494results about "Electric winding testing" patented technology

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 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.

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.

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 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 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.

This invention provides a method for detecting quench in a superconducting coil using leakage current, comprising the following steps: Step 1: Determine a first detection connection point on the main superconducting tape adjacent to a first current connection point, wherein the first detection connection point and the first current connection point are equipotential points; Step 2: Determine a second detection connection point on the sampling superconducting tape, wherein the second detection connection point and the second current connection point are equipotential points; Step 3: Connect a leakage current sampling circuit to the first and second detection connection points. If the leakage current sampling circuit detects leakage current, it is determined that quench exists in the main superconducting tape. This invention uses a leakage current sampling circuit to solve the problem of difficulty in sampling the quench voltage of superconducting magnets.

The invention provides a method for suppressing internal misfire caused by turn-to-turn short circuit of a dry-type air-core reactor, and belongs to the technical field of dry-type air-core reactors. A turn-to-turn short circuit fault detection device combining differential protection and infrared thermal image detection is established to form a three-dimensional turn-to-turn short circuit feature vector, a four-dimensional internal misfire minimum ignition vector is obtained, and a fire development prediction model is established based on an Arrhenius equation. A double-layer game model for minimizing the upper-layer fire loss and minimizing the lower-layer fire extinguishing cost is constructed to optimize five-dimensional suppression vector parameters, and coordinated actions of suppression equipment such as power-off protection, forced ventilation, temperature suppression and inert gas release are controlled in real time through a suppression vector parameter adjustment function. The technical problem of fire suppression measure starting lag or excessive response caused by difficulty in accurately predicting the opportunity and degree of development of the turn-to-turn short circuit fault of the dry-type air-core reactor into the internal fire disaster is solved.

The invention discloses a motor winding fault monitoring method and system, and relates to the technical field of winding fault monitoring. According to the motor winding fault monitoring method, three-phase current waveform signal data and running state data of a set motor are obtained; based on a pre-trained current waveform identification model, in combination with three-phase current waveform signal data of the set motor, a winding fault induction characteristic value of the set motor is analyzed; based on the running state data of the set motor, the winding comprehensive disturbance risk characteristic value of the set motor is analyzed, and the winding fault risk assessment characteristic value of the set motor is analyzed in combination with the winding fault induction characteristic value. Therefore, the dynamic change rule of the current waveform in the time sequence is effectively described, the identification depth and the time response speed of fault causes are improved, and the accuracy of fault risk assessment is enhanced.

The invention relates to the technical field of transformer internal defect diagnosis, in particular to a distribution transformer internal defect diagnosis method and device based on virtual differential current. Comprising the following steps: synchronously acquiring data; calculating an actually measured differential current; establishing a differential current model; according to a differential current model, calculating a deviation value between the actually measured differential current of each phase and a model prediction value, and taking the deviation value as the virtual differential current; carrying out filtering processing on the virtual differential current; and judging defects. According to the method, a differential current model associated with winding impedance and excitation branch parameters is constructed based on operation data when the transformer has no defects, so that the model is bound with physical topological characteristics of the transformer, and virtual differential current (deviation between a measured value and a model predicted value) can reflect actual state change of the winding in the transformer; and the physical relevance and reliability of defect diagnosis are enhanced.

The invention provides a far and near field multi-axis sensing array coupled converter transformer monitoring method and system. The system comprises a near field four-axis sensing array, a far field four-axis sensing array, a signal-to-noise improving unit, an original point processing unit, an AD conversion unit and a background display control seat unit. Acquiring near-field and far-field noise signals without excitation, and coupling the near-field noise signals; taking the far-field noise signal as input, taking the coupled near-field noise signal as output, and training a deep network model; acquiring a to-be-detected signal and a field noise signal under excitation, and coupling the to-be-detected signal; taking the field noise signal as the input of the trained deep network model to obtain a reconstructed coupling noise signal; the method comprises the following steps: obtaining a denoised signal to be detected and a denoised coupling signal by using a coupling noise signal, carrying out signal processing to obtain a corresponding analog signal, converting the analog signal into a corresponding digital signal, and carrying out visual monitoring. The fault recognition rate and the operation and maintenance efficiency are remarkably improved.

The invention relates to a fault detection method and device of a converter transformer, computer equipment and a medium. The method comprises the steps of obtaining a vibration time-domain waveform signal and a current time-domain waveform signal of a winding of the converter transformer in a preset time period, determining vibration frequency response data of the winding according to the vibration time-domain waveform signal and the current time-domain waveform signal, and determining the current time-domain waveform signal of the winding according to the vibration frequency response data and preset reference vibration frequency response data. And determining a fault detection result of the winding. By adopting the method, the timeliness of fault detection of the converter transformer can be improved.

The invention relates to the technical field of power equipment state detection, in particular to a transformer winding state detection method, system and equipment based on dynamic frequency band adaptation and a storage medium. Collecting full-band frequency response data of the healthy reference winding, and performing adaptive adjustment on a preset basic frequency band based on the peak-valley distribution density to obtain a reference target frequency band matched with the frequency spectrum characteristics of the winding; generating a reference polar coordinate feature map through a phase-amplitude mapping rule, and extracting pattern texture features to construct a reference feature library; collecting detected winding data and taking the reference target frequency band as a detection frequency band to generate a detected polar coordinate feature map; a multi-dimensional evaluation result is obtained through graph space similarity comparison and feature difference quantification in combination with amplitude anomaly recognition; and outputting a normal, slight fault or serious fault state judgment result of the tested winding based on the preset evaluation threshold interval and the working condition adaptation correction rule. The problems that in the prior art, frequency band division is fixed, and threshold values lack working condition adaptation are solved.

The invention discloses a multi-signal fusion permanent magnet motor turn-to-turn short circuit fault diagnosis method, which is used for solving the technical problem that turn short is diagnosed only from motor current in the prior art, especially for early turn short diagnosis, the probability of missing report is high, and belongs to the technical field of motor turn-to-turn short circuit fault diagnosis. The three-phase current, the three-phase line voltage and the radial vibration acceleration signal of the bearing area of the bearing end cover of the motor are collected in real time in the operation process of the motor. The method specifically comprises the following steps of 1, current fault feature extraction; step 2, vibration fault feature extraction; s21, vibration spectrum fault feature extraction; s22, vibration envelope spectrum fault feature extraction; s23, calculating a vibration fault feature mark; step 3, turn short online feature mark calculation; 4, judging whether back electromotive force characteristic calculation is started or not; step 5, back electromotive force fault feature extraction; and step 6, final diagnosis of the short turn of the motor.

The invention discloses a transformer fault detection method and device based on a dynamic threshold value, and belongs to the technical field of fault detection, and the method comprises the steps: collecting the current basic parameters, environmental parameters and current actual load of a transformer in real time; calculating a real-time load rate based on the current actual load; obtaining a transformer static threshold value corresponding to each current basic parameter; based on the environmental parameters, converting the static thresholds of the transformers into dynamic thresholds of the transformers; correcting each transformer dynamic threshold based on a preset equipment state correction parameter and a real-time load rate to obtain a corrected transformer dynamic threshold; and on the basis of each corrected transformer dynamic threshold value, performing anomaly detection on each current basic parameter to obtain a detection result of each parameter, and obtaining a transformer fault detection result on the basis of a preset hierarchical fault mechanism table and correlation analysis of each parameter detection result. Therefore, by implementing the method and the device, the problem of low transformer fault detection accuracy in the prior art can be solved.

The invention discloses a relay fault detection circuit, method and system applied to an energy storage system, and relates to the technical field of electrical control, and the method comprises the following steps: resistance dynamic compensation, coil fault analysis, contact fault analysis, mechanical performance analysis and comprehensive fault risk assessment. The comprehensive performance of the relay is analyzed through a comprehensive evaluation algorithm in combination with a coil aging coefficient, a contact health index H and a mechanical degradation index, a comprehensive fault index is obtained, the overall performance condition of the relay is judged according to the comprehensive fault index, and corresponding measures are taken. According to the method, the temperature fluctuation interference is eliminated, the accuracy of contact resistance detection is improved, the mechanical fault of the relay can be found in time through coil aging analysis, contact health evaluation and mechanical performance evaluation algorithms in combination with compensated data, multi-dimensional fault detection is realized, misjudgment and missed judgment are effectively reduced, and the fault judgment accuracy of the relay is remarkably improved.

The invention relates to the technical field of motor monitoring, in particular to a permanent magnet motor fault simulation test system and a fault monitoring method thereof, and the system comprises a test resistance obtaining device which is used for obtaining equivalent resistance parameters of a target model permanent magnet synchronous motor under different fault levels; the fault levels are divided according to magnetic induction intensity information of the motor in a working state; the simulation test system constructs an operation control environment of the target motor based on a current control strategy of the target motor, and introduces equivalent resistance parameters under different fault levels as test resistance variables in the operation control environment; according to the scheme, equivalent resistance parameters corresponding to different fault levels are injected in an environment of simulating a real working current control strategy of a target motor through a pre-constructed simulation test system, and massive current dynamic data covering multiple working conditions are efficiently generated; and therefore, a targeted motor fault monitoring model can be quickly constructed.

The invention relates to a fault monitoring and protection technology for a flexible direct current transmission system, in particular to a fault detection and polarity judgment method and system for the flexible direct current transmission system, and the method comprises the steps: firstly constructing a differential current and a zero-mode current through positive electrode current and negative electrode current, and carrying out the preprocessing through band-pass filtering; then, in the short-time detection window and the long-time confirmation window, key features of difference and zero mode are extracted by adopting an adaptive truncation and robust statistical method, and zero mode compensation is realized in combination with covariance correction; and finally, through multi-scale fusion of short window and long window results, a fusion mean value and variance are calculated, a confidence interval is constructed, and rapid fault detection of the flexible direct-current power transmission system and accurate determination of the polarity of the positive electrode and the negative electrode are realized. According to the method, rapidity and robustness are taken into consideration, interference of noise and abnormal points is effectively suppressed, rapid and accurate fault identification of the flexible direct current system can be realized in a complex operation environment, and technical support is provided for operation safety and protection control of a flexible direct current power grid.

The invention discloses a dry type reactor turn-to-turn short circuit fault positioning method based on a magnetic field direction angle. Obtaining radial and axial magnetic field component data of an outer side designated area under different working conditions; dividing a measurement area according to a reactor structure and magnetic field distribution characteristics; screening optimal measurement areas under different working conditions based on the divided measurement areas and initial moment magnetic field components in the obtained radial and axial magnetic field component data; calculating a magnetic field direction angle of the optimal measurement area under different screened working conditions; making a sensor layout scheme according to the calculated magnetic field direction angles of the optimal measurement area under different working conditions and the change characteristics of the magnetic field direction angles of the measurement points under different working conditions, and determining each measurement point; fault positioning is realized by analyzing the magnetic field direction angle difference between the short-circuit working condition and the normal working condition of each measuring point. According to the method, the problems of insensitive response to the turn-to-turn short circuit fault of the inner layer encapsulation and low positioning precision of a traditional method are solved, and a high-reliability technical scheme is provided for fault detection of the dry-type air-core reactor.

The invention relates to the technical field of motor insulation state monitoring, and discloses a motor winding turn-to-turn discharge monitoring method and device based on Rogowski coils. The high-pass integrator is matched to carry out integral operation on coil output signals in a centralized mode to obtain high-frequency discharge current data, then fault characteristic quantity is constructed based on the data, the fault position of the motor cable is determined, an independent monitoring module does not need to be configured for each phase of cable, and the single high-pass integrator can complete centralized processing of multi-coil signals. Not only is the use amount of core monitoring parts greatly reduced to reduce the monitoring cost, but also the hardware layout is remarkably simplified to save the installation space, and meanwhile, the data processing nodes are reduced to reduce processor resource occupation, and the problems of high monitoring cost, large space and resource consumption and low monitoring efficiency in the prior art are effectively solved.

The invention discloses a current and vibration collaborative perception diagnosis system and method for a rotor turn-to-turn short circuit fault, relates to the field of equipment diagnosis, and realizes full-life-cycle accurate management of the generator rotor turn-to-turn short circuit fault by constructing an intelligent diagnosis and maintenance system driven by multi-dimensional data. The fault mapping model constructed based on historical fault data can quickly identify the fault type and evaluate the severity through current and vibration signal features collected in real time, and the accuracy and timeliness of fault diagnosis are significantly improved; secondly, interval iterative optimization is implemented through preventive measures, and in combination with dynamic prediction of short-circuit occurrence times and repair cost, personalized customization of preventive maintenance strategies is realized, and the full-life-cycle maintenance cost is effectively reduced while the reliability of equipment is guaranteed; in addition, due to introduction of a hierarchical maintenance strategy, the problem of excessive maintenance or insufficient maintenance is avoided, and the use efficiency of maintenance resources is improved.

The invention provides an online power supply switch mutual inductor fault detection method and system, a medium and a product, and relates to the technical field of online monitoring and fault diagnosis of power equipment, and the method comprises the steps: generating a synchronous triggering instruction at the zero crossing point moment of the power frequency current of a mutual inductor; in response to the instruction, injecting an excitation pulse into a secondary circuit of the mutual inductor and synchronously acquiring a feedback voltage to obtain a mixed transient signal; extracting a pure response waveform only containing a high-frequency component from the mixed transient signal to carry out multi-resolution time-frequency transformation, and calculating and outputting a multi-dimensional feature vector representing a winding state; inputting the multi-dimensional feature vector into a pre-trained neural network model for classification operation to obtain a fault classification probability of the current transformer state; and comparing the fault classification probability with a preset confidence threshold to generate a fault diagnosis result of the mutual inductor. By implementing the method, the recognition sensitivity of the system on early weak faults and high-frequency distribution parameter changes of the mutual inductor can be improved.

The invention discloses a fusion filtering method, system, equipment and medium for power transformer winding state monitoring, and belongs to the technical field of power equipment state monitoring, and the method comprises the steps: obtaining an original state monitoring signal of a transformer winding; performing multi-scale decomposition on the original state monitoring signal by adopting self-adaptive wavelet transform; an improved particle filter is embedded, and a suggested distribution function of relative entropy optimization and a dynamic weight updating mechanism are combined; the filtered sub-signals are reconstructed through inverse wavelet transformation; weight parameters are adjusted in a self-adaptive mode; and outputting the key state parameters for transformer winding health assessment. Under the complex electromagnetic environment and the dynamic load fluctuation working condition, the signal-to-noise ratio of the state data of the transformer winding can be remarkably improved, the comprehensive accuracy rate of state evaluation is improved, and reliable data support is provided for health management and preventive maintenance of the transformer winding. And the operation reliability of the transformer and the intelligent level of state evaluation can be obviously improved.

A dry-type reactor turn-to-turn fault monitoring method based on a distributed optical fiber measurement temperature change rate is characterized in that a dry-type hollow series reactor finite element simulation model is built, and temperature distribution during normal operation and turn-to-turn short circuit faults of the dry-type hollow series reactor is acquired. Based on a distributed optical fiber sensing temperature measurement technology of Brillouin scattering, path temperature information in a reactor package is extracted, temperature data after normal operation and turn-to-turn short circuit are compared, and influences of different factors on fault temperature are explored. The early warning method for monitoring the turn-to-turn short circuit fault of the dry-type air-core reactor is provided by taking the temperature change rate as a criterion, and the influence of different factors on the effectiveness of the early warning method is verified. The result shows that the environment temperature, the wind speed and the running current basically have no influence on the method for monitoring the turn-to-turn short circuit of the dry-type air-core reactor by taking the temperature change rate as the criterion, different temperature change rate thresholds need to be set under different sampling intervals, and the smaller the sampling interval is, the larger the threshold is.

The invention provides an ultrasonic de-noising method and system based on an SGF-ASO optimization algorithm, and belongs to the technical field of electrical equipment state monitoring and fault diagnosis, and the method comprises the steps: collecting and preprocessing an ultrasonic echo signal of a transformer winding; sGF analysis is carried out on the preprocessed ultrasonic echo signal, a multi-scale frequency spectrum is generated, spatial gradient features are extracted, and a fusion frequency spectrum gradient map is generated through cross-scale fusion; constructing an oil temperature adaptive ASO constraint model to carry out adaptive sparse optimization on the multi-scale frequency spectrum; and performing inverse short-time Fourier transform on each scale frequency spectrum in the optimized multi-scale frequency spectrum to obtain a multi-scale time domain signal set, and fusing the time domain signal set to output a de-noised ultrasonic echo signal. According to the invention, the structure retention capability of ultrasonic signals in a high-temperature complex environment is enhanced, and high-fidelity noise reduction and oil temperature adaptive processing are realized through a multi-scale gradient guided sparse optimization mechanism.

The invention discloses an early fault monitoring method, device and equipment for a transformer and a storage medium, and the method comprises the steps: arranging magneto-optical measurement sensors at the two ends and the middle part of a winding in an iron core, obtaining the magnetic leakage induction intensity of the sensors and the current of a high-voltage winding, constructing a coefficient analysis model, and calculating the fault of the transformer; and identifying each coefficient of the current coefficient analysis model through a least square parameter algorithm to obtain an identification result, calculating an identification coefficient change rate based on the identification result and a leakage magnetic induction coefficient of the winding in a normal state, and when the transformer is not no-load and the iron core is not magnetized and saturated, if the identification coefficient change rate is greater than a change rate threshold value, determining that the transformer is in a non-load state. And determining that the transformer has turn-to-turn short circuit. It can be seen that the coefficient analytical model reflects the analytical calculation relation between the current and the magnetic flux leakage induction intensity, through the combination of the analytical model and the recursive least square algorithm, signal offset caused by load fluctuation and power grid disturbance is effectively filtered out, and the stability and accuracy of transformer early-stage turn-to-turn short circuit recognition are improved.

The invention provides a fault monitoring method and device for a motor winding of an electric shovel. The method comprises the following steps: collecting a current signal of the motor winding at a preset frequency; calculating the instantaneous change rate of each current sampling point, and dividing the sampling points of the current monitoring period into a sudden change section and / or a stable section according to a self-adaptive threshold value; determining early warning states of all the sudden change sections and all the stable sections, and marking the sudden change sections and the stable sections with abnormal early warning states as abnormal sections; detecting whether a fault section meeting a first preset condition exists or not; and determining the fault type of the fault section according to the fault matching rule and the feature matching score of the fault section. According to the method, fine division of the abrupt change section and the stable section is realized, association of the abrupt change section is completed, segmentation of the same fault is avoided, dynamic segmentation and combination rules are protected, features are extracted in a classified manner, abnormality is dynamically judged, and different from single feature early warning, feature extraction and early warning logic of a sub-scene is protected; and finally, realizing accurate fault distinguishing in combination with feature matching scores.

The invention discloses a transformer component loss detection device and method, and relates to the technical field of transformer detection, the transformer component loss detection device comprises two electric rails, the bottom of each electric rail is provided with a detection correction assembly, and each detection correction assembly comprises a lifting electric push rod, an assembly frame, a fixed seat and a moving block. Guiding sliding rods are installed on the two sides of the bottom of the assembling frame, adjusting screw rods are rotatably installed on the two sides of the bottom of the assembling frame and located at the bottoms of the guiding sliding rods, and servo motors are installed at the two ends of the assembling frame. The lifting electric push rod drives the sleeving hole to sleeve the outer side of the wiring terminal, the cleaning sponge rotates to clean the wiring terminal, when the detection head is in contact with the wiring terminal of the transformer subsequently, the situation that the detection result of the transformer is inaccurate due to stains on the surface of the wiring terminal is avoided, and the detection accuracy of the transformer is improved.

The invention relates to the technical field of transformer winding testing, and provides a transformer winding deformation tester and a testing method thereof.The transformer winding deformation tester comprises a device shell, a tester body fixedly installed in the device shell, a control assembly, a junction box, a wiring assembly and an electrifying assembly; the control assembly is fixedly installed on the top of the device shell, the junction box is installed on the device shell, a plurality of first connecting terminals are fixedly installed in the junction box, the wiring assembly is installed on a wiring structure of the tester body, and the wiring structure comprises an excitation end, a reference end and a response end. The excitation end, the reference end and the response end are all fixedly installed on the tester body, the wiring assembly is connected with an electrifying assembly, and by means of the technical scheme, the technical problem that in the prior art, due to the fact that wiring positions need to be replaced in the testing process, the testing efficiency and accuracy are affected is solved.