4630results about "Electrical measurements" patented technology

The invention relates to the technical field of online calibration, in particular to a multi-parameter fusion intelligent electric energy meter online calibration method and system, and the method comprises the following steps: collecting voltage waveforms, current harmonics and active power data, dividing windows, calculating a covariance matrix, and generating a feature set; a power factor curvature extreme value and a temperature inflection point offset are analyzed to generate an interference identifier, current density distribution and a voltage distortion spectrum are jointly analyzed to extract a harmonic energy ratio to generate a feature vector, and phase compensation is performed on a pulse sequence to generate a calibration instruction set. According to the method, the covariance matrix is constructed by synchronously collecting voltage and current power parameters, the abnormal mark section is generated by combining the temperature change and the covariance difference value, the environment disturbance and the real deviation are effectively distinguished, and the temperature hysteresis effect is identified through the power factor curvature extreme value and the temperature inflection point offset. And combining current density and voltage distortion spectrum analysis to extract a fundamental wave and harmonic wave energy ratio, establishing a composite calibration reference, and dynamically adjusting a pulse duty ratio to realize harmonic wave energy compensation.

The invention provides a multi-source ammeter data intelligent fusion and abnormity identification method. According to the method, original electric parameter data of a plurality of intelligent electric meters are collected under a unified time reference, a cross-equipment measurement data set indexed according to electric meter numbers is constructed, and electric parameter classification and associated information generation are completed in combination with electric wiring information and functional parameters; an electric parameter change curve is extracted through a continuous time window, a value change consistency index is calculated, a response offset feature vector is further introduced for joint judgment, and accurate recognition of an abnormal electric meter and an abnormal time period is achieved; for the intelligent electric meter which is identified as abnormal, constructing an estimated value through data of other electric meters, and calculating residual errors between the estimated value and an original value and between the estimated value and an adjacent estimated value according to a main conductive parameter so as to realize dual residual error verification; judging an anomaly type according to the consistency index and the residual result, and outputting an anomaly recognition result; according to the method, the anomaly detection precision in a multi-table collaboration scene is improved.

The invention provides an intelligent electric energy meter fault prediction method based on multi-modal sensor fusion. The method comprises the following steps: collecting original data of a multi-modal sensor and preprocessing the original data to obtain a standardized data stream; establishing a sensor health state model and monitoring the working state of the sensor; performing feature extraction and pattern recognition on the standardized data to generate an electric energy meter state feature vector; combining a sensor health assessment result and an electric energy meter state feature vector, and applying a double-layer causal reasoning framework to realize separation and identification of a sensor fault and an electric energy meter fault; multi-source uncertainty is quantified, diagnosis reliability is evaluated, and a fault evaluation report is generated. The method can effectively distinguish the sensor fault from the electric energy meter fault, improves the fault prediction accuracy, reduces the false alarm rate, and optimizes the maintenance decision.

The invention discloses an intelligent electric meter abnormal electricity utilization detection method and system based on data analysis, and belongs to the technical field of electricity utilization abnormal detection, and the method specifically comprises the steps: obtaining the historical electricity utilization data of a user according to an intelligent electric meter, and carrying out the preprocessing of the obtained historical electricity utilization data of the user, extracting features of the preprocessed historical power consumption data of the user, carrying out three-layer clustering analysis on the extracted features, analyzing historical power consumption behaviors of the user, identifying abnormal behaviors obviously deviating from a normal power consumption mode according to the historical power consumption behaviors of the user, and adaptively adjusting model parameters through a feedback mechanism. The abnormal electricity utilization of the intelligent electric meter is monitored in real time, the method can adapt to the electricity utilization mode change of the user, the dynamic threshold value adjustment can be adaptively adjusted according to the electricity utilization behavior change of the user, and therefore more accurate anomaly detection is achieved.

The invention relates to the technical field of ammeter monitoring, in particular to a detection method and system for ammeter load optimization, and the method comprises the following steps: collecting voltage and current data based on a load access terminal, judging synchronous offset, recognizing a dominant channel change mode, adjusting sampling rhythm configuration, and constructing amplitude mapping between voltage and power; and analyzing a multi-cycle response trend and classifying load states to obtain a load feature distribution label. According to the invention, by constructing a time sequence comparison structure of voltage and current signals in the same period, the identification capability of channel synchronization characteristics is enhanced, the interference of periodic rhythm drift on data sampling is avoided, the channel dominant trend is judged according to the spacing change proportion, the difference source has clear guidance, and the fluctuation identification precision is improved. In combination with a dynamic regulation and control strategy of a sampling rhythm, adaptive distribution of sampling frequency bands of key channels is realized, the detection granularity is improved, linkage expression among multi-channel signals is completed, and the depiction depth of a load response structure is enhanced.

The invention relates to the technical field of Internet of Things, in particular to an electric energy meter with a wireless communication module, which comprises a parameter acquisition module, a communication optimization module, an interference analysis module, a fluctuation evaluation response module and a load classification module. According to the invention, a mutual inductor captures a single-phase circuit signal, analog-to-digital conversion is combined to realize parameter quantization, interference is eliminated based on an active power identification algorithm, a structured parameter set is integrated, a wavelet packet decomposition parameter and a quantization threshold are dynamically adjusted according to a load rate, and spread spectrum factor configuration is adopted to improve channel efficiency. The method comprises the following steps: extracting key features by using wavelet packet characteristics; continuously monitoring voltage fluctuation by a sliding window; detecting and identifying an instantaneous interference event by a break variable; inhibiting noise by Kalman filtering and predicting a trend; and the performances of the system in the aspects of acquisition precision, transmission efficiency, interference immunity and detection sensitivity are enhanced.

The invention relates to the technical field of electricity meter metering, and discloses an electricity meter adaptive error compensation method and system, and the method comprises the steps: calibrating an electricity meter, collecting baseline data under different environment conditions, and recording a measurement error; environment data and ammeter data are collected in real time through an ammeter, and key features are extracted; constructing an error model, and predicting the error of the current ammeter data; and performing real-time correction and parameter adjustment according to the prediction error, and outputting the corrected measurement data. By introducing a real-time error prediction and dynamic compensation mechanism based on environmental data and combining a particle swarm optimization algorithm to optimize regularization parameters in a ridge regression model, the measurement precision of the electric meter under a complex environmental condition is effectively improved. The method can adaptively adjust the measurement data of the electric meter, reduces errors caused by environmental factors, improves the accuracy and stability of electric power measurement, and provides more reliable data support for an electric power management and monitoring system.

The invention relates to the technical field of electrical engineering and sensors, and provides a high-precision current sensor electrical performance detection method, which comprises the steps of collecting an original output signal of a current sensor and performing multi-dimensional signal preprocessing; extracting multi-dimensional feature parameters from the preprocessed signals based on a dynamic feature extraction algorithm; establishing a dynamic compensation model according to the multi-dimensional characteristic parameters, and generating a compensation control signal; a compensation control signal is injected into the detected current sensor to form closed-loop feedback, and a compensated output signal is collected; calculating a sensor performance degradation parameter according to the signal difference before and after compensation; an electrical performance assessment report is generated based on the performance degradation parameter. According to the invention, the measurement precision and reliability of the sensor can be improved, the service life is prolonged, the maintenance cost is reduced, and a powerful guarantee is provided for safe and stable operation of a power system and electronic equipment.

The invention discloses a metering error correction method and system based on electric power data acquisition, and the method comprises the steps: detecting an operation state through a self-inspection and data acquisition module, and obtaining environment data, electrical parameters and error information; key feature data are selected and standardized through a data preprocessing module; an LSTM model and an SVR model are constructed through a model establishment and fusion module, and prediction results are fused to generate error compensation output; real-time error compensation is carried out on a combined prediction result by utilizing an ARIMA model and error time sequence processing through a dynamic error compensation module; the data smoothing and evaluation module is used for smoothing the voltage data and evaluating the error compensation effect; an error compensation effect is monitored through a monitoring and optimizing module, and feature selection and model parameters are optimized regularly; through the fault diagnosis and emergency module, an abnormal condition is detected, and a corresponding standby scheme is triggered. According to the invention, the accuracy and anti-interference capability of electric power metering are effectively improved, and the method is suitable for complex and changeable metering environments.

The invention discloses a high-precision current detection circuit calibration system, and belongs to the technical field of current detection circuit calibration. Comprising a shunt resistor, a Hall sensor array, a chopping amplifier, a low-noise operational amplifier, a temperature sensor, an ADC acquisition unit, an RC low-pass filter, a signal conditioning unit, a main control unit, an environmental parameter adaptive compensation module and a gain adaptive adjustment module. After an original signal is obtained from a multi-channel sensor, a dynamic error model is constructed in combination with temperature data, a long-term imbalance trend is extracted by using a moving average or median filtering algorithm, and real-time compensation is performed through a pre-calibrated temperature-drift function, so that the system maintenance period is remarkably prolonged.

The invention relates to an environment adaptive radio measurement calibration method and system. The method comprises the following steps: synchronously sampling temperature, humidity, air pressure and electromagnetic interference around radio equipment to obtain an environmental parameter vector; constructing a linear model and a nonlinear target model of a three-layer neural network structure according to the environmental parameter vector; solving an environmental parameter separation vector set by using the linear model and the nonlinear target model; executing one-dimensional iterative search optimization on the environment parameter separation vector set to obtain a target environment parameter vector; and inputting the target environment parameter vector into the nonlinear target model for calibration compensation calculation to obtain dynamic calibration compensation data. According to the method, the updating frequency is reduced when the environment is stable, the calibration precision is ensured, the system resource use is optimized, and the method is particularly suitable for wireless equipment with limited resources.

The invention relates to the technical field of power state monitoring, in particular to an intelligent electric energy meter capable of monitoring and reporting operation errors, and the electric energy meter comprises an error operation parameter monitoring module, an error space-time correlation modeling module, an error propagation path analysis module, an error dynamic regulation and control module, an error adaptability verification module and an error transmission state summarization module. According to the method, the operation deviation is captured in real time, the abnormal time period and nodes are marked, the abnormal event positioning and recording capacity is improved, an abnormal distribution panorama is constructed, path optimization is supported, a high-error path is screened by analyzing a weight value and an influence factor, an error propagation hierarchical structure is optimized, and the load proportion and the connection path are dynamically adjusted; multiple paths are configured to reduce the transmission risk, the system robustness is enhanced, the state response and error correction precision is improved through real-time monitoring and abnormal verification, key information is archived, analyzed and extracted to generate a report record, and intelligence and high efficiency of electric energy meter error monitoring are achieved.

The invention relates to the technical field of electric power measurement, and discloses an electric power measurement equipment anomaly detection method and device based on a knowledge graph. Preprocessing the power state data; according to abnormal state nodes defined in the electric power metering equipment anomaly detection knowledge graph and attributes of the abnormal state nodes, the closest abnormal state is judged through similarity calculation; and according to the node attribute corresponding to the determined closest abnormal state, obtaining an associated abnormal fault reason and operation and maintenance operation guidance, so as to carry out abnormal operation and maintenance disposal on the electric power metering equipment which is determined to be abnormal. The detection accuracy and reliability are improved, misjudgment and missed judgment are reduced, and operation and maintenance personnel are helped to quickly position problems and take effective measures by automatically associating fault reasons and operation and maintenance guidance, so that the equipment operation and maintenance efficiency is improved.

The invention discloses an electric energy metering method of an intelligent electric energy metering box, and relates to the technical field of electric power automation and intelligent metering, and the method comprises the following steps: collecting electric power load data generated in the operation process of power electronic equipment in real time, and carrying out the preprocessing of the collected data, so as to recognize and track the dynamic change characteristics of the equipment load. According to the invention, through real-time acquisition and analysis of equipment load data, dynamic calibration is carried out in combination with frequency spectrum transformation and a machine learning model, contribution factors of a high-order frequency band can be adaptively adjusted, and the influence of harmonic waves on electric energy is compensated. Meanwhile, the data sampling frequency is dynamically adjusted, it is ensured that the metering model responds to load fluctuation in real time, accuracy and timeliness are kept, error amplification caused by static fundamental wave calibration is avoided, and the accuracy and reliability of electric energy metering and the power quality monitoring effect are improved.

The invention discloses an intelligent electric meter anomaly detection method and system based on a federal differential privacy and attention mechanism. The method comprises the following steps: firstly, collecting and regionally grouping intelligent electric meter data; then, a multi-level attention mechanism is adopted to extract regional specific initial features, and spatial correlation features are enhanced through fusion of a graph attention network and multi-head attention; carrying out distributed anomaly preliminary identification; for potential anomalies, user behavior log features are safely obtained and protected through a federated learning framework and an LDP mechanism; secondly, performing security aggregation and optimization on local model parameters which are trained by all parties and subjected to parameter-level differential privacy protection under a federated learning framework; local model fine tuning and secondary cross validation are carried out to confirm true abnormity; performing qualitative traceability on the abnormal event by utilizing multi-dimensional dynamic attention; and finally, dynamically adjusting a feature extraction strategy through federal feedback, and optimizing a data processing scheme in combination with resource awareness. According to the method, the problem of balance of precision, privacy protection and model adaptability in data anomaly detection of the intelligent electric meter is solved.

The invention discloses an electric energy meter error real-time monitoring and intelligent calibration system and method, and relates to the technical field of electric energy meter calibration, and the system comprises a target power data calculation module which is used for collecting voltage and current signals in real time, and carrying out the calculation to obtain target power data; the reference power data calculation module is used for performing real-time calculation to obtain reference power data; the error vector acquisition module is used for acquiring an error vector; the error prediction result generation module is used for carrying out modeling analysis on the error vector and generating an error prediction result; and the target electric energy value output module is used for outputting the target electric energy value. The technical problem that the metering accuracy is insufficient due to the fact that the electric energy meter lacks an effective error analysis and prediction means and a dynamic calibration mechanism in a bidirectional power flow scene in the prior art is solved, and the purposes of real-time monitoring, error analysis and prediction and dynamic calibration of the forward and reverse power data of the electric energy meter are achieved. And the metering accuracy of the electric energy meter in a bidirectional power flow scene is improved.

The invention discloses an electric energy meter fault detection method and device, terminal equipment and a computer readable storage medium, and the method comprises the steps: obtaining the voltage data, current data and temperature and humidity data of an electric energy meter in a current sampling time period, obtaining the historical feedback layer gain of the electric energy meter in a previous sampling time period, and determining a final gain parameter; multiplying the final gain parameter by the current bandwidth and the current cut-off frequency of the anti-interference filter to obtain the updated bandwidth and cut-off frequency of the anti-interference filter; when the anti-interference filter works based on the updated bandwidth and cut-off frequency, acquiring a voltage fluctuation value and a current fluctuation value filtered by the anti-interference filter; and comparing the voltage fluctuation value with a voltage fluctuation threshold value, comparing the current fluctuation value with a current fluctuation threshold value, if the voltage fluctuation value is greater than the voltage fluctuation threshold value and the current fluctuation value is greater than the current fluctuation threshold value, judging that the electric energy meter has a high-interference fault, otherwise, judging that the electric energy meter does not have the high-interference fault.

The invention discloses a high-temperature aging test box health state assessment method and device. The method comprises the following steps: acquiring a nine-point temperature measurement signal, a compressor vibration signal and a fan rotation speed signal of a to-be-assessed high-temperature aging test box; performing feature extraction on the monitoring signal to obtain a multi-dimensional feature sequence; inputting the multi-dimensional feature sequence into a pre-trained time convolutional network model to obtain a degradation feature state of the high-temperature aging test box; and under the condition that the high-temperature aging test box is in a degradation state, determining a state degradation curve of the to-be-evaluated high-temperature aging test box based on the probability space point after the multi-dimensional feature sequence mapping and the health baseline in the time convolutional network model so as to determine the health state of the high-temperature aging test box. According to the method for evaluating the health state of the high-temperature aging test box, the health state of the high-temperature aging test box can be accurately evaluated.

The invention relates to the technical field of electric energy metering and electric power system safety, and discloses an electricity larceny prevention method and system for multi-source data fusion of an electric energy meter, and the method comprises the steps: obtaining and preprocessing current and voltage waveform data and user electricity consumption behavior data; generating multi-dimensional power consumption characteristic parameters, performing screening to form a characteristic vector set, and constructing an abnormal power consumption detection reference set; dynamically verifying power utilization data based on the reference set, and analyzing a phase relation and a mismatch state of current and voltage signals; and generating an electricity consumption behavior deviation index to judge an electricity stealing behavior. The system comprises a multi-source data acquisition module, a feature modeling analysis module, an electrical relation verification module and an electricity stealing judgment module. According to the invention, through multi-source data fusion and multi-dimensional feature analysis, accurate detection of electricity larceny behaviors is realized, accuracy and reliability of electricity larceny prevention are improved, and the method is suitable for power utilization safety management of a smart power grid.

An adaptive calibration method and system for an electric energy meter, relating to the technical field of electric energy meter calibration. The method comprises: acquiring basic data and additional data of an electric energy meter (S1); performing time series data alignment, and performing period division on an environment data set to generate a time series segmentation result (S2); acquiring device information of the electric energy meter, performing key feature analysis within a period, and establishing an interference weight factor for each feature (S3); building a performance degradation model (S4); acquiring usage environment data of the electric energy meter by means of a monitoring sensor (S5); then generating an adaptive calibration result on the basis of the performance degradation model (S6); and finally performing electric energy meter calibration management (S7). The present invention solves the technical problem in the prior art where the accuracy of electric energy metering is affected due to untimely calibration caused by an inability to monitor performance degradation of an electric energy meter in real time, thereby implementing effective calibration management for the electric energy meter, ensuring the long-term accuracy and reliability of the electric energy meter, and improving the efficiency of calibration work.

The invention relates to the technical field of electric energy meter metering, and discloses an electric energy meter metering anomaly analysis method and system, and the method comprises the steps: collecting data, such as voltage waveforms, to generate a metering feature vector set, and constructing an anomaly detection rule base; setting a scene parameter type set, and establishing an abnormal association judgment model; dynamically correcting the threshold value by combining the model, and generating an optimized error threshold value set and an abnormal triggering condition set; and updating a metering analysis strategy to generate an abnormal judgment scheme, and correcting data verification sequential logic. The system comprises a data acquisition module, an abnormal rule base construction module, a scene parameter configuration module, a correlation model training module, a dynamic threshold optimization module, a strategy updating module and a time sequence correction module. Through multi-dimensional data modeling, scene-based threshold configuration and dynamic time sequence calibration, the accuracy and adaptability of electric energy meter measurement anomaly detection are improved, and the method is suitable for measurement anomaly analysis of diversified power consumption scenes in a smart power grid.

The invention relates to the field of power equipment state monitoring, in particular to an electric meter box running state monitoring method and system. An electric meter box operation state monitoring system comprises a state sensing module, an intelligent analysis module, a diagnosis and evaluation module and an operation and maintenance decision module. According to the method, the personalized dynamic health reference model of the electric meter box is constructed, and the real-time acquisition of the data of the multi-dimensional sensor is combined, so that the accurate modeling and dynamic adaptation of the running state of the electric meter box are realized, the misjudgment problem of fixed threshold monitoring under different seasons, time periods and load conditions can be effectively eliminated, and the safety of the electric meter box is improved. Therefore, the accuracy of abnormal recognition and the environment adaptability of the system are improved.

The invention discloses a current transformer real-time state diagnosis system based on Internet of Things, which relates to the technical field of electrical equipment state diagnosis and comprises a reference construction module, a disturbance excitation module, a modeling identification module, a driving unwrapping module, a self-adaptive setting module and a closed-loop control module, in the operation process of a secondary winding loop of the current transformer, a nanosecond time synchronization reference is established, a phase reference baseline is locked, current vector trajectories are continuously collected based on the time synchronization reference and the phase reference baseline, and a discrete phase residual image is constructed. According to the method, through high-precision time synchronization, phase disturbance excitation, sparse modeling identification, driving unwrapping and self-adaptive setting control, hysteresis oscillation of the secondary circuit of the current transformer is accurately identified and suppressed, the fault diagnosis accuracy and the system stability are remarkably improved, and the limitation of a traditional method in the aspects of identification sensitivity and response capability is broken through.

The invention discloses a meter reading system and a meter reading method based on dual-mode communication, and relates to the technical field of meter reading systems and methods, the meter reading system comprises a dual-mode communication module, intelligent data demand switching, strong NB-IoT penetration and far coverage; the BLE is short-distance, high-speed, low-power-consumption and fast in switching, the intelligent electric meter module and the high-precision metering chip measure electric energy accurately, historical data is stored in a large-capacity mode, the shell protection is high, the intelligent electric meter module adapts to severe environments, the data processing and storage module and the high-performance processor compress and process data, the SSD stores in a large-capacity mode and backups redundancy, and the user interaction module and the large-screen LCD clearly display power utilization information. And a button or a touch screen is convenient to operate, and a remote management and monitoring module is provided. Innovative technologies such as dual-mode communication are fused, high-precision metering, intelligent switching communication and convenient interaction are achieved, power line carrier standby and fault early warning diagnosis are achieved, meter reading efficiency, precision and reliability are improved, and development of an intelligent power grid is promoted.

The invention relates to the technical field of electrical variable testing, in particular to a high-precision performance testing processing method and system for a low-impedance voltage transformer. The method comprises the following steps: constructing a controllable test excitation signal, and synchronously collecting a corresponding primary side voltage and a secondary side voltage of a mutual inductor; space electromagnetism elimination is carried out according to the primary side voltage and the secondary side voltage of the mutual inductor, and frequency domain characteristic decomposition and signal instantaneous offset calculation are carried out at the same time, so that a mutual inductor signal characteristic matrix is constructed; acquiring environment parameters corresponding to the low-impedance voltage transformer; constructing a neural network model based on Bayesian optimization, and predicting and outputting a performance parameter set corresponding to frequency response and load characteristics; and comparing the performance parameter set with a preset standard threshold value, feeding back, generating a calibration compensation coefficient, testing and evaluating, and generating a high-precision performance test report corresponding to the low-impedance voltage transformer. According to the invention, high-precision performance testing of the low-impedance voltage transformer can be realized.

The invention relates to the technical field of power grid monitoring, and discloses a voltage sensor multi-parameter real-time monitoring method and device in an Internet of Things environment. According to the method, real-time voltage fluctuation data of a target area is acquired by deploying a multi-channel voltage sensor array and is transmitted to an edge computing node; dynamic feature extraction is executed in the edge nodes, and a multi-dimensional feature matrix containing voltage fluctuation ratio, harmonic distortion and phase deviation features is generated; identifying voltage sag, overvoltage and harmonic resonance event characteristics based on an anomaly detection model; spatial correlation modeling is carried out in combination with power grid topological structure parameters, the coupling strength of adjacent sensor node parameters is calculated, and a multi-parameter correlation map containing event propagation paths and influence ranges is generated; inputting the voltage into a diagnosis model to generate a power grid state diagnosis report, and dynamically adjusting the sampling frequency and filtering parameters of the voltage sensor according to the power grid state diagnosis report. According to the method, real-time monitoring and analysis of multiple parameters of the power grid can be realized, and comprehensive support is provided for power grid state evaluation.

The invention provides a mutual inductor fault diagnosis method based on multi-sensor data fusion and transfer learning, and the method comprises the following steps: S1, installing a plurality of high-precision sensors on the secondary side of a voltage transformer to be subjected to fault diagnosis based on the voltage transformer to be subjected to fault diagnosis; s2, on the basis of the step S1, performing data acquisition and processing on data acquired by various high-precision sensors through a DHDAS dynamic signal acquisition instrument; and S3, based on the step S2, carrying out fault diagnosis on the voltage transformer through a PC (Personal Computer) carrying acquisition analysis software on the DHDAS dynamic signal acquisition instrument. According to the method, multi-mode signals such as voltage, current and temperature are synchronously collected, time-frequency domain features are extracted through continuous wavelet transform, the dynamic characteristics of interlayer short circuit faults can be more comprehensively represented, meanwhile, by combining transfer learning and a deep neural network, the intelligent diagnosis model can remarkably improve the classification performance under the condition that sample data is small, and the classification efficiency is improved. And the limitation of a single-mode signal diagnosis method is effectively overcome.

The invention belongs to the technical field of electric energy meters, and particularly relates to an electric energy meter self-inspection and fault prediction method based on edge calculation. The method comprises the steps that S1, a master station cloud platform issues a self-checking model package matched with the specification of an electric energy meter to each edge node installed in an electric energy meter concentrator; the self-checking model package comprises an encrypted handshake file and an inference engine; s2, the edge node completes communication connection with the master station cloud platform through the encrypted handshake file, then calls an inference engine, and confirms the multi-dimensional weight mask as a confidence coefficient vector and outputs the confidence coefficient vector; s3, after the master station cloud platform receives the confidence vectors of all the edge nodes, the output vectors are uploaded to the master station cloud platform, cross-node time sequence modeling is carried out by the master station cloud platform LSTM, and global fault prediction is generated; according to the invention, the real-time performance of anomaly detection and the robustness of the system are improved.

The invention discloses an electric energy metering method under power grid waveform distortion, and particularly relates to the technical field of electric energy metering. The problems of false triggering of an energy compensation mechanism, metering deviation and electricity price settlement risks caused by zero-sequence current or false phase drift easily generated in a multipoint grounding or grounding fault power system in the prior art are solved. According to the anomaly identification and control method integrating zero-sequence current frequency domain characteristic analysis and three-phase phase deviation trend consistency judgment, the false phase deviation state is accurately identified through synchronous sampling, frequency domain analysis and trend consistency evaluation of three-phase voltage, three-phase current and neutral line current, and the false phase deviation state is accurately identified. The error compensation behavior is shielded in real time, the fundamental wave power metering mode is switched to, the electric energy metering accuracy is guaranteed through an error backtracking correction mechanism after recovery, and the anti-interference capability and metering reliability of the electric meter under complex working conditions are effectively improved.

The invention relates to the technical field of probe calibration, and discloses a probe calibration method and device and probe control equipment, and the method comprises the steps: calculating a vibration wavelength, and constructing a multi-probe measurement structure; synchronously acquiring a wafer surface vibration signal by using a multi-probe measurement structure, and calculating probe vibration characteristic data; acquiring a multi-measurement path electrical signal of the multi-probe measurement structure, and processing the multi-measurement path electrical signal through a baseline automatic encoder to obtain an environment temperature characteristic; calculating a signal deviation value between the measured signal and the baseline signal, and correcting the signal deviation value to obtain an electrical signal after temperature compensation; layered calibration is carried out on the probe vibration characteristic data and the electrical signal after temperature compensation to obtain a wafer electrical characteristic measurement result, so that a more comprehensive and accurate temperature compensation effect is realized, the calibration weight of each layer can be dynamically adjusted according to the probe vibration state and the temperature change degree, the calibration effect is optimized, and the measurement accuracy is improved. And the calibration method has higher environmental adaptability and robustness.