A data feature analysis method and system for single-phase and three-phase meters

Abstract

The present invention belongs to the field of power system monitoring. The present invention discloses a data feature analysis method and system for single-phase and three-phase meters, including: arranging a synchronous calibration multi-channel data acquisition system on a single-phase electric energy meter and a three-phase electric energy meter, obtaining power factor and electric energy meter pulse signal, and performing voltage and current waveform analysis; constructing a prediction model for electric energy consumption, analyzing factors affecting the total power of electric energy through a factor identification model, and extracting characteristic values ​​of harmonic content and phase angle according to the analysis results; through a factor identification model, performing harmonic component analysis and distortion factors according to characteristic values, and performing distortion elimination. The present invention solves the problem of reading accuracy of electric energy meters in variable environments and working conditions, realizes optimization of electric energy quality and prediction of electric energy consumption, improves the overall performance of the electric energy meter system, and also provides strong technical support for the stable operation of the power system and energy management.

Description

Technical Field

[0001] The invention belongs to the field of power system monitoring, and in particular relates to a data feature analysis method and system for single-phase and three-phase meters. Background Art

[0002] With the rapid development of my country's economy, energy demand continues to increase. As an important part of energy, the stable operation and efficient management of the power system are particularly important. As the basic work of the power system, electric energy metering plays a vital role in the safe and economic operation of the power system. Traditional electric energy metering mainly relies on manual meter reading, which is inefficient, prone to errors, and unable to monitor the use of electric energy in real time. To solve this problem, smart electric energy meters came into being. Smart electric energy meters can automatically collect electric energy data, realize remote meter reading and real-time monitoring, and greatly improve the accuracy and efficiency of electric energy metering.

[0003] However, the existing smart energy meters still have some shortcomings in practical applications. First, most of the smart energy meters on the market are single-phase energy meters, and the research and development and application of three-phase energy meters are relatively small. Compared with single-phase energy meters, three-phase energy meters have higher measurement accuracy and stability, but there are certain technical difficulties in data processing and analysis. Secondly, the existing energy data feature analysis methods are mainly aimed at single-phase energy meters, and there is no effective solution for the data feature analysis of three-phase energy meters. This makes it impossible for three-phase energy meters to give full play to their advantages in practical applications, limiting their scope of application in power systems. Summary of the invention

[0004] In view of the above existing problems, the present invention is proposed. The present invention analyzes the data characteristics of single-phase and three-phase electric energy meters, and proposes a universal data characteristic analysis method applicable to single-phase and three-phase electric energy meters, which effectively improves the accuracy and stability of electric energy metering, and can also realize real-time monitoring and analysis of electric energy usage. The present invention aims to solve the problems existing in the existing electric energy metering technology, improve the accuracy and stability of electric energy metering, and provide strong technical support for the stable operation and efficient management of the power system.

[0005] In order to solve the above technical problems, a data feature analysis method for single-phase and three-phase meters is proposed, including:

[0006] Arrange a synchronous calibration multi-channel data acquisition system on the single-phase and three-phase electricity meters to obtain the power factor and meter pulse signal, and perform voltage and current waveform analysis; build a model to predict electricity consumption, analyze the factors affecting the total power of electricity through the factor identification model, and extract the characteristic values ​​of harmonic content and phase angle based on the analysis results; use the factor identification model to analyze the distortion factors of harmonic components based on the characteristic values, and eliminate the distortion.

[0007] As a preferred solution of the data feature analysis method of a single-phase and three-phase meter described in the present invention, the synchronous calibration multi-channel data acquisition system includes a multi-channel data calibration system arranged on the single-phase electric energy meter and a multi-channel data acquisition system arranged on the three-phase electric energy meter, and each system includes three channels.

[0008] The multi-channel data calibration system for a single-phase electric energy meter includes: Channel 1 performs environmental monitoring and preprocessing, monitors the operating environment of the electric energy meter, collects temperature, humidity and vibration signals, and evaluates the impact of environmental factors on the readings of the single-phase electric energy meter:

[0009] ,

[0010] if The value is between -0.5% and 0.5%. The system considers that the influence of environmental factors on the meter reading is negligible. If the value is greater than 1% or less than -1%, the environmental factors have a significant impact on the meter readings and cannot be ignored. The readings affected by the environmental factors are synchronized with the readings in channel 2 for calibration.

[0011] in, is the sum of the environmental factors affecting the correction of the meter readings, T is the temperature, and are the minimum and maximum values ​​of temperature, H is the humidity, and are the minimum and maximum values ​​of humidity, is the vibration signal within the total data collection time t, k is a constant, and the vibration weight is adjusted according to the current environment of the electric energy meter; is the temperature influence coefficient, β is the humidity influence coefficient, is the humidity calibration constant, Vibration influence coefficient.

[0012] Channel 2 collects and analyzes the electric energy parameters, collects voltage and current waveforms in real time, and calculates the real-time power P and power factor :

[0013] ,

[0014] in, is the current signal in time t, is the voltage signal within time t.

[0015] Channel 3 performs synchronous calibration of data. When the value of channel 1 is allowed to be ignored, the final collected voltage and current signals are based on the collected data of channel 2. When the impact of the value in channel 1 on the electric energy meter cannot be ignored, the data of channel 1 and channel 2 are combined for synchronous calibration to perform environmental compensation of the values ​​at the same time to eliminate the deviation caused by environmental factors:

[0016] ,

[0017] ,

[0018] in, and They respectively represent the actual voltage value and actual current value after environmental compensation. After obtaining the actual values, voltage and current waveform analysis is performed.

[0019] The multi-channel data acquisition system for the three-phase electric energy meter includes: channel 1 collects the A phase voltage and current signal according to the change of the A phase voltage and current signal, channel 2 collects the B phase voltage and current signal according to the change of the B phase voltage and current signal, and channel 3 collects the C phase voltage and current signal according to the change of the C phase voltage and current signal. When the loads of the three phases A, B, and C are consistent, the three-phase current and voltage are consistent and the working state is normal. When the loads of the three phases A, B, and C are different, the load is unbalanced and the voltage and current waveform analysis is performed.

[0020] As a preferred solution of the data characteristic analysis method of a single-phase or three-phase meter described in the present invention, the meter pulse signal includes calculating the energy consumption by obtaining the actual voltage and current values. , and is equivalent to an ammeter pulse signal.

[0021] The predicted electric energy consumption model includes:

[0022] ,

[0023] in, is the predicted power consumption, and are the start and end time of the forecast time range, is the power of the ith meter at time t, is the power quality factor of the ith meter at time t, is the power factor at time t, is the optimum value of power factor, =1; is a positive constant that adjusts the shape of the power factor function; S(t) is the meter pulse signal at time t. It is a reference value of the meter pulse signal. and are the actual voltage and current at time t, and are the reference values ​​of voltage and current respectively, and are the standard deviations of voltage and current, respectively, used to adjust the shape of the voltage and current waveform analysis functions; n is the number of electric meters.

[0024] As a preferred solution of the data feature analysis method of a single-phase and three-phase meter described in the present invention, the factor identification model includes analyzing the main factors affecting the total power of electric energy through the factor identification model according to the analysis results of the predicted electric energy consumption for harmonic distortion, including load types including nonlinear loads and linear loads, line impedance, and environmental changes including temperature changes, humidity changes, and vibration changes:

[0025] ,

[0026] The larger the value of W is, the greater the influence of the current factor is. When the characteristic value of W is lower than 1, the influence of the current factor is ignored, the influence of the load type and the environmental changes is calculated, the main factors of the total electric power are determined, and the distortion is analyzed based on the voltage and current waveform analysis results.

[0027] Among them, W represents the comprehensive influence of various factors on the total power of electric energy. represents the angle, m represents the number of factors, represents the dth eigenvalue, which is the contribution rate of the main factors extracted in the factor analysis; is the number of factors, is the eigenvalue of the jth factor, j is the variable index, represents the value of the kth principal factor, represents the value of the kth latent factor in factor analysis, Represents the standard deviation, which is used to calculate the Gaussian distribution. e is the base of the natural logarithm.

[0028] As a preferred solution of the data feature analysis method of a single-phase and three-phase meter described in the present invention, the voltage and current waveform analysis includes analyzing the characteristics of the voltage and current data waveforms of the single-phase and three-phase meters, matching the voltage and current waveforms, and marking as mismatch when the phase difference between the voltage and current waveforms exceeds ±10% of the rated phase difference. When the phase between the voltage and current waveforms falls within the range of ±10% of the rated phase difference, the current and voltage waveforms are considered to match. The distortion conditions of the voltage and current waveforms that are successfully matched are identified, including frequency distortion, phase distortion and harmonic distortion.

[0029] The frequency distortion includes when the fundamental frequency deviates from the rated value of the current electricity meter by more than ±a%, it is marked as frequency distortion, and synchronous calibration is performed to determine whether a calibration error occurs. If the value of the second calibration is different from that of the first calibration, it is considered a calibration error. The system is updated to eliminate the error and then recalibrated until the fundamental frequency does not deviate from the rated value of the current electricity meter. If the value of the second calibration is the same as that of the first calibration, it is checked whether there is any start-up or shutdown of high-power equipment or drastic changes in the grid load, and grid maintenance is performed.

[0030] The phase distortion includes when the phase difference exceeds ±b°, it is marked as phase distortion, whether there are non-linear loads or capacitors that affect the phase relationship, if there are such components, replace the components, if the problem persists, replace the electric energy meter.

[0031] The harmonic distortion includes when the ratio of the amplitude of any subharmonic to the amplitude of the fundamental wave exceeds c%, it is marked as harmonic distortion, harmonic component analysis is performed and the harmonic impact is reduced, wherein a, b and c are thresholds specified according to the current working environment and current working specifications.

[0032] When the voltage or current waveform has the above-mentioned distortion more than three times within 0.1 second, it is classified as short-term distortion. According to the predicted power consumption model and the time when the voltage and current waveforms are distorted, the instantaneous interference source is found according to the time when the distortion occurs.

[0033] When the voltage or current waveform continues to be distorted for 1 minute, it is classified as long-term distortion. The use of the current single-phase electricity meter and three-phase electricity meter is stopped, the scope of the problem is found by predicting the electricity consumption, and the staff is notified to perform grid maintenance and update the electricity meter.

[0034] As a preferred solution of the data feature analysis method of a single-phase and three-phase meter described in the present invention, the harmonic component analysis includes judging the distortion factor in the total factor affecting the total power of electric energy according to the characteristic values ​​of the extracted harmonic content and phase angle, and the harmonic component analysis expression is:

[0035] ,

[0036] in, A function representing the voltage waveform generated by a voltage signal within time t, It represents the function of the current waveform generated by the current signal within time t, t represents the sampling period, s represents the harmonic number, represents the sth harmonic angular frequency, and denote the amplitude of the sth harmonic of voltage and current respectively, and denote the phase angle of the sth harmonic of voltage and current respectively, It represents the energy of voltage and current waveforms at different harmonic frequencies, and D represents the probability value of distortion factor.

[0037] The energy of the voltage and current waveforms at different harmonic frequencies is analyzed by Fourier transform and system filters:

[0038] ,

[0039] Where N is the highest harmonic order, is the length of the periodic sampling, yes The conjugate complex number of , calculate the power; Indicates Fourier transform.

[0040] When D is between 0 and When D is greater than When , it indicates that the power quality distortion is serious, indicating that the distortion is caused by nonlinear load factors, and as the value of D increases, the degree of distortion increases.

[0041] Collecting a dataset for normal situations And the distorted dataset , and calculate the mean of the two data sets and standard deviation :

[0042] ,

[0043] in, It represents the confidence level parameter, using a 95% confidence level, and the z value is 1.96 It is the maximum allowable total harmonic distortion percentage specified in the relevant power quality standards.

[0044] After determining the final distortion factors, a multi-source collaborative optimization algorithm is used to eliminate harmonics to achieve the optimal goal of the electric energy meter.

[0045] As a preferred solution of the data feature analysis method of a single-phase and three-phase meter described in the present invention, the multi-source collaborative optimization algorithm includes using the multi-source collaborative optimization algorithm to adjust the compensation device of the nonlinear load and the environmental compensation to eliminate harmonics to achieve the optimal goal of the electric energy meter, and the environmental compensation is calculated by recalculating To compensate, the compensation device of nonlinear load is expressed as follows:

[0046] ,

[0047] in, represents the optimal objective function for total power distortion elimination of the energy meter, is the operating cost coefficient of the ith nonlinear load, is the active power distortion of the ith nonlinear load, and the control variable Function of The compensation cost coefficient of the i-th nonlinear load, is the reactive power distortion of the ith nonlinear load, and the control variable Function of is the amplitude of the nth harmonic, is the attenuation coefficient of the nth harmonic, is the phase angle of the nth harmonic.

[0048] Another object of the present invention is to provide a data feature analysis system for a single-phase and three-phase meter. The present invention improves the efficiency of electric energy metering and management and ensures the quality of electric energy and the reliability of power supply through accurate data collection, analysis and optimization. The problem of environmental factors affecting the reading of electric energy meters is solved by using methods such as multi-channel data collection, environmental monitoring and preprocessing, electric energy parameter collection and preliminary analysis, and data synchronization calibration, so as to achieve the accuracy and synchronization of data collection. At the same time, an electric energy consumption prediction model is constructed, and the main factors affecting the total power of electric energy are analyzed to provide decision support for electric energy management and optimization. Methods such as voltage and current waveform analysis and harmonic component analysis are used to monitor the quality of electric energy, identify power quality problems, and quantify the degree of distortion of power quality. Finally, through the implementation of a multi-source collaborative optimization algorithm, the compensation equipment is adjusted to eliminate harmonics and optimize the quality of electric energy. This system achieves the effect of improving the efficiency of electric energy metering and management and ensuring the quality of electric energy and the reliability of power supply.

[0049] As a preferred solution of the data feature analysis system for a single-phase and three-phase meter described in the present invention, it is characterized by including a data acquisition and synchronization calibration module, an electric energy consumption prediction and factor identification module, a power quality analysis module, a distortion factor analysis and optimization module, and a power grid maintenance and electric energy meter management module.

[0050] The data acquisition and synchronous calibration module includes multi-channel data acquisition, environmental monitoring and preprocessing, electric energy parameter acquisition and preliminary analysis, and data synchronous calibration. It monitors the operating environment of the electric meter, collects temperature, humidity, and vibration signals, evaluates the impact of environmental factors on the electric energy meter readings, and eliminates the impact of environmental factors on the electric energy meter readings.

[0051] The power consumption prediction and factor identification module constructs a power consumption prediction model, analyzes factors affecting the total power of electric energy, predicts power consumption, identifies the main factors affecting the total power of electric energy, and provides decision support for power management and optimization.

[0052] The power quality analysis module analyzes voltage and current waveforms and harmonic components, monitors power quality, identifies power quality problems, and quantifies the degree of power quality distortion.

[0053] The distortion factor analysis and optimization module analyzes distortion factors, implements a multi-source collaborative optimization algorithm, identifies and analyzes power quality distortion factors, eliminates harmonics by adjusting compensation equipment, and optimizes power quality.

[0054] The grid maintenance and energy meter management module includes calibration error checking of energy meter readings, nonlinear load or capacitor impact analysis, grid maintenance notifications, and energy meter updates to ensure the accuracy of energy meter readings, handle power quality issues in a timely manner, and maintain stable grid operation.

[0055] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, the steps of a method described in data feature analysis of a single-phase meter or a three-phase meter are implemented.

[0056] A computer-readable storage medium stores a computer program, wherein when the computer program is executed by a processor, the computer program implements the steps of a method for analyzing data characteristics of a single-phase or three-phase meter.

[0057] Beneficial effects of the present invention: The present invention uses a synchronous calibration multi-channel data acquisition system to solve the problem of reading accuracy of single-phase and three-phase electric energy meters under the influence of complex environmental factors, synchronously calibrates the influence of environmental factors on the reading of the electric energy meter, and reduces the reading error caused by environmental factors; constructs a prediction model for electric energy consumption, and analyzes various factors affecting the total power of electric energy, such as load type, line impedance, environmental changes, etc. through factor identification technology. This model can not only predict electric energy consumption, but also provide quantitative indicators for electric energy quality evaluation, identify specific causes of electric energy quality distortion, and improve electric energy quality and efficiency. Harmonic component analysis technology is used to conduct an in-depth analysis of distortion factors, and harmonic elimination is achieved through a multi-source collaborative optimization algorithm, thereby improving electric energy quality and efficiency, reducing energy losses and unnecessary costs, and achieving the optimal operation target of the electric energy meter by adjusting the compensation equipment of the nonlinear load and environmental compensation; The present invention solves the problem of reading accuracy of the electric energy meter under variable environments and working conditions, realizes the optimization of electric energy quality and the prediction of electric energy consumption, and the method of the present invention not only improves the overall performance of the electric energy meter system, but also provides strong technical support for the stable operation and energy management of the power system, and has significant economic and social benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work, among which:

[0059] Figure 1 An overall flow chart of a data feature analysis method for a single-phase or three-phase meter provided by an embodiment of the present invention.

[0060] Figure 2 A system functional architecture diagram of a data feature analysis system for a single-phase or three-phase meter provided in accordance with an embodiment of the present invention. DETAILED DESCRIPTION

[0061] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0062] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0063] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it an embodiment that is mutually exclusive with other embodiments, either individually or selectively.

[0064] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0065] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0066] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0067] Example 1, reference Figure 1 , which is the first embodiment of the present invention, provides a data feature analysis method for a single-phase meter and a three-phase meter, comprising:

[0068] S1: Arrange a synchronous calibration multi-channel data acquisition system on the single-phase and three-phase electric energy meters to obtain the power factor and meter pulse signal, and perform voltage and current waveform analysis.

[0069] Furthermore, the synchronous calibration multi-channel data acquisition system includes a multi-channel data calibration system arranged on a single-phase electric energy meter and a multi-channel data acquisition system arranged on a three-phase electric energy meter, and each system includes three channels.

[0070] The multi-channel data calibration system for a single-phase electric energy meter includes: channel 1 performs environmental monitoring and preprocessing, monitors the operating environment of the electric energy meter, collects temperature, humidity and vibration signals, and evaluates the impact of environmental factors on the readings of the single-phase electric energy meter.

[0071] First, we need to determine the temperature effect coefficient , obtained through historical data analysis, it represents the relative change of the meter reading when the temperature changes by one unit. Then, the temperature data T over a period of time is collected and its range is determined. arrive , calculate the cumulative effect of temperature on the meter reading, by f(T) = exist arrive Integrate within the range.

[0072] Similarly, the humidity impact assessment is performed in the same way, the cumulative impact of humidity on the meter reading is calculated, the cumulative impact of temperature and humidity is added together, and the total impact of environmental factors on the meter reading is obtained based on the vibration impact factor. :

[0073] ,

[0074] if The value is between -0.5% and 0.5%. The system considers that the influence of environmental factors on the meter reading is negligible. If the value is greater than 1% or less than -1%, the environmental factors have a significant impact on the meter readings and cannot be ignored. The readings affected by the environmental factors are synchronized with the readings in channel 2 for calibration.

[0075] in, is the sum of the environmental factors affecting the correction of the meter readings, T is the temperature, and are the minimum and maximum values ​​of temperature, H is the humidity, and are the minimum and maximum values ​​of humidity, is the vibration signal within the total data collection time t, k is a constant, and the vibration weight is adjusted according to the current environment of the electric energy meter; is the temperature influence coefficient, β is the humidity influence coefficient, is the humidity calibration constant, Vibration influence coefficient.

[0076] Channel 2 collects and analyzes the electric energy parameters, collects voltage and current waveforms in real time, and calculates the real-time power P and power factor :

[0077] ,

[0078] in, is the current signal in time t, is the voltage signal within time t.

[0079] Channel 3 performs synchronous calibration of data. When the value of channel 1 is allowed to be ignored, the final collected voltage and current signals are based on the collected data of channel 2. When the impact of the value in channel 1 on the electric energy meter cannot be ignored, the data of channel 1 and channel 2 are combined for synchronous calibration to perform environmental compensation of the values ​​at the same time to eliminate the deviation caused by environmental factors:

[0080] ,

[0081] ,

[0082] in, and They respectively represent the actual voltage value and actual current value after environmental compensation. After obtaining the actual values, voltage and current waveform analysis is performed.

[0083] The multi-channel data acquisition system for the three-phase electric energy meter includes: channel 1 collects the A phase voltage and current signal according to the change of the A phase voltage and current signal, channel 2 collects the B phase voltage and current signal according to the change of the B phase voltage and current signal, and channel 3 collects the C phase voltage and current signal according to the change of the C phase voltage and current signal. When the loads of the three phases A, B, and C are consistent, the three-phase current and voltage are consistent and the working state is normal. When the loads of the three phases A, B, and C are different, the load is unbalanced and the voltage and current waveform analysis is performed.

[0084] It should be noted that the actual voltage and current values ​​obtained are used to calculate the power consumption , and is equivalent to an ammeter pulse signal.

[0085] S2: Construct a model to predict power consumption, analyze the factors that affect the total power of electric energy through the factor identification model, and extract the characteristic values ​​of harmonic content and phase angle based on the analysis results.

[0086] Furthermore, the prediction model for power consumption includes,

[0087] ,

[0088] in, is the predicted power consumption, and are the start and end time of the forecast time range, is the power of the ith meter at time t, is the power quality factor of the ith meter at time t, is the power factor at time t, is the optimum value of power factor, =1; is a positive constant that adjusts the shape of the power factor function; S(t) is the meter pulse signal at time t. It is a reference value of the meter pulse signal. and are the actual voltage and current at time t, and are the reference values ​​of voltage and current respectively, and are the standard deviations of voltage and current, respectively, used to adjust the shape of the voltage and current waveform analysis functions; n is the number of electric meters.

[0089] It should be noted that the factor identification model includes the analysis of the main factors affecting the total power of electric energy through the factor identification model based on the analysis results of the predicted power consumption, including load types including nonlinear loads and linear loads, line impedance, and environmental changes including temperature changes, humidity changes, and vibration changes:

[0090] ,

[0091] The larger the value of W is, the greater the influence of the current factor is. When the characteristic value of W is lower than 1, the influence of the current factor is ignored, the influence of the load type and the environmental changes is calculated, the main factors of the total electric power are determined, and the distortion is analyzed based on the voltage and current waveform analysis results.

[0092] Among them, W represents the comprehensive influence of various factors on the total power of electric energy. represents the angle, m represents the number of factors, represents the dth eigenvalue, which is the contribution rate of the main factors extracted in the factor analysis; is the number of factors, is the eigenvalue of the jth factor, j is the variable index, represents the value of the kth principal factor, represents the value of the kth latent factor in factor analysis, Represents the standard deviation, which is used to calculate the Gaussian distribution. e is the base of the natural logarithm.

[0093] It should also be noted that the voltage and current waveform analysis is performed according to S1: the characteristics of the voltage and current data waveforms of the single-phase and three-phase meters are analyzed, and the voltage and current waveforms are matched. When the phase difference between the voltage and current waveforms exceeds ±10% of the rated phase difference, it is marked as mismatched. When the phase between the voltage and current waveforms falls within the range of ±10% of the rated phase difference, the current and voltage waveforms are considered to match. The distortion conditions of the voltage and current waveforms that have been successfully matched are identified, including frequency distortion, phase distortion, and harmonic distortion.

[0094] The frequency distortion includes when the fundamental frequency deviates from the rated value of the current electricity meter by more than ±a%, it is marked as frequency distortion, and synchronous calibration is performed to determine whether a calibration error occurs. If the value of the second calibration is different from that of the first calibration, it is considered a calibration error. The system is updated to eliminate the error and then recalibrated until the fundamental frequency does not deviate from the rated value of the current electricity meter. If the value of the second calibration is the same as that of the first calibration, it is checked whether there is any start-up or shutdown of high-power equipment or drastic changes in the grid load, and grid maintenance is performed.

[0095] The phase distortion includes when the phase difference exceeds ±b°, it is marked as phase distortion, whether there are non-linear loads or capacitors that affect the phase relationship, if there are such components, replace the components, if the problem persists, replace the electric energy meter.

[0096] The harmonic distortion includes when the ratio of the amplitude of any subharmonic to the amplitude of the fundamental wave exceeds c%, it is marked as harmonic distortion, harmonic component analysis is performed and the harmonic impact is reduced, wherein a, b and c are thresholds specified according to the current working environment and current working specifications.

[0097] When the voltage or current waveform has the above-mentioned distortion more than 3 times within 0.1 second, it is classified as short-term distortion. According to the predicted power consumption model and the time when the voltage and current waveforms are distorted, the instantaneous interference source is found according to the time when the distortion occurs.

[0098] When the voltage or current waveform continues to be distorted for 1 minute, it is classified as long-term distortion. The use of the current single-phase electricity meter and three-phase electricity meter is stopped, the scope of the problem is found by predicting the electricity consumption, and the staff is notified to perform grid maintenance and update the electricity meter.

[0099] S3: Through the factor identification model, the distortion factors are analyzed by harmonic components according to the eigenvalues, and the distortion is eliminated.

[0100] Furthermore, the distortion factor in the total factor affecting the total power of electric energy is determined based on the characteristic values ​​of the extracted harmonic content and phase angle. The harmonic component analysis expression is:

[0101] ,

[0102] in, A function representing the voltage waveform generated by a voltage signal within time t, It represents the function of the current waveform generated by the current signal within time t, t represents the sampling period, s represents the harmonic number, represents the sth harmonic angular frequency, and denote the amplitude of the sth harmonic of voltage and current respectively, and denote the phase angle of the sth harmonic of voltage and current respectively, It represents the energy of voltage and current waveforms at different harmonic frequencies, and D represents the probability value of distortion factor.

[0103] The energy of the voltage and current waveforms at different harmonic frequencies is analyzed by Fourier transform and system filters:

[0104] ,

[0105] Where N is the highest harmonic order, is the length of the periodic sampling, yes The conjugate complex number of , calculate the power; Indicates Fourier transform.

[0106] When D is between 0 and When D is greater than When , it indicates that the power quality distortion is serious, indicating that the distortion is caused by nonlinear load factors, and as the value of D increases, the degree of distortion increases.

[0107] Collecting a dataset for normal situations And the distorted dataset , and calculate the mean of the two data sets and standard deviation :

[0108] ,

[0109] in, It represents the confidence level parameter, using a 95% confidence level, and the z value is 1.96 It is the maximum allowable total harmonic distortion percentage specified in the relevant power quality standards.

[0110] After determining the final distortion factors, a multi-source collaborative optimization algorithm is used to eliminate harmonics to achieve the optimal goal of the electric energy meter.

[0111] It should be noted that the multi-source collaborative optimization algorithm is used to adjust the compensation equipment of the nonlinear load and the environmental compensation to eliminate harmonics to achieve the optimal goal of the electric energy meter. The environmental compensation is calculated by recalculating To compensate, the compensation device of nonlinear load is expressed as follows:

[0112] ,

[0113] in, represents the optimal objective function for total power distortion elimination of the energy meter, is the operating cost coefficient of the ith nonlinear load, is the active power distortion of the ith nonlinear load, and the control variable Function of The compensation cost coefficient of the i-th nonlinear load, is the reactive power distortion of the ith nonlinear load, and the control variable Function of is the amplitude of the nth harmonic, is the attenuation coefficient of the nth harmonic, is the phase angle of the nth harmonic.

[0114] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0115] Example 2, an embodiment of the present invention, provides a data feature analysis method for a single-phase meter and a three-phase meter. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through experiments.

[0116] The experiment aims to verify the performance of the proposed single-phase and three-phase meter data feature analysis method in practical applications. To this end, we selected a group of single-phase energy meters and three-phase energy meters as experimental objects, marked as group A for single-phase energy meters and group B for three-phase energy meters. Each group contains 5 energy meters, a total of 10 energy meters, and the experiment is carried out according to the process of Example 1.

[0117] Table 1 Experimental data

[0118] Energy meter number Temperature (°C) humidity(%) Vibration signal (m / s²) Voltage (V) Current (A) Power Factor Harmonic distortion (%) Phase distortion(°) Frequency distortion(%) Voltage after calibration (V) Current after calibration (A) A1 25 50 0.5 220 5 0.9 2 1 0.5 219.5 5.1 A2 23 55 0.6 221 5.2 0.92 1.8 0.8 0.4 220.2 5.3 A3 24 53 0.55 220 5.1 0.91 2.1 1.1 0.6 219.7 5.2 A4 26 51 0.53 221 5.3 0.93 1.9 0.9 0.7 220.3 5.4 A5 22 49 0.48 219 4.9 0.89 2.2 1.2 0.5 218.5 5 B1 24 53 0.55 380 10 0.95 1.5 0.9 0.6 379.5 10.2 B2 26 51 0.53 381 9.8 0.93 2.1 1.1 0.7 380.3 9.9 B3 23 55 0.6 380 10.2 0.96 1.6 0.8 0.5 379.7 10.3 B4 25 52 0.5 381 9.9 0.94 1.4 0.7 0.6 380.2 10 B5 21 48 0.45 379 10.1 0.92 1.8 1 0.8 378.5 10.2

[0119] According to the experimental data, the average temperature of the energy meter in group A is 24.0°C, with a standard deviation of 1.2°C; the average temperature of the energy meter in group B is 24.4°C, with a standard deviation of 1.2°C, which indicates that there is little difference in temperature between the two groups of energy meters. The average humidity of group A is 51.6%, with a standard deviation of 1.6%; the average humidity of group B is 51.8%, with a standard deviation of 1.8%, and the humidity is relatively consistent between the two groups. The average vibration signal of group A is 0.54 m / s², with a standard deviation of 0.04 m / s²; the average vibration signal of group B is 0.54 m / s², with a standard deviation of 0.03 m / s², which indicates that the vibration signal is basically the same between the two groups, keeping the variables consistent.

[0120] The average voltage of group A is 220.2V, with a standard deviation of 0.6V; the average voltage of group B is 380.2V, with a standard deviation of 0.6V, and the voltage level difference between single-phase and three-phase energy meters. The average current of group A is 5.1A, with a standard deviation of 0.2A; the average current of group B is 10.0A, with a standard deviation of 0.2A, and the current difference between single-phase and three-phase energy meters is also the same.

[0121] The average power factor of group A is 0.91, and the standard deviation is 0.02; the average power factor of group B is 0.94, and the standard deviation is 0.02, which shows that the power factor of the three-phase energy meter is slightly higher than that of the single-phase energy meter. The average harmonic distortion of group A is 2.0%, and the standard deviation is 0.2%; the average harmonic distortion of group B is 1.6%, and the standard deviation is 0.3%. This shows that the harmonic distortion of the three-phase energy meter is slightly lower than that of the single-phase energy meter.

[0122] Phase distortion and frequency distortion: There was little difference between the two groups in terms of phase distortion and frequency distortion.

[0123] By comparing the voltage and current data before and after calibration, we can see that the calibration process has slightly adjusted the voltage and current readings, which helps to improve the accuracy of the energy meter readings.

[0124] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0125] Embodiment 3, the third embodiment of the present invention, is different from the first two embodiments in that:

[0126] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0127] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0128] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0129] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0130] Example 4, reference Figure 2 , which is the fourth embodiment of the present invention, provides a data feature analysis system for single-phase and three-phase meters, including a data acquisition and synchronization calibration module, an electric energy consumption prediction and factor identification module, a power quality analysis module, a distortion factor analysis and optimization module, and a power grid maintenance and electric energy meter management module.

[0131] The data acquisition and synchronous calibration module includes multi-channel data acquisition, environmental monitoring and preprocessing, electric energy parameter acquisition and preliminary analysis, and data synchronous calibration. It monitors the operating environment of the electric meter, collects temperature, humidity, and vibration signals, evaluates the impact of environmental factors on the electric energy meter readings, and eliminates the impact of environmental factors on the electric energy meter readings.

[0132] The power consumption prediction and factor identification module constructs a power consumption prediction model, analyzes factors affecting the total power of electric energy, predicts power consumption, identifies the main factors affecting the total power of electric energy, and provides decision support for power management and optimization.

[0133] The power quality analysis module analyzes voltage and current waveforms and harmonic components, monitors power quality, identifies power quality problems, and quantifies the degree of power quality distortion.

[0134] The distortion factor analysis and optimization module analyzes distortion factors, implements a multi-source collaborative optimization algorithm, identifies and analyzes power quality distortion factors, eliminates harmonics by adjusting compensation equipment, and optimizes power quality.

[0135] The grid maintenance and energy meter management module includes calibration error checking of energy meter readings, nonlinear load or capacitor impact analysis, grid maintenance notifications, and energy meter updates to ensure the accuracy of energy meter readings, handle power quality issues in a timely manner, and maintain stable grid operation.

[0136] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A data feature analysis method for single-phase and three-phase meters, characterized in that: include, Arrange a synchronous calibration multi-channel data acquisition system on single-phase and three-phase electric energy meters to obtain power factor and meter pulse signals and perform voltage and current waveform analysis; Construct a model to predict power consumption, analyze the factors that affect the total power through the factor identification model, and extract the characteristic values ​​of harmonic content and phase angle based on the analysis results; Through the factor identification model, the distortion factor is analyzed by harmonic component according to the eigenvalue, and the distortion is eliminated; The synchronous calibration multi-channel data acquisition system includes arranging a multi-channel data calibration system on a single-phase electric energy meter and arranging a multi-channel data acquisition system on a three-phase electric energy meter, each system including three channels; The multi-channel data calibration system for a single-phase electric energy meter includes: Channel 1 performs environmental monitoring and preprocessing, monitors the operating environment of the electric energy meter, collects temperature, humidity and vibration signals, and evaluates the impact of environmental factors on the readings of the single-phase electric energy meter: If the value of E(t) is between -0.5% and 0.5%, the system considers that the influence of environmental factors on the reading of the energy meter is negligible. If the value of E(t) is greater than 1% or less than -1%, the environmental factors have a significant influence on the reading of the energy meter and cannot be ignored. The reading affected by the environment is synchronized with the reading in channel 2 for calibration. Where E(t) is the sum of the environmental factors used to correct the meter reading, T is the temperature, and T min and T max are the minimum and maximum values ​​of temperature, H is the humidity, and H min and H max are the minimum and maximum values ​​of humidity, respectively; Shake(t) is the vibration signal within the total data acquisition time t; k is a constant that adjusts the vibration weight according to the current environment of the energy meter; α is the temperature influence coefficient, β is the humidity influence coefficient, γ is the humidity calibration constant, and δ is the vibration influence coefficient; Channel 2 collects and analyzes the electric energy parameters, collects voltage and current waveforms in real time, and calculates the real-time power P(t) and power factor cosφ: Where I(t) is the current signal within time t, and V(t) is the voltage signal within time t; Channel 3 performs synchronous calibration of data. When the value of channel 1 is allowed to be ignored, the final collected voltage and current signals are based on the collected data of channel 2. When the impact of the value in channel 1 on the electric energy meter cannot be ignored, the data of channel 1 and channel 2 are combined for synchronous calibration to perform environmental compensation of the values ​​at the same time to eliminate the deviation caused by environmental factors: V actual (t)=V(t)-E(t)·V(t) I actual (t)=I(t)-E(t)·I(t) Among them, V actual (t) and I actual (t) respectively represent the actual voltage value and the actual current value after environmental compensation. After obtaining the actual values, the voltage and current waveform analysis is performed; The multi-channel data acquisition system for the three-phase electric energy meter includes: channel 1 collects the voltage and current signal of phase A according to the change of the voltage and current signal of phase A, channel 2 collects the voltage and current signal of phase B according to the change of the voltage and current signal of phase B, and channel 3 collects the voltage and current signal of phase C according to the change of the voltage and current signal of phase C. When the loads of the three phases A, B, and C are consistent, the three-phase current and voltage are consistent and the working state is normal. When the loads of the three phases A, B, and C are different, the load is unbalanced and the voltage and current waveform analysis is performed; The meter pulse signal includes calculating the energy consumption h=∫V by obtaining the actual voltage and current values actual (t)·I actual (t)·cosφ, and is equivalent to the meter pulse signal; The predicted electric energy consumption model includes: Among them, P pred is the predicted power consumption, t0 and t f are the start and end time of the forecast time range, P i (t) is the power of the ith meter at time t, Q i (t) is the power quality factor of the ith meter at time t, φ(t) is the power factor at time t, φ0 is the optimal value of the power factor, φ0 = 1; ε is a positive constant that adjusts the shape of the power factor function; S(t) is the meter pulse signal at time t, S0 is a reference value of the meter pulse signal, V actual (t) and I actual (t) represent the actual voltage and current values ​​after environmental compensation, V0 and I0 are the reference values ​​of voltage and current, respectively. V and σ I are the standard deviations of voltage and current, respectively, which are used to adjust the shapes of voltage and current waveform analysis functions; n is the number of meters; The factor identification model includes analyzing the main factors affecting the total power of electric energy through the factor identification model according to the analysis results of the predicted electric energy consumption for harmonic distortion, wherein the main factors include load type, line impedance, and environmental changes, wherein the load type includes nonlinear load and linear load, and the environmental changes include temperature change, humidity change, and vibration change: When the value of W is larger, it means that the influence of the current factor is greater. When the characteristic value of W is lower than 1, the influence of the current factor is ignored, the influence of the load type and the environmental change is calculated, the main factor of the total electric power is determined, and the distortion is analyzed according to the voltage and current waveform analysis results; Among them, W represents the comprehensive influence of various factors on the total power of electric energy, ∈ represents the angle, m represents the number of factors, and λ d Represents the eigenvalue of the dth main factor, which is the contribution rate of the main factor extracted in factor analysis; j is the eigenvalue of the jth main factor, j is the variable index; PC d represents the value of the kth principal factor, FA d represents the value of the kth latent factor in factor analysis, Represents the standard deviation, which is used to calculate the Gaussian distribution. e is the base of the natural logarithm.

2. A data characteristic analysis method for a single-phase meter or a three-phase meter as claimed in claim 1, characterized in that: The voltage and current waveform analysis includes analyzing the characteristics of the voltage and current data waveforms of the single-phase and three-phase meters, matching the voltage and current waveforms, marking as mismatch when the phase difference between the voltage and current waveforms exceeds ±10% of the rated phase difference, and considering the current and voltage waveforms as matched when the phase between the voltage and current waveforms falls within the range of ±10% of the rated phase difference, and identifying the distortion of the voltage and current waveforms that are successfully matched, including frequency distortion, phase distortion and harmonic distortion; The frequency distortion includes when the fundamental frequency deviates from the rated value of the current energy meter by more than ±a%, it is marked as frequency distortion, synchronous calibration is performed, and it is determined whether a calibration error occurs. If the value of the secondary calibration is different from that of the primary calibration, it is considered to be a calibration error. The system is updated to eliminate the error and then recalibrated until the fundamental frequency does not deviate from the rated value of the current energy meter. If the value of the secondary calibration is the same as that of the primary calibration, it is checked whether there is a start-up or stop of high-power equipment or a drastic change in the grid load, and grid maintenance is performed; The phase distortion includes when the phase difference exceeds ±b°, it is marked as phase distortion, whether there are nonlinear loads or capacitors that affect the phase relationship, if there are such components, replace the components, if the problem persists after replacing the components, replace the electric energy meter; The harmonic distortion includes when the ratio of the amplitude of any subharmonic to the amplitude of the fundamental wave exceeds c%, it is marked as harmonic distortion, harmonic component analysis is performed and harmonic influence is reduced, wherein a, b and c are thresholds specified according to the current working environment and the current working use specification; When the voltage or current waveform has the above distortion more than 3 times within 0.1 second, it is classified as short-term distortion. According to the predicted power consumption model and the time when the voltage and current waveforms are distorted, the instantaneous interference source is found according to the time when the distortion occurs; When the voltage or current waveform continues to be distorted for 1 minute, it is classified as long-term distortion. The use of the current single-phase electricity meter and three-phase electricity meter is stopped, the scope of the problem is found by predicting the electricity consumption, and the staff is notified to perform grid maintenance and update the electricity meter.

3. A data characteristic analysis method for a single-phase meter or a three-phase meter as claimed in claim 2, characterized in that: The harmonic component analysis includes determining the distortion factor in the total factors affecting the total power of electric energy according to the characteristic values ​​of the extracted harmonic content and phase angle. The harmonic component analysis expression is: Among them, V V(t) The function representing the voltage waveform generated by the voltage signal within time t, I I(t) The function that represents the current waveform generated by the current signal within time t, s represents the harmonic order, ω s represents the sth harmonic angular frequency, and denote the amplitude of the sth harmonic of voltage and current respectively, and Represents the phase angle of the sth harmonic of voltage and current, K(V V(t) ,I I(t) ) represents the energy of voltage and current waveforms at different harmonic frequencies, and D represents the probability value of distortion factor; The energy of the voltage and current waveforms at different harmonic frequencies is analyzed by Fourier transform and system filters: Among them, s max is the highest harmonic order, I * (t) is I I(t) The complex conjugate of , calculate the power; Indicates Fourier transform; When D is between 0 and D th When D is greater than D th When , it indicates that the power quality distortion is serious, indicating that the distortion is caused by nonlinear load factors, and as the value of D increases, the degree of distortion increases; Collect a normal dataset D normal And the distorted dataset D distortedl , and calculate the mean μ of the two data sets normal and standard deviation σ normal : D th =(μ normal +z·s normal )×THD max Where z represents the confidence level parameter, and the confidence level is 95%, and the value of z is 1.96; THD max It is the maximum permissible total harmonic distortion percentage specified in the relevant power quality standards; After determining the final distortion factors, a multi-source collaborative optimization algorithm is used to eliminate harmonics to achieve the optimal goal of the electric energy meter.

4. A data characteristic analysis method for a single-phase meter or a three-phase meter as claimed in claim 3, characterized in that: The multi-source collaborative optimization algorithm includes using the multi-source collaborative optimization algorithm to adjust the compensation device of the nonlinear load and the environmental compensation to eliminate harmonics to achieve the optimal goal of the electric energy meter. The environmental compensation is compensated by recalculating E(t). The compensation device of the nonlinear load is expressed as follows: Among them, P opt represents the optimal objective function for eliminating the total power distortion of the electric energy meter, L i is the operating cost coefficient of the ith nonlinear load, P di is the active power distortion of the ith nonlinear load, and the control variable x i Function of G i is the compensation cost coefficient of the ith nonlinear load, J di is the reactive power distortion of the ith nonlinear load, and the control variable x i Function of Γ s is the amplitude of the sth harmonic, η s is the attenuation coefficient of the sth harmonic, is the phase angle of the sth harmonic, and N is the number of nonlinear loads.

5. A system using a data characteristic analysis method of a single-phase or three-phase meter as claimed in any one of claims 1 to 4, characterized in that: It includes data acquisition and synchronization calibration module, power consumption prediction and factor identification module, power quality analysis module, distortion factor analysis and optimization module, and power grid maintenance and power meter management module; The data acquisition and synchronous calibration module includes multi-channel data acquisition, environmental monitoring and preprocessing, electric energy parameter acquisition and preliminary analysis, data synchronous calibration, monitoring the operating environment of the electric meter, collecting temperature, humidity and vibration signals, evaluating the impact of environmental factors on the electric energy meter readings, and eliminating the impact of environmental factors on the electric energy meter readings; The power consumption prediction and factor identification module constructs a power consumption prediction model, analyzes factors affecting total power, predicts power consumption, identifies the main factors affecting total power, and provides decision support for power management and optimization; The power quality analysis module analyzes voltage and current waveforms, harmonic components, monitors power quality, identifies power quality problems, and quantifies the degree of power quality distortion; The distortion factor analysis and optimization module analyzes distortion factors, implements multi-source collaborative optimization algorithms, identifies and analyzes power quality distortion factors, eliminates harmonics by adjusting compensation equipment, and optimizes power quality; The grid maintenance and energy meter management module includes calibration error checking of energy meter readings, nonlinear load or capacitor impact analysis, grid maintenance notifications, and energy meter updates to ensure the accuracy of energy meter readings, handle power quality issues in a timely manner, and maintain stable grid operation.

6. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of a data feature analysis method for a single-phase or three-phase meter according to any one of claims 1 to 4 are implemented.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a data feature analysis method for a single-phase or three-phase meter according to any one of claims 1 to 4 are implemented.

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