912results about "Electric winding testing" patented technology

The present invention relates to the technical field of secondary device monitoring. Disclosed are an online monitoring method for an operating state of a secondary current loop neutral line, and a system. The method comprises: by using a high-precision sensor for collection and a composite filtering algorithm for processing, obtaining a secondary current fundamental component of each neutral line of a transformer substation which is not influenced by harmonic waves and transient states, and uploading the secondary current fundamental component to a protection information master station; retrieving originally configured access protection information master station information to obtain three-phase secondary current loop information of each bay of the transformer substation, summarizing secondary current information of all monitoring ranges, and performing grouping and classification marking on said information by transformer substation, bay and winding; and using the collection information for operation analysis, automatically identifying and confirming an operating state of the line, and on the basis of the operating state, calculating the outlier deviation linearity and the continuous dynamic trend deviation degree of each winding of each bay. The present invention solves the problem that intermittent faults in secondary current loop neutral lines are concealed and are not easily found, realizes comprehensive monitoring for the whole system, and can quickly and accurately position a specific winding and issue an abnormal early warning.

The invention discloses a method and a device for calculating an electromagnetic-heat flow field under a turn-to-turn short circuit fault of a converter transformer. The method comprises the following steps: establishing a converter transformer multi-physical field numerical calculation theoretical model based on circuit-electromagnetic-heat flow coupling; based on the multi-physical field numerical calculation theoretical model, establishing a converter transformer electromagnetic-heat flow field model under the turn-to-turn short circuit fault; setting turn-to-turn short circuit faults at different positions in the electromagnetic-heat flow field model of the converter transformer; and performing winding current waveform, voltage waveform and electromagnetic field analysis and heat flow field analysis of the converter transformer under the set turn-to-turn short circuit fault.

The invention relates to the field of motor health management and predictive maintenance, in particular to a motor stator winding insulation state evaluation method and system based on a digital model. Comprising the following steps: S1, collecting high frequency of a motor stator winding, and generating multi-physical-quantity real-time data; s2, calculating a dynamic capacitance reference value according to real-time data of multiple physical quantities; s3, performing subtraction operation on the high-frequency equivalent capacitance measurement value and the dynamic capacitance reference value, and extracting an insulation degradation residual signal; s4, constructing a self-adaptive dynamic detection threshold according to multi-physical-quantity real-time data; s5, judging whether the absolute value of the insulation degradation residual signal is greater than a self-adaptive dynamic detection threshold or not: if so, judging that an insulation degradation event occurs; if not, judging that the operation state is a normal operation state; and S6, in response to the insulation degradation event, updating the insulation degradation index, and generating insulation state evaluation based on the updated insulation degradation index. According to the invention, false alarm under severe load fluctuation is avoided, and the accuracy and reliability of evaluation are significantly improved.

The invention discloses a motor fault intelligent diagnosis method based on digital twinning, and belongs to the field of motor fault diagnosis. According to the method, in a model training stage, characteristic quantities are selected through a random forest method to train an extreme learning machine model with an autoregressive cycle function as a digital twin motor. According to the method, in an open-circuit fault diagnosis stage, a physical entity motor characteristic quantity is collected and input into a digital twin virtual motor to obtain a digital twin current, the difference between actual winding current data and the digital twin winding current is obtained to obtain a current residual error, and the difference value of the current residual error is used as a threshold value to carry out open-circuit fault diagnosis. Fault positioning is carried out by adopting a moving average window method; in the turn-to-turn short-circuit fault diagnosis stage, a current symmetric mean absolute error is used as a judgment standard to diagnose a short-circuit fault. According to the method, accurate design parameters and fault data of the motor are not needed, the open-circuit fault and the turn-to-turn short-circuit fault of the motor can be quickly diagnosed in real time only through the data during normal operation of the motor, and the robustness is very high.

This invention presents a method for detecting circuit anomalies in a current transformer. The method is executed by a module integrated into an intelligent electronic device. The process involves a multi-step analysis that starts with comparing the frequency of a detected current signal from the transformer's secondary winding with the frequency of a detected voltage signal from a primary conductor. If the frequency difference exceeds a predetermined threshold, the method advances to assess the magnitude of the detected current signal. If this magnitude falls below another predetermined threshold, the method proceeds to analyze the Total Harmonic Distortion (THD) of the current signal. Depending on whether the THD is above or below a set threshold, the method determines the presence of either an open or a short circuit within the current transformer. This approach provides a reliable and accurate means for identifying circuit anomalies, enhancing the safety of electrical systems.

The invention discloses a power transformer winding insulation state monitoring method, a power transformer winding insulation state monitoring device, power transformer winding insulation state monitoring equipment and a medium, and relates to the technical field of power system equipment state monitoring. And an improved particle swarm optimization algorithm is adopted to inversely calculate the insulation parameters of a plurality of windings of the transformer to the ground, so that the insulation state of each winding can be accurately evaluated. Compared with a traditional monitoring method, the method has the advantages that rich information contained in the common-mode leakage current can be fully utilized, the insulation degradation conditions of different windings can be accurately distinguished, more accurate insulation state evaluation is provided, and the safe operation level of the transformer is effectively improved. Moreover, multi-stage evaluation is carried out on the insulation state through a fuzzy comprehensive evaluation method, the insulation state can be comprehensively reflected, different severity degrees of the insulation state can be distinguished, the current state can be evaluated, the degradation trend can be predicted through dynamic indexes, and a scientific basis is provided for operation and maintenance decision making.

The invention discloses a stator winding grounding fault arc extinction method and system based on grounding transition resistance. The method comprises the following steps: S1, acquiring fundamental voltage of a neutral point and a machine end after normal operation and a grounding fault in real time; s2, calculating a fault phase potential EA1, and calculating a phase angle delta between the fault phase potential EA1 and the neutral point ground voltage UN1; s3, constructing a generator stator winding grounding fundamental wave fault analysis positioning equation, and substituting the measurement and calculation amount to complete fundamental wave fault positioning; s4, if multiple solutions occur in fundamental wave fault positioning, introducing a low-frequency injection quantity check criterion, and screening out a unique correct transition resistance value; and S5, carrying out arc extinction on the fault point of the neutral point of the generator in a high-resistance grounding mode. According to the method, the accuracy and reliability of fault positioning are remarkably improved, the possibility of misjudgment is eliminated, and an accurate compensation basis is provided for arc extinction.

The embodiment of the invention discloses a transformer winding short circuit fault detection method. The method comprises the following steps: acquiring magnetic flux leakage data between transformer windings; determining a short-circuit fault area according to the magnetic flux leakage data; constructing a finite element model of the transformer; simulating the finite element model of the transformer based on the short-circuit fault area to obtain a short-circuit database; and acquiring magnetic flux leakage data between the transformer windings in real time, and determining the short-circuit fault position of the transformer windings according to the short-circuit database. According to the method, accurate detection of the short-circuit fault of the transformer winding is achieved by obtaining the magnetic flux leakage data, determining the short-circuit fault area, constructing the finite element model, obtaining the short-circuit database through simulation and locating the fault in real time, the accuracy and efficiency of fault detection are improved, and powerful guarantee is provided for safe operation of the transformer.

The invention discloses a transformer winding monitoring and diagnosis data acquisition method, system and device and a medium, and belongs to the technical field of power equipment state monitoring, and the method comprises the steps: constructing a multi-scale wavelet transform and improved adaptive Kalman filtering fusion architecture, the multi-scale wavelet transform and improved adaptive Kalman filtering fusion architecture is used for acquiring a monitoring signal of a transformer winding; performing frequency division processing on the monitoring signal by using the multi-scale wavelet transform, and determining each scale coefficient; adjusting a noise covariance matrix based on improved adaptive Kalman filtering, and carrying out filtering processing on each scale coefficient to determine a filtering result; and reconstructing the filtering result into a reconstructed signal, and performing signal quality evaluation on the reconstructed signal to determine the signal quality of the transformer winding. Through multi-scale decomposition and adaptive filtering, the limitation of a traditional method under non-stationary signal processing and strong noise interference is solved, and the retention degree and the signal-to-noise ratio of signal features are improved.

The invention belongs to the technical field of power equipment maintenance, and particularly relates to a transformer winding fault diagnosis method, a terminal and a storage medium, and the method comprises the steps: obtaining frequency response curves of a transformer winding in a normal state and an abnormal state through a frequency sweep test, the head end of the primary side winding is connected with a sweep frequency signal and the tail end is suspended; determining the number of units of an equivalent network according to the number of the frequency response curve characteristic frequencies of the windings in the normal state, and reconstructing a normal winding equivalent network composed of a plurality of RLC units; according to the method, the high-precision equivalent network is constructed, the frequency response residual error analysis is performed, and the Pearson's correlation coefficient is combined to screen the high-correlation element, so that the specific position of the winding deformation or fault can be accurately positioned, the fault type is distinguished, and the limitation that the traditional frequency response method can only judge the overall deformation degree is thoroughly solved.

The invention provides a transformer winding state detection method based on temperature characteristics. The method comprises the following steps: (1) collecting operation data; (2) preprocessing the operation data; (3) taking the normalized transformer load current, the normalized active power, the normalized top oil temperature, the normalized ambient temperature and the normalized oil flow speed as input data, taking the normalized winding hot-spot temperature as output data, and constructing a data set; (4) establishing a VSN-TKAN-GRN deep learning network parallel computing model of the transformer winding hot-spot temperature considering the multi-head attention mechanism; (5) predicting the hot-spot temperature of the transformer winding based on a VSN-TKAN-GRN deep learning network parallel computing model; and (6) calculating the average relative error percentage of the transformer winding hot-spot temperature prediction result and the actual measurement result, and judging the winding state according to the change of the average relative error percentage. According to the method, the state of the transformer winding in operation can be efficiently and accurately judged, so that effective transformer operation and maintenance measures can be taken in time, and large faults are avoided.

The invention discloses a transformer fault intelligent diagnosis method and system, and the method comprises the steps: reading a longitudinal differential protection action switching value from transformer fault recording, according to the fault recording information, calculating rated current of each side of the transformer, longitudinal three-phase differential current amplitude, voltage and current amplitude of each side and zero-sequence voltage and zero-sequence current amplitude of each side; and combining different criteria to intelligently identify fault types such as a non-internal fault, a three-phase symmetric fault, a single-phase earth fault, a two-phase earth fault, a two-phase interphase short circuit and an interturn fault of the transformer, and fault sides and fault phases. According to the invention, the fault type and the fault position of the transformer can be rapidly and automatically positioned, manual fault analysis is assisted or replaced, and an intelligent automatic solution is provided for accident handling of the transformer fault in an engineering field.

The invention discloses a column type distribution transformer winding abnormity detection system and detection method. The detection system comprises a frequency response detection instrument, a signal coupling assembly, an impedance matching module and a data processing module. The frequency response detection instrument is used for outputting a frequency sweep excitation signal and collecting a winding response signal; the signal coupling assembly is used for isolating power frequency voltage and filtering low-frequency interference; the impedance matching module is used for adjusting output impedance and input impedance to realize matching with electrical characteristics of different windings; and the data processing module is used for constructing an equivalent detection loop model, completing frequency response simulation calculation and carrying out frequency domain analysis and anomaly identification on an actually measured response curve. According to the invention, electrified state diagnosis of distribution transformer winding structure abnormity (such as deformation and loosening) can be realized.

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

An insulation inspection system includes: a set of input terminals; a brush; a circuit connected to the set of input terminals, the brush, and a part being inspected, where the circuit includes at least one pair of resistors and a capacitor; and a control module configured to detect when a short circuit exists between a conductive element of the part and one or more conductive bristles due to insulative material missing on the part and as a result the one or more conductive bristles of the brush contacting the conductive element of the part.

The invention discloses a remote fault diagnosis method for a generator excitation system, and belongs to the technical field of generator excitation systems. The diagnosis method is realized based on a diagnosis system, the diagnosis system comprises a generator excitation system, the generator excitation system can maintain the stability of generator terminal voltage, adjust reactive power distribution, improve the stability of a power system, improve the dynamic performance of the power system and the like, and the generator excitation system is connected with a data acquisition device. The data acquisition device is connected with a monitoring center and a data transmission module. According to the remote fault diagnosis method for the generator excitation system, the structure is reasonable, various operation parameters of the excitation system can be monitored in real time, the advanced data analysis technology is utilized, the fault is deeply analyzed, operation and maintenance personnel are helped to give an early warning in advance and make a quick response, the reliability of power generation equipment can be greatly improved, the downtime is shortened, and the remote fault diagnosis method is worthy of popularization.

The invention relates to the technical field of power equipment detection, in particular to a method for detecting turn-to-turn short circuit of an iron core reactor, which is used for solving the problems that the turn-to-turn short circuit risk of the iron core reactor cannot be comprehensively evaluated, the state change of equipment cannot be captured in real time and the sensitivity and accuracy of turn-to-turn short circuit risk monitoring are reduced in the prior art. According to the method, the inter-turn short circuit risk of the iron core reactor is comprehensively evaluated by integrating the operating current, the direct-current resistance, the inductance value and the temperature parameters of key parts, and the state change of equipment is captured in real time by dynamically calculating the change rate of each parameter, so that the monitoring sensitivity and accuracy are remarkably improved; the logistic regression model trained based on historical data can accurately predict the turn-to-turn short circuit probability, automatic risk monitoring is achieved, manual intervention is reduced, and the misjudgment rate is reduced.

The invention provides a power distribution network zero sequence mutual inductor wiring polarity on-line proofreading method and system. The method comprises the following steps that 1, an active inverter is directly connected between a phase line of a power distribution network or a neutral point of a grounding transformer and the ground, or is connected between the phase line of the power distribution network or the neutral point of the grounding transformer and the ground through a boosting transformer; step 2, performing closed-loop control on the active inverter to inject zero-sequence current into the power distribution network; 3, in the injection process, judging whether the wiring polarity of the zero-sequence mutual inductor is wrong or not; 4, if only one of the zero-sequence voltage transformer and the zero-sequence current transformer has a wiring polarity error, correcting by modifying a software flag bit; and step 5, each power distribution terminal and the accessory device thereof complete online calibration of the wiring polarity of the zero sequence mutual inductor. By applying the technical scheme, the problem that the grounding fault section positioning research and judgment result is wrong due to the wiring polarity error of the zero-sequence mutual inductor of the power distribution terminal and the accessory device thereof can be avoided, and the method has good engineering application value.

The invention belongs to the field of high-voltage switch fault diagnosis, and discloses a high-voltage switch coil fault assessment method and related equipment, and the method comprises the steps: obtaining a real-time operation coil current curve, and enabling the real-time operation coil current curve to be aligned with a preset standard coil current curve through a dynamic time warping method; the problem of large discrete type of a coil current curve caused by complex and changeable field operation environments is effectively solved; according to the method, key features, namely a deconstructed sequence feature set, of an aligned curve are further extracted through time sequence deconstruction, the deconstructed sequence feature set is input into a pre-trained coil fault assessment model, and therefore the fault type and the fault degree of the high-voltage switch coil can be accurately assessed. By adopting the method, the accuracy and the stability of fault diagnosis are improved, the practicability and the adaptability of the method are enhanced, and the method can be better applied to a complex field operation environment.

The invention relates to the field of transformer detection, in particular to a power transformer winding deformation state detection method and system, and the method comprises the steps: obtaining a data set which comprises a three-phase winding abnormal index and a harmonic difference score of an output frequency domain signal of each phase winding, and inputting the data set into a preset prediction model to obtain the deformation state of the transformer winding. According to the method, the three-phase winding abnormal index of the linear sweep frequency domain signal is calculated, the abnormal degree of the winding is measured by using the three-phase winding abnormal index, and then the deformation state of the winding is detected by using the prediction model, so that the accuracy of a detection result is improved.

The invention discloses a motor fault diagnosis method and system based on color image fusion symmetric point mode, and the method comprises the steps: converting a vibration signal and an electromagnetic signal of a motor into symmetric point mode images, and generating a color signal image fusing feature information; respectively abstracting the color signal images fused with the feature information into nodes and edges in a high-dimensional semantic space so as to construct graph structure data; and performing diagnosis classification on the graph structure data of the vibration signals and the electromagnetic signals by using respective capsule graph network models, and fusing diagnosis classification results of the vibration signals and the electromagnetic signals through a voting mechanism to obtain a final diagnosis classification result. According to the method, multi-channel time domain signals are converted into image expressions with dense information and consistent geometry, and unified feature modeling is carried out on the images based on a depth map structure network with topology perception capability, so that motor fault diagnosis with high diagnosis precision and strong robustness is realized.

To process measurements acquired using measurement instrumentation (41, 42), a processing system (50) is operative to perform a physics informed data-driven processing technique to determine at least one parameter related to an asset (20) of an electric power system (10). The processing system (50) is operative to perform a preprocessing (70) of measurements to generate input data, with the preprocessing (70) comprising identifying a subset of measurement locations for which the measurements are reliable for inputting to the data-driven processing technique and using measurements obtained at the identified subset for generating the input data. The processing system (50) is operative to estimate local heat loss, using the data-driven processing technique.

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

The invention discloses a permanent magnet motor turn-to-turn short circuit fault diagnosis method considering complex working condition influence, belongs to the technical field of permanent magnet motor fault diagnosis, and aims to solve the problems that a turn-to-turn short circuit diagnosis result of a permanent magnet synchronous motor for an electric vehicle is easily influenced by working conditions and slight faults are difficult to diagnose under complex operation working conditions. The method comprises the following steps: step 1, high-frequency signal injection and information acquisition; injecting the high-frequency rotating voltage signal into a motor control system under a natural coordinate system, and collecting each phase of high-frequency current and each phase of high-frequency voltage of the motor through a band-pass filter; 2, subtracting the high-frequency voltage from the healthy voltage under the condition that the high-frequency signal is injected, and calculating to obtain a high-frequency voltage residual error of each phase; 3, according to the high-frequency voltage residual error of each phase, obtaining two fault characteristic quantities, namely the standard deviation of the high-frequency voltage residual error and the absolute average value of the high-frequency voltage residual error of each phase; and 4, judging whether a turn-to-turn short circuit fault occurs or not according to the fault characteristic quantity, and judging a fault phase.

The invention relates to a turn-to-turn short circuit fault diagnosis method, device and equipment for a dry-type air-core reactor, a readable storage medium and a program product, and relates to the technical field of electric power safety. By adopting the method, the initial turn-to-turn short circuit condition of the reactor can be detected in time, and the accuracy of fault diagnosis is improved. The method comprises the following steps: obtaining a reconstruction signal according to historical voltage and current data of the dry-type air-core reactor; on the basis of the reconstruction signal, constructing a Bowdrill curve, and calculating a characteristic parameter of the Bowdrill curve; calculating the change rate of the current state of the characteristic parameters relative to the health state, and obtaining the weight corresponding to the change rate through an entropy weight method; determining a standard value for judging the fault according to the change rate and the weight, and taking a target standard value corresponding to the target fault degree as a fault threshold value; and obtaining a current standard value according to the current voltage and current data of the dry-type air-core reactor, and comparing the current standard value with a fault threshold to obtain a turn-to-turn short circuit fault diagnosis result of the dry-type air-core reactor.

The invention discloses a high-precision transformer fault detection method and system, relates to the technical field of transformer detection, is used for solving the problems of insensitive fault characterization and delayed diagnosis under a light load condition, and judges whether to enter a load judgment mechanism or not by acquiring input side access voltage information and counting low-load accumulated duration. When entering a load discrimination mechanism, detecting the start-stop behavior of a load branch and the temperature rise trend of a winding, calculating a light load index, identifying a light load state, carrying out region division on an iron core, collecting magnetic flux density data, and calculating iron loss balance characteristics aiming at iron loss deviation which is easy to occur under a light load condition; the insulation degradation coefficient is calculated by fusing the oil temperature peak value and the dielectric loss factor, and the abnormal index is generated with the iron loss balance characteristic, so that the early warning of the early hidden fault of the transformer can be realized in a light load scene in which the traditional temperature rise and current indexes are insensitive, and the operation reliability and maintenance efficiency of the transformer in a power distribution system are improved.

The invention discloses a method and a device for positioning a turn-to-turn short circuit fault position of a transformer, a medium and a terminal, relates to the technical field of transformer fault diagnosis, and mainly aims to solve the problem of internal faults of the transformer caused by untimely response, low action sensitivity and incapability of accurately positioning the turn-to-turn short circuit fault position in the prior art. Comprising the steps that when it is detected that a turn-to-turn short circuit fault exists in a target transformer, micro-temperature field simulation data of the target transformer are obtained, and the micro-temperature field simulation data are obtained based on a micro-temperature field digital twinborn model of the target transformer; based on a convolutional neural network, extracting turn-to-turn short circuit fault characteristic parameters from the micro-temperature field simulation data; and determining the position of the turn-to-turn short circuit fault of the target transformer based on a Bayesian reasoning method according to the turn-to-turn short circuit fault characteristic parameter.

The embodiment of the invention discloses a transformer invisible fault diagnosis method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring voltage and current recording data of a transformer measured on line; performing influence factor elimination operation on the voltage and current recording data, specifically comprising the following steps of: eliminating a higher harmonic component, a direct current component and an attenuation direct current component by adopting a full-wave differential Fourier algorithm to obtain a fundamental wave voltage and a fundamental wave current; when the elliptical orbit is calculated, normalization processing is carried out on different loads; eliminating the influence of abnormal numerical values by adopting a Gaussian regression algorithm; constructing a voltage difference and current elliptic track by using a transformer port relational expression compensated by the exciting current; correcting the elliptical trajectory by using the primary side voltage data, the secondary side voltage data and the primary side current data of the two groups of transformers measured within the sampling time; and after the operation of eliminating the influence factors, realizing the recognition and diagnosis of the invisible fault of the transformer through Lissajous ellipse fitting, characteristic quantity extraction and an artificial intelligence inversion algorithm.

The invention discloses a distributed wireless control system and method for transformer winding deformation detection. The distributed wireless control system comprises a plurality of transformers, a first deformation detection device, a second deformation detection device, an edge computing server and a cloud platform, wherein the first deformation detection device and the second deformation detection device are electrically connected with windings of the transformers; the first deformation detection device is used for generating a first fault signal when a winding deformation fault is detected; the edge computing server is used for analyzing according to the type of the fault winding and the short-circuit impedance variable quantity to obtain a first fault credibility level; and the cloud platform is used for obtaining the second fault credibility according to the historical data of the transformer winding, and determining a fault secondary judgment strategy according to the first fault credibility level and the second fault credibility. According to the invention, two different types of winding deformation detection devices are arranged to work cooperatively, and a secondary judgment strategy is executed based on judgment of the fault credibility level and the fault credibility, so that the problem of deformation fault false alarm in actual work is reduced, and the accuracy and reliability of deformation fault detection are improved.

The embodiment of the invention discloses a waveform identification and correction method and system for winding deformation diagnosis, and relates to the technical field of transformer winding monitoring, and the method comprises the steps: synchronously collecting voltage and current signals, and carrying out the preprocessing; performing waveform disturbance type identification on the preprocessed signal, and dividing the disturbance type into harmonic dominant disturbance, voltage sag disturbance or compound disturbance, and harmonic dominant disturbance, voltage sag disturbance or compound disturbance; dynamically selecting a corresponding waveform processing path according to a waveform disturbance type identification result; processing the signal by adopting the selected waveform processing path so as to correct and reconstruct the signal and obtain compensated voltage and current waveform data; by constructing a disturbance identification and path adaptive processing mechanism, the problems of processing lag and compensation distortion of a general algorithm during dynamic signal disturbance are solved, the identification precision is improved, and real-time error correction is realized.