7817results about "Current/voltage measurement" patented technology

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

The invention relates to the technical field of power equipment state monitoring, in particular to a lightning arrester state monitoring method, system and equipment based on multi-physics field coupling and a storage medium. Acquiring current-voltage characteristic parameters, temperature distribution data and mechanical stress data of the lightning arrester, performing electro-thermal-mechanical coupling analysis based on multi-physical field monitoring data, identifying overlapping positions of an electric field distortion area, a temperature abnormal area and a stress concentration area, and determining the overlapping positions as a degradation key area; establishing a correlation response relationship between the leakage current and the temperature and a transfer response relationship between the temperature and the mechanical stress for the deteriorated key area; on the basis of the leakage current change trend of the degradation key area and the coupling influence of the superimposed temperature field and stress field, a degradation feature fusion factor is constructed, the evolution law of the degradation feature fusion factor in the time sequence is analyzed, and the degradation threshold value under the multi-field synergistic effect is determined in combination with the electric-thermal coupling acceleration effect and the thermal-mechanical coupling weakening effect.

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

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

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

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

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

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

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

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

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

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

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

The invention relates to the technical field of test and measurement, and discloses a series fault arc detection method, which comprises the following steps: collecting current data of fault arc waveforms and normal arc waveforms of a plurality of different loads; performing wavelet transformation on the preprocessed current data, dynamically extracting detail coefficients of a target layer number, calculating a standard deviation, a normalized energy ratio and an energy entropy based on the wavelet coefficients, splicing peak-to-peak values to form a four-dimensional feature vector, adding a normal or fault label, and converting the feature vector into a three-dimensional tensor; the residual shrinkage module is used for constructing a deep residual shrinkage network model based on a one-dimensional convolutional neural network and introducing an attention threshold generation and soft thresholding mechanism; training the model and monitoring the performance by adopting an early stop method; and inputting label-free sample data to the model, and outputting a detection result. The problems that in the prior art, deep features cannot be reflected, the training cost is high, and overfitting is prone to occurring are solved, and the purposes of improving the accuracy, being high in stability and efficient in detection are achieved.

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

The invention discloses an on-line qualitative detection method for partial discharge of power equipment, which comprises a detection system and detection steps, and also comprises the following steps: the detection system comprises a high-frequency partial discharge detector for detecting a high-frequency current transformer; the ultrahigh frequency detection system is used for detecting electromagnetic wave radiation which is generated by partial discharge and has extremely short duration time; the ultrasonic detection system is used for detecting sound waves and ultrasonic signals generated along with energy release when partial discharge occurs; a transient ground voltage method detection system is used for live-line detection of equipment such as a medium-voltage switch cabinet. According to the online qualitative detection method for the partial discharge of the power equipment, different detected equipment and different detection methods are adopted to perform online detection on the partial discharge of the equipment, so that the early partial discharge phenomenon of the power equipment can be found in time, the long-term discharge monitoring data of the power equipment can be stored, and the overall detection effect is improved.

The invention provides a current sensor. The current sensor adopts a magnetic-core-free design, and adopts a diversion row group with a special structure to guide a target current so as to enhance the electrifying capability of the current sensor and reduce the adverse effect of a high-frequency current skin effect on measurement. Based on the structural characteristics of the diversion row group, the magnetic sensitive component for measuring the target current and the corresponding sensing signal processing method are arranged in a matched manner, so that the measurement precision of high-frequency large current is improved, the suppression capability of an interference magnetic field is improved, and the signal-to-noise ratio of an effective measurement signal is increased. The current sensor provided by the invention is compact in structure, simple in manufacturing process, strong in electrifying capability and high in signal-to-noise ratio of a sensing signal, not only widens the measuring range of the current sensor, but also effectively improves the measurement precision of high-frequency large current, and expands the application range of the magnetic-core-free current sensor.

The invention discloses a method and device for determining the health degree of a direct current contactor and electronic equipment. The method comprises the following steps: acquiring historical life data of the DC contactor; determining initial accumulated damage of the direct current contactor based on the historical life data; based on a single loss environmental influence coefficient, the initial accumulated damage is corrected, target accumulated damage of the direct current contactor is obtained, and the single loss environmental influence coefficient is used for reflecting the loss degree of the direct current contactor caused by environmental factors when the direct current contactor executes closing or opening operation; and determining the health degree of the DC contactor based on the target accumulated damage. The technical problem that the accuracy of the health degree determination result of the direct current contactor is low in the prior art is solved.

The invention discloses a wide temperature range compensation method for resistive current of a lightning arrester, which belongs to the technical field of online monitoring of power equipment, and comprises the following steps: acquiring waveform data under a reference low-temperature working condition, and establishing an initial capacitive harmonic base; acquiring working condition data at any temperature in real time, performing harmonic decomposition, and analyzing to generate a nonlinear distortion feature vector; dynamically correcting the initial base by using the feature vector, and carrying out subtraction operation on the total current harmonic wave to obtain a final resistive current harmonic wave vector; and reconstructing an instantaneous resistive current waveform through inverse transformation and calculating a characteristic value. According to the technical scheme, the initial capacitive harmonic wave base is established, the nonlinear distortion feature vector is generated based on the harmonic distortion of the real-time working condition quantification temperature and valve plate nonlinear coupling, and dynamic feedback correction is conducted on the base, so that the real resistive current waveform in the wide temperature range can be accurately separated and reconstructed, and the accuracy of the real resistive current waveform in the wide temperature range is improved. And the accuracy and reliability of lightning arrester state evaluation are improved.

The invention provides a grid-connected inverter impedance detection method based on an L-shaped filter, and belongs to the technical field of grid-connected inverter impedance detection. A multi-frequency-point disturbance signal is injected into a preset grid-connected inverter working point, time domain data of inverter side current and common coupling point voltage are collected, fast Fourier transform analysis is carried out on the collected data, amplitude and phase information corresponding to each injection frequency point is extracted, and a frequency domain feature vector matrix is established; and establishing a frequency compensation matrix model to compensate and correct high-frequency band measurement data based on a frequency characteristic attenuation vector, calculating to obtain optimal estimated values of power grid impedance and grid-connected inverter output impedance, performing error analysis and precision verification on an impedance detection result, and outputting a verified impedance parameter value. The technical problem of insufficient power grid impedance detection precision caused by high-frequency band attenuation of the L-shaped filter is solved.

The invention discloses a high-precision charging pile direct-current electric energy metering method based on ripple compensation, and relates to the technical field of charging pile control. Dual-path signal acquisition and processing are executed to obtain a direct-current component metering result and ripple characteristic parameters; averaging the sampling points to obtain a high-frequency direct current component; subtracting the high-frequency direct-current component from the original sampling signal to obtain a pure ripple signal, performing FFT conversion and time domain analysis on the pure ripple signal, and extracting ripple characteristic parameters; according to the charging pile direct-current electric energy high-precision metering method based on ripple compensation, a main metering path and ripple analysis path dual-path architecture is adopted, the main path guarantees the direct-current component metering precision through a low-sampling-rate and high-precision ADC, the ripple path only extracts features rather than reconstructs waveforms, high hardware cost is not needed, and the charging pile direct-current electric energy metering precision is improved. Metering errors caused by ripples can be reduced to meet and be superior to national standard requirements. The transient scene is defined through the ripple mutation coefficient Ktrans and the ripple decay time tau, and the problem that the transient precision of a traditional method is out of control is solved.

The invention discloses a machine learning-based low-voltage transformer area electric leakage risk intelligent identification method and system. The identification method comprises the following steps of 1, multi-dimensional electric leakage feature construction and transformer area information aggregation analysis; 2, dynamically evaluating the electric leakage risk and adaptively adjusting a threshold value; and step 3, an electric leakage type intelligent identification and confidence degree determination mechanism. The electric leakage risk identification and classification method has the beneficial effects that by introducing a graph nerve enhanced gradient boosting tree model (G-GTBoost) and an improved residual error convolution-time sequence neural network (Res-CNN-LSTM) model and cooperating with a multi-modal feature fusion and dynamic threshold adjustment mechanism, the performance is remarkably improved in electric leakage risk identification and classification.

The invention discloses a cable operation environment optimization method based on cable trench collection information, and relates to the technical field of intelligent operation and maintenance of a power system, and the method comprises the steps: collecting an original parameter data set in a cable trench; a pulse current signal is collected at a cable grounding wire connection point, and a standard pulse discharge capacity sequence is generated through band-pass filtering and amplitude integral conversion; based on the original relative humidity measurement value and the environment temperature value, calculating a compensated relative humidity value through a multi-scale heat conduction-diffusion coupling equation; calculating a cable insulation risk index through a quantum annealing algorithm by using the compensated relative humidity value and the standard pulse discharge capacity sequence; the dynamic measurement deviation of the temperature and humidity sensor is subjected to double compensation of thermal radiation and molecular adsorption by constructing a multi-scale heat conduction-diffusion coupling equation, so that the reliability of environmental state sensing is greatly improved, and the operation safety, stability and intelligent operation and maintenance level of a power cable system are effectively enhanced.

The invention discloses a high-voltage switch cabinet partial discharge fault identification method and a storage medium. The method comprises the following steps: synchronously acquiring acoustic, electric, magnetic and thermal signals through a multi-physical field sensor array; performing preprocessing and feature extraction on the signals; using a cascade deep learning network to fuse multi-modal features to identify fault types and levels; realizing accurate positioning of the discharge source in the digital twinborn model by adopting a dynamic simulation coherent positioning method; and risk assessment and early warning are carried out by integrating diagnosis and positioning results. According to the method, deep learning and physical mechanism simulation are deeply fused through a data and model dual-drive normal form, so that the defects of shallow fusion level, low positioning precision, lack of interpretability and the like in the prior art are overcome, and intelligent, precise and prospective identification and early warning of the partial discharge fault of the high-voltage switch cabinet are realized.

The invention relates to the technical field of power cable fault detection and positioning, in particular to a middle and high voltage cable insulation defect live detection and positioning method, which comprises the following steps: step 1, injecting a composite detection signal into a cable conductor through a signal injection device; 2, arranging signal acquisition devices at a plurality of preset point positions of the cable; step 3, carrying out adaptive processing on the acquired response signal; step 4, extracting insulation defect features from the response signals after adaptive processing; step 5, based on the extracted insulation defect features, classifying defect types by using a pattern recognition algorithm, and calculating defect positions based on a signal propagation time difference principle; and 6, outputting a defect identification and positioning result to a monitoring system, verifying, and if the deviation between the positioning result and historical data exceeds a threshold value, re-executing the steps 1 to 5. By combining a multi-point synchronous measurement technology and a precise space-time analysis method, the position where the defect occurs can be positioned more accurately.

The invention discloses a zero-sequence current and vibration signal fusion optimal transmission-based transmission chain cross-domain diagnosis method. The method comprises the following steps of data acquisition and preprocessing; constructing a multi-sensor fusion feature optimal transmission domain adaptive diagnosis model: firstly, extracting multi-scale features in a source domain and a target domain by using multi-scale depth separable convolution, and enhancing feature representation through a space channel attention mechanism; then calculating domain adaptation loss, calculating local maximum mean value difference loss between source domain and target domain features, and aligning loss of source domain and target domain distribution by using an optimal transmission method; and carrying out model training and fault diagnosis. According to the method, the local maximum mean value difference loss and the optimal transmission distance loss are combined to construct a double-constraint optimization target, the local maximum mean value difference loss is responsible for capturing distribution differences more finely, the optimal transmission distance loss is responsible for finding an optimal transmission scheme to reduce the differences, and the generalization ability of the model is improved.

The invention discloses a power transmission line fault positioning method and system, and relates to the technical field of power transmission lines. The method comprises the following steps: dividing a power transmission line into a plurality of logic sections, and setting a basic logic rule; performing coarse-grained threshold judgment and fine-grained logic check on the real-time monitoring parameters of each logic section, and screening out candidate fault logic sections; establishing a node logic link, detecting a breakpoint node of the node logic link, comparing the breakpoint node in a previous time period and a later time period, judging whether the breakpoint node is a persistent fault or not, and obtaining a breakpoint judgment result; performing logic consistency check on breakpoint judgment results of different monitoring points and different parameter types, and when conflicts occur, making decisions according to priorities to generate a unified judgment result; and synthesizing the candidate fault logic sections and the unified judgment result to generate a final fault positioning result of the power transmission line. According to the invention, high-precision, reliable and intelligent positioning of power transmission line faults is realized.

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 power frequency phase adaptive acquisition device, and relates to the technical field of electric power signal measurement, the device acquires electric field data at a gap through an electric field sensor integrated on an edge shell, calculates electric field intensity fluctuation, and generates interference diagnosis information in combination with a preset interference determination threshold value; when the interference diagnosis information shows that effective interference exists, the electric field intensity variable quantity is calculated, a mapping model is established based on a proportionality coefficient obtained through an experiment so as to obtain zero crossing point offset, and the sampling trigger moment of a zero crossing point detection module is corrected; the clock synchronization module calculates a phase difference by taking the corrected trigger moment as a reference, dynamically adjusts a calibration frequency by combining a phase difference threshold value, and outputs a synchronized system clock signal; and the control system takes the synchronized system clock signal as a trigger signal, collects power frequency phase data according to a power frequency period, and stores the power frequency phase data to a database through a transmission unit, thereby realizing high-precision power frequency phase collection.

The invention discloses an electricity stealing behavior detection method and system based on an intelligent electric energy meter, and the method comprises the following steps: constructing a phase locking reference frame at the end of the intelligent electric energy meter, synchronously collecting a voltage phase and a current phase through high-precision time, generating a direction discrimination baseline, setting a zero-power crossing point and a direction overturning threshold value, and carrying out the detection of an electricity stealing behavior. Outputting a direction baseline consistency index as a direction identification reference; according to the invention, through phase locking, high-precision synchronization, neutral line drift analysis, multi-table data alignment and phase disturbance verification, accurate identification and causal confirmation of a current backward flow behavior are realized, a disturbance user is reversely positioned in combination with a disturbance fingerprint, an intervention instruction is output, and an identification-verification-positioning-control closed loop mechanism is constructed. And the anti-electricity-stealing capability and the metering accuracy of the intelligent electric energy meter in a complex power supply environment are obviously improved.