault mode identification module, a fault diagnosis module, an early warning module and a decision support module, multi-source data related to power system fault diagnosis are collected in real time, the data processing module carries out preprocessing and feature extraction on the collected data, and the fault mode recognition module constructs a fault mode recognition model based on a long short-term memory network, a random forest and a convolutional neural network and recognizes a fault mode. The fault diagnosis module carries out fault diagnosis based on real-time data, the early warning module carries out early warning on possible faults in advance according to a diagnosis result, and the decision support module generates an emergency scheduling scheme according to fault diagnosis and early warning information. And the stability and safety of the power system are improved.

The invention discloses a distribution network cable dielectric loss withstand voltage partial discharge intelligent detection and evaluation method and system, and relates to the technical field of power equipment state monitoring, and the method comprises the steps: synchronously collecting a dielectric loss spectrum, a withstand voltage leakage current curve and a partial discharge spectrum of a distribution network cable through a phase resolution measurement device; processing the partial discharge spectrum by using a frequency domain adaptive filter to generate a denoised partial discharge characteristic spectrum; constructing a cable multi-dimensional parameter normal domain hyperspherical model, and calculating a deviation distance value; and inputting the deviation distance value into a fuzzy inference engine, performing nonlinear conversion according to a preset fuzzy rule base, generating an insulation state evaluation index and a fault risk probability distribution diagram of the distribution network cable, and realizing accurate evaluation of the cable insulation state. According to the invention, the accurate evaluation of the cable insulation state and the accurate positioning of the fault position are realized, and the operation reliability and maintenance efficiency of the power distribution network are improved.

The invention discloses an anomaly detection and intelligent diagnosis method, system and device based on digital power grid multi-source data and a medium, and belongs to the technical field of anomaly detection, and the method comprises the steps: obtaining multi-source operation data from a power grid operation process, carrying out the preprocessing, and generating a standardized data set; time sequence features are extracted based on historical data, a power grid state reference model is constructed, and normal operation states in different load scenes are represented; on the basis of deviation calculation of the standardized data set and the power grid state reference model, abnormal candidate signals are detected, and high-confidence-coefficient abnormal signals are screened and generated; determining an abnormal source based on the high-confidence abnormal signal in combination with a power grid topological structure, and performing analysis to obtain fault type information; and generating a control instruction according to the fault type information and issuing the control instruction to a power grid control system. According to the method, a complete technical scheme of multi-dimensional data fusion, dynamic deviation detection, high-confidence anomaly screening, anomaly source accurate positioning and fault type rapid diagnosis is realized.

The invention discloses a cable fault monitoring method and device based on multivariate time series data fusion, and relates to the technical field of cable multi-parameter fault monitoring, and the method comprises the steps: obtaining key time series characteristics representing the health state of a cable; dividing health state grades of the cable according to the electrical parameters and the environmental parameters, performing model training by taking the key time sequence characteristics and the health state grades as input parameters based on a multivariate neural network algorithm, and constructing a health assessment model; predicting the real-time electrical parameters and the environment time sequence parameters of the to-be-tested cable based on the health assessment model, and outputting a health state grade result of the to-be-tested cable; responding to the health state grade result and sending out a corresponding fault monitoring alarm instruction; the problem that the fault judgment condition is unreliable due to the fact that the time sequence dynamic characteristics of monitoring data are not considered in a monitoring method is solved, and the reliability, accuracy and detection efficiency of cable fault monitoring are remarkably improved.

A system-side harmonic impedance estimation method and apparatus, and a storage medium and an electronic device. The method comprises: acquiring an h-th harmonic parameter equivalent circuit simplified on the basis of power grid characteristics, h being a positive integer greater than or equal to 2 (S1); on the basis of the h-th harmonic parameter equivalent circuit, establishing a system-side harmonic impedance estimation mathematical model (S2); acquiring amplitude and phase data of a harmonic voltage and a harmonic current at a PCC (S3); dividing the amplitude data of the harmonic voltage at the PCC into several stable intervals (S4); on the basis of the amplitude and phase data of the harmonic voltage and the harmonic current in the several stable intervals, establishing a variable coefficient regression model between a harmonic voltage phasor and a harmonic current phasor (S5); selecting an optimal window width of a kernel function of the variable coefficient regression model (S6); and calculating a system harmonic impedance on the basis of the variable coefficient regression model and the optimal window width (S7).

The invention relates to the technical field of cable fault detection, in particular to a cable fault analysis system based on big data, which comprises a disturbance feature extraction module, a time sequence hierarchy judgment module, a reflection characteristic analysis module, a topological section identification module and a fault path mapping module. According to the method, gradient inflection points are extracted by calculating voltage and current increment included angles at adjacent moments, spatial-temporal characteristics of disturbance signals are correlated, interference of noise to a starting point mark is reduced, time difference is fitted in combination with cable length, deviation over-threshold abnormal points are dynamically filtered, influences of synchronization errors on sorting are eliminated, and fault path mapping stability is improved; the method comprises the following steps: generating characteristic parameters by fusing amplitude values and wave crest intervals of main waves and reflection waves, quantifying amplitude attenuation and phase offset coupling, enhancing the multi-path superposed signal separation capability, judging the reflection characteristic similarity of a physical section based on a Manhattan distance and a support vector machine, and dynamically dividing a fault section in combination with line topology. The problem of fuzzy positioning in a branch parallel scene in a traditional method is solved.

The invention provides a distribution line fault location optimization method based on single-ended traveling wave location, and relates to the technical field of traveling wave detection. The method comprises the following steps: carrying out segmented modeling on a line, calculating the traveling wave propagation speed and wave impedance of each segment, and correcting the traveling wave propagation speed and wave impedance; collecting a voltage / current traveling wave signal of a detection point, and extracting a line mode component; performing wavelet transformation on the line mode component, detecting the wave head position of the initial traveling wave through a modulus maximum search algorithm, and recording the arrival time of the initial traveling wave, a corresponding first energy value and polarity; calculating a reflected wave time window based on segmented modeling parameters, screening reversed polarity waves with qualified energy attenuation, and calculating a fault distance; if no effective reflected wave is detected in the reflected wave time window, triggering a pseudo double-end mode; a result is verified through quadruple constraints; the problems of poor parameter adaptability, low signal processing precision, strong reflected wave dependence and incomplete verification in traveling wave distance measurement of a complex distribution line are solved, and the accuracy and reliability of fault distance measurement are improved.

The present invention discloses a method and system for smart grid fault self-healing based on generative artificial intelligence, which relates to the field of smart grid fault self-healing. The method includes: obtaining and preprocessing multi-source heterogeneous data; generating a pre-trained language model and performing domain adaptability fine-tuning, adding a task-specific output layer; performing feature fusion using a multimodal data fusion method, and using annotated historical fault data sets for supervised training to obtain fault diagnosis results. Fault self-healing is modeled as a Markov decision process, and a deep reinforcement learning algorithm is used to interactively learn the optimal decision-making strategy with a power grid simulation environment, and an optimal fault self-healing plan is generated and converted into specific operation instructions. The present invention integrates multi-source heterogeneous data, performs accurate positioning and description generation, improves the reliability and self-healing ability of the smart grid, and ensures rapid recovery of normal operation when a power grid fails.

The invention provides a power distribution network line fault testing and positioning method based on optical fiber pulse transmission, and relates to the technical field of power distribution networks, and the method comprises the steps: obtaining a phase voltage signal, a phase current signal and an optical fiber transmission signal of a power distribution network line, and judging a fault through wavelet transformation; when a fault exists, probe light is generated by using a narrow linewidth laser, a scattering signal is obtained after modulation of a Bragg grating array, and gain adjustment and coherent demodulation are performed on the scattering signal to determine a mutation point; based on the catastrophe point, a reflection signal is obtained through bidirectional reflection measurement, and the fault position is determined through combination of a group velocity dispersion equation and self-adaptive multi-stage dispersion compensation. Through photoelectric joint detection and multi-stage signal processing, the accuracy of fault positioning is improved, rapid and accurate positioning of the power distribution network fault is realized, and the method has relatively strong anti-interference capability and environmental adaptability.

A method and system for analyzing fault data features of an active distribution network. The method comprises: analyzing a voltage variation after a fault in an active distribution network and analyzing a single-phase ground fault (S1); acquiring distribution network fault data by means of analog simulation (S2); and establishing a fault location prediction model to analyze fault data features and realize fault localization (S3). In the method, a voltage variation after a fault in an active distribution network is analyzed, the voltage variation is equivalently represented using an injected current, a single-phase ground fault is analyzed, and the localization of a fault point is realized. By simulating a current variation during an actual fault injection process by means of an injected current function, a model can more closely reflect an actual operating state, thereby enhancing the authenticity of fault simulation and the accuracy of prediction. A complex relationship between a current variation and node impedance is deeply analyzed by means of a complex information filtering function, and more refined fault features are extracted, thereby optimizing the precision of fault localization, and enhancing the adaptability of the model to different fault scenarios.

The invention relates to the technical field of power distribution network fault positioning, and discloses a power distribution network fault positioning method and system, and the method comprises the steps: constructing an initial topological graph G0 = (V0, E0) of a power distribution network; when a topology change event occurs, an increment updating module is triggered, an increment node set and an increment edge set which are influenced by the topology change event are obtained, and the initial topological graph is updated according to the increment node set and the increment edge set to obtain a target topological graph G1; constructing a graph data set H = (G1, Xt) according to the target topological graph and multi-channel time sequence data Xt, and inputting the graph data set into a graph neural network model to obtain a fault propagation probability matrix; identifying a suspected fault area according to the fault propagation probability matrix, synchronously injecting pulse signals into nodes in the suspected fault area, and collecting node response signals; and positioning the fault position of the power distribution network according to the fault propagation probability matrix and a node response signal. According to the method and the system provided by the invention, the fault position can be quickly and accurately positioned under the condition that the topological structure of the power distribution network dynamically changes.

The invention relates to the technical field of power grid monitoring, in particular to a power operation risk identification method, system and device based on multi-modal data fusion and a storage medium. In order to solve the problems of multi-modal data splitting, topological constraint missing and the like in traditional power disturbance analysis, an improved BERT model is constructed, and electrical signal time-frequency features and text semantic information are mapped to a unified vector space through a multi-modal embedding mechanism; a time sequence attention mechanism is adopted to establish a time dependency relationship between signals and texts, and a graph attention network is combined to realize risk propagation modeling under power grid topology constraints; and collaborative optimization of disturbance classification, risk prediction and trend analysis is carried out through a multi-task learning framework. The technical problems that heterogeneous data fusion is difficult and risk identification precision is insufficient are effectively solved, accurate identification and intelligent early warning of electric power operation risks are achieved, and the safe operation level of a power grid is improved.

The invention relates to the technical field of cable asset management and fault prediction, and discloses a cable fault positioning method based on deep learning clustering analysis test waveform characteristics, and the method comprises the steps: collecting waveform and environment data in a cable operation period, and constructing a historical feature library comprising waveform, environment and position features; a self-adaptive detection model is adopted, and parameters are dynamically adjusted to adapt to different working conditions; multi-dimensional feature fusion and matching analysis are combined; a fault point distance is calculated through a signal propagation model and a time difference positioning algorithm, precise positioning is realized by fusing environment compensation and multi-point cross validation, and a three-dimensional geographic coordinate is generated by combining a laying path; and after multiple verifications, a structured report containing a fault type, a risk level, a prediction position, confidence and operation and maintenance suggestions is generated. According to the system, intelligent monitoring, fault risk prediction, asset optimization management and operation and maintenance decision support of a cable operation state are realized, and scientificity and economy of cable management in a complex environment are improved.

The invention discloses a power distribution station room monitoring method and device and a storage medium, and the method comprises the steps: obtaining the electromagnetic interference intensity distribution of a power distribution station room and the real-time state data of a line, and determining a sensor type selection scheme through combining with the working condition of equipment; calculating a fault propagation path through the fault traveling wave arrival time difference, the line model parameters and the wave velocity difference, determining the position of a fault point, and generating a positioning result; performing associated sensor type selection and fault positioning, judging whether a fault point is in a layout area, and preliminarily judging a fault type; generating a recovery path candidate scheme based on the real-time state data and the fault positioning result; and fusing the fault traveling wave characteristics and the recovery path line state parameters, establishing a comprehensive evaluation model, and determining an optimal recovery path scheme by taking the power supply recovery efficiency and the load loss rate as optimization targets. The method has the following effects: rapid and accurate positioning of the power distribution network fault and intelligent power supply recovery are realized, and the reliability and efficiency of the operation of the power distribution network are improved.

The invention provides a power transmission network equipment fault diagnosis and life prediction method and system, and relates to the technical field of fault diagnosis, and the method comprises the steps: obtaining the time sequence electrical characteristic data of a plurality of monitoring nodes, carrying out the window segmentation and statistical characteristic extraction, building a dynamic association graph structure based on a space-time association constraint model, and obtaining the time sequence electrical characteristic data; and calculating the abnormal contribution degree of each node, marking candidate abnormal nodes, determining a fault propagation path through reverse tracing and path analysis, and finally outputting a fault positioning result. According to the invention, abnormal nodes can be accurately identified, a fault propagation path can be accurately tracked, and the accuracy and timeliness of power transmission network fault diagnosis are improved.

The invention provides an intelligent line dynamic monitoring system integrating Beidou positioning and AI. The system comprises inspection equipment, a sensor network and central control equipment. The inspection equipment is used for receiving the inspection task sent by the central control equipment, planning an inspection path and executing the inspection task according to the inspection task and the position information obtained through the Beidou receiver, and obtaining an environment image through the visual monitoring equipment in the process of executing the inspection task and sending the environment image to the central control equipment. The sensor network is used for collecting line data of the power transmission line and environment data of the surrounding environment of the power transmission line and sending the data to the central control device. The central control device is used for generating an inspection report according to the environment image, the line data and the environment data, evaluating the health state of the power transmission line based on a line state analysis and prediction model, and generating an early warning notification and an inspection task. Omnibearing and multi-level monitoring of the power transmission line and the surrounding environment thereof is realized, and the inspection efficiency and safety are remarkably improved.

The invention discloses a safety monitoring and early warning method for a power system, and relates to the technical field of power monitoring, and the method comprises the steps: obtaining a target detection line and a real-time monitoring data set; performing task disassembly on the real-time monitoring data set, and screening an identification task set; establishing a fault probability resonance model, inputting the identification task set into the fault probability resonance model to obtain the fault probability of each power device, and calculating a resonance effect value between any two devices; obtaining a transfer matrix based on the fault probability and the resonance effect value of each power device; generating a probability graph based on the transfer matrix in combination with the time sequence; predicting a fault propagation path based on the probability graph, adjusting the fault probability of the power equipment, identifying a key path and risk equipment based on the adjusted fault probability, and generating a monitoring analysis report; the response efficiency of the monitoring data of the power transmission and transformation equipment is improved; the effect of dynamic correlation analysis between faults is achieved; and the effects of fault equipment and a fault propagation path are accurately predicted.

The invention provides a harmonic detection method and system for a vehicle network electrical system, and relates to the technical field of harmonic detection, and the harmonic detection method for the vehicle network electrical system comprises the steps: obtaining a current signal of a vehicle network bus, and carrying out the band limit processing of the current signal; constructing a variable window width S transformation kernel function with self-adaptive frequency; based on bispectrum analysis of a third-order cumulant, harmonic phase coupling characteristics are extracted, and an enhanced spectrum is generated through principal component projection; generating a dynamic detection threshold by using a non-harmonic interval background noise model; and carrying out harmonic parameter fitting by adopting an exponential decay model with frequency domain priori constraint. According to the method, through a cascade architecture of adaptive time-frequency analysis, high-order statistical feature extraction, dynamic threshold detection and prior constraint fitting, full-process closed-loop optimization from signal preprocessing to parameter optimization is realized, and the method is particularly suitable for accurate detection of heavy-frequency, nonlinear and time-varying harmonics of a vehicle network system; and a diagnosis basis with higher reliability can be provided for electric energy quality treatment.

The invention discloses a power grid visual diagnosis system based on multi-source data fusion, and relates to the field of power grid diagnosis systems. The system comprises an information acquisition module, an electrical fault alarm module, a non-electrical quantity fault alarm module, a fault discrimination module, a communication module and a fault recording module. Firstly, information of a power grid is collected through the information collection module, then the information is transmitted to the electrical fault alarm module and the non-electrical fault alarm module for monitoring, corresponding actions are made when abnormity occurs, then the information is transmitted to the fault judgment module, input information is analyzed, and the fault type is obtained; meanwhile, the fault recording module records and broadcasts the power grid; the communication module collects information of other modules in the system and exchanges information with the monitoring system. According to the method, the abnormal information of the power grid is monitored and alarmed in combination with the multi-source data, and the fault type is judged through the data model, so that the omnibearing monitoring of the power grid is realized, and data support is provided for a subsequent maintenance plan.

The invention discloses a power distribution network fault early warning and positioning system based on distributed traveling wave online measurement, and belongs to the technical field of power distribution networks, and the system comprises a traveling wave signal collection module which is configured to collect a power distribution network fault traveling wave signal; the traveling wave signal processing module is configured to process the collected power distribution network fault traveling wave signal and determine a power distribution network fault characteristic traveling wave signal; the fault identification and early warning module is configured to rapidly identify the fault type of the power distribution network according to the fault characteristic traveling wave signal of the power distribution network and perform fault early warning; and the fault position positioning module is configured to accurately position the fault position of the power distribution network according to a double-end traveling wave distance measurement principle. According to the method, the problem that the use effect of the power distribution network is poor due to the fact that rapid identification, early warning and accurate positioning cannot be carried out on the power distribution network fault based on distributed traveling wave online measurement in the prior art is solved. According to the invention, rapid identification, early warning and accurate positioning can be carried out on the power distribution network fault based on distributed traveling wave online measurement, and the use effect of the power distribution network can be improved.

The invention relates to an electrical equipment fault monitoring and positioning method and system based on data analysis, and belongs to the technical field of electrical equipment monitoring. The method comprises the following steps: sensing a transient traveling wave signal of a fault current by adopting a traveling wave detection technology to determine an initial fault area; acquiring state data of the secondary equipment in the fault area through mapping of the secondary equipment in the fault area; constructing a secondary loop connection model based on the configuration file, and designing an action sequence rule base to verify the action logic and time sequence matching of a regional data centralized protection device, a circuit breaker and a communication link in real time; based on logic verification, action triggering conditions are extracted to be fused with the regional data set; and inputting the fault feature vector into a classification model for fault identification by obtaining the classification model. According to the invention, through traveling wave detection, EEMD decomposition, data synchronization and logic verification, feature information related to the fault is accurately extracted, and through data fusion and real-time learning, the fault identification precision and efficiency are improved.