340 results about "Transient signal" patented technology

A power distribution network cable fault point location method and system, a computer device and a medium, belonging to the technical field of power distribution networks. The location method comprises: S101, when a cable has a fault, a transient signal of a wide-area frequency band is generated, a fault traveling wave containing high-frequency components; a traveling wave line mode component having a relatively small chromatic dispersion is used to perform measurement; decoupling an extracted fault traveling wave signal; S102: the traveling wave voltage and current will have a sharp change when the traveling wave generated by the fault point reaches a measurement end, and the traveling wave head can appear as a high-frequency sudden change in a time-frequency graph, the sudden change point being the position of the wave head; S103: performing EEMD decomposition on the fault traveling wave line mode component, and extracting a first IMF component for Hilbert transform to obtain a time-frequency graph thereof, a sampling moment corresponding to the position of a first frequency sudden change point on the time-frequency graph being the arrival time of the fault traveling wave head; and using a double-end fault location algorithm to calculate a distance with respect to the fault point. The result of the location method has relatively high precision, and, compared with the actual fault position, has a relative error not more than 4%.

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

The invention discloses a self-adaptive multi-dimensional adjustment metering box based on the Internet of Things, and relates to the technical field of intelligent monitoring of power equipment. The problem of measurement distortion caused by high fixed threshold false triggering rate, weak anti-aliasing capability and multi-source data asynchronization in a high-dynamic industrial scene in the prior art is solved. Multi-parameter acquisition is realized through the sensing acquisition module and the annular buffer area; a reconfigurable FIR filtering module is adopted to dynamically switch a low-pass / band elimination mode to suppress aliasing interference; noise features are extracted by combining spectral kurtosis analysis and concept drift detection, and a dynamic threshold value is generated by using exponentially weighted moving average and a sliding window standard deviation; a multi-channel time sequence is aligned through an IEEE 1588 protocol, and cloud parameter closed-loop optimization is realized based on extended Kalman filtering and a particle swarm algorithm; according to the method, the threshold fault tolerance, the high-frequency transient signal capturing precision and the multi-source heterogeneous data fusion reliability in a high-noise environment are remarkably improved.

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

The invention relates to the technical field of remote control of sensing equipment, in particular to a remote control method and system of wireless sensing equipment. The method comprises the following steps: collecting real-time network monitoring parameters of wireless sensing equipment; performing transient signal fluctuation identification and multi-time-point load state evolution on the real-time network monitoring parameters, and constructing a multi-time-point network load state curve; performing sliding time window segmentation on the multi-time-point network load state curve, and performing future time period state transition prediction so as to construct a future time period network state trend chart; acquiring a remote control instruction of the wireless sensing equipment; and performing dynamic instruction transmission rate adjustment on the remote control instruction of the wireless sensing equipment according to the network state trend chart in the future time period to obtain a dynamic transmission rate adjustment strategy. According to the invention, the remote control stability and performance of the wireless sensing equipment are improved.

The invention provides a transformer fault intelligent diagnosis method and system based on a deep convolutional network, and the method comprises the steps: capturing a transformer global transient signal through a heterogeneous physical field synchronous sampling array which is deployed in a spatial discretization manner; inputting three kinds of heterogeneous data streams of vibration spectrum, electromagnetic leakage wave and partial discharge pulse into a multi-scale feature decoupling program; forming an initial feature primitive set of fault diagnosis; inputting the generated vibration mode tensor, the electromagnetic eddy current characteristic spectrum and the discharge pulse characteristic cloud into a characteristic interaction contribution degree analysis program; generating a fault sensitive feature topological graph carrying a weight identifier; training a depth differentiable diagnosis decision tree by using the fault sensitive feature topological graph, and analyzing and positioning key diagnosis nodes; and millisecond-level synchronous output of fault types and positioning information is realized. The system comprises a multi-modal feature generation module, an adaptive fusion module and a lightweight diagnosis module. The invention provides a new-generation interpretable, low-delay and high-precision diagnosis normal form.

The invention discloses a power distribution network fault analysis and positioning method and system based on artificial intelligence, and relates to the technical field of intelligent power grids, and the method comprises the steps: collecting the multi-modal data of a power distribution network through a distributed multi-pulse source and D-PMU equipment, carrying out the preprocessing, and extracting the features of a transient signal through wavelet transform; constructing a feature graph based on the extracted transient signal features, analyzing the space-time association strength between nodes according to the feature graph, and performing dynamic adjustment and weighted fusion of the space-time association weight; positioning and classifying fault points by using a graph attention network model based on the weighted fusion feature graph; according to the method, the time-space correlation strength among the nodes is analyzed and dynamically adjusted, so that the accuracy and flexibility of power distribution network fault point recognition are effectively improved, and the fault points are positioned and classified by using the graph attention network model based on the weighted fusion fault feature graph, so that the fault point recognition accuracy is improved. And the automation degree and the accuracy of fault response are greatly improved.

The invention belongs to the technical field of power grid fault prediction, and particularly relates to a power transmission line fault prediction method, system and terminal based on spatio-temporal information fusion, and the method comprises the steps: collecting a transient signal of a power transmission line and multi-dimensional environment data of a region where the power transmission line is located in real time, and the multi-dimensional environment data comprises meteorological parameters and geographic parameters; time periods are divided based on 24 solar terms, a dynamic weight matrix is constructed, and initial weights of all environmental factors under different solar terms are trained through historical fault data; according to the method, the transient signals of the power transmission line and the multi-dimensional environment data of the area where the power transmission line is located are collected in real time, the dynamic weight matrix is constructed based on 24 solar terms, and the influence of meteorological parameters, geographic parameters and time factors on the line fault is fully considered. According to the method, the actual operation state of the line can be reflected more comprehensively, so that the accuracy of fault prediction is improved.

The invention provides a high-precision electric energy metering and fault diagnosis method adaptive to multiple working conditions, and the method comprises the following steps: carrying out the multi-rate synchronous sampling of an electric parameter through a double-AD parallel sampling architecture, collecting a fundamental wave voltage / current signal through a low-speed AD module at a sampling rate of 4kHz, capturing a high-frequency harmonic wave and a transient signal through a high-speed AD module at a sampling rate of 256kHz, and carrying out the fault diagnosis of the high-frequency harmonic wave and the transient signal; a sampling mode is dynamically switched according to the load harmonic content; a sliding window mechanism is adopted to carry out multiple times of repeated detection on the same electric parameter, five times of independent measurement are completed in a 200ms time window, an outlier is eliminated based on a Pauta criterion, then a weighted mean value of a signal-to-noise ratio is calculated, AD reference voltage is calibrated in real time through a PT100 temperature sensor, and the accuracy of the AD reference voltage is improved. Carrying out nonlinear dynamic compensation by combining a cubic spline interpolation method of a pre-stored load characteristic curve; and constructing a composite electric energy calculation model, summing the fundamental wave electric energy and the harmonic electric energy weighted according to the standard to generate a total electric energy value, and comparing a metering result with a theoretical value in real time by using a digital phase-locked loop.

The invention provides an intelligent fault diagnosis method and system for an intelligent pod. According to the method, a power line voltage fluctuation signal and a motor control line transient signal are synchronously collected, and multi-frequency-band feature extraction and transient energy density analysis are carried out respectively. Establishing a contrastive analysis mechanism of voltage fluctuation amplitude and transient amplitude based on the synchronous trigger point, calculating a time difference and generating a composite rule containing an amplitude threshold interval and a phase correlation value; a multi-dimensional space model fusing amplitude, energy and time difference is constructed, a fault prediction rule base is established, finally, an abnormal point distribution mode is recognized through feature vector matching degree, a fault type is accurately positioned, a diagnosis result is output, and fault diagnosis and positioning of the intelligent pod are achieved. According to the technical scheme provided by the invention, accurate fault positioning and diagnosis of the intelligent pod power supply and control system are realized through multi-dimensional signal feature fusion and an intelligent matching algorithm.

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 provides an adaptive stochastic resonance weak fault detection method based on kurtosis optimization, and belongs to the technical field of fault detection, and the method comprises the steps: collecting and preprocessing a transient signal on a cable line; constructing a bistable stochastic resonance model; kurtosis of the output signal is used as a fitness function, and potential well parameters of the bistable stochastic resonance model are optimized based on a particle swarm optimization algorithm; and substituting the optimized potential well parameter into the bistable stochastic resonance model, and processing the preprocessed transient signal to obtain an enhanced output signal for fault detection. The method has the beneficial effects that the stochastic resonance system is constructed on the basis of the classical bistable model, the kurtosis of the output signal is used as the fitness function of the particle swarm optimization algorithm, the potential well parameters are adaptively optimized, the potential well parameters are driven to automatically converge to the optimal state, and the robustness of the system is improved. The high-impedance grounding fault traveling wave signal can be extracted and enhanced from a strong noise background, and the detection sensitivity and reliability are improved.

The invention discloses a silicon-based NMOS internal defect detection method, system and device and a medium, and the method comprises the steps: enabling a source electrode and a drain electrode to be in short circuit, respectively applying a high level and a low level to a grid electrode and a Bulk end, applying a reverse voltage to the grid electrode at a single temperature point, and recording a capacitance transient signal in a deep energy level trap filling and emission process in a space charge region; a digital system is used for recording N transient measurement values of the capacitor at different temperature points; Fourier calculation is used for converting the N transient measurement values into a relational graph of discrete Fourier coefficients and temperature, so that information such as trap level, defect concentration and capture cross section of the deep level trap can be accurately obtained. According to the method, a capacitance transient spectrogram obtained by voltage pulse excitation under a variable-temperature condition is converted into a relational graph of a discrete Fourier coefficient and temperature by Fourier calculation, so that the defect level of a deep level trap is accurately obtained. And the transient test time can be further shortened for the discrete trap level, so that the result is accurately controlled.

The invention relates to a power transmission line fault positioning method based on an improved YOLOv8 model and a double-end TWM, and the method comprises the steps: collecting three-phase current transient signals at M and N ends of a line after a fault occurs, carrying out the phase-mode conversion of the three-phase current transient signals through employing a Karen's transformation method, and selecting a traveling wave current line-mode component as a fault traveling wave research object; using a wavelet analysis method to decompose the traveling wave current line mode component to obtain traveling wave generation pictures with different sampling rates; building a Pix2PixHD model and training the Pix2PixHD model; constructing a fault target detection model, and obtaining a time range of a fault traveling wave head by using the fault target detection model; constructing a traveling wave main body segmentation model, and obtaining a traveling wave head maximum value point by using the traveling wave main body segmentation model; and according to the time range of the fault traveling wave head and the traveling wave head maximum value point information, calculating to obtain a line fault point position. According to the invention, accurate line fault positioning is realized, and the problem of low line fault positioning accuracy in the prior art is solved.

The invention relates to the technical field of discharge monitoring and early warning, in particular to a partial discharge real-time monitoring and early warning system based on multi-source data fusion, which is characterized in that decision tree classification is carried out on a mixed transient signal flow through a signal physical separation module, and pure electromagnetic wave speed type and mechanical wave speed type signal sources are separated; the feature marking module extracts feature points with a unified time reference and marks physical attribute categories; the double-path credibility fusion module executes time difference logic verification on the two types of feature points, a credible discharge event is marked only when an electromagnetic wave verification result and a mechanical wave verification result are logic values 1 at the same time, event confidence parameters are generated through dynamic confidence fusion, and physical interlocking verification and uniqueness locking are achieved; the anti-interference response module dynamically adjusts a response threshold value based on the event confidence parameter, and only triggers a targeted response to the threat which is physically and essentially confirmed to be real discharge and has confidence reaching the standard; the problem of distinguishing a real discharge event from multi-source interference is solved, and the real discharge event is reliably identified.

The invention relates to the technical field of power distribution network fault positioning, in particular to a power distribution network fault traveling wave positioning method and system, and the method comprises the steps: synchronously collecting a transient signal through a distributed traveling wave sensing unit, and extracting an initial traveling wave arrival time; constructing an adaptive wave velocity model based on line physical parameters to calculate an equivalent propagation velocity; dynamically generating a traveling wave propagation tree according to the real-time switch state to determine an effective monitoring path; based on the propagation path and the actual arrival time, an optimization algorithm is adopted to determine a preliminary fault point; and calculating a consistency index of the polarity of the traveling waves of the adjacent nodes, starting a multi-hypothesis tracking mechanism in a preliminary point neighborhood when the consistency index is lower than a threshold value, performing confidence coefficient weighted evaluation by combining a traveling wave attenuation model, and outputting an accurate positioning result. According to the method, through cooperation of dynamic topology mapping, adaptive wave velocity correction and multi-source closed-loop verification, the precision, reliability and adaptive capability of fault positioning in a complex power distribution network environment are remarkably improved, and the operation and maintenance requirements of rapid self-healing are met.

The invention provides an unmanned aerial vehicle identification method fusing heading, micro-motion spectrum and transient signal features. The method comprises the following steps: extracting unmanned aerial vehicle target heading features; unmanned aerial vehicle target micro-motion spectrum feature extraction; extracting transient features of a target communication signal of the unmanned aerial vehicle; constructing a fusion feature vector; and classifying and identifying the target. According to the method, the heading features, the micro-motion spectrum features and the transient signal features of the light and small unmanned aerial vehicle are extracted, the unmanned aerial vehicle target is classified and identified by using the random forest classification method through feature normalization fusion, and the robustness and accuracy of unmanned aerial vehicle identification are improved.

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 relates to the technical field of power system fault detection, and discloses a 10kV cable single-phase earth fault detection method based on an adaptive algorithm, and the method comprises the following steps: S1, employing a voltage collector and a current collector to collect the power frequency and transient signals of a switching station integrated intelligent terminal strip; s2, filtering and de-noising the collected signals through a self-adaptive digital signal processing algorithm; s3, adaptively determining a single-phase earth fault signal component, and identifying the most significant frequency band; s4, extracting features of the fault signal by using a multi-band multi-criterion adaptive algorithm; and S5, based on the extracted features, selecting the most significant criterion for single-phase earth fault detection. Through an adaptive algorithm and a deep learning model, the detection and recognition precision of a complex single-phase earth fault is improved, real-time intelligent processing is realized through an integrated monitoring module, misjudgment is reduced, the operation and maintenance efficiency is improved, the system adapts to multi-scene application, and the intelligentization and power supply reliability of the system are enhanced.

The invention provides a power distribution network fault section rapid calibration method based on a feeder terminal. According to the method, power distribution network line electrical quantity and distributed power supply output information are collected in real time through a feeder terminal and preprocessed, then fault transient signals are processed through complete set empirical mode decomposition of mean value self-adaptive noise, and intrinsic mode components are obtained to judge fault types. Thirdly, fault section preliminary positioning is carried out, and an approximate area is determined according to a positive sequence voltage vector for a symmetric short-circuit fault, a negative sequence voltage vector for an asymmetric short-circuit fault and a zero sequence current amplitude for a single-phase earth fault; then accurate calibration is carried out, a single-phase earth fault is judged through a filter and correlation coefficients, and short circuit and broken line faults are calibrated through a model or an intelligent algorithm in combination with line topology and electrical parameters. And finally, establishing a target function containing a penalty term, converting the target function into a Markov decision process, solving through an approximate dynamic programming algorithm, realizing rapid and accurate calibration of a fault section, and improving the power supply reliability of the power distribution network.

The invention relates to the field of machine learning, particularly discloses an artificial intelligence-based power system fault analysis and diagnosis method, and effectively solves the problem of information loss caused by neglecting a key waveform form in a transient signal in the prior art through a local feature extraction and serialization module. An original signal is converted into a local feature sequence with more characterization significance. Aiming at the averaging bottleneck of an existing model in an information aggregation stage, a traditional feature compression method is abandoned, and a sequence information aggregation and decision-making mechanism is provided. According to the mechanism, a context sensing sequence is regarded as a probability event, and modeling is carried out on the sequence from three orthogonal dimensions of a content center, time sequence dispersion and distribution uncertainty by calculating feature expectation, time sequence variance and information entropy of the context sensing sequence. The method can deeply insight and quantify the essential difference of different events in the time sequence dynamic evolution mode, thereby fundamentally solving the problem of misjudgment caused by feature confusion.

The invention discloses an ICEEMDAN and improved YOLOv8-based power transmission line fault location method, which comprises the following steps of: respectively constructing a fault identification model and a fault location model by using an improved YOLOv8 network, carrying out phase-mode transformation on a three-phase voltage transient signal of a fault line by using a Karen transform method, selecting a line-mode component as a fault traveling wave research object, decomposing the line-mode component by using an ICEEMDAN algorithm, and carrying out fault location on the three-phase voltage transient signal of the fault line. Using an NTEO algorithm to strengthen a modal component of the line mode component, highlighting fault signal features, and obtaining a fault traveling wave energy spectrogram and an amplified traveling wave energy spectrogram; an initial fault time range is determined by using a fault identification model, and then a fault time point, namely the accurate arrival time of fault traveling wave heads at two ends of the line, is obtained by using a fault positioning model according to the amplified traveling wave energy spectrogram. The method improves the precision of line fault distance measurement, is not affected by transition resistance, fault distance and transmission lines, and is high in reliability and good in practicability.

The invention relates to an underwater acoustic transient signal detection method, in particular to an underwater acoustic transient signal detection method under Alpha stable distribution noise. The invention provides an underwater acoustic transient signal detection method under Alpha stable distribution noise, and aims to solve the problems that the performance of a traditional signal detection method based on Gaussian hypothesis is remarkably reduced and the estimation precision of the arrival time is low due to the fact that actual marine environment noise usually has non-Gaussian characteristics such as sudden peaks and the like. The method comprises the following steps: step 1, acquiring an original underwater acoustic transient signal under a non-Gaussian background; step 2, carrying out noise reduction processing on the collected original underwater acoustic transient signal under the non-Gaussian background to obtain a noise-reduced underwater acoustic transient signal under the non-Gaussian background; and step 3, a short-time cross-correlation entropy detector is adopted to process the hydro-acoustic transient signal subjected to noise reduction processing in the step 2 under the non-Gaussian background, and the short-time cross-correlation entropy and the arrival time of the target signal are obtained. The method is applied to the field of underwater acoustic transient signal detection.

The invention discloses a new energy grid-connected line fault section positioning system based on an ant colony algorithm, and relates to the technical field of power grid fault diagnosis, and the system comprises a wire signal collection module and a fog end intelligent analysis module. According to a distributed architecture, an optimal section where a fault occurs in a grid-connected line is analyzed. The method has the advantages that real-time positioning is high, the network pressure of data centralized transmission and the complexity of centralized calculation are reduced, the fault analysis precision is improved, and the criterion reliability is improved.

The invention discloses an ultratrace mercury isotope analysis method, and relates to the technical field of environmental analysis. The method comprises the following steps: purging a liquid sample to be detected by nitrogen in a stannous chloride atmosphere, and trapping and concentrating mercury into a gold trapping tube; pyrolyzing the pyrolytic gold trapping tube to release mercury vapor; pyrolytic mercury vapor and thallium isotope internal standard aerosol are mixed to serve as a sample injection sample, and the sample injection sample is introduced into a multi-receiver inductively coupled plasma mass spectrometry; acquiring a mercury instantaneous signal and a thallium isotope signal; and calculating the mercury isotope ratio by adopting least square linear regression fitting in combination with a thallium internal standard ratio correction algorithm. According to the method, the instantaneous mercury isotope signal is generated by online combination of gold trapping and multi-receiver inductively coupled plasma mass spectrometry, the instantaneous signal is strategically collected and processed, the determination concentration of isotope analysis can be greatly reduced, the sensitivity of the method is remarkably improved, and direct determination of mercury isotope composition in a water sample with the concentration of 10 ng L <-1 > is met.

The invention belongs to the technical field of cable fault positioning, and discloses a cable branch box fault early warning and positioning system based on online partial discharge monitoring. Comprising a topology building module which is used for building a topology model in a cable branch box and defining a signal propagation path rule; pre-calculating signal expectation characteristics from each topological node to all monitoring sensors to form a structured expectation library; the acquisition and analysis module is used for acquiring multi-source transient signals and performing feature extraction to obtain an information fusion matrix; the decoupling positioning module is used for performing analysis based on the information fusion matrix and the structured expectation library to obtain a fault positioning point; the evaluation and early warning module is used for extracting all transient events in a preset time period corresponding to the fault positioning point, forming a time sequence feature sequence, extracting and analyzing an evolution index of the time sequence feature sequence, obtaining a risk transition probability, dividing risk levels and carrying out corresponding early warning; according to the invention, non-confusion accurate positioning of the partial discharge source of a specific joint or a cable section in the branch box is realized.

According to the power distribution network fault protection and positioning method and system based on the fast switch, an edge calculation node adopts noise self-adaptive improved Morlet wavelet transform, and a current change rate and a voltage break variable are extracted from a transient signal; constructing a three-dimensional decision model taking the current change rate, the voltage break variable and the line characteristic impedance as variables; based on a three-dimensional decision-making model, when it is judged that an internal fault occurs by using the extracted current change rate and voltage break variable, the distributed detection unit sends the action state quantity of the equipment where the distributed detection unit is located to an edge computing node; after the edge computing node utilizes the action state matrix of the equipment, a state reasoning method based on a fuzzy Petri network is adopted to determine a fault section; verifying the fault section according to the matching degree of the fault current weighting and the characteristic impedance of the equipment in the fault section; after the fault section is verified, fault isolation is executed; and after fault isolation, fault positioning is optimized, and it is ensured that the cooperative breaking time sequence of the multi-stage switch is accurately matched with a fault diffusion path.

The invention relates to the technical field of battery science, in particular to a battery health real-time diagnosis method based on voltage internal resistance correlation analysis in the battery replacement process. The method comprises the following specific implementation steps: firstly, capturing a high-frequency transient signal at the moment of battery connection and disconnection, and carrying out real-time evaluation and error correction on the high-frequency transient signal based on a signal symmetry evaluation mechanism; thirdly, a battery health model is designed, the model generates a nonlinear internal resistance spectrum through an AI model part, and a health state physical model is used as priori knowledge for constraint; and the health state physical model outputs an initial health state according to the internal resistance spectrum adjustment parameters. And finally, training the battery health verification model by utilizing meta learning, outputting a health state predicted value by the model, and obtaining a fusion result of an error of the battery health verification model and an error of the health state predicted value through an error intelligent fusion method. And performing weighted average on the health state prediction value and the initial battery health state according to a fusion result to determine a final battery health state.

The invention discloses a permanent magnet motor starting control method and system based on harmonic injection, and aims to improve the harmonic injection precision and the dynamic adaptive capacity in the motor starting process. According to the method, frequency domain features are extracted by adopting CNN of a multi-scale convolution kernel, dynamic changes of a time sequence are captured in combination with Bi-LSTM, and an attention mechanism is introduced to dynamically weight key transient signals, so that comprehensive modeling and optimization of harmonic signals on frequency domain, time domain and transient features are realized; and an efficient closed-loop dynamic optimization system is constructed by further combining the global search capability and the real-time error feedback mechanism of the genetic algorithm. The method can adaptively capture the key change when the working condition suddenly changes, correct the harmonic injection parameters in real time, significantly reduce the power loss caused by torque fluctuation and high-frequency harmonic in millisecond response, and improve the stability of harmonic injection and the energy efficiency optimization effect.

Transient signal suppression is provided. A system includes a choke. The choke includes an inner surface defining a cavity, the inner surface configured to surround a plurality of conductive elements. The choke includes an outer surface configured to couple with a housing for the plurality of conductive elements.