Electric energy meter fault detection method and system

Abstract

The invention discloses an electric energy meter fault detection method and system, relates to the technical field of electric power measurement and detection, and solves the problems that a current electric energy meter regular inspection mode is high in omission ratio and lagged in response, and an existing detection mode is low in utilization rate of collected original data. The method comprises the following steps: acquiring standard acquisition data of three-phase power corresponding to a standard electric energy meter based on sampling duration and sampling frequency, and performing noise suppression and drift correction on the standard acquisition data; calculating the to-be-detected data of the three-phase power corresponding to the standard electric energy meter through the standard acquisition data; according to the to-be-detected data and the boundary data, calculating a standard window feature vector of three-phase electricity corresponding to the standard electric energy meter, and then constructing an electric energy meter detection formula of the electric energy meter through the standard window feature vector; whether the test electric energy meter has a fault or not is judged based on the real-time monitoring data of the test electric energy meter. According to the method, the data feature utilization efficiency is improved while the fault omission ratio of the electric energy meter is reduced.

Description

Technical Field

[0001] This invention belongs to the field of power metering and testing technology, specifically a method and system for detecting faults in electricity meters. Background Technology

[0002] An electricity meter is an energy metering device used to measure and record electricity consumption. It is a core device for metering and billing in power systems and on the user side. Its basic principle is to collect voltage and current signals and perform calculations to obtain key electrical parameters such as active energy, reactive energy, and power, thereby achieving real-time monitoring and cumulative metering of user electricity consumption. Modern electronic electricity meters not only perform basic energy metering but also have multi-functional expansion capabilities, providing data support for the construction and management of smart grids.

[0003] In existing technologies, traditional electricity meter fault detection relies on periodic inspections. Periodic inspections have problems such as high missed detection rates and slow response. Furthermore, traditional detection methods have low utilization rates of raw data such as voltage and current values ​​collected, and it is difficult to extract effective features.

[0004] Therefore, this invention proposes a method and system for detecting faults in electricity meters. Summary of the Invention

[0005] To address the shortcomings of existing technologies, the purpose of this invention is to provide a method and system for detecting faults in electricity meters.

[0006] The technical problem to be solved by this invention is:

[0007] How to reduce the rate of missed detection of electricity meter faults and improve the efficiency of data feature utilization.

[0008] To achieve the above objectives, the present invention adopts the following technical solution:

[0009] A method for detecting faults in an electricity meter, the method comprising:

[0010] Step S100: Based on the sampling duration and sampling frequency, standard data of the three-phase electricity corresponding to the standard energy meter is collected, and noise suppression and drift correction are performed on the standard data.

[0011] Step S200: Calculate the test data of the three-phase electricity corresponding to the standard energy meter using standard collected data;

[0012] Step S300: Calculate the standard window feature vector of the standard energy meter corresponding to the three-phase electricity based on the data to be tested and the boundary data, and then construct the energy meter detection formula through the standard window feature vector.

[0013] Step S400: Determine whether the test energy meter has a fault based on the real-time monitoring data of the test energy meter.

[0014] As a further technical solution of the present invention, the standard collected data are the standard three-phase voltage value and standard three-phase current value of the standard energy meter corresponding to the three-phase electricity.

[0015] The data to be tested are the voltage crest factor, current crest factor, voltage kurtosis, current kurtosis, voltage harmonic distortion rate, and current harmonic distortion rate corresponding to the three-phase electricity of the standard energy meter.

[0016] The boundary data are the ranges of voltage crest factor, current crest factor, voltage kurtosis, current kurtosis, voltage harmonic distortion rate, and current harmonic distortion rate for the three-phase electricity corresponding to the standard energy meter.

[0017] As a further technical solution of the present invention, step S100 includes the following sub-steps:

[0018] Step S101: Multiply the sampling duration and the sampling frequency to calculate the total number of sampling points within the sampling duration;

[0019] Step S102: Distribute all sampling points evenly over the sampling duration and obtain the timestamp of each sampling point;

[0020] Step S103: Collect the standard three-phase voltage and standard three-phase current values ​​of the corresponding three-phase electricity at all sampling points using a standard electricity meter;

[0021] Step S104: Summarize all standard three-phase voltage values ​​and standard three-phase current values ​​into standard data collected by the standard energy meter for the corresponding three-phase electricity.

[0022] Step S105: Noise suppression is performed on the standard acquired data, and then drift correction is performed on the noise-suppressed standard acquired data.

[0023] As a further technical solution of the present invention, step S200 includes the following sub-steps:

[0024] Step S201: Take the absolute value of the standard three-phase voltage value of any phase in the three-phase power at all sampling points to obtain the absolute three-phase voltage value, and iterate through all the absolute three-phase voltage values. The largest absolute three-phase voltage value is obtained as the voltage peak value of the current phase. Similarly, the voltage peak values ​​of all phases are obtained.

[0025] Step S202: Take the absolute value of the three-phase current value of any phase in the three-phase power at all sampling points to obtain the absolute three-phase current value. Iterate through all the absolute three-phase current values ​​and obtain the largest absolute three-phase current value as the current peak value of the current phase. Similarly, obtain the current peak values ​​of all phases.

[0026] Step S203: Calculate the mean square value of voltage and mean square value of current for each phase of the three-phase power supply.

[0027] Step S204: Divide the voltage peak value by the corresponding root mean square voltage value to calculate the voltage crest factor of the three-phase electricity. Similarly, divide the current peak value by the corresponding root mean square current value to calculate the current crest factor of the three-phase electricity.

[0028] As a further technical solution of the present invention, step S200 further includes the following sub-steps:

[0029] Step S205: Add the standard three-phase voltage values ​​of any one phase of the three-phase electricity and take the average value to calculate the average voltage value of the corresponding phase electricity. Similarly, calculate the average current value of the corresponding phase electricity. Calculate the voltage kurtosis value and current kurtosis value of the three-phase electricity corresponding to the standard energy meter respectively.

[0030] Step S206: The three-phase voltage values ​​of the phase electricity are converted by Fast Fourier Transform, and the conversion result is divided by the square root of two to calculate the harmonic voltage values ​​of the three-phase electricity corresponding to the standard energy meter. The harmonic voltage value with the number 1 corresponding to the harmonic voltage frequency is selected as the fundamental voltage value HVn1. Similarly, the harmonic current values ​​of the three-phase electricity corresponding to the standard energy meter are calculated, and the harmonic current frequency with the number 1 corresponding to the harmonic voltage frequency is selected as the fundamental current value. Then, the voltage harmonic distortion rate and current harmonic distortion rate of the three-phase electricity corresponding to the standard energy meter are calculated.

[0031] Step S207: Summarize the voltage crest factor, current crest factor, voltage kurtosis value, current kurtosis value, voltage harmonic distortion rate, and current harmonic distortion rate into the test data for the three-phase electricity corresponding to the standard energy meter.

[0032] As a further technical solution of the present invention, step S300 includes the following sub-steps:

[0033] Step S301: Obtain the maximum and minimum values ​​corresponding to the range of voltage crest factor values, and normalize the voltage crest factor using a normalization formula to obtain the standard voltage crest factor for three-phase electricity corresponding to the standard energy meter.

[0034] If the standard voltage crest factor is less than zero, then the standard voltage crest factor is recorded as zero.

[0035] If the standard voltage crest factor is greater than one, then the standard voltage crest factor is recorded as one.

[0036] Step S302: Obtain the range of voltage kurtosis values ​​and the range of voltage harmonic distortion rates. Repeat step S301 to calculate the standard voltage kurtosis value and standard voltage harmonic distortion rate for the three-phase electricity corresponding to the standard energy meter.

[0037] Step S303: Construct a standard window feature vector of the three-phase voltage in the three-phase electricity corresponding to the standard energy meter by using the standard voltage crest factor, standard voltage kurtosis and standard voltage harmonic distortion rate.

[0038] As a further technical solution of the present invention, step S300 further includes the following sub-steps:

[0039] Step S304: Sum all values ​​in the window feature vector and take the average value to calculate the standard sample mean vector of the window feature vector. Then, calculate the standard covariance matrix of the window feature vector using the formula for calculating the sample mean vector and the covariance matrix.

[0040] Step S305: Calculate the standard voltage Mahalanobis distance between the standard window feature vector and the standard sample mean vector using the Mahalanobis distance formula;

[0041] Step S306: Obtain the value range of current crest factor, current kurtosis value and current harmonic distortion rate respectively. Repeat steps S301 to S305 to calculate the standard current Mahalanobis distance between the standard window feature vector and the standard sample mean vector.

[0042] Step S307: The calculation formulas for the standard voltage Mahalanobis distance and the standard current Mahalanobis distance are summarized into the standard energy meter test formula.

[0043] As a further technical solution of the present invention, the real-time monitoring data is the real-time three-phase voltage value and real-time three-phase current value corresponding to the three-phase electricity of the test power meter.

[0044] As a further technical solution of the present invention, step S400 includes the following sub-steps:

[0045] Step S401: Obtain the real-time three-phase voltage value of the three-phase electricity corresponding to the test energy meter, and calculate the real-time voltage crest factor, real-time voltage kurtosis and real-time voltage harmonic distortion rate corresponding to the real-time three-phase voltage value. Then, construct the real-time window feature vector of the three-phase voltage in the three-phase electricity corresponding to the test energy meter, and substitute the real-time window feature vector into the calculation formula of the standard voltage Mahalanobis distance in the meter detection formula to calculate the real-time voltage Mahalanobis distance between the test energy meter and the standard energy meter.

[0046] Step S402: Obtain the real-time three-phase current value corresponding to the three-phase power of the test energy meter. Repeat step S401 to calculate the real-time current Mahalanobis distance between the test energy meter and the standard energy meter.

[0047] Step S403: If the real-time voltage Mahalanobis distance is greater than or equal to the first distance threshold, the test energy meter is determined to be faulty; if the real-time voltage Mahalanobis distance is less than the first distance threshold, proceed to step S404.

[0048] In step S404, if the real-time current Mahalanobis distance is greater than or equal to the second distance threshold, the test energy meter is determined to be faulty; if the real-time current Mahalanobis distance is less than the second distance threshold, the test energy meter is determined to be faultless.

[0049] The present invention also provides an electricity meter fault detection system, including a data acquisition module, a preprocessing module, a data analysis module, a parameter calculation module, a sample detection module, and a database module;

[0050] The database module is used to store the sampling duration, sampling frequency, and boundary data of three-phase electricity for standard energy meters and test energy meters; the data acquisition module is used to acquire standard acquisition data of three-phase electricity corresponding to standard energy meters and real-time monitoring data of three-phase electricity corresponding to test energy meters.

[0051] The preprocessing module is used to suppress noise and correct drift in the standard collected data; the data analysis module is used to calculate the test data of the three-phase electricity corresponding to the standard electricity meter based on the standard collected data.

[0052] The parameter calculation module is used to construct the meter testing formula for a standard energy meter using the data to be tested and the boundary data; the sample testing module is used to determine whether the test energy meter has a fault by using the real-time monitoring data of the three-phase electricity corresponding to the test energy meter.

[0053] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0054] 1. This invention collects standard data of three-phase electricity corresponding to a standard energy meter based on sampling duration and sampling frequency, performs noise suppression and drift correction on the standard data, and then uses the standard data to analyze and obtain the test data of three-phase electricity corresponding to the standard energy meter.

[0055] 2. This invention combines the data to be tested and boundary data analysis to calculate the standard window feature vector corresponding to the three-phase electricity of the standard energy meter, and constructs the energy meter detection formula through the standard window feature vector. The real-time monitoring data of the test energy meter is substituted into the energy meter detection formula to calculate the real-time voltage Mahalanobis distance and real-time current Mahalanobis distance between the test energy meter and the standard energy meter. The real-time voltage Mahalanobis distance and the real-time voltage Mahalanobis distance are compared with the corresponding distance threshold to determine whether the test energy meter has a fault. This invention effectively improves the utilization efficiency of energy meter data features while reducing the missed detection rate of energy meter faults. Attached Figure Description

[0056] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0057] Figure 1 This is a flowchart of the method of the present invention;

[0058] Figure 2 This is a flowchart illustrating the calculation process for the Mahalanobis distance of the energy meter in this invention.

[0059] Figure 3 This is an overall system block diagram of the present invention. Detailed Implementation

[0060] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0061] Example 1: Please refer to Figure 1 and Figure 2 As shown, the technical solution provided by the present invention is: a method for detecting faults in an electricity meter, comprising the following steps;

[0062] Step S100: Based on the sampling duration and sampling frequency, standard data of the three-phase electricity corresponding to the standard energy meter is collected, and noise suppression and drift correction are performed on the standard data.

[0063] Specifically, the standard data collected includes the standard three-phase voltage and standard three-phase current values ​​corresponding to the standard energy meter; the sampling duration and sampling frequency are preset.

[0064] It should be noted that since energy meters that can detect three-phase electricity have backward compatibility, they can usually also detect single-phase electricity; however, energy meters that can detect single-phase electricity do not have backward compatibility. Therefore, this embodiment analyzes energy meters that can detect three-phase electricity.

[0065] In this embodiment, step S100 includes the following sub-steps:

[0066] Step S101: Multiply the sampling duration and the sampling frequency to calculate the total number of sampling points within the sampling duration;

[0067] In practice, the sampling duration can be one second and the sampling frequency can be 10kHz; the sampling interval between adjacent sampling points is specifically one divided by the sampling frequency.

[0068] Step S102: Distribute all sampling points evenly over the sampling time and obtain the timestamp t for each sampling point, t=1, 2, ..., m, where m is the total number of sampling points;

[0069] Step S103: Collect the standard three-phase voltage value Vn(t) and standard three-phase current value In(t) of the corresponding three-phase electricity at all sampling points using a standard electricity meter, where n is the number of the three-phase electricity, n=1, 2, 3;

[0070] Step S104: Summarize all standard three-phase voltage values ​​and standard three-phase current values ​​into standard data collected by the standard energy meter for the corresponding three-phase electricity.

[0071] Step S105: Noise suppression is performed on the standard acquired data, and then drift correction is performed on the noise-suppressed standard acquired data.

[0072] When collecting standard three-phase voltage and current values ​​of three-phase electricity using a standard energy meter, the power grid itself is affected by factors such as load fluctuations and harmonic interference, resulting in high-frequency noise superimposed on the standard data. In this embodiment, wavelet transform is preferred to eliminate the noise generated by power grid fluctuations in the standard data. When collecting standard three-phase voltage and current values ​​of three-phase electricity using a standard energy meter, baseline drift may occur in the three-phase voltage and current values ​​over time due to sensor aging, temperature changes, or accumulated errors in the measurement link. In this embodiment, Kalman filtering is preferred to correct the drift in the standard three-phase voltage and current values. Specifically, wavelet transform for noise reduction and Kalman filtering for drift correction are existing technologies and will not be elaborated here.

[0073] Step S200: Calculate the test data of the three-phase electricity corresponding to the standard energy meter using standard collected data;

[0074] The specific data to be tested are the voltage crest factor, current crest factor, voltage kurtosis, current kurtosis, voltage harmonic distortion rate, and current harmonic distortion rate of the three-phase electricity corresponding to the standard energy meter.

[0075] In this embodiment, step S200 includes the following sub-steps:

[0076] Step S201: Take the absolute value of the standard three-phase voltage value of any phase in the three-phase power at all sampling points to obtain the absolute three-phase voltage value, and iterate through all the absolute three-phase voltage values. The largest absolute three-phase voltage value is obtained as the voltage peak value of the current phase. Similarly, the voltage peak value VFn of all phases is obtained.

[0077] Specifically, phase electricity refers to a single phase of three-phase electricity;

[0078] Step S202: Take the absolute value of the three-phase current value of any phase in the three-phase power at all sampling points to obtain the absolute three-phase current value. Iterate through all the absolute three-phase current values ​​and obtain the largest absolute three-phase current value as the current peak value of the current phase. Similarly, obtain the current peak value IFn of all phases.

[0079] Step S203: Calculate the root mean square value of voltage VJn and the root mean square value of current IJn for each phase of the three-phase electricity using the root mean square formula. The specific formula is as follows: ; ;

[0080] It should be specifically noted that, since this embodiment uses three-phase electricity to test the energy meter, and three-phase electricity is alternating current, the waveform formed by the change of three-phase voltage over time is a sine wave. Therefore, the standard three-phase voltage value is a positive voltage value for half the time and a negative voltage value for half the time within one cycle. Here, both the positive and negative voltage values ​​represent the direction of the current. Similarly, the standard three-phase current value is a positive current for half the time and a negative current for half the time within one cycle. Here, both the positive and negative current values ​​represent the direction of the current. When directly calculating the average voltage and average current within the sampling period, the average voltage and average current will approach zero.

[0081] Step S204: Divide the voltage peak value by the corresponding root mean square voltage value to calculate the voltage crest factor YBn of the three-phase electricity. Similarly, divide the current peak value by the corresponding root mean square current value to calculate the current crest factor LBn of the three-phase electricity.

[0082] Among them, the crest factor is used to reflect the waveform distortion caused by problems in the sampling or measurement of the electricity meter, which results in the three-phase voltage values ​​corresponding to the sine wave or the three-phase current values ​​corresponding to the sine wave; the crest factor is a dimensionless number.

[0083] Step S205: Sum the standard three-phase voltage values ​​of any one phase of the three-phase electricity and take the average value to calculate the average voltage value PYn of the corresponding phase. Similarly, calculate the average current value PLn of the corresponding phase. Calculate the voltage kurtosis value YQn and current kurtosis value LQn of the corresponding three-phase electricity using the formulas as follows: ; ;

[0084] The kurtosis value is used to identify instantaneous impacts in three-phase electricity. If there are problems with the circuit, sampling link, or filtering of the electricity meter, it will cause spike signals in the sine waves of the three-phase voltage and the sine waves of the three-phase current.

[0085] Step S206: The three-phase voltage values ​​of the phase electricity are converted using Fast Fourier Transform, and the conversion result is divided by the square root of two to calculate the harmonic voltage value HVnj corresponding to the standard energy meter, where j is the harmonic voltage frequency number, j=1, 2, ..., 0, and 0 is a positive integer. The harmonic voltage value corresponding to j=1 is selected as the fundamental voltage value HVn1. Similarly, the harmonic current value HInj corresponding to the standard energy meter is calculated, and the harmonic current frequency corresponding to j=1 is selected as the fundamental current value HIn1. Then, the voltage harmonic distortion rate THDVn and the current harmonic distortion rate THDIn corresponding to the standard energy meter are calculated using the following formula: ; ;

[0086] Among them, the harmonic distortion rate is used to measure the energy meter's ability to respond to harmonic components. A faulty energy meter will cause deviations in the calculation of harmonic amplitude.

[0087] Specifically, the three-phase electricity collected by the standard energy meter consists of three-phase voltages and three-phase currents, which are discrete signals that change with time. The discrete signals are converted into frequency domain signals by Fast Fourier Transform (FFT). The frequency domain signals contain Dolby spectrum components. The harmonic voltage, fundamental voltage, harmonic current, and fundamental current values ​​are obtained based on the harmonic frequency numbering. The conversion of discrete signals into frequency domain signals by FFT is an existing technology and will not be elaborated here.

[0088] Step S207: Summarize the voltage crest factor, current crest factor, voltage kurtosis value, current kurtosis value, voltage harmonic distortion rate, and current harmonic distortion rate into the test data for the three-phase electricity corresponding to the standard energy meter.

[0089] Step S300: Calculate the standard window feature vector of the standard energy meter corresponding to the three-phase electricity based on the data to be tested and the boundary data, and then construct the energy meter detection formula through the standard window feature vector.

[0090] Specifically, the boundary data includes the voltage crest factor range, current crest factor range, voltage kurtosis range, current kurtosis range, voltage harmonic distortion rate range, and current harmonic distortion rate range for the three-phase electricity corresponding to the standard energy meter.

[0091] In this embodiment, step S300 includes the following sub-steps:

[0092] Step S301: Obtain the maximum endpoint value c1 and the minimum endpoint value c2 corresponding to the voltage crest factor range. Normalize the voltage crest factor using a normalization formula to obtain the standard voltage crest factor BYn for three-phase electricity corresponding to the standard energy meter. The specific formula is as follows: BYn=(YBn-c2) / (c1-c2);

[0093] If the standard voltage crest factor is less than zero, then the standard voltage crest factor is recorded as zero.

[0094] If the standard voltage crest factor is greater than one, then the standard voltage crest factor is recorded as one.

[0095] Step S302: Obtain the range of voltage kurtosis values ​​and the range of voltage harmonic distortion rates. Repeat step S301 to calculate the standard voltage kurtosis value BQn and standard voltage harmonic distortion rate BJn corresponding to the three-phase electricity of the standard energy meter.

[0096] Step S303: Construct a standard window feature vector Xg for the three-phase voltage in the three-phase electricity corresponding to the standard energy meter using the standard voltage crest factor, standard voltage kurtosis, and standard voltage harmonic distortion rate. Here, g is the number of the feature vector in the standard window feature vector, g = 1, 2, ..., d, and d is the total number of feature vectors. In this embodiment, d = 9. The matrix is ​​as follows: ;

[0097] Step S304: Sum all values ​​in the window feature vector, take the average, and calculate the standard sample mean vector JZ of the window feature vector. Then, calculate the standard covariance matrix XF of the window feature vector using the formula for calculating the sample mean vector and the covariance matrix. The specific formula is as follows: ;

[0098] Step S305: Calculate the standard voltage Mahalanobis distance JLV(Xg) between the standard window feature vector and the standard sample mean vector using the Mahalanobis distance formula. The specific formula is as follows: ;

[0099] It should be noted that the Euclidean distance formula cannot take into account the correlation between all feature values ​​in the standard window feature vector. Therefore, the Mahalanobis distance formula is used to standardize the scale of all features in the standard window feature vector.

[0100] Step S306: Obtain the value range of current crest factor, current kurtosis value and current harmonic distortion rate respectively. Repeat steps S301 to S305 to calculate the standard current Mahalanobis distance between the standard window feature vector and the standard sample mean vector.

[0101] Step S307: The calculation formulas for the standard voltage Mahalanobis distance and the standard current Mahalanobis distance are summarized into the standard energy meter test formula.

[0102] Step S400: Determine whether the test energy meter has a fault based on the real-time monitoring data of the test energy meter;

[0103] Specifically, the real-time monitoring data includes the real-time three-phase voltage and current values ​​corresponding to the three-phase power of the test energy meter. In particular, the test energy meter and the standard energy meter are produced in the same batch. The test energy meter and the standard energy meter are connected in series, and the real-time three-phase voltage value of the test energy meter is collected. Then, the test energy meter and the standard energy meter are connected in parallel, and the real-time three-phase current value of the test energy meter is collected.

[0104] It should be specifically noted that the circuit only contains a test energy meter and a standard energy meter. When the test energy meter is fault-free and is connected in series with the standard energy meter, the three-phase voltage values ​​of the test energy meter and the standard energy meter are the same. When the test energy meter is fault-free and is connected in parallel with the standard energy meter, the three-phase current values ​​of the test energy meter and the standard energy meter are the same.

[0105] In this embodiment, step S400 includes the following sub-steps:

[0106] Step S401: Obtain the real-time three-phase voltage value of the three-phase electricity corresponding to the test energy meter, and calculate the real-time voltage crest factor, real-time voltage kurtosis and real-time voltage harmonic distortion rate corresponding to the real-time three-phase voltage value. Then, construct the real-time window feature vector of the three-phase voltage in the three-phase electricity corresponding to the test energy meter, and substitute the real-time window feature vector into the calculation formula of the standard voltage Mahalanobis distance in the meter detection formula to calculate the real-time voltage Mahalanobis distance between the test energy meter and the standard energy meter.

[0107] Step S402: Obtain the real-time three-phase current value corresponding to the three-phase power of the test energy meter. Repeat step S401 to calculate the real-time current Mahalanobis distance between the test energy meter and the standard energy meter.

[0108] Step S403: If the real-time voltage Mahalanobis distance is greater than or equal to the first distance threshold, then it is determined that the test energy meter is faulty.

[0109] If the real-time voltage Mahalanobis distance is less than the first distance threshold, proceed to step S404;

[0110] Step S404: If the real-time current Mahalanobis distance is greater than or equal to the second distance threshold, then it is determined that the test energy meter is faulty.

[0111] If the real-time current Mahalanobis distance is less than the second distance threshold, it is determined that the test energy meter is not faulty;

[0112] There is no size comparison between the first distance threshold and the second distance threshold.

[0113] Example 2: Please refer to Figure 3 As shown, based on another concept of the same invention, a fault detection system for electricity meters is proposed, including a data acquisition module, a preprocessing module, a data analysis module, a parameter calculation module, a sample detection module, and a database module;

[0114] The database module is used to store the sampling duration, sampling frequency, and boundary data of three-phase electricity for standard and test energy meters.

[0115] The data acquisition module is used to collect standard acquisition data for three-phase electricity from standard energy meters and real-time monitoring data for three-phase electricity from test energy meters.

[0116] The preprocessing module is used to suppress noise and correct drift in the standard acquired data;

[0117] The data analysis module is used to calculate the test data of the three-phase electricity corresponding to the standard energy meter based on the standard collected data;

[0118] The parameter calculation module is used to construct the standard energy meter testing formula based on the data to be tested and the boundary data.

[0119] The sample detection module is used to determine whether the test energy meter has a fault by using real-time monitoring data of the three-phase electricity corresponding to the test energy meter.

[0120] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for detecting faults in an electricity meter, characterized in that, The methods include: Step S100: Based on the sampling duration and sampling frequency, standard acquisition data of the three-phase electricity corresponding to the standard energy meter is acquired, and noise suppression and drift correction are performed on the standard acquisition data; the standard acquisition data are the standard three-phase voltage value and standard three-phase current value of the three-phase electricity corresponding to the standard energy meter. Step S200: Calculate the test data of the three-phase electricity corresponding to the standard energy meter using standard acquisition data; the test data are the voltage crest factor, current crest factor, voltage kurtosis value, current kurtosis value, voltage harmonic distortion rate, and current harmonic distortion rate of the three-phase electricity corresponding to the standard energy meter. Step S300: Calculate the standard window feature vector of the standard energy meter corresponding to the three-phase electricity based on the data to be tested and the boundary data. Then, construct the meter detection formula of the energy meter through the standard window feature vector. The boundary data are the voltage crest factor value range, current crest factor value range, voltage kurtosis value range, current kurtosis value range, voltage harmonic distortion rate value range, and current harmonic distortion rate value range of the standard energy meter corresponding to the three-phase electricity. Step S400: Determine whether the test energy meter has a fault based on the real-time monitoring data of the test energy meter.

2. The method for detecting faults in an electricity meter according to claim 1, characterized in that, Step S100 includes the following sub-steps: Step S101: Multiply the sampling duration and the sampling frequency to calculate the total number of sampling points within the sampling duration; Step S102: Distribute all sampling points evenly over the sampling duration and obtain the timestamp of each sampling point; Step S103: Collect the standard three-phase voltage and standard three-phase current values ​​of the corresponding three-phase electricity at all sampling points using a standard electricity meter; Step S104: Summarize all standard three-phase voltage values ​​and standard three-phase current values ​​into standard data collected by the standard energy meter for the corresponding three-phase electricity. Step S105: Noise suppression is performed on the standard acquired data, and then drift correction is performed on the noise-suppressed standard acquired data.

3. The method for detecting faults in an electricity meter according to claim 1, characterized in that, Step S200 includes the following sub-steps: Step S201: Take the absolute value of the standard three-phase voltage value of any phase in the three-phase power at all sampling points to obtain the absolute three-phase voltage value, and iterate through all the absolute three-phase voltage values. The largest absolute three-phase voltage value is obtained as the voltage peak value of the current phase. Similarly, the voltage peak values ​​of all phases are obtained. Step S202: Take the absolute value of the three-phase current value of any phase in the three-phase power at all sampling points to obtain the absolute three-phase current value. Iterate through all the absolute three-phase current values ​​and obtain the largest absolute three-phase current value as the current peak value of the current phase. Similarly, obtain the current peak values ​​of all phases. Step S203: Calculate the mean square value of voltage and mean square value of current for each phase of the three-phase power supply. Step S204: Divide the voltage peak value by the corresponding root mean square voltage value to calculate the voltage crest factor of the three-phase electricity. Similarly, divide the current peak value by the corresponding root mean square current value to calculate the current crest factor of the three-phase electricity.

4. The method for detecting faults in an electricity meter according to claim 3, characterized in that, Step S200 further includes the following sub-steps: Step S205: Add the standard three-phase voltage values ​​of any one phase of the three-phase electricity and take the average value to calculate the average voltage value of the corresponding phase electricity. Similarly, calculate the average current value of the corresponding phase electricity. Calculate the voltage kurtosis value and current kurtosis value of the three-phase electricity corresponding to the standard energy meter respectively. Step S206: The three-phase voltage values ​​of the phase electricity are converted by Fast Fourier Transform, and the conversion result is divided by the square root of two to calculate the harmonic voltage values ​​of the three-phase electricity corresponding to the standard energy meter. The harmonic voltage value with the number 1 corresponding to the harmonic voltage frequency is selected as the fundamental voltage value HVn1. Similarly, the harmonic current values ​​of the three-phase electricity corresponding to the standard energy meter are calculated, and the harmonic current frequency with the number 1 corresponding to the harmonic voltage frequency is selected as the fundamental current value. Then, the voltage harmonic distortion rate and current harmonic distortion rate of the three-phase electricity corresponding to the standard energy meter are calculated. Step S207: Summarize the voltage crest factor, current crest factor, voltage kurtosis value, current kurtosis value, voltage harmonic distortion rate, and current harmonic distortion rate into the test data for the three-phase electricity corresponding to the standard energy meter.

5. The method for detecting faults in an electricity meter according to claim 1, characterized in that, Step S300 includes the following sub-steps: Step S301: Obtain the maximum and minimum values ​​corresponding to the range of voltage crest factor values, and normalize the voltage crest factor using a normalization formula to obtain the standard voltage crest factor for three-phase electricity corresponding to the standard energy meter. If the standard voltage crest factor is less than zero, then the standard voltage crest factor is recorded as zero. If the standard voltage crest factor is greater than one, then the standard voltage crest factor is recorded as one. Step S302: Obtain the range of voltage kurtosis values ​​and the range of voltage harmonic distortion rates. Repeat step S301 to calculate the standard voltage kurtosis value and standard voltage harmonic distortion rate for the three-phase electricity corresponding to the standard energy meter. Step S303: Construct a standard window feature vector of the three-phase voltage in the three-phase electricity corresponding to the standard energy meter by using the standard voltage crest factor, standard voltage kurtosis and standard voltage harmonic distortion rate.

6. The method for detecting faults in an electricity meter according to claim 5, characterized in that, Step S300 further includes the following sub-steps: Step S304: Sum all values ​​in the window feature vector and take the average value to calculate the standard sample mean vector of the window feature vector. Then, calculate the standard covariance matrix of the window feature vector using the formula for calculating the sample mean vector and the covariance matrix. Step S305: Calculate the standard voltage Mahalanobis distance between the standard window feature vector and the standard sample mean vector using the Mahalanobis distance formula; Step S306: Obtain the value range of current crest factor, current kurtosis value and current harmonic distortion rate respectively. Repeat steps S301 to S305 to calculate the standard current Mahalanobis distance between the standard window feature vector and the standard sample mean vector. Step S307: The calculation formulas for the standard voltage Mahalanobis distance and the standard current Mahalanobis distance are summarized into the standard energy meter test formula.

7. The method for detecting faults in an electricity meter according to claim 1, characterized in that, The real-time monitoring data consists of the real-time three-phase voltage and real-time three-phase current values ​​corresponding to the three-phase electricity meter being tested.

8. The method for detecting faults in an electricity meter according to claim 7, characterized in that, Step S400 includes the following sub-steps: Step S401: Obtain the real-time three-phase voltage value of the three-phase electricity corresponding to the test energy meter, and calculate the real-time voltage crest factor, real-time voltage kurtosis and real-time voltage harmonic distortion rate corresponding to the real-time three-phase voltage value. Then, construct the real-time window feature vector of the three-phase voltage in the three-phase electricity corresponding to the test energy meter, and substitute the real-time window feature vector into the calculation formula of the standard voltage Mahalanobis distance in the meter detection formula to calculate the real-time voltage Mahalanobis distance between the test energy meter and the standard energy meter. Step S402: Obtain the real-time three-phase current value corresponding to the three-phase power of the test energy meter. Repeat step S401 to calculate the real-time current Mahalanobis distance between the test energy meter and the standard energy meter. Step S403: If the real-time voltage Mahalanobis distance is greater than or equal to the first distance threshold, then it is determined that the test energy meter is faulty. If the real-time voltage Mahalanobis distance is less than the first distance threshold, proceed to step S404; Step S404: If the real-time current Mahalanobis distance is greater than or equal to the second distance threshold, then it is determined that the test energy meter is faulty. If the real-time current Mahalanobis distance is less than the second distance threshold, the test energy meter is determined to be fault-free.

9. A fault detection system for electricity meters, characterized in that, A method for detecting faults in an electricity meter according to any one of claims 1-8 includes a data acquisition module, a preprocessing module, a data analysis module, a parameter calculation module, a sample detection module, and a database module; The database module is used to store the sampling duration, sampling frequency, and boundary data of three-phase electricity for standard energy meters and test energy meters; the data acquisition module is used to acquire standard acquisition data of three-phase electricity corresponding to standard energy meters and real-time monitoring data of three-phase electricity corresponding to test energy meters. The preprocessing module is used to suppress noise and correct drift in the standard collected data; the data analysis module is used to calculate the test data of the three-phase electricity corresponding to the standard electricity meter based on the standard collected data. The parameter calculation module is used to construct the meter testing formula for a standard energy meter using the data to be tested and the boundary data; the sample testing module is used to determine whether the test energy meter has a fault by using the real-time monitoring data of the three-phase electricity corresponding to the test energy meter.

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