3822results about "Short-circuit testing" patented technology

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 present invention discloses a method for diagnosing micro-short circuit fault of a battery, comprising: acquiring real-time voltage data of each battery in battery module of electric vehicle, obtaining voltage matrix after noise elimination; calculating dynamic reference voltage sequence of the each battery in the voltage matrix within sliding window length, extracting eigenvalues of the each battery at different times; calculating correlation coefficients between voltages of the each battery at different times and reference voltage within the sliding window; forming feature point matrix of the battery module based on the eigenvalues and the correlation coefficients; calculating dynamic reference feature point sequence based on the feature point matrix, and calculating improved Frechet distances between feature point sequences of the each battery and the dynamic reference feature point sequence, using Frechet distance values as anomaly scores of the each battery; thus, judging whether a short-circuit fault occurs in the each battery.

The invention relates to the technical field of bushing detection, and discloses a bushing insulation state detection method based on multi-parameter real-time monitoring. The method comprises the following steps: acquiring multi-dimensional monitoring data such as a partial discharge pulse sequence, dielectric loss angle tangent value fluctuation data and surface leakage current density distribution in a bushing operation environment in real time; carrying out insulation defect feature extraction on the data set, and generating an insulation defect distribution map containing a creeping discharge track, an internal air gap position mark and a carbonization channel density index; high-risk and potential defect areas are divided by adopting a dynamic threshold segmentation algorithm, boundary coordinates are determined, and an insulation state evaluation vector containing parameters such as a surface discharge intensity index and the like is constructed according to the boundary coordinates; and inputting the vector into a pre-trained insulation aging prediction model, and outputting a bushing residual insulation life estimated value and a defect development priority sequence. According to the method, all-around accurate monitoring and pre-judgment of the insulation state of the sleeve are achieved, and scientific support is provided for operation and maintenance decision making of the sleeve.

The invention relates to the technical field of power equipment state monitoring, in particular to a lightning arrester state monitoring method, system and equipment based on multi-physics field coupling and a storage medium. Acquiring current-voltage characteristic parameters, temperature distribution data and mechanical stress data of the lightning arrester, performing electro-thermal-mechanical coupling analysis based on multi-physical field monitoring data, identifying overlapping positions of an electric field distortion area, a temperature abnormal area and a stress concentration area, and determining the overlapping positions as a degradation key area; establishing a correlation response relationship between the leakage current and the temperature and a transfer response relationship between the temperature and the mechanical stress for the deteriorated key area; on the basis of the leakage current change trend of the degradation key area and the coupling influence of the superimposed temperature field and stress field, a degradation feature fusion factor is constructed, the evolution law of the degradation feature fusion factor in the time sequence is analyzed, and the degradation threshold value under the multi-field synergistic effect is determined in combination with the electric-thermal coupling acceleration effect and the thermal-mechanical coupling weakening effect.

The invention provides a multi-dimensional electrical equipment insulation analysis and evaluation method, and relates to the technical field of electrical equipment insulation online detection and intelligent diagnosis. According to the method, an electrically isolated direct current detection channel is constructed in a neutral point small resistance grounding system, controllable direct current detection signals are injected, micro direct current leakage response signals are collected, and after zero drift correction, temperature compensation and power frequency ripple suppression are carried out, layered attribution is carried out in combination with primary system topology and an equipment ledger, and a multi-dimensional response matrix is formed. According to the method, environment and working condition normalization is realized through factor rejection and robust pull-back, a mechanism model containing leakage channel equivalent parameters and path constraints is established, multi-working-condition data is fused to calculate equivalent insulation parameters and credibility indication quantity, and a multi-dimensional insulation representation vector is generated. And performing time sequence analysis on the representation vector, extracting degradation and abrupt change characteristics, outputting a health index and a risk level, and realizing quantitative evaluation and intelligent early warning of the insulation state.

The invention discloses a CPLD-driven IGBT short-circuit current dynamic monitoring method and system, and relates to the technical field of power electronics, and the method comprises the specific steps of S100 real-time data acquisition, S200 dynamic threshold calculation, S300 short-circuit state judgment, S400 rapid protection execution, and S500 fault data recording and uploading. Accurate and prospective judgment of the short circuit state of the IGBT is achieved, two stress parameters including the direct-current bus voltage and the junction temperature serve as independent variables, when the bus voltage rises, the voltage stress borne by the IGBT in the turn-off period is increased, meanwhile, the current peak at the short circuit moment is more remarkable, early warning is conducted in advance by dynamically lowering the threshold value, and therefore the short circuit state of the IGBT is accurately judged. According to the mechanism, the monitoring system does not simply respond to the occurred overcurrent, but can pre-judge the safety boundary of the device under the current working condition, so that intervention is implemented before the current really reaches a dangerous peak value, and a protection blind area under a severe working condition is effectively avoided.

The invention particularly relates to a leakage current testing method, device and system for an intelligent circuit breaker, and relates to the technical field of electrical safety detection. A composite signal generation and injection module; a broadband signal acquisition and conditioning module; and a line fault positioning and impedance analysis module. According to the invention, the composite signal generation module realizes the non-interference isolation injection of a leakage current signal and a high-frequency positioning signal, and guarantees the high fidelity of data through adaptive filtering, multi-channel synchronous sampling and high-precision timing. The line positioning module integrates TDR and a frequency domain impedance method, and through multi-frequency point deviation correction and topology-GIS association, the fault type is accurately identified, and the test and troubleshooting period is significantly shortened.

The invention provides a lithium battery internal short circuit fault early warning and positioning method and system based on deep learning. The early warning and positioning method comprises the following steps: step S10, multi-dimensional data semantic acquisition and working condition self-adaptive preprocessing; step S20, collaborative extraction and alignment of cross-scale spatio-temporal features; step S30, performing dynamic threshold adaptive internal short circuit early warning and feedback optimization; s40, fault accurate positioning and verification of topology perception are carried out; and step S50, performing embedded collaborative optimization and self-diagnosis deployment. According to the invention, early weak signals of an internal short circuit fault can be found in time, and the efficiency and timeliness of battery safety monitoring are improved; the sensitivity of early warning is improved; the robustness in high-temperature, low-temperature and rapid charging and discharging environments is enhanced; and the positions of the single battery and the electrode with the fault can be accurately identified.

The invention discloses a diesel generator turn-to-turn short circuit fault protection method and system based on multi-parameter characteristics, and relates to the technical field of intelligent fault diagnosis of power equipment, and the method comprises the steps: carrying out the feature extraction of operation state monitoring data, carrying out the integration according to a time sequence, and generating a real-time feature sequence set; performing spatio-temporal feature coding and fusion on the real-time feature sequence set to generate a multi-dimensional spatio-temporal feature map; mapping the comprehensive matching score set into fault confidence according to a linear relation, obtaining a fault confidence sequence, judging a fault level in combination with a characteristic amplitude overrun condition, and generating a fault diagnosis result set; and executing hierarchical response based on the fault diagnosis result set, generating a hierarchical protection action execution record, and generating a short-circuit fault protection report in combination with the fault diagnosis result set. According to the method, quantitative similarity analysis of real-time features and standard modes is realized, the fault probability is accurately evaluated through a multi-parameter weighted fusion mechanism, and the reliability and interpretability of a diagnosis result are improved.

The invention relates to a battery internal short circuit early warning method and system based on a physical information neural network, and belongs to the technical field of battery fault detection, and the method comprises the steps: constructing a first-stage PINN model through a linearized Butler-Volmer equation, and carrying out the training through a multi-source signal of a battery; taking the output of the first-stage PINN model as prior input, and constructing a second-stage PINN model by using a nonlinear electrochemical-thermal-mechanical coupling equation, and training the second-stage PINN model; using the trained first-stage PINN model and the trained second-stage PINN model to output prediction results of the voltage and the expansive force of the target battery; comparing the difference between the prediction result of the model and the measured value of the sensor to generate a voltage residual error and an expansive force residual error; and realizing identification and alarm of the short circuit risk in the target battery based on the voltage residual error and the expansive force residual error. According to the method, a multi-fidelity PINN hierarchical modeling mechanism is introduced, so that high-precision characterization and prediction of the internal state of the battery can be realized by using experimental data and mechanism model information at the same time under different precision and complexity hierarchies.

The invention relates to the technical field of battery management, in particular to a battery pack multi-fault diagnosis method and system based on signal decomposition and entropy feature fusion. The method comprises the following steps: acquiring voltage data of each single battery in a battery pack in real time, and performing segmentation processing on voltage time sequence data by adopting a sliding window mechanism based on a Fibonacci sequence; adaptively optimizing key parameters of variational mode decomposition by using a Schrodinger optimization algorithm, and performing adaptive decomposition on the voltage signal in each window to obtain a plurality of intrinsic mode functions; calculating the Shannon entropy value of each intrinsic mode function, preferably selecting a preset number of representative mode components according to the Shannon entropy characteristic contribution degree, and carrying out superposition reconstruction to obtain a reconstructed voltage signal; extracting the Shannon entropy, the spectrum sparsity index and the time domain stability index of the reconstructed voltage signal to form a three-dimensional fault feature vector; and identifying a connection fault, a short circuit fault and a sensor fault through a multi-level threshold judgment strategy based on the three-dimensional fault feature vector.

The invention discloses an on-line qualitative detection method for partial discharge of power equipment, which comprises a detection system and detection steps, and also comprises the following steps: the detection system comprises a high-frequency partial discharge detector for detecting a high-frequency current transformer; the ultrahigh frequency detection system is used for detecting electromagnetic wave radiation which is generated by partial discharge and has extremely short duration time; the ultrasonic detection system is used for detecting sound waves and ultrasonic signals generated along with energy release when partial discharge occurs; a transient ground voltage method detection system is used for live-line detection of equipment such as a medium-voltage switch cabinet. According to the online qualitative detection method for the partial discharge of the power equipment, different detected equipment and different detection methods are adopted to perform online detection on the partial discharge of the equipment, so that the early partial discharge phenomenon of the power equipment can be found in time, the long-term discharge monitoring data of the power equipment can be stored, and the overall detection effect is improved.

The invention relates to the technical field of power distribution fault positioning, in particular to a power distribution single-phase earth fault positioning method and device based on multi-source information fusion. The device comprises a data acquisition module, a fault line selection module, a grading emergency module, a fault positioning module and a parameter optimization module. According to the method, the zero-sequence current is monitored in real time, the dynamic change curve is generated, the fault line is accurately identified in combination with amplitude and phase characteristics, electrical parameters, environmental factors and load importance are fused to carry out comprehensive risk assessment and start a hierarchical response mechanism, and the fault point of the fault line is accurately positioned by adopting a voltage drop method. And the monitoring frequency and the decision threshold value are adaptively adjusted according to the fault characteristics and historical data, so that response lag and insufficient precision of a traditional fault positioning method are avoided, full-flow intelligent management from fault detection, accurate positioning to parameter self-optimization is realized, the accuracy and efficiency of fault processing are improved, and safe and stable operation of a power grid is ensured.

The invention relates to the technical field of fault testing, in particular to a dry-type air-core reactor turn-to-turn short circuit monitoring method based on a magnetic field difference method. According to the method, regularization constraint is introduced in an initial baseline stage to construct a reference electromagnetic coupling matrix, a heat accumulation index is recursively analyzed based on three-phase current energy, a thermally-induced magnetic field drift model is fitted in combination with magnetic field residual errors, and combined with combined constraint of the reference electromagnetic coupling matrix and the thermal drift model, a three-phase electromagnetic coupling model is established. The normal thermally induced magnetic field drift component is dynamically stripped in real-time monitoring; and under the constraint of a reference electromagnetic coupling matrix, fitting and optimizing a current vector through weighted least square state estimation, and integrating magnetic field and current two-dimensional deviation to obtain a structure consistency deviation index for fault monitoring. According to the invention, monitoring is carried out in consideration of a linear electromagnetic transfer rule and nonlinear thermal drift compensation, a complex magnetic field environment and a variable load operation condition of the dry-type air-core reactor are adapted, and continuous and reliable monitoring guarantee is provided for long-term stable operation of equipment.

The invention discloses an insulator leakage current non-contact measurement method and system based on magnetic field sensing, and belongs to the technical field of electrician measurement, and the method comprises the steps: collecting original magnetic field data and environmental parameter data; performing interference stripping processing on the original magnetic field data based on the environmental parameter data to generate corrected magnetic field data; performing time-frequency analysis and noise suppression processing on the corrected magnetic field data to obtain leakage current characteristic data; performing inversion calculation by using the leakage current characteristic data to obtain a leakage current value; in combination with historical leakage current data, executing state evaluation processing to generate an insulator state; and according to the insulator state, automatic optimization processing is executed, and magnetic field sensor array gain and interference stripping processing parameters are adjusted. According to the invention, the technical scheme of multi-source sensing data fusion, interference stripping, physical inversion calculation and state evaluation closed-loop feedback optimization is adopted, and high-precision non-contact measurement and intelligent state evaluation of the leakage current of the insulator can be realized.

The invention relates to the technical field of power distribution network fault early warning, and discloses an early warning system and method for a power distribution network ground fault. The data acquisition module of the system acquires multi-mode monitoring data such as zero-sequence current waveform, grounding wire current amplitude phase, insulator leakage current and environment temperature and humidity of the power distribution network in real time, and comprehensively reflects the operation state of the power distribution network. And the feature fusion module performs spatial-temporal feature compensation on the multi-modal monitoring data to generate a fault feature tensor, so that the insulation degradation evolution period of the topological node of the power distribution network is determined, and the insulation state change is accurately evaluated. And the risk deduction module analyzes a dynamic communication path of the topological structure of the power distribution network, performs fault conduction modeling, determines a ground fault risk situation level in combination with multi-modal monitoring data, and comprehensively considers fault conduction characteristics and operation conditions. The early warning decision module generates a dynamic early warning strategy according to the insulation degradation evolution period and the risk situation level, and the early warning mode is flexibly adjusted.

A turn-to-turn short-circuit fault diagnosis method and system for a reactor, and a medium. The method comprises: using operating data of a reactor to be tested to plot a target Bowditch curve of said reactor (101); calculating feature parameters of the target Bowditch curve (102); inputting the target Bowditch curve into a preset embedded target diagnosis network model, so as to extract a feature map of the target Bowditch curve (103); using the feature parameters and the feature map of the target Bowditch curve to generate feature information (104); and comparing whether the feature information is consistent with preset feature information, and on the basis of a comparison result, determining a fault state of said reactor (105). The method solves the technical problem of existing detection methods having poor sensitivity and being unable to detect incipient turn-to-turn short-circuit conditions of reactors in a timely manner, and realizes the real-time detection and determination of turn-to-turn short-circuit faults in dry-type air-core reactors.

The invention relates to the technical field of power distribution network fault detection, and discloses a power distribution network high-resistance grounding fault intelligent detection method, system and device and a medium, and the method comprises the steps: collecting a power distribution network line transient signal which comprises a transient zero-sequence voltage signal and a transient zero-sequence current signal; preprocessing the transient signal and extracting a multi-dimensional feature set for representing high-resistance grounding fault characteristics, wherein the multi-dimensional feature set comprises a transient energy feature, a wavelet entropy feature and a harmonic component feature; and inputting the extracted multi-dimensional feature set into a pre-trained machine learning model, and simultaneously outputting a judgment result of the high-resistance grounding fault and an estimated value of the grounding resistance through the machine learning model. According to the method, the transient energy, the multi-scale wavelet entropy, the odd harmonic amplitude and the phase, which are derived from different analysis domains and have complementary physical meanings, are deeply fused, so that the problem that a single feature is weak in characterization capability and easy to fail under the conditions of high noise and high resistance is effectively solved.

The invention discloses a high-voltage cable grounding current abnormity monitoring system, which belongs to the technical field of electric power facilities and comprises an inspection acquisition module, an association analysis module, a collaborative reasoning module and a self-optimization module. The inspection acquisition module is used for synchronously acquiring multi-source monitoring parameters and acquiring position information so as to generate a grounding parameter matrix; the association analysis module is used for screening suspected defect types, dividing a local overheating area, performing overheating cause judgment, constructing a coupling association matrix of a multi-physical field, performing evolution stage judgment in combination with historical data, and generating a preliminary mechanism hypothesis in combination with credibility rating; the collaborative reasoning module is used for constructing a dynamic knowledge graph and a constraint prediction network, constructing a collaborative reasoning mechanism based on a two-way real-time interaction channel, performing preliminary reasoning, and dividing early warning levels in combination with comprehensive confidence so as to perform operation and maintenance feedback; and the self-optimization module is used for carrying out directional iteration on the double engines by utilizing manual feedback and carrying out cross-line migration optimization according to different lines.

The invention belongs to the technical field of equipment state monitoring and fault diagnosis, and relates to a pole-mounted switch damp defect diagnosis method and system based on multi-source information fusion, and the method comprises the steps: collecting a leakage current signal and a high-frequency pulse current signal flowing through a grounding wire in real time, and environment parameters around a target pole-mounted switch; preprocessing the leakage current signal and the high-frequency pulse current signal, and performing feature extraction based on the preprocessed leakage current signal, the preprocessed high-frequency pulse current signal and environmental parameters to obtain a leakage current feature quantity, a high-frequency discharge feature quantity and an environmental coupling factor; and based on the leakage current characteristic quantity, the high-frequency discharge characteristic quantity and the environment coupling factor, judging a damp fault of the target pole-mounted switch, and combining a D-S evidence theory and fusing multi-source information confidence to complete damp defect diagnosis of the target pole-mounted switch. According to the invention, complementarity between different information sources can be fully utilized, and the damp state of the pole-mounted switch can be accurately judged.

The invention relates to the technical field of on-line health monitoring of wide bandgap semiconductor power devices, and discloses an on-line gate leakage current monitoring method and system for a GaN power device, and the method comprises the steps: extracting an average gate driving current which is within a complete switching period, an average value of instantaneous currents flowing into or out of the gate of the GaN power device; and on the basis of the average gate driving current, gate leakage current is obtained, so that online gate leakage current monitoring of the GaN power device can be carried out. The system corresponds to the method. According to the invention, the core problem that the traditional voltage detection method is insufficient in resolution and cannot realize early warning is fundamentally solved; meanwhile, a source electrode series sampling and non-intrusive signal conditioning circuit is adopted, a switch and a voltage stabilizing circuit are prevented from being introduced into a grid electrode driving loop, and therefore interference on parasitic parameters of the grid electrode loop is eliminated, and feasibility and reliability of application in a high-frequency and high-sensitivity GaN power device are ensured.

The invention discloses a rectifier bridge arm fault identification method and system circuit based on multi-characteristic current waveform analysis, and relates to the technical field of rectifier bridge arm fault identification, and the method comprises the steps: collecting a current signal of an AC input side of a rectifier bridge through a current sensor, and obtaining an original current time domain waveform after filtering conditioning; performing period division on the original current time domain waveform based on a voltage synchronization signal, and outputting a positive half-cycle conduction interval and a negative half-cycle conduction interval in each power frequency period; in each power frequency period, performing square integral operation on the current waveforms of the positive half-cycle conduction interval and the negative half-cycle conduction interval to obtain a positive half-cycle energy value and a negative half-cycle energy value, and calculating an energy unbalance degree; and windowing fast Fourier transform is carried out on the original current time domain waveform, amplitudes and phases of third, fifth and seventh characteristic harmonics are extracted to form a harmonic characteristic vector, the vector is matched with a pre-stored bridge arm fault harmonic fingerprint template, and the matching degree of each fault type is obtained.

The invention discloses a line diagnosis method and circuit, and the method and circuit are used for a data communication system, and the method comprises the steps: detecting a first current on a high-side switching tube and / or a second current on a low-side switching tube; judging the states of the high-side switch tube and the low-side switch tube based on the detection result; and judging whether open circuit and short circuit exist at the line positive end and the line negative end and whether short circuit exists between the line positive end and the line negative end and the power supply voltage and the ground voltage based on the states of the high-side switch tube and the low-side switch tube. According to the line diagnosis method and the diagnosis circuit, the accuracy of fault diagnosis in the bus power supply and power-on process can be improved, false report and missing report of faults are reduced, and chip and line burnout caused by long-time overcurrent is avoided; by means of fault detection of the communication state, the line fault diagnosis precision in bus power supply communication is improved, false alarms and missing alarms are reduced, and overcurrent is avoided.

The invention discloses a power distribution network single-phase grounding reason diagnosis method based on confidence and ambiguity evaluation. The method comprises the following steps: acquiring original diagnostic data corresponding to a waveform to be detected; determining a confidence level corresponding to the original diagnosis data according to a preset grading rule based on the similarity distance corresponding to each of the plurality of neighbor sample waveforms in the original diagnosis data; determining a plurality of candidate reasons based on the single-phase grounding reasons corresponding to the plurality of neighbor sample waveforms; based on the number of the plurality of neighbor sample waveforms, determining occurrence frequencies corresponding to the plurality of candidate reasons; determining target ambiguity based on the occurrence frequencies corresponding to the candidate reasons and the reason label semantic association matrix; and based on the confidence level and the target ambiguity, adaptively generating a diagnosis result. According to the method, the technical problem that the information completeness of the conclusion output by the existing analysis method is insufficient, so that an objective judgment basis for the conclusion reliability is lacked when the operation and maintenance personnel make decisions is solved.

The invention discloses a high-precision positioning method and device for a wiring error electric leakage user, and a storage medium, and relates to the technical field of electric leakage detection. The method comprises the following steps: collecting residual current of a tested transformer area and time sequence data of load current of each user in the tested transformer area; based on the time sequence data, calculating the time sequence correlation strength between the load current and the residual current of each user; generating a differentiated penalty coefficient for each user based on the time sequence correlation intensity; constructing a regression model taking the residual current as a dependent variable and the load current of each user as an independent variable, and reconstructing a target loss function of the regression model by taking a penalty coefficient as a coefficient of a regularization term; and through minimizing the target loss function, a weight coefficient corresponding to the load current of each user is estimated, and then the user with a wiring error electric leakage fault is determined. According to the method, the problem of misjudgment caused by normal user interference in a traditional electric leakage user identification method is effectively solved.

The invention discloses a rapid pilot protection method for a flexible DC line, and the method comprises the steps: firstly collecting the positive and negative current data of the DC line in real time, calculating the current change rate, and starting protection if the current change rate exceeds a preset threshold value; locking a fault moment data window, and calculating an H distance of a positive and negative electrode current sequence based on a Hausdorff distance so as to distinguish an inter-electrode fault or a single-electrode fault; thirdly, calculating a fault current break variable sequence by using the current data in the data window to obtain a current included angle of the side; then, current included angle information of the two sides is interacted with an opposite-side protection device; calculating a product average value, namely a fault current included angle entropy, according to the current included angles on the two sides; and finally, by judging whether the included angle entropy is greater than an internal and external fault threshold value or not, accurately identifying an internal fault and triggering tripping, or judging the internal fault as an external fault. According to the method, millisecond-level rapid protection is realized through the current included angle characteristic quantity and the included angle entropy criterion, and the method has high transition resistance resistance and low communication synchronization requirements.

The invention discloses a vehicle-mounted power supply short-circuit heat prediction method based on big data analysis, and the method comprises the following steps: collecting real-time operation data, and storing the data in a data cache, thereby obtaining a cache time sequence signal; filtering the cached time sequence signal, and extracting a basic feature sequence; constructing an RC network model based on the basic feature sequence, and converting a fractional order operator into an equivalent integer order model; performing parameter estimation on the equivalent integer order model to generate a thermal prediction model; taking the thermal prediction model and the basic feature sequence as joint input and output power loss features; inputting the power loss characteristics into a quantile regression forest model to form a heat prediction result; comparing the heat prediction result with a heat threshold value and a temperature rise threshold value, and outputting a control trigger signal; and calling parameters in a control strategy library according to the control trigger signal to generate a control strategy. According to the invention, vehicle-mounted power supply short-circuit heat prediction is realized.

The embodiment of the invention discloses a high-resistance grounding fault active line selection method, device and system, relates to the technical field of power distribution network relay protection, and aims to solve the problems of low sensitivity and poor reliability of high-resistance grounding fault line selection. The method comprises the following steps: when fault starting is monitored, controlling a switch assembly connected with a damping resistor in a neutral point grounding loop to act so as to change the access state of the damping resistor; collecting bus zero-sequence voltage and zero-sequence current of each feeder line before and after the action of the switch assembly; calculating the zero-sequence active power variation of each feeder line before and after the action; and finally, comparing the zero-sequence active power variation of each feeder line to determine a fault feeder line. According to the invention, excitation is actively injected and the power variation is analyzed, so that the fault characteristics are enhanced, and the method has the beneficial effects of high sensitivity and high reliability.

The invention discloses a power transmission cable ground fault adaptive diagnosis method and system, and the method comprises the steps: collecting multi-modal data, and carrying out the preprocessing and extraction of the multi-modal data, and forming a statistical feature vector; the method comprises the following steps: adaptively decomposing sheath current data by adopting OVMD, calculating segmented TKEO energy operators, and realizing fault early warning through a ratio criterion of maximum values of TKEO derivatives of adjacent segments; inputting the real-time acquisition time sequence working condition class feature vector into a trained LSTM to obtain a fault probability, dynamically updating a fault probability threshold based on LSTM output, and judging that a fault occurs when the fault probability exceeds a continuous set period of the fault probability threshold; and inputting the statistical class feature vector into ANN, inputting the sheath current data into 1D-CNN for fault classification, and performing adaptive weighted voting integrated decision on the output of the two types of networks to identify the fault type. According to the invention, the problems of low fault detection sensitivity, poor positioning precision, lack of early warning capability and the like in the prior art are solved.

The invention discloses a battery internal short circuit detection method and device which are applied to a battery pack comprising a plurality of single batteries, and the method comprises the steps: obtaining the current information of the battery pack and the voltage information of each single battery; constructing observed quantity representing monomer difference, and estimating a first difference parameter representing monomer voltage source characteristic difference and a second difference parameter representing monomer impedance characteristic difference on line by adopting nonlinear filtering to obtain a corresponding time sequence; segmenting the time sequence according to a preset operation period, and performing weighted convergence according to weights related to electric quantity changes to obtain corresponding characteristic parameters; and constructing a first statistical discrimination quantity and a second statistical discrimination quantity which reflect extreme value deviation and fluctuation deviation based on the periodic characteristic parameters, performing joint discrimination with a threshold value, and determining an internal short circuit when abnormal monomers are consistent and meet a continuous periodic criterion. According to the method, through difference observation, filtering estimation, periodic weighting and double-discriminant-quantity combined judgment, the discriminant stability and reliability under working condition fluctuation and noise interference are improved.