An error source-based smart meter correction method and system

Abstract

The application relates to the technical field of smart meters, in particular to a smart meter correction method and system based on error sources, which are used for improving the correction accuracy of smart meters. The main scheme is as follows: if the meter error probability value of the current time period is greater than the preset probability value and the error influence value is greater than the preset influence value, the error source of the smart meter is determined according to the time series data of the smart meter; the corresponding target compensation value is respectively calculated through the shunt fault error, the reference source fault error, the phase fault error and / or the aging fault error and the respective corresponding adjustment coefficients; the original measurement value of the smart meter is compensated through the target compensation value to obtain the corrected measurement value; and the residual error is calculated according to the corrected measurement value and the standard meter electric energy value; and if the residual error is greater than the preset precision threshold, the adjustment coefficients corresponding to the shunt fault error, the reference source fault error, the phase fault error and / or the aging fault error are adjusted.

Description

Technical Field

[0001] This application relates to the field of smart meter technology, and in particular to a smart meter correction method and system based on the root cause of error. Background Technology

[0002] As the functions of smart energy meters in my country become more complex and are put into operation on a large scale by power grid companies, issues related to their quality and reliability have gradually emerged. As a result, power grid companies are focusing their attention on the accuracy of metering and the reliability of operation.

[0003] Currently, multiple calibration points are selected evenly across the entire measurement range of the meter (such as voltage and current) for calibration, which helps correct nonlinear errors. However, nonlinear loads can distort the current and voltage waveforms in the power grid, resulting in low accuracy of smart energy calibration. Summary of the Invention

[0004] In view of this, this application provides a smart meter correction method and system based on the root cause of error, which is used to improve the accuracy of smart meter correction based on the root cause of error.

[0005] In a first aspect, embodiments of this application provide a smart meter correction method based on error root causes. This method is applied to a processor in a smart meter correction system based on error root causes. The system includes: the processor, a data acquisition device, and an environmental data acquisition device. The method includes:

[0006] Obtain time-series data of smart meters corresponding to the periods of abnormal electricity consumption from historical electricity consumption data;

[0007] The time series data of the abnormal electricity consumption period and the time series data of the current period are input into the error probability prediction model to obtain the meter error probability value of the current period and the error impact value of the time series data of the abnormal electricity consumption period on the time series data of the current period.

[0008] If the meter error probability value in the current time period is greater than the preset probability value and the error impact value is greater than the preset impact value, then the root cause of the smart meter error is determined based on the time series data of the smart meter. The root cause of the error includes shunt fault error, reference source fault error, phase fault error and / or aging fault error.

[0009] The target compensation value is calculated based on the shunt fault error, the reference source fault error, the phase fault error, and / or the aging fault error, and their respective adjustment coefficients; the initial value of the adjustment coefficient is set by the user.

[0010] The original measurement value of the smart meter is compensated according to the target compensation values ​​corresponding to the shunt fault error, the reference source fault error, the phase fault error and / or the aging fault error in a preset order to obtain the corrected measurement value.

[0011] Secondly, embodiments of this application also provide a smart meter correction system based on the root cause of error, the system comprising:

[0012] The acquisition module is used to obtain time series data of smart meters corresponding to the time periods of abnormal electricity consumption from historical electricity consumption data;

[0013] The prediction module is used to input the time series data of the abnormal electricity consumption period and the time series data of the current period into the error probability prediction model to obtain the meter error probability value of the current period and the error impact value of the time series data of the abnormal electricity consumption period on the time series data of the current period.

[0014] The determination module is used to determine the root cause of the smart meter's error based on the smart meter's time series data if the meter's error probability value for the current time period is greater than a preset probability value and the error impact value is greater than a preset impact value. The root cause of the error includes shunt fault error, reference source fault error, phase fault error, and / or aging fault error.

[0015] The calculation module is used to calculate the corresponding target compensation value based on the shunt fault error, the reference source fault error, the phase fault error, and / or the aging fault error and their respective adjustment coefficients; the initial value of the adjustment coefficient is set by the user.

[0016] The compensation module is used to compensate the original measurement value of the smart meter according to a preset sequence using the target compensation values ​​corresponding to the shunt fault error, the reference source fault error, the phase fault error, and / or the aging fault error, respectively, to obtain the corrected measurement value.

[0017] Thirdly, embodiments of this application also provide an electronic device, including: a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor and the memory communicate via the bus. The machine-readable instructions are executed by the processor to perform the steps of the smart meter correction method based on the root cause of error in the first aspect.

[0018] Fourthly, embodiments of this application also provide a computer-readable storage medium storing a computer program, which, when run by a processor, executes the steps of the smart meter correction method based on error root causes described in the first aspect.

[0019] This application provides a method and system for correcting smart meters based on error root causes. First, time-series data of smart meters corresponding to periods of abnormal electricity consumption are obtained from historical electricity consumption data. The time-series data of the abnormal electricity consumption periods and the current time period are input into an error probability prediction model to obtain the meter error probability value for the current time period and the error impact value of the time-series data of the abnormal electricity consumption periods on the current time period's time-series data. If the meter error probability value for the current time period is greater than a preset probability value and the error impact value is greater than a preset impact value, then the error root causes of the smart meter are determined based on the smart meter's time-series data. Error root causes include shunt fault error, reference source fault error, phase fault error, and / or aging fault error. Target compensation values ​​are calculated for each of the shunt fault error, reference source fault error, phase fault error, and / or aging fault error, along with their corresponding adjustment coefficients. The initial values ​​of the adjustment coefficients are user-defined. The original measured values ​​of the smart meter are compensated according to a preset order using the target compensation values ​​corresponding to the shunt fault error, reference source fault error, phase fault error, and / or aging fault error to obtain corrected measured values. Compared to existing technologies that use multiple calibration points for calibration, this application determines the root cause of error based on the acquired time series data of the smart meter. Then, it compensates the smart meter according to the target compensation value corresponding to the determined root cause of error to obtain the corrected measurement value. This process continues until the residual error calculated based on the corrected measurement value and the standard meter's energy value is less than or equal to a preset accuracy threshold. Thus, this application can guarantee the correction accuracy of the smart meter based on the root cause of error.

[0020] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0021] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 A flowchart of a smart meter correction method based on error root causes provided in an embodiment of this application is shown;

[0023] Figure 2 A flowchart of another smart meter correction method based on error root causes provided in an embodiment of this application is shown;

[0024] Figure 3This paper shows a structural block diagram of a smart meter correction system based on error root causes, provided in an embodiment of this application.

[0025] Figure 4 A schematic diagram of a computer device provided in an embodiment of this application is shown. Detailed Implementation

[0026] The terms "first," "second," and "third," etc., used in this application specification, claims, and the aforementioned drawings are used to distinguish different objects, not to limit a specific order.

[0027] In the embodiments of this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design that is described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a specific manner to facilitate understanding.

[0028] In the description of this application, unless otherwise stated, " / " indicates that the objects before and after it are in an "or" relationship. For example, A / B can mean A or B. "And / or" in this application is merely a description of the relationship between the related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone, where A and B can be singular or plural. Furthermore, in the description of this application, unless otherwise stated, "multiple" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0029] In the embodiments of this application, at least one can also be described as one or more, and multiple can be two, three, four or more, and this application does not impose any restrictions.

[0030] like Figure 1 As shown, this application provides a smart meter correction method based on the root cause of errors. This method is applied to a processor in a smart meter correction system based on the root cause of errors. The system includes: the processor, a running data acquisition device, and an environmental data acquisition device. The smart meter correction method based on the root cause of errors provided in this application may include:

[0031] S10. Obtain the time series data of the smart meters corresponding to the time periods of abnormal electricity consumption from historical electricity consumption data.

[0032] The time series data includes operational data and environmental data. The operational data may include measured values ​​of voltage, current, power, and power factor for each phase; the environmental data may include temperature values, humidity values, etc., which are not specifically limited in this embodiment.

[0033] In this embodiment, historical electricity consumption data can be statistically analyzed to determine abnormal electricity consumption periods. For example, when electricity consumption suddenly surges without any additional increase in electricity consumption, this period can be identified as an abnormal electricity consumption period. Other examples include power exceeding a certain upper limit or voltage exceeding the national standard range. Each abnormal electricity consumption period has a start time, end time, and anomaly type label (such as voltage sag, power consumption surge for unknown reasons, etc.).

[0034] S20. Input the time series data of the abnormal electricity consumption period and the time series data of the current period into the error probability prediction model to obtain the meter error probability value of the current period and the error impact value of the time series data of the abnormal electricity consumption period on the time series data of the current period.

[0035] It should be noted that multiple time periods with abnormal electricity consumption can be obtained from historical electricity consumption data, such as 10:00-12:00, 20:00-22:00. The length of each abnormal electricity consumption time period is a fixed value, which can be 2 hours, 3 hours, or 5 hours, etc.

[0036] In this embodiment, abnormal electricity consumption periods within the most recent day can be obtained. Then, all abnormal electricity consumption periods and the time series data of the current period are input into an error probability prediction model to predict the meter error probability value for the current period. This meter error probability value represents the probability that a meter error may occur during the current period. The error probability prediction model in this embodiment is a time prediction model, which predicts the meter error probability value for the current period using time series data from multiple input periods.

[0037] Among them, the error impact value is used to represent the impact of the time series data of the abnormal power consumption period on the time series data of the current period. It can be divided into 1-10 levels, and the larger the value, the greater the impact.

[0038] Specifically, this embodiment calculates the baseline average value of various data (i.e., operational data and environmental data) based on time series data of normal electricity consumption periods. Then, it calculates the abnormal period state index and the current period state index based on the baseline average value. Finally, it calculates the absolute value of the difference between the abnormal period state index and the current period state index to obtain the error impact value; or the error impact value = |current period average - average value of abnormal electricity consumption period| ÷ |average value of normal electricity consumption period - average value of abnormal electricity consumption period|. The abnormal period state index and the current period state index are calculated by dividing the average value of the abnormal electricity consumption period and the average value of the normal electricity consumption period by the baseline average value, respectively.

[0039] For example, the average power consumption during normal periods is the average power consumption of the previous month's normal working days = 100 kW; the average power consumption during abnormal periods is the average power consumption during last week's faults = 150 kW; and the current period's average power consumption is the average power consumption monitored today = 130 kW; then the error impact value = |130 - 150| ÷ |100 - 150| = |-20| ÷ |-50| = 0.4 (or 40%)

[0040] Error impact value = 0: The current state is the same as the normal state (complete recovery);

[0041] Error impact value = 1: The current state is the same as the abnormal state (completely affected);

[0042] Error impact value = 0.4: The current state is 40% biased towards an abnormal state and 60% biased towards a normal state;

[0043] In this example, an impact value of 0.4 indicates that the abnormal event has a 40% residual impact on the current power consumption, and the equipment operation has partially recovered but is not yet fully normal.

[0044] S30. If the meter error probability value for the current time period is greater than the preset probability value and the error impact value is greater than the preset impact value, then the root cause of the smart meter error is determined based on the time series data of the smart meter.

[0045] The root causes of error include shunt fault error, reference source fault error, phase fault error, and / or aging fault error. The preset probability value and preset influence value are set according to actual needs; for example, the preset probability value can be 70% or 60%, and the preset influence value can be 50% or 60%, etc. This embodiment does not impose specific limitations on these.

[0046] Shunt fault error refers to the measurement error caused by changes in the characteristics of the shunt resistor in the current sampling circuit. This error is mainly caused by the temperature coefficient of the shunt resistor. When the ambient temperature or its own operating temperature changes, the resistance value of the shunt resistor will drift, resulting in a gain error in the current sampling value. The magnitude of the error is closely related to the load current and temperature. The larger the load current, the more severe the shunt self-heating and the more obvious the temperature drift effect. In this embodiment, the shunt fault error can be calculated using the following formula:

[0047]

[0048]

[0049] in, This is due to shunt fault error. For temperature coefficient, The current temperature. This is a reference temperature (usually 25°C). Reference temperature The nominal resistance value below, This represents the change in resistance caused by temperature.

[0050] Reference source fault error refers to the drift error of the internal reference voltage source (Vref) of the metering chip due to temperature changes or its own aging. The reference voltage is the reference for all ADC sampling, and its drift directly affects the accuracy of all measurement channels (voltage, current), causing a global gain error. This error is usually strongly correlated with chip temperature and independent of the load current.

[0051] Phase fault error refers to the power measurement error caused by phase mismatch between the voltage and current sampling channels. This error is almost zero with purely resistive loads (power factor = 1), but increases significantly with inductive or capacitive loads (low power factor). It mainly originates from component tolerances in anti-aliasing filters, signal path delay mismatches, etc.

[0052] Aging-related fault errors refer to measurement errors caused by the performance degradation of meter components over time. This type of error typically exhibits a slow, monotonous drift trend and is related to factors such as the meter's cumulative operating time and historical operating temperature. Key affected components include shunt resistors, reference voltage sources, and integrating capacitors.

[0053] In one optional embodiment provided in this application, determining the root cause of error in the smart meter based on the time series data of the smart meter includes:

[0054] S301. Extract key feature vectors based on the time series data of the smart meter.

[0055] The key feature vector includes: average power error, error-temperature correlation coefficient, error-temperature sensitivity slope, error-current correlation coefficient, error-current change rate, power factor sensitivity, and error drift rate. The key feature vector can be expressed by the formula... express, The average error of electrical energy. The error-temperature correlation coefficient. The slope of error-temperature sensitivity. The error-current correlation coefficient, The error is the rate of change of current. For power factor sensitivity, This represents the error drift rate.

[0056] Specifically, the average energy error represents the systematic deviation level of the meter's measurement error over a period of time. It reflects whether the meter is generally running fast (positive error) or slow (negative error), and is a fundamental indicator for assessing the meter's accuracy. It can be calculated by taking the arithmetic mean of all error samples over a period of time. The error-temperature correlation coefficient quantifies the strength and direction of the linear correlation between the meter's error and the chip temperature. The closer the value is to +1 or -1, the greater the influence of temperature on the error. A positive value indicates that the error increases with increasing temperature. It can be obtained by calculating the Pearson correlation coefficient between the error series and the temperature series. The error-temperature sensitivity slope represents the percentage change in the average error for every 1°C change in temperature. It can be calculated using a linear regression function. The error-current correlation coefficient quantifies the strength of the linear correlation between the meter's error and the magnitude of the load current. A positive value indicates that the larger the current, the larger the error. The error-current correlation coefficient can be obtained by calculating the Pearson correlation coefficient between the error series and the current series; the error-current change rate describes the average rate of change of the error over a specific current range (such as from light load to full load), which can be obtained by calculating the quotient of the difference between the average error at two typical load points and the difference in current at that load point; the power factor sensitivity measures the degree of dependence of the error on the power factor. The larger its absolute value, the greater the difference between the error characteristics under low power factor conditions and those under purely resistive loads. It can be obtained by calculating the difference between the average error under low power factor conditions and the average error under high power factor conditions; the error drift rate represents the long-term trend of the error over time (aging rate). A sustained positive value indicates that the meter is gradually speeding up at a constant rate, which can be calculated by a linear regression function over time.

[0057] S302. Determine the root cause of the error in the smart meter using the key feature vector.

[0058] Specifically, determining the root cause of error in the smart meter through the key feature vector includes:

[0059] S3021. If the average error of electrical energy is greater than the preset value, then determine whether the error-temperature correlation coefficient and the error-temperature sensitivity slope are both greater than the corresponding preset coefficient and preset slope.

[0060] In this embodiment, the absolute value of the average energy error μΔE is first checked to see if it exceeds the significance threshold (a preset value θμ, which can be set according to actual needs, such as 0.5%). If it does not exceed the threshold, it indicates that the meter currently has no significant systematic error and may be in a healthy state or the error is random noise, requiring no further diagnosis. Only when a significant deviation exists will the error root cause analysis process begin.

[0061] This embodiment determines whether the error-temperature correlation coefficient and the error-temperature sensitivity slope are both greater than the corresponding preset coefficient and preset slope, that is, the judgment condition is (| |>θρ) AND (| The parameters |>θk) (e.g., θρ = 0.7, θk = 0.01 % / °C) are used to check whether the correlation between error and temperature ρT and the sensitivity kT are both significant. A high correlation indicates that the root cause of the fault is a temperature-sensitive component, and further analysis is needed to determine the root cause of the error.

[0062] S3022. If both are greater than the corresponding preset coefficient and preset slope, then the shunt fault error and the reference source fault error are determined according to the error-current correlation coefficient and the error-current change rate.

[0063] Specifically, determining the shunt fault error and the reference source fault error based on the error-current correlation coefficient and the error-current change rate includes: if the error-current change rate is greater than the corresponding target slope or the error-current correlation coefficient is greater than the corresponding target coefficient, then the root cause of the error is determined to be the shunt fault error; if the error-current change rate is less than or equal to the corresponding target slope and the error-current correlation coefficient is less than or equal to the corresponding target coefficient, then the root cause of the error is determined to be the reference source fault error.

[0064] After confirming temperature sensitivity, it is necessary to determine whether the error is also related to the load current I, i.e., the judgment condition is (| |>θρ) OR (| |>θS). It should be noted that the shunt resistor's resistance changes with temperature (temperature drift), and it is directly used for current sampling. The larger the current, the more severe the self-heating, and the more pronounced the temperature drift effect. This results in the error being strongly positively correlated with both temperature and load current. Since it is a pure gain error, it is independent of the power factor (PF). (Not significant). That is, when the error-current change rate is greater than the corresponding target slope or the error-current correlation coefficient is greater than the corresponding target coefficient, the root cause of the error is determined to be a shunt fault error. The internal reference voltage of the chip serves as the reference for all measurements. Its temperature drift will cause a global, proportional gain error in all measured values ​​such as voltage, current, and power. Therefore, the error is only strongly correlated with temperature and is independent of the load current magnitude and power factor. That is, when the error-current change rate is less than or equal to the corresponding target slope and the error-current correlation coefficient is less than or equal to the corresponding target coefficient, the root cause of the error is determined to be a reference source fault error.

[0065] S3023. If the error is less than or equal to the corresponding preset coefficient or preset slope, then the phase fault error and the aging fault error are determined based on the power factor sensitivity and the error drift rate.

[0066] Specifically, determining the phase fault error and the aging fault error based on the power factor sensitivity and the error drift rate includes: if the power factor sensitivity is greater than a preset sensitivity, then the root cause of the error is determined to be the phase fault error; if the power factor sensitivity is less than or equal to the preset sensitivity, and the error drift rate is greater than a preset drift rate, then the root cause of the error is determined to be the aging fault error.

[0067] In this embodiment, if the error is independent of temperature, it is checked whether it is sensitive to changes in the power factor. Defects in the input channel or filter cause a fixed phase shift between the voltage and current sampling signals. This error is almost zero when the power factor PF = 1; however, at low power factors (such as motor starting or appliance standby), this phase shift introduces a significant power calculation error. Therefore, the phase fault error is strongly correlated with the power factor but independent of temperature and load current.

[0068] If the error is independent of temperature and power factor, check for a long-term, unidirectional, and slow trend of change. Components such as resistors and capacitors will experience irreversible and slow changes in their parameters under long-term operation and thermal stress (e.g., oxidation, material degradation). This causes the gain of the metering circuit to change monotonically over time, resulting in aging fault errors that are strongly correlated with operating time but independent of instantaneous operating conditions (temperature, current, power factor).

[0069] For example, the values ​​of each parameter in the key feature vector are shown below:

[0070]

[0071] Perform the diagnostic procedure according to steps S021-S023:

[0072] The average error of +1.8% > 0.5%, indicating a significant deviation. ρT = 0.82 > 0.7 and kT = 0.04 > 0.01, showing a strong correlation between the error and temperature. ρI = 0.05 < 0.7 and SI = 0.0001 are very small, indicating that the error is independent of the load current. DPF = 0.1% < 0.3%, indicating that it is independent of the power factor. This yields a characteristic pattern consistent with reference source failure errors. The error is strongly correlated only with chip temperature and independent of other factors, indicating that a global reference drift has occurred.

[0073] S40. Calculate the corresponding target compensation value based on the shunt fault error, reference source fault error, phase fault error and / or aging fault error and their respective adjustment coefficients.

[0074] Specifically, in this embodiment, the shunt fault error, reference source fault error, phase fault error, and aging fault error can be calculated using the following formulas:

[0075]

[0076] in, This is the target compensation value corresponding to the shunt fault error. The temperature coefficient of the shunt resistor. The shunt temperature is measured in real time. Reference temperature (usually 25°C); The target compensation value corresponding to the fault error of the reference source. To calibrate at temperature The nominal value of the reference voltage is below. The first-order temperature drift coefficient of the reference voltage. The second-order temperature drift coefficient of the reference voltage. This represents the target compensation value corresponding to the phase fault error. This is the phase error compensation coefficient. For real-time power factor, This represents the target compensation value corresponding to the aging fault error. The aging rate constant is This represents the cumulative running time of the electricity meter.

[0077] in, The initial values ​​for each adjustment coefficient are custom-defined.

[0078] S50. The original measurement value of the smart meter is compensated according to the target compensation values ​​corresponding to the shunt fault error, reference source fault error, phase fault error and / or aging fault error in a preset order to obtain the corrected measurement value.

[0079] In one optional embodiment provided in this application, the step of compensating the original measurement value of the smart meter according to a preset order using the target compensation values ​​corresponding to the shunt fault error, the reference source fault error, the phase fault error, and / or the aging fault error to obtain a corrected measurement value includes: calculating the compensated voltage sampling value using the target compensation value corresponding to the reference source fault error; calculating the compensated current sampling value using the target compensation value corresponding to the shunt fault error; calculating the compensated accumulated energy value using the compensated voltage sampling value, the compensated current sampling value, and the target compensation value corresponding to the phase fault error; and calculating the corrected measurement value using the compensated accumulated energy value and the target compensation value corresponding to the aging fault error.

[0080] 1. The formula for calculating the compensated voltage sample value based on the target compensation value corresponding to the reference source fault error is as follows:

[0081]

[0082] in, The voltage sample value after compensation. These are the original sampled values ​​of the voltage channel ADC.

[0083] 2. The formula for calculating the compensated current sample value based on the target compensation value corresponding to the shunt fault error is as follows:

[0084]

[0085] in, The current sample value after compensation. The original sampled value of the current channel ADC

[0086] 3. The formula for calculating the cumulative electrical energy value after compensation using the compensated voltage sample value, the compensated current sample value, and the target compensation value corresponding to the phase fault error is as follows:

[0087]

[0088]

[0089] in, The instantaneous power value after compensation. The accumulated electrical energy value after compensation.

[0090] 4. The formula for calculating the corrected measurement value by combining the accumulated electrical energy value after compensation and the target compensation value corresponding to the aging fault error is as follows:

[0091]

[0092] in, These are the corrected measurements.

[0093] This application provides a smart meter correction method based on error root causes. First, time-series data of the smart meters corresponding to periods of abnormal electricity consumption are obtained from historical electricity consumption data. The time-series data of the abnormal electricity consumption periods and the current time period are input into an error probability prediction model to obtain the meter error probability value for the current time period and the error impact value of the time-series data of the abnormal electricity consumption periods on the current time period's time-series data. If the meter error probability value for the current time period is greater than a preset probability value and the error impact value is greater than a preset impact value, then the error root causes of the smart meter are determined based on the smart meter's time-series data. Error root causes include shunt fault error, reference source fault error, phase fault error, and / or aging fault error. Target compensation values ​​are calculated for each of the shunt fault error, reference source fault error, phase fault error, and / or aging fault error, along with their corresponding adjustment coefficients. The initial values ​​of the adjustment coefficients are user-defined. The original measured values ​​of the smart meter are compensated according to a preset order using the target compensation values ​​corresponding to the shunt fault error, reference source fault error, phase fault error, and / or aging fault error to obtain corrected measured values. Compared to existing technologies that use multiple calibration points for calibration, this application determines the root cause of error based on the acquired time series data of the smart meter. Then, it compensates the smart meter according to the target compensation value corresponding to the determined root cause of error to obtain the corrected measurement value. This process continues until the residual error calculated based on the corrected measurement value and the standard meter's energy value is less than or equal to a preset accuracy threshold. Thus, this application can guarantee the correction accuracy of the smart meter based on the root cause of error.

[0094] Further, such as Figure 2 As shown, after compensating the original measurement value of the smart meter to obtain the corrected measurement value, the method further includes:

[0095] S60. Calculate the residual error based on the corrected measurement value and the standard meter energy value.

[0096] S70. If the residual error is greater than the preset accuracy threshold, then update the adjustment coefficients corresponding to the shunt fault error, the reference source fault error, the phase fault error and / or the aging fault error respectively.

[0097] That is, update and adjust parameters

[0098] Then jump to the step of calculating the corresponding target compensation value by means of the shunt fault error, the reference source fault error, the phase fault error and / or the aging fault error and their respective adjustment coefficients, and continue to execute until the residual error is less than or equal to the preset accuracy threshold.

[0099] Specifically, updating the adjustment coefficients corresponding to the shunt fault error, the reference source fault error, the phase fault error, and / or the aging fault error respectively includes: updating the adjustment coefficients corresponding to the shunt fault error, the reference source fault error, the phase fault error, and / or the aging fault error respectively using a binary search method.

[0100] This application provides a smart meter correction method based on error root causes. First, time-series data of the smart meter is acquired, including operational and environmental data corresponding to multiple time points. Then, the error root causes of the smart meter are determined based on the time-series data. These error root causes include shunt fault error, reference source fault error, phase fault error, and / or aging fault error. Next, target compensation values ​​are calculated for each of the shunt fault error, reference source fault error, phase fault error, and / or aging fault error, along with their corresponding adjustment coefficients. The original measurement value of the smart meter is compensated using the target compensation values ​​to obtain a corrected measurement value. A residual error is calculated based on the corrected measurement value and the energy value of a standard meter. If the residual error is greater than a preset accuracy threshold, the adjustment coefficients corresponding to the shunt fault error, reference source fault error, phase fault error, and / or aging fault error are adjusted. The process then jumps to the step of calculating the corresponding target compensation values ​​for each of the shunt fault error, reference source fault error, phase fault error, and / or aging fault error, and continues until the residual error is less than or equal to the preset accuracy threshold. Instead of selecting multiple calibration points for calibration, this application determines the root cause of error based on the acquired time series data of the smart meter, and then compensates the smart meter according to the target compensation value corresponding to the determined root cause of error to obtain the corrected measurement value, until the residual error calculated based on the corrected measurement value and the standard meter energy value is less than or equal to the preset accuracy threshold. Thus, this application can guarantee the correction accuracy of the smart meter based on the root cause of error.

[0101] When dividing each function into modules according to its corresponding function. Figure 3 This diagram illustrates a possible composition of the smart meter correction system based on error root causes involved in the above and embodiment examples, such as... Figure 3 As shown, the smart meter correction system based on the root cause of the error may include:

[0102] The acquisition module 31 is used to acquire time series data of smart meters corresponding to the time periods of abnormal electricity consumption from historical electricity consumption data;

[0103] Prediction module 32 is used to input the time series data of the abnormal electricity consumption period and the time series data of the current period into the error probability prediction model to obtain the meter error probability value of the current period and the error impact value of the time series data of the abnormal electricity consumption period on the time series data of the current period.

[0104] The determination module 33 is used to determine the root cause of the smart meter's error based on the time series data of the smart meter if the meter error probability value in the current time period is greater than a preset probability value and the error impact value is greater than a preset impact value. The root cause of the error includes shunt fault error, reference source fault error, phase fault error and / or aging fault error.

[0105] Calculation module 34 is used to calculate the corresponding target compensation value based on the shunt fault error, the reference source fault error, the phase fault error, and / or the aging fault error and their respective adjustment coefficients; the initial value of the adjustment coefficient is set by the user.

[0106] The compensation module 35 is used to compensate the original measurement value of the smart meter according to a preset sequence by using the target compensation values ​​corresponding to the shunt fault error, the reference source fault error, the phase fault error and / or the aging fault error, respectively, to obtain the corrected measurement value.

[0107] In an optional embodiment provided by the present invention, the calculation module 34 is further configured to calculate the residual error based on the corrected measurement value and the standard meter energy value;

[0108] The update module 36 is used to update the adjustment coefficients corresponding to the shunt fault error, the reference source fault error, the phase fault error, and / or the aging fault error respectively if the residual error is greater than the preset accuracy threshold; and jump to the step of calculating the corresponding target compensation value by the shunt fault error, the reference source fault error, the phase fault error, and / or the aging fault error and their corresponding adjustment coefficients respectively to continue execution until the residual error is less than or equal to the preset accuracy threshold.

[0109] In an optional embodiment provided by the present invention, the determining module 33 is specifically used for:

[0110] Key feature vectors are extracted from the time series data of the smart meter. The key feature vectors include average energy error, error-temperature correlation coefficient, error-temperature sensitivity slope, error-current correlation coefficient, error-current change rate, power factor sensitivity, and error drift rate.

[0111] The root causes of errors in the smart meter are determined by the key feature vectors.

[0112] In an optional embodiment provided by the present invention, the determining module 33 is specifically used for:

[0113] If the average error of electrical energy is greater than the preset value, then determine whether the error-temperature correlation coefficient and the error-temperature sensitivity slope are both greater than the corresponding preset coefficient and preset slope.

[0114] If both are greater than the corresponding preset coefficient and preset slope, then the shunt fault error and the reference source fault error are determined according to the error-current correlation coefficient and the error-current change rate.

[0115] If the error is less than or equal to the corresponding preset coefficient or preset slope, then the phase fault error and the aging fault error are determined based on the power factor sensitivity and the error drift rate.

[0116] In an optional embodiment provided by the present invention, the determining module 33 is specifically used for:

[0117] If the error-current change rate is greater than the corresponding target slope or the error-current correlation coefficient is greater than the corresponding target coefficient, then the root cause of the error is determined to be the shunt fault error.

[0118] If the error-current change rate is less than or equal to the corresponding target slope and the error-current correlation coefficient is less than or equal to the corresponding target coefficient, then the root cause of the error is determined to be the reference source fault error.

[0119] In an optional embodiment provided by the present invention, the determining module 33 is specifically used for:

[0120] If the power factor sensitivity is greater than the preset sensitivity, then the root cause of the error is determined to be a phase fault error.

[0121] If the power factor sensitivity is less than or equal to the preset sensitivity, and the error drift rate is greater than the preset drift rate, then the root cause of the error is determined to be the aging fault error.

[0122] In an optional embodiment of the present invention, the compensation module 35 is specifically used for:

[0123] The compensated voltage sample value is calculated using the target compensation value corresponding to the fault error of the reference source.

[0124] The compensated current sampling value is calculated based on the target compensation value corresponding to the fault error of the shunt.

[0125] The cumulative electrical energy value after compensation is calculated using the compensated voltage sample value, the compensated current sample value, and the target compensation value corresponding to the phase fault error.

[0126] The corrected measurement value is calculated by using the accumulated electrical energy value after compensation and the target compensation value corresponding to the aging fault error.

[0127] In an optional embodiment of the present invention, the updating module 36 is specifically used for:

[0128] The adjustment coefficients corresponding to the shunt fault error, the reference source fault error, the phase fault error, and / or the aging fault error are updated respectively using the binary division method.

[0129] For specific system limitations, please refer to the limitations of the smart meter correction method based on error root causes mentioned above, which will not be repeated here. Each module in the above-mentioned device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of the computer device in hardware form or independent of it, or stored in the memory of the computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0130] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 4 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements a smart meter correction method based on the root cause of errors.

[0131] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps:

[0132] Obtain time-series data of smart meters corresponding to the periods of abnormal electricity consumption from historical electricity consumption data;

[0133] The time series data of the abnormal electricity consumption period and the time series data of the current period are input into the error probability prediction model to obtain the meter error probability value of the current period and the error impact value of the time series data of the abnormal electricity consumption period on the time series data of the current period.

[0134] If the meter error probability value in the current time period is greater than the preset probability value and the error impact value is greater than the preset impact value, then the root cause of the smart meter error is determined based on the time series data of the smart meter. The root cause of the error includes shunt fault error, reference source fault error, phase fault error and / or aging fault error.

[0135] The target compensation value is calculated based on the shunt fault error, the reference source fault error, the phase fault error, and / or the aging fault error, and their respective adjustment coefficients; the initial value of the adjustment coefficient is set by the user.

[0136] The original measurement value of the smart meter is compensated according to the target compensation values ​​corresponding to the shunt fault error, the reference source fault error, the phase fault error and / or the aging fault error in a preset order to obtain the corrected measurement value.

[0137] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0138] Obtain time-series data of smart meters corresponding to the periods of abnormal electricity consumption from historical electricity consumption data;

[0139] The time series data of the abnormal electricity consumption period and the time series data of the current period are input into the error probability prediction model to obtain the meter error probability value of the current period and the error impact value of the time series data of the abnormal electricity consumption period on the time series data of the current period.

[0140] If the meter error probability value in the current time period is greater than the preset probability value and the error impact value is greater than the preset impact value, then the root cause of the smart meter error is determined based on the time series data of the smart meter. The root cause of the error includes shunt fault error, reference source fault error, phase fault error and / or aging fault error.

[0141] The target compensation value is calculated based on the shunt fault error, the reference source fault error, the phase fault error, and / or the aging fault error, and their respective adjustment coefficients; the initial value of the adjustment coefficient is set by the user.

[0142] The original measurement value of the smart meter is compensated according to the target compensation values ​​corresponding to the shunt fault error, the reference source fault error, the phase fault error and / or the aging fault error in a preset order to obtain the corrected measurement value.

[0143] In one embodiment, a computer program product is provided, the computer program product comprising a computer program that is executed by a processor to perform the following steps:

[0144] Obtain time-series data of smart meters corresponding to the periods of abnormal electricity consumption from historical electricity consumption data;

[0145] The time series data of the abnormal electricity consumption period and the time series data of the current period are input into the error probability prediction model to obtain the meter error probability value of the current period and the error impact value of the time series data of the abnormal electricity consumption period on the time series data of the current period.

[0146] If the meter error probability value in the current time period is greater than the preset probability value and the error impact value is greater than the preset impact value, then the root cause of the smart meter error is determined based on the time series data of the smart meter. The root cause of the error includes shunt fault error, reference source fault error, phase fault error and / or aging fault error.

[0147] The target compensation value is calculated based on the shunt fault error, the reference source fault error, the phase fault error, and / or the aging fault error, and their respective adjustment coefficients; the initial value of the adjustment coefficient is set by the user.

[0148] The original measurement value of the smart meter is compensated according to the target compensation values ​​corresponding to the shunt fault error, the reference source fault error, the phase fault error and / or the aging fault error in a preset order to obtain the corrected measurement value.

[0149] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0150] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above.

[0151] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for correcting smart meters based on the root causes of errors, characterized in that, This method is applied to a processor in a smart meter correction system based on the root cause of error. The system includes: the processor, a data acquisition device, and an environmental data acquisition device. The method includes: Obtain time-series data of smart meters corresponding to the periods of abnormal electricity consumption from historical electricity consumption data; The time series data of the abnormal electricity consumption period and the time series data of the current period are input into the error probability prediction model to obtain the meter error probability value of the current period and the error impact value of the time series data of the abnormal electricity consumption period on the time series data of the current period. If the meter error probability value in the current time period is greater than the preset probability value and the error impact value is greater than the preset impact value, then the root cause of the smart meter error is determined based on the time series data of the smart meter. The root cause of the error includes shunt fault error, reference source fault error, phase fault error and / or aging fault error. The target compensation value is calculated based on the shunt fault error, the reference source fault error, the phase fault error, and / or the aging fault error, and their respective adjustment coefficients; the initial value of the adjustment coefficient is set by the user. The original measurement value of the smart meter is compensated according to the target compensation values ​​corresponding to the shunt fault error, the reference source fault error, the phase fault error and / or the aging fault error in a preset order to obtain the corrected measurement value.

2. The method according to claim 1, characterized in that, After compensating the original measurement value of the smart meter to obtain the corrected measurement value, the method further includes: The residual error is calculated based on the corrected measured value and the standard meter energy value. If the residual error is greater than the preset accuracy threshold, then update the adjustment coefficients corresponding to the shunt fault error, the reference source fault error, the phase fault error, and / or the aging fault error respectively; and jump to the step of calculating the corresponding target compensation value by the shunt fault error, the reference source fault error, the phase fault error, and / or the aging fault error and their respective adjustment coefficients to continue execution until the residual error is less than or equal to the preset accuracy threshold.

3. The method according to claim 1, characterized in that, The step of determining the root cause of error in the smart meter based on its time-series data includes: Key feature vectors are extracted from the time series data of the smart meter. The key feature vectors include average energy error, error-temperature correlation coefficient, error-temperature sensitivity slope, error-current correlation coefficient, error-current change rate, power factor sensitivity, and error drift rate. The root causes of errors in the smart meter are determined by the key feature vectors.

4. The method according to claim 3, characterized in that, The process of determining the root cause of error in the smart meter using the key feature vector includes: If the average error of electrical energy is greater than the preset value, then determine whether the error-temperature correlation coefficient and the error-temperature sensitivity slope are both greater than the corresponding preset coefficient and preset slope. If both are greater than the corresponding preset coefficient and preset slope, then the shunt fault error and the reference source fault error are determined according to the error-current correlation coefficient and the error-current change rate. If the error is less than or equal to the corresponding preset coefficient or preset slope, then the phase fault error and the aging fault error are determined based on the power factor sensitivity and the error drift rate.

5. The method according to claim 4, characterized in that, The determination of the shunt fault error and the reference source fault error based on the error-current correlation coefficient and the error-current change rate includes: If the error-current change rate is greater than the corresponding target slope or the error-current correlation coefficient is greater than the corresponding target coefficient, then the root cause of the error is determined to be the shunt fault error. If the error-current change rate is less than or equal to the corresponding target slope and the error-current correlation coefficient is less than or equal to the corresponding target coefficient, then the root cause of the error is determined to be the reference source fault error.

6. The method according to claim 4, characterized in that, The determination of the phase fault error and the aging fault error based on the power factor sensitivity and the error drift rate includes: If the power factor sensitivity is greater than the preset sensitivity, then the root cause of the error is determined to be a phase fault error. If the power factor sensitivity is less than or equal to the preset sensitivity, and the error drift rate is greater than the preset drift rate, then the root cause of the error is determined to be the aging fault error.

7. The method according to claim 1, characterized in that, The process of compensating the original measurement value of the smart meter according to a preset sequence using target compensation values ​​corresponding to the fault error of the shunt, the fault error of the reference source, the phase fault error, and / or the aging fault error to obtain a corrected measurement value includes: The compensated voltage sample value is calculated using the target compensation value corresponding to the fault error of the reference source. The compensated current sampling value is calculated based on the target compensation value corresponding to the fault error of the shunt. The cumulative electrical energy value after compensation is calculated using the compensated voltage sample value, the compensated current sample value, and the target compensation value corresponding to the phase fault error. The corrected measurement value is calculated by using the accumulated electrical energy value after compensation and the target compensation value corresponding to the aging fault error.

8. The method according to claim 2, characterized in that, The updating of the adjustment coefficients corresponding to the shunt fault error, the reference source fault error, the phase fault error, and / or the aging fault error includes: The adjustment coefficients corresponding to the shunt fault error, the reference source fault error, the phase fault error, and / or the aging fault error are updated respectively using the binary division method.

9. A smart meter correction system based on the root cause of error, characterized in that, The system includes: The acquisition module is used to obtain time series data of smart meters corresponding to the time periods of abnormal electricity consumption from historical electricity consumption data; The prediction module is used to input the time series data of the abnormal electricity consumption period and the time series data of the current period into the error probability prediction model to obtain the meter error probability value of the current period and the error impact value of the time series data of the abnormal electricity consumption period on the time series data of the current period. The determination module is used to determine the root cause of the smart meter's error based on the smart meter's time series data if the meter's error probability value for the current time period is greater than a preset probability value and the error impact value is greater than a preset impact value. The root cause of the error includes shunt fault error, reference source fault error, phase fault error, and / or aging fault error. The calculation module is used to calculate the corresponding target compensation value based on the shunt fault error, the reference source fault error, the phase fault error, and / or the aging fault error and their respective adjustment coefficients; the initial value of the adjustment coefficient is set by the user. The compensation module is used to compensate the original measurement value of the smart meter according to a preset sequence using the target compensation values ​​corresponding to the shunt fault error, the reference source fault error, the phase fault error, and / or the aging fault error, respectively, to obtain the corrected measurement value.

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