3518results 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 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 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 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 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 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 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 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.

The invention relates to the technical field of electronic timer calibration, in particular to an ammeter clock intelligent calibration method and system based on multi-modal fusion, and the method comprises the steps: firstly obtaining ammeter clock voltage and environment data, carrying out the preprocessing, and generating an instantaneous disturbance sequence and a steady-state voltage time sequence; respectively calculating a voltage chronic influence factor, a voltage acute influence factor and an environment acceleration factor; determining the influence coefficient of each factor through historical data regression of similar ammeters, carrying out fusion calculation on total life loss, and carrying out iteration to generate a dynamic correction period adaptive to individual damage; and finally, constructing a same-batch and same-environment electric meter reference group, screening similar clusters through clustering, comparing corrected time consistency, judging unconventional abnormity, and outputting a targeted calibration strategy. According to the method, the multi-dimensional error factors are quantified through multi-modal fusion, the differentiation model is constructed, the dynamic correction period is generated through iteration, the traditional fixed period calibration defect is overcome, and the long-term timing precision stability of the electric meter is improved.

The invention relates to the technical field of power system measurement, and discloses a current transformer error dynamic monitoring method and system.The current transformer error dynamic monitoring method comprises the steps that current transformer time sequence data and a system event log are obtained; constructing a time sequence causal graph to represent the time correlation between the event and the error change; identifying potential causal links by applying a counter causal model; designing a multi-world simulation engine to generate an anti-fact scene; quantifying a causal effect by comparing actual observation with an anti-fact simulation result; establishing a monitoring mechanism to track key trigger events in real time; generating a dynamic causal interpretation report and adjusting a compensation strategy; according to the method, the limitation of traditional correlation analysis is broken through, the causal relationship and the correlation can be accurately distinguished, the real triggering factor of the error change of the current transformer can be accurately identified, the false alarm rate and the missing report rate are reduced, and the accurate dynamic monitoring of the error of the current transformer is realized.

The invention relates to the technical field of dynamic correction and intelligent compensation of measured data of a current sensor, and discloses a self-adaptive data compensation method for the current sensor, which comprises the following steps of: acquiring an original output signal and environmental parameters in real time, identifying multi-dimensional error sources such as temperature drift, nonlinear error and electromagnetic coupling error, establishing an influence factor model, and calculating the dynamic correction and intelligent compensation of the measured data of the current sensor. Dynamic compensation is realized by fusing directivity judgment of each error source and a weighting mechanism, and online learning and model parameter updating are carried out by introducing a long-short-term memory network structure, so that the problems of poor environmental adaptability, limited compensation precision and lagging model updating in the prior art are effectively solved.

The invention relates to the field of load monitoring, in particular to an electric energy meter time-sharing load monitoring method and system based on multi-dimensional electricity utilization characteristics, and the method comprises the steps: collecting and preprocessing the electrical time sequence data of a user electric energy meter; extracting multi-dimensional statistical characteristics of current and voltage of the users under a plurality of analysis windows, and clustering to obtain a cluster and membership degree of each user; for the target user, respectively calculating a first anomaly degree of the target user and a historical sequence thereof and a second anomaly degree of the target user and a sequence of other users in the same period in the cluster to which the target user belongs; according to the membership degree of the user under each analysis window, carrying out weighted fusion on the second anomaly degree to obtain a third anomaly degree; and calculating a comprehensive abnormality based on the first abnormality and the third abnormality, and determining whether the load is abnormal. According to the invention, through dual reference of individual history and a dynamic group and multi-scale credible fusion, the accuracy, the adaptive ability and the reliability of load anomaly monitoring are significantly improved.

The invention relates to the technical field of electric energy meter data analysis, and discloses a power consumption data analysis operation and maintenance method and system of an electric energy meter. The method comprises the following steps of: acquiring and preprocessing electricity utilization state data, namely synchronously acquiring voltage, current and power data, and performing timestamp alignment, data cleaning and resampling; classifying according to preset peak, flat and valley time intervals, and extracting time-frequency domain characteristics such as voltage fluctuation amplitude and current change rate in each interval to generate a set; detecting time period switching frequency, and marking an abnormal interval if the time period switching frequency exceeds a threshold value; generating an abnormal feature subset according to similarity decomposition of historical anomalies and current features; correcting the abnormal feature subset based on the electrical and time sequence parameter association relationship; and reversely backtracking a potential fault source along the fault association map, determining an operation and maintenance priority according to the number of abnormal parameters, position depth and the like, and generating an operation and maintenance instruction. The system comprises corresponding modules such as data acquisition and processing. According to the scheme, data can be accurately analyzed, a fault source is positioned, and the operation and maintenance efficiency is improved.

The invention relates to the field of electric energy metering, and discloses a real-time metering method of an intelligent modular electric energy metering box, which comprises the following steps: deploying a sensor array in the electric energy metering box; acquiring original electric energy data and environmental parameters through the sensor array to obtain an original electric energy metering data stream; performing dynamic threshold filtering on the original electric energy metering data stream to obtain clean electric energy metering data; constructing an electric energy topology model according to a graph neural network, and inputting the key electric energy parameters into the electric energy topology model to obtain initial electric energy topology network data; and abnormal nodes in the electric energy topology model are detected through an isolated forest algorithm to obtain optimized electric energy topology data, the dynamic threshold filtering and Kalman filtering algorithms are adopted, noise and abnormal values in the original electric energy data can be effectively removed, the reliability of the electric energy metering data is ensured, and especially in a complex environment, the reliability of the electric energy metering data is improved. Errors may be caused by factors such as electromagnetic interference.

The invention relates to the technical field of electric power intelligent judgment, in particular to an AI-based intelligent electric energy meter fault diagnosis system, which comprises an operation data acquisition module, a jump correlation judgment module, an operation deviation judgment module, a current waveform diagnosis module and an abnormity alarm module. According to the invention, through synchronous acquisition of the current data and the operation data of the refrigeration storage, whether the current jump is related to the gating action is judged, and the refrigeration performance abnormity of the refrigeration storage and the fault type of the intelligent electric meter are effectively identified by further combining multiple cross validation of baseline energy consumption and temperature change; an ideal current waveform is constructed through an AI algorithm and is compared with an actually measured waveform, so that intelligent judgment and classification of fault types are realized; meanwhile, the diagnosis result is linked with the operation and maintenance system, the response efficiency is improved, and the method is particularly suitable for scenes with high requirements on the stability of the electric meter, such as cold chain storage, and has good practical value and popularization prospect.

The invention discloses an electricity meter terminal temperature anomaly identification method, system and device and a storage medium, and relates to the technical field of intelligent electricity meter monitoring and anomaly detection.The method comprises the steps that currents of a live line loop and a zero line loop of an intelligent electricity meter are sampled, and the deviation rate of the live line current relative to the zero line current is calculated; estimating the temperature of the terminal of the electric energy meter based on the deviation rate and by using the resistance characteristic metering error change trend of the manganese-copper shunt; and when the terminal temperature estimated value of the electric energy meter exceeds a preset threshold value, determining that the temperature of the live wire terminal is abnormal, and recording an alarm event. The method realizes real-time acquisition of current difference characteristics, provides a reliable data basis for subsequent terminal temperature estimation, realizes a method of indirectly reflecting terminal temperature rise through electrical characteristics, ensures that alarm can be triggered and a complete tracing chain can be formed when temperature abnormity occurs, and improves the accuracy of terminal temperature estimation. Therefore, the controllability of the operation safety of the electric energy meter and the completeness of post analysis are improved.

The invention discloses an electricity meter adaptive error compensation method, system and device based on multi-physical coupling and hidden variable modeling and a storage medium, and relates to the technical field of electric energy metering and detection, and the method comprises the steps: constructing a multi-physical coupling basis vector containing a single variable and a cross variable based on a sensing output signal; hidden variable joint modeling is carried out based on the multi-physical coupling basis vector and parasitic capacitance change, magnetic conductivity drift and shunt resistance offset, and a compensation mapping relation of electromagnetic, thermal and stress coupling is formed; generating a compensation coefficient by adopting the compensation mapping relation, and applying the compensation coefficient in an ammeter metering link to complete an error compensation process; according to the method, voltage, current and phase compensation coefficients are generated through multi-physical coupling basis vector and hidden variable modeling, dynamic description and self-adaptive correction of error sources are achieved, dynamic accurate correction of voltage, current and phase is achieved, and it is ensured that the ammeter keeps high-precision metering for a long time under the complex environment and the device drift condition.

The invention relates to the technical field of electrical measurement, and discloses an electrical measurement instrument dynamic calibration error compensation method, which comprises the following steps: S1, multi-source data real-time acquisition: integrating a temperature sensor, a humidity sensor and an electromagnetic interference detection module in a measurement instrument, configuring voltage, current, frequency and other measurement channels, and carrying out multi-source data real-time acquisition at the frequency of not less than 100HZ; environmental parameters (temperature T, humidity H and electromagnetic interference intensity E) and measurement data (model characteristic parameters X1, X2,..., Xn such as voltage, current and frequency) are synchronously acquired. According to the electrical measuring instrument dynamic calibration error compensation method, through multi-source data real-time acquisition and composite error model construction, in combination with multiple linear regression and an improved BP neural network, environment and measurement signal changes are comprehensively captured, an error compensation value is accurately calculated, a measurement error can be effectively controlled within an extremely small range, and the error compensation accuracy is improved. Compared with a traditional method, the measurement precision is greatly improved.

The invention discloses a dynamic digital compensation method for the metering error of wide-temperature-interval electric energy meter equipment, and the method comprises the steps: collecting the metering error data of the equipment at different temperatures through a temperature sensing module, so as to construct a historical temperature-error relation library; training an adaptive compensation model based on a historical temperature-error relation library to establish a nonlinear mapping relation between the temperature and the error; in the real-time operation process of the equipment, the current equipment temperature is collected through a temperature sensing module, and the current equipment temperature and the real-time load rate are input into the self-adaptive compensation model to obtain error correction; and the metering result is dynamically adjusted in real time according to the current equipment temperature and the error correction amount so as to output a compensated accurate metering value. According to the method, a temperature sensor does not need to be newly added, dynamic error correction is realized only by utilizing the existing temperature sensing capability of equipment, the metering precision and stability in a wide-temperature environment can be improved, the comprehensive cost is reduced, and the high-precision metering requirements in special environments such as extremely cold and high temperature environments are met.

The invention relates to the technical field of mutual inductor tests, and provides a mutual inductor test abnormal data automatic filtering analysis method and system, and the method comprises the steps: obtaining magnetic field data, electric field data and thermal field data, respectively carrying out the feature extraction and weighted fusion, and obtaining a multi-physical field fusion feature vector; constructing an electromagnetic induction chain type propagation graph, calculating the weight of an edge in the graph, constructing a magnetic flux conservation constraint graph convolutional neural network, learning propagation and evolution modes of transformer test abnormity, and obtaining a coupling abnormity feature vector; the coupling anomaly feature vectors are classified, normal data, single physical field abnormal data and multi-physical field coupling abnormal data are recognized, and corresponding data are judged as abnormal data and filtered; and carrying out physical mechanism analysis on the filtered abnormal data to obtain an abnormal detection report. According to the invention, high-precision automatic identification, filtering analysis and physical mechanism traceability diagnosis of transformer test abnormal data are realized.

The invention provides an intelligent electric meter calibration method and device, computer equipment and a storage medium, and the method comprises the steps: constructing a single meter-transformer area double-layer data system through a special transformer collection terminal, and collecting transformer area total intelligent electric meter data, user meter temperature and humidity, an electric energy observation value, transformer area line impedance and load unbalance degree data; constructing an energy conservation optimization function according to the user meter electric energy observation value and the total meter electric energy by combining the deviation with the observation value; determining line loss data according to total meter current and user temperature and humidity, determining power supply stability through total meter voltage, and determining optimization function constraint conditions according to corresponding data; and solving an optimal user table software compensation coefficient and a transformer area line loss correction coefficient by a generalized genetic algorithm, updating a humidity-error correlation coefficient and a line loss reference value by a feedback performance attenuation model to determine an adjusted single table calibration error range, and performing single table calibration by combining the user table temperature and humidity and an electric energy observation value. By adopting the scheme, the accuracy, the reliability and the environmental adaptability of the calibration of the intelligent electric meter are improved.

The invention discloses a precision self-calibration method and system for a current transformer, and relates to the technical field of current transformer calibration, and the method comprises the steps: collecting and obtaining primary side current data, secondary side current data, a transformer environment data flow and a load change data flow; establishing a theoretical model of the current transformer, performing precision deviation evaluation on the primary side current data, the secondary side current data, the transformer environment data flow and the load change data flow based on the theoretical model of the current transformer, and determining precision deviation parameters of the current transformer; and carrying out calibration analysis on the current transformer precision deviation parameter based on a current transformer calibration strategy, determining a self-calibration correction parameter, and carrying out closed-loop self-calibration control on the target current mutual inductance. The technical effects of performing adaptive calibration on the current transformer in combination with various working condition factors, improving the reliability and stability of calibration of the current transformer and ensuring the measurement precision of the current transformer are achieved.

A system includes control circuitry for identifying, during operation in a first operating mode wherein electrical power is limited by protection devices, an indicator to enter a second operating mode. The indicator corresponds to power consumption of the electrical system exceeding a power capacity of the electrical system, nearing, achieving, or exceeding a limit. In response to the indicator, the system operates in the second operating mode by retrieving reference information comprising load preferences and limits, and managing one or more loads, sources, or both to modify power consumption, power capacity, or both based on the indicator and based on the reference information. The system communicates with loads or sources, and manages branch circuit using controllable elements to prevent power consumption from exceeding power capacity. The system modifies setpoints, turns devices on or off, schedules device operation or otherwise manages the electrical system based on predetermined preferences.