441 results about "INSULATION FAILURE" patented technology

The invention relates to a low-voltage motor insulation resistance online monitoring and early warning method, system and device and a medium. The method comprises the following steps: firstly, acquiring multi-dimensional operation data through a data acquisition link to form an original monitoring data set; preprocessing the original monitoring data set, and focusing core information through dimension reduction processing to obtain a key parameter set; and then inputting the key parameter set into a dynamic correlation model constructed based on a long short-term memory network, and outputting a prediction result of the variation trend of the insulation resistance value along with time and the deterioration degree quantification. And finally, carrying out early warning analysis based on a prediction result, and generating early warning fault features in combination with a real degradation judgment process. According to the method, the insulation resistance trend and the degradation degree are accurately predicted, the defect of insufficient real-time performance of off-line detection is overcome, the accuracy and the pertinence of fault early warning are improved, and equipment shutdown and safety risks caused by insulation faults are effectively reduced.

The invention relates to the technical field of power equipment fault diagnosis, in particular to a transformer overheating fault intelligent diagnosis system based on a physical information neural network. According to the invention, physical constraint filtering is carried out on signals through the adaptive noise suppression module; a loss function fusing a heat conduction equation, a gas decomposition kinetic equation and an insulation aging kinetic equation is constructed through a physical information neural network, and coupling modeling of temperature and gas and collaborative prediction of the aging state of insulation paper are achieved; spatial topological features and time sequence features are extracted through a multi-modal feature fusion module, and cross-modal association is established by adopting an attention mechanism; predicting a future feature trajectory through a fault evolution prediction module and outputting hierarchical early warning; and finally, outputting fault types, severity and maintenance suggestions. According to the method, a physical mechanism and data intelligence are deeply fused, advanced early warning and accurate positioning of an overheat fault are realized, and meanwhile, the blank that insulation failure cannot be pre-judged in the prior art is filled.

The invention relates to the technical field of high-voltage electrical equipment, and particularly discloses a high-insulating-property thermoplastic sleeve and a method for preparing the same based on a co-extrusion process, the surface of a nonlinear layer is wrapped with an insulating material, so that the corona discharge phenomenon is avoided; and meanwhile, the same thermoplastic material is adopted as the insulating layer (including the first thermoplastic layer and the second thermoplastic layer) material and the base material of the nonlinear layer, so that no obvious interface exists between the insulating layer and the nonlinear layer, charge accumulation is avoided, insulation failure is avoided, the insulation and voltage resistance of the sleeve is improved, and the operation reliability is improved.

The invention discloses an insulation fault edge self-healing method and device for a power conversion system, and belongs to the technical field of fault diagnosis and automatic maintenance of power electronic equipment, and the method comprises the steps: obtaining multi-modal sensing data, synchronously collected by a multi-modal sensor array, of the power conversion system; a preset lightweight CNN-Transform hybrid model is used to process the multi-modal sensing data, and a feature vector of each modal is obtained; fusing each modal feature vector by adopting a dynamic weight attention mechanism to obtain a fused fault feature vector; classifying the fused fault feature vectors based on a fault classifier, and determining a fault level; and according to the determined fault level, sending a corresponding control instruction to an execution mechanism, and triggering a hierarchical self-healing action. According to the invention, fault identification and self-healing decision can be rapidly completed, and the accuracy of fault detection is effectively improved.

The invention relates to the technical field of on-line monitoring, in particular to an insulation fault on-line monitoring method and system for a medical IT system control cabinet. The method comprises the following steps: selecting a non-integer frequency multiplication detection frequency according to equivalent ground distributed capacitance in a previous monitoring period, and superposing the non-integer frequency multiplication detection frequency with a fundamental frequency to form a composite signal injection system; collecting signals to calculate a voltage instantaneous harmonic distortion rate change gradient, and selecting a time window to split the leakage current according to the change gradient; on the basis of dispersion estimation signal-to-noise ratio weighted fusion current vectors, insulation resistance and distributed capacitance are calculated; constructing a four-dimensional original feature vector composed of the insulation parameter, the phase difference and the intermodulation component energy; constructing a diagonal weighting matrix based on the resistance-capacitance ratio and the gradient to transform a feature vector, and determining the fault attribution through the Mahalanobis distance calculation with the fault template library. According to the scheme, harmonic interference can be avoided, calculation stability can be improved, insulation instantaneous sudden change can be quickly responded, and different fault types can be accurately distinguished to guide maintenance.

The invention relates to a ring main unit intelligent detection and fault early warning method and system. The method comprises the following steps: deploying an ultrahigh frequency sensor, an infrared thermal imager, an ultraviolet sensor and a high-frequency current transformer, and synchronously acquiring multi-modal data such as partial discharge, temperature distribution, corona discharge and grounding current of the ring main unit; carrying out feature extraction on the collected data, wherein the feature extraction comprises a time-frequency statistical feature and a temperature-load correlation feature of the partial discharge signal; the Dempster-Shafer evidence theory is utilized to carry out fusion calculation on the multi-source feature evidence, and confidence coefficients for insulation degradation, poor contact, arc faults and other fault types are obtained; arc fault prediction is carried out in combination with a long-short-term memory network model; and finally executing a grading early warning strategy according to the fault confidence. The problems of low detection reliability and fault early warning lag of a single sensor are solved, early accurate diagnosis and predictive maintenance of the insulation fault of the ring main unit are realized, and the operation safety and reliability of power equipment are remarkably improved.

The invention provides an insulation fault detection method and device of a power battery, equipment and a medium. The method comprises the steps of obtaining multiple pieces of state time sequence data of the power battery; wherein the multiple pieces of state time sequence data comprise insulation resistance value time sequence data, collected by an insulation monitoring unit, of a battery module in the power battery and sensor time sequence data, collected by each preset sensor, of the power battery; performing weighted data fusion on the plurality of pieces of state time sequence data, and determining an insulation health score of the power battery; and comparing the insulation health score with a plurality of preset score thresholds, and determining an insulation fault detection result of the power battery. Through the method and the device, the degradation trend of the insulation performance can be recognized earlier, the sensitivity and the accuracy of fault detection are improved, safety accidents caused by lagging alarm of insulation faults are effectively avoided, and the operation safety and the reliability of a power battery system are remarkably improved.

The invention discloses an on-line insulation monitoring system and method for a national railway vehicle, and relates to the technical field of on-line insulation monitoring, and the system comprises a detection signal injection module which is used for injecting a preset broadband active detection signal, and synchronously collecting a response signal set transmitted through a vehicle body grounding network; the fingerprint feature construction module is used for constructing network fingerprint features in a current insulation state; the spatial distribution atlas acquisition module is used for carrying out inversion calculation to obtain an admittance spatial distribution atlas of the whole vehicle; and the insulation abnormal area identification module is used for identifying an insulation abnormal area. According to the invention, the technical problems of long troubleshooting time and poor accuracy caused by incapability of online real-time monitoring of the insulation state of the vehicle body grounding network of the national railway vehicle and low abnormal area positioning precision in the prior art are solved, and online monitoring of the insulation state of the vehicle body grounding network of the national railway vehicle and accurate positioning of the abnormal area are realized. And the efficiency and the accuracy of insulation fault checking are improved.

The invention relates to the technical field of power equipment on-line monitoring and fault diagnosis, and discloses a converter transformer bushing end screen voltage abnormity detection device and early warning method.The method comprises the steps that voltage data are collected through a wireless intelligent sensor, soft synchronization and self-adaptive windowing are achieved through a commutation notch feature locking mechanism, and a preprocessing signal is output; topological correlation clustering is executed to construct a virtual health reference, a dynamic residual sequence is extracted through time domain difference, and system side and equipment side features are decoupled; constructing a variational mode decomposition model, and calculating a characteristic entropy value of the intrinsic mode function; and comparing the entropy value with a threshold value, triggering an active impedance disturbance verification mechanism, controlling a sensor to access a load and comparing an actual voltage drop rate with a theoretical voltage drop rate, and determining an insulation degradation or poor contact fault. High-precision synchronization can be achieved without a hardware clock, and the problems that weak fault features are difficult to extract and virtual connection and insulation faults are difficult to distinguish in a high-impedance loop are effectively solved.

The invention provides a ground insulation resistance detection circuit, a detection method and a product, which are applied to a detection scene of ground insulation resistance of a direct current side of a non-isolated high-voltage energy storage converter. According to the ground insulation impedance detection circuit provided by the embodiment of the invention, a complex isolation circuit in an isolation type scheme is not needed, an additional bridge arm resistor in a non-isolation type scheme is also avoided, the detection circuit is constructed through a small number of resistors, switches and voltage acquisition modules, the number of devices and the occupation of PCB space are reduced, the design and manufacturing cost can be effectively reduced, and the detection circuit is suitable for large-scale popularization and application. And the compact layout requirement of the non-isolated high-voltage energy storage converter is met. On the basis, the embodiment of the invention further provides a ground insulation impedance detection method, the voltage acquisition module stably collects the first voltage and the second voltage through on-off of the switch, whether the risk of insulation failure exists or not is judged, the situation of sampling signal distortion in a non-isolation scheme can be reduced, and the detection precision is effectively improved.

The invention relates to an insulation fault identification method and system, electronic equipment, a medium and a product, and belongs to the technical field of electronics. The method comprises the steps that when the insulation resistance value of a vehicle is smaller than a preset first resistance value, whether the vehicle is subjected to direct current charging operation within a preset first time period or not is judged; if the direct current charging operation is not carried out on the vehicle, whether a first difference value between the maximum value and the minimum value of the insulation resistance value in a preset second time period is larger than a preset second resistance value or not is judged; if the first difference value is not larger than the preset second resistance value, whether the air conditioner of the vehicle is used or not within a preset third time period is judged; if the air conditioner is used, the insulation fault type of the vehicle is determined according to the temperature parameters related to the air conditioner. By analyzing the big data related to the vehicle insulation fault, the insulation fault type can be quickly determined, so that the insulation fault positioning efficiency is improved, the troubleshooting period is shortened, the economic loss caused by continuous existence of the fault is reduced, and the maintenance cost is reduced.

The invention discloses a power equipment insulation fault early warning method and system based on machine learning, and relates to the technical field of fault early warning, and the method comprises the following steps: constructing a time sequence connection relation graph of discharge pulses based on a multi-point discharge time sequence set in an insulation structure; identifying a discharge closed loop based on the time sequence connection relation graph, and constructing a closed loop evolution sequence according to a time sequence; by taking the evolution sequence as a training target, constructing a machine learning prediction model for predicting the closing trend of the discharge path; and based on the prediction model, predicting a discharge path closing state of the target power equipment during future operation, and outputting an insulation fault early warning signal based on a prediction result. According to the invention, the insulation fault early warning signal is generated by identifying the closed loop and predicting the closing trend of the discharge path, so that the problem that the prior art only depends on the discharge intensity and is difficult to accurately warn the insulation fault is solved.

The invention relates to the technical field of ring main unit engineering, in particular to a state evaluation method and system for a gas insulation environment-friendly ring main unit, and the method comprises the steps: obtaining space probe distribution coordinates and gas concentration data in the environment-friendly ring main unit, and generating a leakage gas three-dimensional concentration matrix according to the leakage gas three-dimensional concentration matrix; constructing a gas concentration distribution prediction model matched with the environment-friendly ring main unit, inputting the leakage gas three-dimensional concentration matrix and the geometric parameters of the cabinet body into the gas concentration distribution prediction model, and outputting to obtain predicted gas concentration distribution; generating a gas leakage behavior parameter, and generating an insulation failure probability and an electric ignition risk index; and generating an insulation performance evaluation index, and generating state evaluation of the environment-friendly ring main unit according to the insulation performance evaluation index. According to the method, the gas leakage behavior is accurately simulated, so that the insulation failure probability and the electric ignition risk index are quantified, and the accuracy of state evaluation of the ring main unit is greatly improved.

One aspect of the invention relates to a bidirectional on-board charger 2 for a vehicle 1, comprising:a bidirectional AC-to-DC converter 8 designed to supply power to an electrical power supply socket 6 for an external electrical device;means for detecting an electrical insulation fault 9, connected to a phase line 12 and to a neutral line 13 of said electrical power supply socket 6; a switch 16 arranged on said phase line 12; a switching means 18 designed to control the switch 16 between a first position P1 in which the means 9 are connected to the socket 6 and a second position P2; a resistor 20 which, when the switch 16 is in the second position P2, is electrically connected to the switch 16 and to a ground 10 of the vehicle.

The invention relates to the technical field of photovoltaic inverter grid connection, and discloses a photovoltaic panel assembly insulation impedance fault positioning method, a storage medium and a system. Comprising the steps of collecting voltage between positive and negative electrodes of an inverter under first voltage, voltage to ground of the negative electrode of the inverter and a direct-current component of leakage current of an output end, close to a power grid side, of the inverter; collecting the voltage between the positive electrode and the negative electrode of the inverter under the second voltage after voltage disturbance, the voltage to ground of the negative electrode of the inverter and the direct-current component of the leakage current of the output end, close to the power grid side, of the inverter; calculating a first insulation resistance value corresponding to the plurality of photovoltaic panel assemblies under the first voltage, and calculating a second insulation resistance value of the plurality of photovoltaic panel assemblies under the second voltage; and according to the first insulation resistance value and the second insulation resistance value, positioning the photovoltaic panel assembly with the insulation resistance abnormity. According to the invention, the accurate positioning of the insulation fault of the photovoltaic panel assembly level is realized, and the specific photovoltaic panel assembly with the insulation abnormity can be accurately identified from the series photovoltaic string.

The invention relates to the technical field of new energy automobile high-voltage safety, and particularly provides a new energy automobile high-voltage safety protection method and system based on multistage linkage outage, and the method comprises the steps: a sensor layer continuously collects high-voltage system parameters, and forms an original data stream; the high-voltage system parameters comprise insulation resistance, collision acceleration, battery pack pressure, temperature and the like; original data flows are processed by a multi-level diagnosis center, and fault level judgment is obtained by combining fault codes and timestamps recorded by a fault latch memory through multi-source data fusion and threshold comparison; and an execution instruction is formed through fault level judgment, and an execution mechanism is driven to perform corresponding power-off operation. The system comprises a data flow acquisition module, a diagnosis central processing module and a power-off operation execution module. According to the invention, when abnormal working conditions such as collision, insulation fault, short circuit and the like of the vehicle occur, a rapid, accurate and reliable high-voltage safety protection device is realized through multi-stage collaborative diagnosis and staged power-off.

The invention relates to the technical field of digital twinning, and discloses a digital twinning-based subway electrical hidden danger checking and intelligent fire-fighting linkage method, which comprises the following steps of: firstly, constructing a digital twinning model of an electrical component and establishing a synchronous time sequence taking a driving interval as a reference; inputting environment and voltage data acquired in real time into the model to calculate the drift rate of the insulating surface water film under the driving of the electric field; counting the cumulative migration volume in the driving period, solving a periodic water film migration proportion index, and carrying out dynamic reduction on the nominal creepage distance of the assembly by using the index; and finally, comparing the calculated dynamic effective creepage distance with the minimum safety distance specified by the insulation matching standard, and when the dynamic effective creepage distance is lower than the safety standard, automatically judging hidden dangers and generating an operation and maintenance work order. According to the invention, early warning and automatic disposal of the electrical insulation fault in the metro humid and hot environment are realized through mechanism modeling.

The invention discloses an insulation fault detection method, an inverter and a photovoltaic system, and the method comprises the steps: detecting an MPPT unit with an insulation fault, controlling the switching tube of the MPPT unit with the insulation fault to be switched off, and controlling the switching tubes of other normal MPPT units to be switched on; and a turn-off device corresponding to the MPPT unit with the insulation fault is controlled, so that the m photovoltaic strings corresponding to the MPPT unit with the insulation fault independently charge the direct current bus capacitor in sequence, and the photovoltaic string with the insulation fault is detected. According to the invention, the photovoltaic string with the insulation fault can be accurately positioned, and the manpower, time and economic cost for manually checking the position of the fault string for the second time can be saved.

The invention belongs to the technical field of GIS fault detection, and discloses a GIS latent insulation fault diagnosis method and system under strong noise, and the method achieves the omnibearing signal capture through synchronously obtaining optical, mechanical, electrical and acoustic signals, and solves a problem of weak signal leak detection. For strong noise interference, noise and fault characteristic frequency bands are effectively separated by using multi-modal signal difference and constructing a time domain and wavelet domain cascaded double-layer dictionary. A discriminative sparse model is combined with a K-SVD algorithm to carry out end-to-end iterative optimization, sparse features with higher discriminative force are automatically mined, and the problem of deep feature mining is solved. For the problem of weak generalization ability of small samples, a classifier error term is introduced into an objective function, a joint optimization objective function fusing reconstruction and classification errors is constructed, dictionary learning and classifier training are combined into one, the generalization ability of the model is significantly enhanced, and the method is suitable for large-scale popularization and application. Therefore, timely and accurate identification of the latent insulation fault under the conditions of strong noise and small samples is realized.

The invention provides an insulation detection method and device of a photovoltaic system, a controller and the photovoltaic system. The method comprises the following steps: detecting whether the photovoltaic system has an insulation fault; if the photovoltaic system has the insulation fault, determining the position of the insulation fault in the fault photovoltaic string according to the ratio of the voltage to ground of the first pole of the photovoltaic system to the open-circuit voltage of the photovoltaic string; the first electrode is any one of a direct-current bus positive electrode and a direct-current bus negative electrode; and respectively and independently controlling the positive and negative electrodes of the input end of each direct current conversion circuit to be short-circuited, and for each MPPT module, determining whether the insulation fault position is located in the MPPT module based on the change of the voltage to ground of the first electrode before and after the short circuit of the MPPT module. According to the photovoltaic system insulation fault detection method and the photovoltaic system insulation fault detection device, insulation faults from a system level to a component level can be spanned, additional devices are not needed, manpower, time and economic cost required by field troubleshooting is reduced, and the detection efficiency of the photovoltaic system insulation faults is improved.

A box body, a battery, and an electrical device, the box body comprising a main box body and a separator. Through holes are formed on the main box body, and the separator is arranged in the main box body and blocks the through holes. The separator is configured to be melted through when thermal runaway occurs in a battery cell. In the embodiments of the present application, the separator can block the through holes, such that the inside and outside of the main box body are not in communication, and impurities such as metal particles outside of the main box body cannot enter the main box body from the through holes, thereby reducing the probability of insulation failure caused by metal particles outside of the main box body entering the main box body.

The embodiment of the invention discloses a photovoltaic inverter insulation detection method and device and a photovoltaic inverter. The method comprises the steps that voltage parameters of an MPPT branch are acquired; the voltage parameters comprise a first voltage, a second voltage and a third voltage; based on the Kirchhoff's law, the ground insulation impedance of the MPPT branch is calculated according to the first voltage, the second voltage and the third voltage; and determining whether the MPPT branch has an insulation fault or not according to the ground insulation impedance of the MPPT branch. According to the scheme, the insulation resistance of each MPPT branch can be calculated one by one, so that the MPPT branch with the insulation fault can be quickly positioned.

The invention provides an insulation online monitoring method, device and system for an outer sheath of a high-voltage cable, which are used for diagnosing an insulation fault of the outer sheath of the high-voltage cable under the condition of single-end grounding of a metal sheath, and the method comprises the following steps: obtaining a current three-phase grounding ring current; when the current three-phase grounding ring current meets the preset monitoring requirement, the capacitance current of the cable is calculated based on the cable structure parameters; judging whether the high-voltage cable outer sheath has an insulation fault or not according to a ratio between the grounding ring current and the capacitance current of the cable, if the ratio is greater than a preset multiple threshold value, determining that the high-voltage cable outer sheath has the insulation fault, and if the ratio is not greater than the preset multiple threshold value, determining that the high-voltage cable outer sheath does not have the insulation fault, the insulation fault is that a penetrating hole exists in the outer sheath of the high-voltage cable, and the reliability and timeliness of insulation on-line monitoring of the outer sheath of the high-voltage cable under the condition that the single end of the metal sheath is grounded are improved.

The invention discloses an electrothermal aging test method and device for simulating the space charge effect of a cable terminal. The method comprises the following steps: S1, treating a cable sample; s2, constructing an electric heating aging platform; s3, carrying out five aging stage tests on the cable sample; s4, carrying out space charge detection on the cable samples at different aging stages; s5, analyzing correlation between a space charge accumulation rule and insulation degradation based on a charge mobility formula and an injection current formula; according to the invention, the actual working condition of the cable terminal is accurately simulated through the electrothermal collaborative aging platform, and the limitation that the space charge evolution law is difficult to reproduce in the traditional method is overcome; a weighted all-phase averaging method is adopted to process signals, and an arc-shaped sensor and a frequency domain deconvolution algorithm are combined, so that interference is effectively filtered out, and the detection stability and accuracy are improved; through correlation of charge accumulation and insulation degradation processes, an insulation failure mechanism is disclosed, fault early warning is realized, and theoretical and test support is provided for design optimization, life evaluation and operation and maintenance of the cable terminal.

The invention relates to the technical field of electrical engineering and high-voltage insulation, discloses a multi-objective optimized green insulating gas and solid material configuration method and system, and aims to solve the problems of nanometer air gap accumulation and insulation failure caused by asynchronous static matching and control of a gas-solid interface under a dynamic electric field. According to the method, nanoscale strain and temperature data are obtained in real time through an interface state high-precision sensing module, electric field gradient information is combined, the data are input into a multi-target collaborative optimization control module based on a Hamiltonian mechanical model, and a 100-nanosecond-level synchronous solid pre-tightening force and gas pressure regulation and control instruction is generated with the purpose of minimizing the system energy change rate; and the piezoelectric actuator and the micro valve array are used for executing microsecond-level dynamic adjustment. According to the system, ultrahigh temporal-spatial resolution monitoring, hardware-level synchronous regulation and control and predictive control of physical model driving are realized, the reliability of green insulation equipment is remarkably improved, and the service life of the green insulation equipment is remarkably prolonged.

The invention discloses a transformer iron core insulation fault distinguishing method based on electromagnetic acoustic cross-modal fusion, which comprises the following steps: acquiring iron core grounding current, UHF electromagnetic signals and acoustic signals, fusing the acquired iron core grounding current, UHF electromagnetic signals and acoustic signals to obtain fusion features, processing the UHF electromagnetic signals and acoustic signals to obtain a transformer iron core insulation fault distinguishing result, and distinguishing the transformer iron core insulation fault distinguishing result. And respectively obtaining a final fault coordinate and an acoustic positioning coordinate, carrying out signal-to-noise ratio weighted fusion on the final fault coordinate and the acoustic positioning coordinate to obtain a fault three-dimensional coordinate after final fusion, processing the fusion feature and the fault three-dimensional coordinate after final fusion, and outputting a final diagnosis report. The cross-modal attention mechanism is introduced, the purified iron core current, the UHF electromagnetic signal and the acoustic signal are deeply fused, the iron core and insulation fault distinguishing accuracy is remarkably improved, and the fault diagnosis precision and stability are enhanced.

The invention relates to the technical field of whole vehicle fault detection, and particularly provides a whole vehicle insulation fault detection method and device, a medium and equipment, the method is applied to a controller in a whole vehicle high-voltage system, and the method comprises the steps that the current state of a whole vehicle is acquired; the current state comprises a static non-charging and non-high-voltage state and other states, and the other states comprise a static non-charging and high-voltage state, an alternating current charging state, a direct current charging state or a driving state; analyzing the insulation resistance value in the current state to determine the insulation fault type of the whole vehicle; wherein the insulation fault type comprises a battery hardware fault, an electric drive system fault, an air conditioning system fault, a heater system fault, a storage battery charging system fault, an assembly system fault or a charging pile fault. According to the embodiment of the invention, automatic and accurate detection and positioning of the vehicle insulation fault can be realized.

The invention belongs to the technical field of electrical safety detection, and relates to an electrical fireplace electrical safety performance test system. By constructing a multi-parameter acquisition unit, a data correction unit, a performance evolution analysis unit and a failure mode recognition unit, synchronous acquisition and fusion analysis of insulation resistance, leakage current and environment temperature and humidity of the electric fireplace are realized; processing the original data by using an insulation resistance correction model based on temperature and humidity, analyzing and identifying a high-risk coupling state in combination with sliding window correlation, accurately judging an insulation performance failure critical point, and further dividing a degradation stage; and establishing a differential variable correlation model at each stage, generating a performance trend vector, and identifying the current insulation failure mode through the characteristic change of the performance trend vector. The system effectively solves the problem that the traditional test method is difficult to dynamically evaluate the insulating property evolution process and early warning failure risk, and improves the accuracy, the intelligent level and the risk pre-judgment capability of the electrical safety performance test of the electric fireplace.

The invention relates to the technical field of electrical detection, solves the technical problem of monitoring confusion caused by coupling of electrode aging drifting and real insulation fault signals in a frequency domain in a periodic dynamic burst electrical system, and particularly relates to a line insulation inspection and power-on experiment method. Comprising three-phase current signals and line voltage signals; welding periods are divided based on amplitude abrupt change characteristics of the current signals, and each welding period is divided into an arc starting stage, an arc stabilizing stage and an arc quenching stage; and the period stability index is calculated through the stage duration of the welding period, and effective data are screened. According to the method, the fundamental frequency energy ratio, the harmonic pollution index and the phase offset are extracted, the progressive trend of stripping electrode aging is fitted in combination with least square linear fitting, and the frequency domain energy concentration ratio is utilized to accurately distinguish the insulation fault and the electrode aging signal; false fault signals caused by electrode aging are prevented from being misjudged as insulation faults, and unnecessary shutdown maintenance is reduced.

The invention is suitable for the technical field of electrical systems, and provides an insulation state monitoring method, system and device based on mode recognition and a medium, monitoring nodes are determined based on a power grid topological structure and node attribute data, key monitoring positions can be accurately positioned, and the monitoring efficiency is improved; preprocessing and multi-modal feature extraction are carried out on operation data, so that data under a complex electromagnetic environment and variable insulation characteristics can be effectively processed, and features comprehensively reflecting node states are extracted; a node working mode is determined by using a pre-trained graph neural network model, insulation characteristics in different working states can be accurately identified, and the problems of difficult reliable monitoring, low fault diagnosis speed and low positioning precision of a traditional method are solved; dynamic diagnosis and grading early warning are carried out based on the working mode, the difference and noise interference of AC-DC side insulation fault characteristics can be effectively processed in time, real-time and accurate monitoring and early warning of the insulation state are realized, and safe and reliable operation of the power grid flexible capacity expansion system is guaranteed.