9934results about "Current/voltage measurement" patented technology

The invention relates to the technical field of industrial safety, and discloses an electrolytic aluminum short circuit port operation safety early warning system based on multi-parameter collaborative awareness and intelligent diagnosis, and the system comprises a parameter collaborative awareness module, a dynamic diagnosis module, an early warning decision module, and an execution feedback module. By constructing a multi-dimensional parameter collaborative sensing mechanism, fusing temperature field distribution, current balance degree and insulation state multi-source data in real time and dynamically capturing early abnormal symptoms of a short circuit port, the hysteresis problem of traditional single-parameter threshold monitoring is solved, conversion from passive response to active defense is achieved, and the comprehensiveness and timeliness of operation state monitoring are improved; and meanwhile, based on a historical fault database and a real-time evolution model, a health index is generated and a fault path is predicted, so that maintenance personnel can pre-judge a development trend and a time window of potential risks in advance, and sudden equipment accidents are avoided.

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 discloses a power battery health state on-line monitoring and early warning method and system based on multi-data fusion. The method comprises the following steps: synchronously acquiring multi-dimensional heterogeneous data in a battery operation process through a multi-sensor data acquisition module; carrying out standardization processing and preprocessing on the multi-dimensional heterogeneous data, extracting a comprehensive feature vector and carrying out multi-sensor data fusion; a multi-sensor data fusion algorithm is adopted to carry out weight distribution on the fused comprehensive feature vector, a dynamic weight coefficient is calculated according to the sensitivity and reliability of each parameter to the health state of the battery, and a fused battery state feature descriptor is obtained; and performing mode recognition and state classification on the fused battery state feature descriptors by using a pre-established evaluation model to realize quantitative evaluation of the health degree of the battery. According to the invention, real-time monitoring, evaluation and early warning of the health state of the battery in the whole life cycle are realized, and a reliable basis is provided for safe operation and maintenance decision of a battery system.

The invention discloses a method for diagnosing an IGBT open-circuit fault and a current sensor fault of a T-type three-level inverter, belongs to the technical field of inverter fault diagnosis, and is used for solving the problems of diagnosis and positioning when a three-level T-type inverter IGBT switch tube and a sensor have faults. Comprising the following steps: classifying and marking and numbering fault types of an open-circuit fault of a T-type three-level inverter IGBT and a fault of a current sensor; fault samples are collected and divided into a training set and a test set according to the proportion of 7: 3; a TCN-LSTM-SelfAction fault diagnosis model is constructed, and model training is carried out by using the training set; the trained model is used for evaluating the accuracy of fault type diagnosis of the training set and the test set. According to the diagnosis method, the fault types and the fault positions of the IGBT open-circuit fault and the current sensor fault can be diagnosed at the same time, the TCN and the LSTM are selected to be combined, local information and global relation of fault features can be captured at the same time, and meanwhile, the performance of a model is improved by combining a self-attention mechanism in the LSTM, and the accuracy is improved.

The invention belongs to the technical field of electrical safety, and particularly relates to a human body electric shock protection method and system based on multi-parameter fusion, multi-dimensional characteristic parameters are formed by extracting a high-frequency harmonic component, a voltage abrupt change slope and a magnetic field intensity fluctuation value of a residual current waveform, and the weight is dynamically adjusted according to the instantaneous change rate of each characteristic, so that the human body electric shock protection is realized. Generating a weighted feature vector; and a comprehensive criterion value is calculated in combination with a preset rule base and is compared with a dynamic threshold value, so that whether the circuit breaker is triggered to act or not is determined. According to the method, through fusion analysis of a plurality of physical characteristics, the identification capability of the system in a complex electromagnetic environment is improved, the probability of misoperation caused by external interference is effectively reduced, the action accuracy and stability of the circuit breaker are improved, and the power utilization safety and the power supply continuity are guaranteed.

The invention discloses a self-adaptive switching system and method for power supply and distribution, and the system comprises a data collection module, an analysis module, a modeling module, a decision module, an execution module and an early warning module, and the data collection module generates a real-time monitoring signal; the analysis module performs dynamic weighted evaluation according to the real-time monitoring signal and generates an evaluation signal; the modeling module constructs a virtual model of the power supply system and generates an analog signal; the decision signal generates a control signal containing a path selection instruction; the execution module controls the semiconductor device to switch the physical connection state of the power supply according to the control signal and generates a switch feedback signal; and the early warning module forms an equipment health assessment result according to the switch feedback signal and an abnormal arc signal when the execution module switches the connection state and transmits the result to the decision module. The self-adaptive switching system for power supply and distribution can solve the problem that a traditional power supply and distribution switching system depends on a single threshold to judge the state of a power supply and cannot comprehensively evaluate the quality of electric energy.

The invention discloses a dual-channel time-frequency fusion driving Mama main power distribution network detection and classification method, which comprises the following steps: S1, collecting fault voltage and current signal data under different main power distribution network topologies, and constructing a training set; s2, constructing a model based on dual-channel time-frequency fusion driving Mama, wherein the model is used for calculating and obtaining the fault probability and the fault category label of the kth fault; s3, the training set trains the model by using a back propagation and gradient descent method to obtain a trained main and distribution network fault detection network which is used for inputting a fault data set and then mapping a corresponding fault classification label; and S4, inputting fault voltage and current signal data of the main power distribution network by using the trained model, and executing detection classification operation. According to the method, through multi-scale feature reconstruction and cross-modal fusion, the detection robustness in a complex noise environment is remarkably improved, and the fault starting moment, the duration time and the propagation path are accurately captured.

The invention relates to the technical field of voltage control, in particular to a synchronous main shift gear automatic compensation voltage control system, which comprises an area load information acquisition module, a voltage instantaneous value and a current instantaneous value are periodically read through a voltage transformer and a current transformer which are deployed on a key feeder line or a user side in a specified area, and the voltage instantaneous value and the current instantaneous value are acquired; and calculating to obtain an active power value and a reactive power value, adding a synchronous timestamp and a node position code to each collected value, transmitting and collecting the coded voltage value, active power value, reactive power value and timestamp information, organizing and storing according to a regional and time sequence, and establishing a regional load real-time measurement data set. According to the method, real-time capturing and structured processing of the system call number, the parameter and the return value are carried out on the L4T kernel event triggered by the target process PID, a serialized system call track is obtained, and an execution process recording path mainly driven by process behaviors is formed.

The invention relates to the technical field of traction backflow monitoring, and provides a railway traction backflow monitoring analysis method and system based on multi-path current fusion. The method comprises the steps of generating multi-path current waveform data based on a mapping relation between a phase variation and traction backflow intensity, performing time-frequency domain combined noise reduction, and aligning with a time axis to obtain aligned multi-path current waveform data; and cross-path collaboration analysis is carried out, synchronous deviation characteristics of multi-path current are extracted, matching verification is carried out on the synchronous deviation characteristics and a preset traction load dynamic change interval, and an abnormal current event caused by harmonic distortion or transient impact and positioning information of the abnormal current event in the longitudinal direction of the steel rail are determined. According to the invention, high-precision detection and positioning of railway traction backflow and identification of harmonic and transient impact can be realized, and the problem of low-precision detection of railway traction backflow caused by weak anti-interference capability, insufficient spatial resolution and multipath current collaboration deficiency is solved.

The invention discloses an asynchronous motor energy-saving monitoring and dynamic early warning method based on situation awareness, and relates to the technical field of asynchronous motor energy-saving monitoring. The method comprises the steps that S1, multi-source heterogeneous data are synchronously collected, power supply parameters and operation parameters are obtained in real time, and a frequency conversion working condition self-adaptive deviation value and a current stability coefficient are obtained; s2, generating four-dimensional situation factors including an overtemperature danger value, a current stability coefficient, a voltage stability coefficient and a frequency deviation value; s3, constructing a fourth-order transmission network, and generating a primary early warning signal based on the path mark combination; s4, calculating the motor efficiency in real time, triggering an efficiency abnormity mark, fusing the efficiency abnormity mark with the primary early warning signal, and reconstructing a final early warning instruction; and S5, executing hierarchical control according to the final early warning instruction. Through multi-parameter coupling and dynamic early warning transmission, the early-stage capture capability of the composite fault is improved, the early warning response delay is shortened, energy-saving optimization and fault protection of the motor are realized, and the method has a remarkable practical value.

The invention discloses a superconducting magnet quench detection early warning system and method based on skeleton current, and relates to the field of superconducting magnet quench detection, and the method comprises the steps: constructing an electrothermal coupling finite element model of a superconducting magnet and a conductive skeleton; simulating different quench events based on the electrothermal coupling finite element model; according to different quench events, quench judgment threshold values and different levels of quench severity index grading threshold values are determined; when the superconducting magnet is out of service, acquiring a skeleton current signal that the current of the superconducting magnet is shunted to the conductive skeleton; based on the skeleton current signal, according to the quench judgment threshold value and the quench severity index grading threshold value, grading early warning and protection response are conducted on the superconducting magnet quench, nondestructive testing can be conducted on the superconducting magnet quench, grading evaluation and optimization protection of the quench severity degree are achieved, and the safety of the superconducting magnet quench is improved. And the cost of the quench monitoring system is reduced.

The invention relates to the field of on-line monitoring of electrical equipment, in particular to an on-line monitoring method of the electrical equipment, which comprises the following steps: acquiring multi-dimensional monitoring parameters of the electrical equipment, including an electrical parameter, a temperature parameter and an insulation parameter; performing feature extraction on the collected multi-dimensional monitoring parameters to obtain feature parameters corresponding to the monitoring parameters, and constructing a feature vector set; based on historical normal operation data and real-time acquisition data of the electrical equipment, establishing a dynamic threshold model, and calculating a threshold corresponding to the characteristic parameter through the dynamic threshold model; feature parameters in the feature vector set are compared with corresponding thresholds output by the dynamic threshold model, and whether the electrical equipment is abnormal or not is judged; when the abnormity exists, outputting an abnormity monitoring result; and if not, returning to S1. According to the invention, by collecting multi-dimensional parameters, calibrating, correcting, de-noising and extracting features, and combining historical and real-time data to build a model and verify, data preprocessing, threshold adaptation and anomaly judgment are realized, and the monitoring reliability is improved.

The invention provides a remote intelligent control distribution box, which belongs to the technical field of distribution boxes and comprises a control chip, a wireless communication device, a current monitoring device, a voltage monitoring device, a temperature monitoring device, a leakage protection device, a circuit breaker state monitoring device, a grounding state monitoring device, a humidity monitoring device and a power management device. A control module is arranged in the control chip and is used for acquiring output data of the current monitoring device, constructing a linear temperature rise prediction model, identifying potential fault types by using a decision tree algorithm, operating a lightweight power distribution network sensing model to process dimension-reduced time sequence data and realizing low-calculation-amount feature extraction by adopting a sparse attention mechanism; and when the system health state risk score exceeds a threshold value, a protection strategy is adjusted, and fault precursor characteristics are recorded. The technical problem that early warning of the abnormal state of the power distribution system is difficult to realize in a low-computing-resource environment is solved.

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

The invention belongs to the technical field of fault early warning, and discloses a heater fault early warning method and system based on current analysis. Comprising the steps that transient current signals of all heating wire loops in the copper bush heater are collected in real time, harmonic characteristic parameters are extracted, and a dynamic current fingerprint database and a multi-dimensional harmonic characteristic spectrum are generated respectively; dynamically calculating the contact impedance between each heating wire and the copper sleeve groove according to the dynamic current fingerprint database and the multi-dimensional harmonic characteristic spectrum; the current distribution path of each heating wire in the copper sleeve groove is restored in real time, the abnormal contact form of each heating wire is identified, the falling risk probability and falling risk strength of each heating wire are dynamically predicted by fusing the falling precursor signals of each heating wire detected in real time, and a grading early warning strategy report is generated in real time; according to the invention, fault early warning can be converted into preventive maintenance from post-maintenance, the intelligent level of operation and maintenance of the heater is improved, and the continuity and safety of production are guaranteed.

The invention provides a method and a system for measuring plasma current in a quasi-ring symmetrical star simulator, and relates to the technical field of star simulators, and the method comprises the steps: determining a sensor design index according to a bootstrap current simulation calculation result, and converting the design index into an actual arrangement scheme according to an installation environment constraint; all the magnet coils are independently excited in a plasma-free state, wall eddy current interference signals generated by the magnet coils are calibrated, and thus wall eddy current calibration voltage signals are output; a sensor in an actual arrangement scheme is used for synchronously collecting a total original induced voltage signal including plasma current and wall eddy current; an integral voltage signal representing the total current is output; and compensating the integral voltage signal by using the wall eddy current calibration voltage signal, and inverting the final measurement value of the plasma current in the quasi-ring symmetric star simulator. The device and the method have the beneficial effects that the interference of a stray field on a measurement result is weakened, and a high-precision diagnostic tool is provided for the plasma physical research of the star simulator.

The present inventions, in one aspect, are directed to techniques and / or circuitry to applying a charge pulse to the terminals of the battery during a charging operation, measure a plurality of voltages of the battery which are in response to the first charge pulse, determine a charge pulse voltage (CPV) of the battery, wherein the charge pulse voltage is a peak voltage which is in response to the first charge pulse, determine whether the CPV of the battery is within a predetermined range or greater than a predetermined upper limit value and adapt one or more characteristics of a charge packet if the CPV is outside the predetermined range or is greater than a predetermined upper limit value.

The invention provides an intelligent monitoring and early warning method based on multi-source information fusion, and belongs to the technical field of intelligent diagnosis and early warning, and the method comprises the steps: synchronously collecting the multi-modal data of a voltage transformer, the multi-modal data comprising voltage / current waveform, partial discharge signal, temperature and vibration data; preprocessing the multi-modal data, wherein a preprocessing method comprises waveform segmentation, spectrogram generation and scalar normalization; constructing a dynamic weight distribution mechanism: calculating a weight matrix based on the relevance between the time sequence features and the spatial features, and dynamically adjusting scalar weights by combining the physical coupling relationship between the temperature and the vibration to generate feature fusion weights; iteratively updating the weight matrix according to the real-time prediction error; fusing the time sequence features, the spatial features and the scalar features through the dynamic weight distribution mechanism to generate a comprehensive feature vector; and synchronously outputting a fault type classification result and an equipment health score based on the comprehensive feature vector, and calculating a real-time prediction error.

The invention discloses an electronic component early fault detection method based on infrared thermal imaging, and the method comprises the following steps: collecting an infrared thermal imaging image, synchronously collecting current and voltage data, and obtaining an infrared thermal imaging sequence; distortion correction, noise suppression and radiance correction are carried out on the sequence, and metal reflection false high-temperature points are removed; executing time sequence difference to extract a continuous heating area, and generating a time sequence temperature feature vector; inputting the feature vector into an improved Times Net model, and outputting time sequence feature representation; calculating a reconstruction error, a prediction residual error and a frequency spectrum deviation, and fusing into an abnormal score; comparing the abnormal score with a dynamic threshold value, and outputting an electronic component identifier, a potential fault type and an abnormal type; and an early fault trend report is generated by combining historical data comparison. The method realizes early detection and trend prediction of electronic component faults, and is suitable for state monitoring and reliability guarantee of a complex circuit system.

The invention discloses a cable accessory fault accurate positioning method and system based on multi-physics field cooperation, and belongs to the technical field of cable fault detection, and the method comprises the steps: installing and deploying a plurality of signal monitoring sensors in a key region of a cable accessory, and forming a multi-physics field sensing matrix; the method comprises the following steps: monitoring a cable accessory in real time by adopting heterogeneous sampling frequency, collecting a multi-physics signal, extracting a multi-physics signal feature in the multi-physics signal based on a time-frequency domain joint feature algorithm, performing signal analysis on the multi-physics signal feature, and obtaining a multi-physics signal analysis result; a multi-physical field fault model is constructed based on a multi-field coupling positioning algorithm, and a fault point in the cable accessory is positioned according to a multi-physical field signal analysis result, so that the fault of the cable accessory can be quickly and accurately identified and positioned, and the reliability and stability of a power system are improved.

The invention discloses an error correction method and device for non-intrusive voltage differential measurement, a medium and a product, and relates to the technical field of voltage measurement, and the method is applied to a scene in which a differential electric field sensor array is arranged in a set range with a measured conductor as the center. Comprising the following steps: constructing an initial correction model based on a sensor distance and a measured conductor voltage; under the actual working condition, the initial correction model is processed through a least square method, and a correction model is obtained; when non-intrusive voltage differential measurement is carried out, the current measured conductor voltage and the sensor distance are obtained, and a correction coefficient is determined by adopting the correction model; and completing error correction of non-intrusive voltage differential measurement based on the correction coefficient. According to the invention, the complexity of an inversion algorithm and the special requirement on the spatial position of the sensor can be reduced.

The invention relates to a power cable insulation state real-time monitoring and alarm device, and belongs to the technical field of power equipment state monitoring. Comprising a multi-source sensing unit which is installed at a cable body and a joint; the edge computing terminal is fixed in the cable well or the ring main unit; and the cloud early warning platform receives the processing data and outputs an alarm instruction. According to the invention, multi-parameter deep fusion is realized, four-dimensional monitoring of grounding current, temperature, partial discharge and humidity is initiated for the first time, and limitation of a single parameter is broken through; and an insulation health index IHI is proposed, and the insulation deterioration degree is quantitatively evaluated. According to the method, tan delta and IHI are calculated on site through an edge-cloud collaborative architecture edge terminal, so that the data transmission quantity is reduced; the cloud dynamic threshold value is adjusted, and the alarm accuracy is improved. According to the invention, intelligent early warning mechanism three-level alarm linkage protection equipment is arranged; and the self-calibration function ensures long-term reliability.

The invention discloses a distribution network fault positioning method, system and device based on variational mode decomposition and a medium, and relates to the technical field of power system fault positioning, and the method comprises the steps: collecting a traveling wave signal of a power distribution network, and carrying out the preprocessing of the collected traveling wave signal; performing modal decomposition on the preprocessed traveling wave signal, and outputting a plurality of modal components; based on the mode component obtained through decomposition, wave head arrival time is extracted; performing correction compensation based on the wave head arrival time difference, the phase difference and the propagation characteristics, and calculating the relative distance between the fault point and the known node; calculating the position coordinates of the fault point in the distribution network according to the geographic coordinates and the direction vectors of the nodes in combination with the relative distance; and constructing a visual interface, displaying the position of a fault point, and storing the traveling wave signal data and a fault positioning result into a database. According to the method, the adaptive correction function and the time difference compensation model are constructed, so that time difference errors caused by factors such as frequency change and phase nonlinearity can be corrected, and the accuracy of fault positioning is improved.

The invention relates to the technical field of energy storage equipment monitoring, and discloses an energy storage power station equipment state monitoring and maintenance decision system. The system comprises an equipment topology overview construction module, a multi-source monitoring module and a health state compensation module. Wherein the equipment topology overview construction module generates an equipment topology overview containing electrical connection parameters and working condition parameters based on a physical connection relationship of an energy storage power station; the multi-source monitoring module collects equipment operation state data streams from the distributed monitoring nodes and obtains environment disturbance data in real time; and the health state compensation module performs missing node compensation on the equipment operation state data stream in the equipment state fusion monitoring mode to obtain a health state index of the energy storage equipment. According to the system, equipment topology information and multi-source data are integrated, accurate assessment of the equipment health state is realized, support is provided for maintenance decision of energy storage power station equipment, and equipment operation and maintenance efficiency can be improved.

The invention discloses a lightning arrester on-line monitoring method and device, and relates to the field of intelligent monitoring, and the method comprises the steps: firstly collecting original current and voltage waveform data, carrying out the frequency domain decomposition operation, then introducing an intelligent prediction mechanism, carrying out the intelligent prediction of a phase error based on the fundamental voltage phase, temperature change and harmonic characteristics, and finally, carrying out the calculation of the phase error. And performing phase angle correction operation and preliminary calculation of resistive current on the fundamental current and voltage by using the corrected phase information, and introducing a historical data smoothing algorithm to perform noise reduction optimization on a result. And finally, comparing the analyzed and processed resistive current with a preset threshold value, and judging whether an alarm prompt is generated or not. Therefore, real-time evaluation and abnormal alarm of the operation state of the lightning arrester are realized, so that a powerful guarantee is provided for safe operation of a power system.

The invention relates to the technical field of self-adaptive starting, and discloses a self-adaptive starting method and system based on voltage and current monitoring, and the method comprises the steps that an MCU (Microprogrammed Control Unit) triggers an auxiliary silicon controlled rectifier and a main silicon controlled rectifier through preset starting logic to start a motor, and carries out the high-frequency sampling of current and voltage data in real time; once starting failure is detected, short-time voltage sequences and short-time current sequences before and after a failure occurrence point can be automatically captured and stored. Then, key features are extracted from the sequences, and failure causes are determined through decision tree model analysis. And based on the analysis result and the current retry times, the MCU outputs a corresponding retry strategy to ensure that the motor can be successfully started or enter a safety mode. Thus, the reliability of motor starting is improved, subsequent fault diagnosis is supported through detailed data recording, and the maintainability and safety of the system are greatly improved.

The invention relates to the field of lithium battery intelligent monitoring, in particular to a low-temperature-resistant lithium battery BMS monitoring method and system based on cooperative sensing. The method comprises the following steps: acquiring surface temperature distribution, environment temperature and electrochemical parameters of the low-temperature tolerant lithium battery, and constructing a multi-source state characteristic matrix through space-time alignment and data fusion; applying a composite current excitation signal, and combining impedance analysis and spectrum analysis to extract impedance parameters, nonlinear distortion components and electrochemical stress factors to form a multi-dimensional feature vector; obtaining a state monitoring result by utilizing decision level fusion; through multi-sensor fusion and multi-dimensional feature fusion, the special internal state of the low-temperature tolerant lithium battery is comprehensively represented, early risk identification and early warning are carried out aiming at unique electrochemical behavior features of the low-temperature tolerant lithium battery, and compared with a common lithium battery monitoring method, the method has the advantages that the safety is high; and the accuracy, the robustness and the real-time performance of monitoring the state of the low-temperature tolerant lithium battery in the low-temperature environment are remarkably improved.

The invention belongs to the field of power distribution automation, and relates to a power grid equipment frequency adaptive resampling method based on a dynamic scale factor, and the method comprises the steps: carrying out the point compensation through employing a linear interpolation method when a power grid equipment detects that the number of continuous lost points is smaller than or equal to 3; selecting a voltage signal channel or a current signal channel to carry out zero crossing point detection and real-time frequency calculation; when the real-time frequency obtained through calculation deviates from the nominal frequency, the power grid equipment introduces a dynamic scale factor for resampling, sampling points are increased when the real-time frequency is higher than the nominal frequency, and sampling points are reduced when the real-time frequency is lower than the nominal frequency; writing data obtained by resampling into a calculation annular cache region of the power grid equipment; and performing FFT calculation on the data in the calculation annular cache region to obtain power grid parameters. The sampling interval of the data acquisition unit is dynamically adjusted by increasing or reducing the sampling points, so that the sampling time sequence and the actual signal period are synchronized, the sampling frequency and the real-time frequency are adaptively matched, and the sampling distortion problem under the power grid frequency fluctuation is solved.

The invention discloses a distribution line fault detection and positioning method based on current signal analysis, and belongs to the technical field of power system fault diagnosis. The method comprises the steps of collecting three-phase and zero-sequence current data before and after a fault, calculating current variation, judging whether the fault is an interphase short-circuit fault, and determining a first fault interval by combining an impedance method; and performing wavelet packet transformation on the transient voltage signal to obtain an actual transient signal, injecting a test signal in the first fault interval, collecting a response signal, constructing an autoregression model, solving an inverse noise function, and performing denoising processing on the actual transient signal to obtain a target transient signal. Wavelet packet decomposition is carried out on the denoised target transient signals, energy distribution and signal-to-noise ratio information of all frequency bands are combined, a corrected positioning formula is introduced, and accurate positioning of a second fault interval of the fault point is achieved. The method effectively inhibits noise interference, improves the positioning precision, and is suitable for a real-time fault detection and positioning system in an intelligent power distribution network.

The invention relates to the technical field of power system fault diagnosis, and discloses a flexible DC power distribution network fault diagnosis method and system. The method comprises the steps of collecting a fault current time sequence signal of a direct current side of the flexible direct current power distribution network, inputting the fault current time sequence signal into a pre-trained fault diagnosis model, and performing adaptive time frequency feature extraction on the fault current time sequence signal by using an interpretable complex time frequency convolution layer to obtain a time-frequency-amplitude tensor, a time-amplitude trajectory, a time-frequency trajectory and a frequency-amplitude trajectory are constructed, the trajectories are subjected to Gramer difference angular field coding, a three-channel time-frequency feature matrix is generated, the three-channel time-frequency feature matrix is input to a parallel double-branch feature extraction module, and global features and local features are extracted; and carrying out adaptive weighted fusion on the extracted global features and local features, classifying the fused features, and outputting a diagnosis result of a fault category. According to the invention, the accuracy of fault diagnosis of the flexible DC power distribution network is improved.