A method and system for monitoring operation errors of smart electric meters

Abstract

The present invention provides a method and system for monitoring the operation error of a smart meter, and relates to the technical field of monitoring the operation error of a smart meter. The method comprises: generating an adjustment factor according to the operation data, the use environment and the model of the smart meter; calculating the real-time operation error of the smart meter according to the operation data and the adjustment factor; comparing the real-time operation error with a preset error threshold, and triggering an error exceeding standard warning when the real-time operation error exceeds the error threshold; recording and storing the identification, exceeding standard time, exceeding standard error value and related operation data of the smart meter that triggers the error exceeding standard warning; and performing fault diagnosis on the smart meter that triggers the error exceeding standard warning to obtain a fault diagnosis result. The present invention can more accurately calculate the real-time operation error of the meter, thereby improving the accuracy of data processing and the credibility of meter readings.

Description

Technical Field

[0001] The present invention relates to the technical field of smart electric meters, and in particular to a method and system for monitoring operation errors of smart electric meters. Background Art

[0002] In traditional meter monitoring methods, the accuracy of meters is usually ensured through regular calibration and manual inspection. However, this method has problems such as low efficiency and poor real-time performance, and it is difficult to detect and correct errors in meter operation in a timely manner. In order to solve this problem, some smart meter operation error monitoring methods have emerged in recent years. These methods collect meter operation data and use data analysis technology to monitor the operation status of meters.

[0003] However, the existing smart meter error monitoring methods still have some defects in data processing:

[0004] Some monitoring methods may only focus on the meter’s electricity data, while ignoring key parameters such as current, voltage, and power, which are essential for fully evaluating the meter’s operating status and calculating operating errors.

[0005] The environment and model of the meter will affect its operating status. Existing monitoring methods may not fully consider these factors, resulting in low accuracy of error calculation. There is a lack of a mechanism that can dynamically adjust according to the environment and model of the meter.

[0006] The data processing process of some monitoring methods is relatively complicated and cannot achieve real-time monitoring and feedback. In the operation of the power system, it is crucial to detect and deal with meter errors in a timely manner. Therefore, the real-time performance of the monitoring method needs to be improved. Summary of the invention

[0007] The technical problem to be solved by the present invention is to provide a method and system for monitoring the operation error of a smart meter, which can more accurately calculate the real-time operation error of the meter, thereby improving the accuracy of data processing and the credibility of the meter readings.

[0008] In order to solve the above technical problems, the technical solution of the present invention is as follows:

[0009] In a first aspect, a method for monitoring operation errors of a smart meter is provided, the method comprising:

[0010] Periodically collect the operation data of smart meters, including current value, voltage value, power value and power data;

[0011] Generate an adjustment factor based on the operation data, usage environment and meter model of the smart meter;

[0012] Calculate the real-time operation error of the smart meter based on the operation data and adjustment factors;

[0013] Compare the real-time operation error with the preset error threshold, and when the real-time operation error exceeds the error threshold, trigger an error exceeding threshold warning;

[0014] Record and store the identification, exceeding time, exceeding error value and related operation data of the smart meter that triggers the exceeding error warning;

[0015] Perform fault diagnosis on the smart meter that triggers the error exceeding limit warning to obtain the fault diagnosis result;

[0016] According to the fault diagnosis results, the smart meter is repaired or replaced.

[0017] Furthermore, an adjustment factor is generated according to the operation data, usage environment and meter model of the smart meter, including:

[0018] According to the operation data, usage environment and meter model of the smart meter, Calculate adjustment factors;

[0019] Where AF represents the adjustment factor; a1 and a2 represent the weight coefficients; I c and V c Respectively represent the current effective value of current and voltage; I h and V h Respectively represent the historical current and voltage effective values; b1 and b2 represent weight coefficients; T d Indicates the difference between the current temperature and the standard temperature; T s Indicates standard temperature; H d Indicates the difference between the current humidity and the standard humidity; H s represents standard humidity; c1 and c2 represent weight coefficients; P Fc Indicates the current power factor; P Fh represents the historical power factor; M represents the parameter used to adjust the measurement error of different models of smart meters; d represents the weight coefficient; H Dc Indicates the current harmonic distortion; H Dh Indicates historical harmonic distortion.

[0020] Furthermore, the real-time operation error of the smart meter is calculated based on the operation data and the adjustment factor, including:

[0021] According to the operating data and adjustment factors, Calculate the real-time operation error R of the smart meter;

[0022] Among them, α, β, γ and δ represent weight coefficients; P c and Ph Respectively represent the current power and historical power values; E c and E h Respectively represent the current power and historical power values.

[0023] Furthermore, a fault diagnosis is performed on the smart meter that triggers the error exceeding limit warning to obtain a fault diagnosis result, including:

[0024] Determine the parameter space for fault diagnosis, including fault type and fault location;

[0025] Initialize a particle swarm, where each particle in the particle swarm represents a fault scenario, including a parameter combination of a current channel fault, a voltage channel fault, and a metering circuit fault;

[0026] Construct a fitness function to evaluate how well each failure scenario matches the actual situation;

[0027] Through iteration, the velocity and position of each particle are updated to obtain the final failure solution;

[0028] According to the final fault plan, the specific fault type is determined, including current channel fault, voltage channel fault and metering circuit fault.

[0029] Furthermore, the calculation formula of the fitness function is:

[0030]

[0031] Among them, α, β and γ are weight coefficients; It is the sum of the absolute errors between the actual measured data and the simulated data; represents the sum of error terms associated with the fault scenario x; represents the sum of the diagnostic information differences associated with fault scenario x; i is the variable used to index the actual measured data points, N is the total number of actual measured data points; j is the variable used to index the error terms associated with fault scenario x; K is the total number of error terms; k is the variable used to index the diagnostic information differences associated with fault scenario x, L is the total number of diagnostic information differences.

[0032] Furthermore, the speed and position of each particle are updated, where the speed update formula is:

[0033]

[0034] in, represents the velocity of particle i at the t+1th iteration in dimension d; w(t) is the inertia weight that changes dynamically with time t; represents the velocity of particle i at the tth iteration in dimension d; c1(t) represents the learning factor that changes with time t; r1 represents a random number in the range [0,1]; p id represents the final position of particle i in dimension d; represents the position of particle i at the tth iteration on dimension d; c2(t) represents the learning factor that changes with time t; r2 represents a random number in the range [0,1]; g d represents the global final position of the entire particle swarm in dimension d; c3(t) represents the learning factor that changes with time t; r3 represents a random number in the range [0,1]; l d represents the final position of particle i in the neighborhood; ∈ represents the random perturbation term;

[0035] in, w max Indicates the maximum value of inertia weight; w min Represents the minimum value of the inertia weight; t represents the current number of iterations; T represents the total number of iterations.

[0036] Furthermore, the speed and position of each particle are updated, where the update formula of the position is:

[0037]

[0038] Among them, λ is the parameter that controls the slope of the S-shaped curve, and the sigmoid function is used to map the speed into the interval; Represents the position of particle i at the t+1th iteration in dimension d.

[0039] In a second aspect, a smart meter operation error monitoring device includes:

[0040] The acquisition module is used to periodically collect the operation data of the smart meter, including current value, voltage value, power value and power data;

[0041] A generating module, used for generating an adjustment factor according to the operation data, the use environment and the model of the smart meter;

[0042] A calculation module, used to calculate the real-time operation error of the smart meter according to the operation data and the adjustment factor;

[0043] A comparison module is used to compare the real-time operation error with a preset error threshold, and when the real-time operation error exceeds the error threshold, an error exceeding threshold warning is triggered;

[0044] A recording module, used to record and store the identification of the smart meter that triggers the error exceeding limit warning, the exceeding limit time, the exceeding limit error value and related operation data;

[0045] A diagnosis module, used for performing fault diagnosis on the smart meter that triggers the error exceeding standard warning to obtain a fault diagnosis result;

[0046] The processing module is used to repair or replace the smart meter according to the fault diagnosis result.

[0047] According to a third aspect, a computing device includes:

[0048] one or more processors;

[0049] The storage device is used to store one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the above method.

[0050] In a fourth aspect, a computer-readable storage medium stores a program, and the program implements the above method when executed by a processor.

[0051] The above solution of the present invention includes at least the following beneficial effects:

[0052] By periodically collecting the operation data of the smart meter (such as current value, voltage value, power value and electricity data), this method can monitor the operation status of the meter in real time. Combined with the adjustment factor, the real-time operation error of the meter can be calculated more accurately, thereby improving the accuracy of data processing and the credibility of the meter reading.

[0053] By comparing the real-time operating error with the preset error threshold, the system can immediately trigger an alarm once the error exceeds the limit. This instant feedback mechanism helps staff to respond quickly and handle abnormal situations, reducing economic losses or resource waste caused by meter errors.

[0054] This method can record and store the information of the electric meter that triggers the error exceeding the standard warning, including the exceeding time, the exceeding error value and the related operation data. By diagnosing the fault of the electric meter that triggers the warning, the problem can be located more accurately, and then the electric meter can be repaired or replaced in a targeted manner according to the diagnosis results. This intelligent processing method not only improves work efficiency, but also reduces maintenance costs. The entire monitoring process involves a large amount of data collection, analysis and processing. Through the automated data processing process, this method can efficiently manage this data, ensure the accuracy and consistency of the data, and thus improve the overall efficiency and reliability of data processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is a flow chart of a method for monitoring operation errors of a smart meter provided in an embodiment of the present invention.

[0056] Figure 2It is a schematic diagram of a smart meter operation error monitoring device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0057] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0058] like Figure 1 As shown, an embodiment of the present invention provides a method for monitoring operation errors of a smart meter, the method comprising:

[0059] Step 11, periodically collect the operation data of the smart meter, including current value, voltage value, power value and power data;

[0060] Step 12, generating an adjustment factor according to the operation data, usage environment and meter model of the smart meter;

[0061] Step 13, calculating the real-time operation error of the smart meter according to the operation data and the adjustment factor;

[0062] Step 14, comparing the real-time operation error with a preset error threshold, and when the real-time operation error exceeds the error threshold, triggering an error exceeding threshold warning;

[0063] Step 15, recording and storing the identification, exceeding time, exceeding error value and related operation data of the smart meter that triggers the error exceeding warning;

[0064] Step 16, performing fault diagnosis on the smart meter that triggers the error exceeding limit warning to obtain a fault diagnosis result;

[0065] Step 17: repair or replace the smart meter according to the fault diagnosis result.

[0066] In an embodiment of the present invention, by periodically collecting the operation data (such as current value, voltage value, power value and electricity data) of the smart meter, the method can monitor the operation status of the meter in real time. Combined with the adjustment factor, the real-time operation error of the meter can be calculated more accurately, thereby improving the accuracy of data processing and the credibility of the meter reading. By comparing the real-time operation error with the preset error threshold, once the error exceeds the standard, the system can immediately trigger an alarm. This instant feedback mechanism helps the staff to respond to and handle abnormal situations quickly, reducing economic losses or resource waste caused by meter errors. The method can record and store the meter information that triggers the error exceeding the standard alarm, including the exceeding time, the exceeding error value and related operation data. By performing fault diagnosis on the meter that triggers the alarm, the problem can be more accurately located, and then the meter can be repaired or replaced in a targeted manner according to the diagnosis results. This intelligent processing method not only improves work efficiency, but also reduces maintenance costs. The entire monitoring process involves a large amount of data collection, analysis and processing. Through the automated data processing process, the method can efficiently manage these data, ensure the accuracy and consistency of the data, thereby improving the overall efficiency and reliability of data processing.

[0067] In a preferred embodiment of the present invention, the above step 11 may include:

[0068] Periodic collection means that data is not collected randomly or sporadically, but at certain time intervals, such as every hour, every day, or every week. This periodic collection ensures the continuity and comparability of data, and helps to analyze the operating status of the meter in different time periods. The operating data includes current value, voltage value, power value and power data.

[0069] The current value indicates the magnitude of the current passing through the meter, in amperes (A), which reflects the load of the meter. The voltage value indicates the voltage across the meter, in volts (V). The power value indicates the instantaneous power measured by the meter, in watts (W) or kilowatts (kW), which reflects the conversion rate of electrical energy. The power data indicates the total amount of electrical energy consumed over a period of time, in kilowatt-hours (kWh). Smart meters can record and transmit this data in real time, making remote monitoring and data analysis possible. This data can not only be used to monitor the operating status of the meter, but also help power companies better understand users' electricity usage behaviors and habits, thereby optimizing power supply and electricity billing.

[0070] Specific examples:

[0071] Assume that a city power company decides to periodically collect and analyze data from smart meters under its jurisdiction in order to improve service quality and efficiency. It sets a schedule to automatically collect operating data from all smart meters at 3 a.m. every day.

[0072] During the one-day data collection cycle, the system automatically obtained the following data from each meter:

[0073] The current value, for example, the current value of a household at 3 a.m. is 5.6A, which indicates that the household's electrical appliances are consuming the corresponding current at this time.

[0074] Voltage value: In this same household, the voltage value displayed on the electric meter is 230V, which is a standard household electricity voltage value, indicating that the voltage is stable.

[0075] The power value of this household at this time is 1280W, which means that these electrical devices are running at this power.

[0076] Electricity data: From the last data collection to this data collection period, the household consumed a total of 10.5 kWh of electricity.

[0077] By analyzing this data, power companies can understand users' peak electricity consumption hours and electricity consumption habits, and provide users with more personalized services, such as time-of-use electricity prices and electricity usage recommendations. At the same time, this data also helps power companies predict electricity demand and optimize grid operation. In addition, if the data of a meter is abnormal (such as excessive fluctuations in current or voltage), the system can detect it in time and issue an alarm so that staff can conduct maintenance to ensure the accurate operation of the meter and the safety of users' electricity use.

[0078] In a preferred embodiment of the present invention, the above step 12, generating an adjustment factor according to the operation data, usage environment and meter model of the smart meter, may include:

[0079] Step 121, according to the operation data, usage environment and meter model of the smart meter, Calculate adjustment factors;

[0080] Where AF represents the adjustment factor; a1 and a2 represent the weight coefficients; I c and V c Respectively represent the current effective value of current and voltage; I h and V h Respectively represent the historical current and voltage effective values; b1 and b2 represent weight coefficients; T d Indicates the difference between the current temperature and the standard temperature; T s Indicates standard temperature; H d Indicates the difference between the current humidity and the standard humidity; H s represents standard humidity; c1 and c2 represent weight coefficients; P Fc Indicates the current power factor; P Fhrepresents the historical power factor; M represents the parameter used to adjust the measurement error of different models of smart meters; d represents the weight coefficient; H Dc Indicates the current harmonic distortion; H Dh Indicates historical harmonic distortion.

[0081] In an embodiment of the present invention, the calculation of the adjustment factor takes into account a variety of factors, including current, voltage, temperature, humidity, power factor, meter model, and harmonic distortion, all of which may affect the operation error of the meter. By comprehensively considering these factors, a more accurate error evaluation result can be obtained. Since the adjustment factor is calculated based on real-time operation data and environmental conditions, it can dynamically reflect the error changes of the meter under different conditions. This dynamic adjustment helps to timely discover and deal with potential error problems and improve the measurement accuracy of the meter. The method is applicable to smart meters of different models, and the measurement errors of meters of different models are adjusted by parameter M. This makes the method more widely applicable and can meet the error monitoring requirements of different meter models. Through automated data collection and processing procedures, the method can efficiently calculate the adjustment factor and evaluate the operation error. This helps to reduce manual intervention and improve the efficiency and accuracy of data processing. Through continuous monitoring and analysis of operation data and adjustment factors, the changing trend of meter performance can be found, providing strong support for preventive maintenance. This helps to extend the service life of the meter and reduce maintenance costs.

[0082] In a preferred embodiment of the present invention, the above step 13, calculating the real-time operation error of the smart meter according to the operation data and the adjustment factor, includes:

[0083] Step 131, according to the operation data and adjustment factors, Calculate the real-time operation error R of the smart meter;

[0084] Among them, α, β, γ and δ represent weight coefficients; P c and P h Respectively represent the current power and historical power values; E c and E h Respectively represent the current power and historical power values.

[0085] In an embodiment of the present invention, the calculation of the real-time operation error takes into account a variety of factors, including current, voltage, power, electricity, and adjustment factors, all of which may affect the operation error of the electric meter. By comprehensively considering these factors, a more accurate error evaluation result can be obtained. Since the adjustment factor is calculated based on real-time operation data and environmental conditions, it can dynamically reflect the error changes of the electric meter under different conditions. This dynamic adjustment helps to timely discover and deal with potential error problems and improve the measurement accuracy of the electric meter. Through automated data collection and processing processes, the method can efficiently calculate the real-time operation error. This helps to reduce manual intervention and improve the efficiency and accuracy of data processing. Through continuous monitoring and analysis of real-time operation errors, the changing trend of the performance of the electric meter can be discovered, providing strong support for preventive maintenance. This helps to extend the service life of the electric meter and reduce maintenance costs. By accurately evaluating the real-time operation error of the electric meter, potential error problems can be discovered and corrected in a timely manner, thereby enhancing the reliability and accuracy of the electric meter. This helps to improve the overall efficiency and stability of the power system.

[0086] Step 14, in this step, the system compares the real-time calculated operating error with the preset error threshold. This threshold is a standard used to determine whether the operating error of the meter is within an acceptable range. If the real-time operating error exceeds this threshold, the system will trigger an error exceeding standard warning. The real-time operating error is calculated by step 13, which reflects the error of the meter under the current operating conditions. The error threshold is a preset value, which is set according to the specifications, accuracy requirements and operating environment of the meter, and it represents the maximum allowable value of the meter's operating error.

[0087] Step 15, when the system triggers the error exceeding the limit warning, step 15 will be automatically executed. The purpose of this step is to record and store all important information related to the error exceeding the limit for subsequent analysis and processing. The identification of the smart meter is the unique identification code of the meter, which is used to distinguish different meters; the exceeding time refers to the specific time point when the real-time operation error exceeds the threshold; the exceeding error value refers to the specific value of the real-time operation error exceeding the threshold; the relevant operation data includes the current, voltage, power and electricity of the meter when the error exceeds the limit.

[0088] Specific examples:

[0089] Assume that a city power company monitors the operation error of smart meters under its jurisdiction. At a certain time of the day, the system finds that the real-time operation error of a smart meter exceeds the preset error threshold.

[0090] Step 14: The system compares the real-time operation error of the electric meter (assuming 1.5%) with a preset error threshold (assuming 1%) and finds that the real-time operation error exceeds the threshold.

[0091] Step 15: The system immediately triggers an error exceeding limit warning and records the following information:

[0092] Smart meter ID: Assume it is "Meter123".

[0093] Exceeding time: Assume it is "2023-04-01 14:00:00".

[0094] Exceeding standard error value: 1.5%.

[0095] Relevant operating data: at the time of exceeding the standard, the current of the electric meter was 10A, the voltage was 230V, the power was 2300W, and the electricity was 100kWh.

[0096] The power company's staff can then conduct further fault diagnosis and repair of the meter based on the recorded information.

[0097] In a preferred embodiment of the present invention, the above step 16, performing fault diagnosis on the smart meter that triggers the error exceeding limit warning to obtain a fault diagnosis result, includes:

[0098] Step 161, determining the parameter space of fault diagnosis, including the fault type and the fault location;

[0099] Step 162, initializing a particle swarm, wherein each particle in the particle swarm represents a fault scenario, including a parameter combination of a current channel fault, a voltage channel fault, and a metering circuit fault;

[0100] Step 163, constructing a fitness function for evaluating the degree of matching between each fault scenario and the actual situation;

[0101] Step 164, updating the speed and position of each particle through iteration to obtain a final fault solution;

[0102] Step 165, according to the final fault solution, determine the specific fault type, including current channel fault, voltage channel fault and metering circuit fault.

[0103] In the embodiment of the present invention, by determining the parameter space of fault diagnosis, including the fault type and the fault location, the specific fault point of the electric meter can be accurately located, thereby improving the efficiency of fault handling. The particle swarm is initialized, and each particle represents a fault scheme, which can cover a variety of possible fault conditions such as current channel fault, voltage channel fault and metering circuit fault, providing a comprehensive solution for fault diagnosis. By constructing a fitness function to evaluate the degree of match between each fault scheme and the actual situation, and by iteratively updating the speed and position of each particle, the final fault scheme can be obtained, thereby improving the accuracy of fault diagnosis. According to the final fault scheme, the specific fault type can be determined, including current channel fault, voltage channel fault and metering circuit fault, etc., which provides a basis for quickly repairing the electric meter and helps to restore the normal operation of the electric meter as soon as possible. By accurately diagnosing and repairing the smart meter in a timely manner, the impact of the meter failure on the power system can be reduced, which helps to improve the overall stability of the power system.

[0104] In a preferred embodiment of the present invention, the calculation formula of the fitness function is:

[0105]

[0106] Among them, α, β and γ are weight coefficients; It is the sum of the absolute errors between the actual measured data and the simulated data; represents the sum of error terms associated with the fault scenario x; represents the sum of the diagnostic information differences associated with fault scenario x; i is the variable used to index the actual measured data points, N is the total number of actual measured data points; j is the variable used to index the error terms associated with fault scenario x; K is the total number of error terms; k is the variable used to index the diagnostic information differences associated with fault scenario x, L is the total number of diagnostic information differences.

[0107] In an embodiment of the present invention, the fitness function comprehensively considers the absolute error between the actual measurement data and the simulation data, the error term related to the fault scheme, and the difference in diagnostic information related to the fault scheme. This comprehensive consideration method helps to more comprehensively evaluate the degree of match between each fault scheme and the actual situation. Through the weight coefficients α, β and γ, the proportion of each part in the fitness function can be flexibly adjusted, so as to adjust the focus of fault diagnosis according to actual needs. For example, if more attention is paid to the degree of match between the actual measurement data and the simulation data, the value of α can be appropriately increased. By minimizing the fitness function, the degree of match between the fault scheme and the actual situation can be maximized, thereby improving the accuracy of fault diagnosis. This method helps to reduce the possibility of misdiagnosis and missed diagnosis. The design of the fitness function takes into account a variety of possible sources of error, including measurement error, model error and diagnostic information difference. This design enables the algorithm to show good robustness when facing different types of errors. Through a reasonable fitness function design, unnecessary calculations can be reduced and the calculation efficiency of the algorithm can be improved.

[0108] In a preferred embodiment of the present invention, the speed and position of each particle are updated, wherein the speed update formula is:

[0109]

[0110] in, represents the velocity of particle i at the t+1th iteration in dimension d; w(t) is the inertia weight that changes dynamically with time t; represents the velocity of particle i at the tth iteration in dimension d; c1(t) represents the learning factor that changes with time t; r1 represents a random number in the range [0,1]; p id represents the final position of particle i in dimension d; represents the position of particle i at the tth iteration on dimension d; c2(t) represents the learning factor that changes with time t; r2 represents a random number in the range [0,1]; g d represents the global final position of the entire particle swarm in dimension d; c3(t) represents the learning factor that changes with time t; r3 represents a random number in the range [0,1]; l d represents the final position of particle i in the neighborhood; ∈ represents the random perturbation term;

[0111] in, w max Indicates the maximum value of inertia weight; w min Represents the minimum value of the inertia weight; t represents the current number of iterations; T represents the total number of iterations.

[0112] In an embodiment of the present invention, the inertia weight w(t) decreases linearly with the increase of the number of iterations t. This strategy enables the algorithm to have a stronger global search capability in the early stage (a larger inertia weight is conducive to jumping out of the local optimum), and to focus more on local search in the later stage (a smaller inertia weight is conducive to fine search), thereby increasing the probability of finding the global optimal solution. By adjusting the learning factors c1(t), c2(t) and c3(t), the ability of particles between global search and local search can be balanced, which helps the algorithm to quickly locate the approximate area of ​​the global optimal solution during the search process, and to perform fine search within the area to find a more accurate optimal solution. The random perturbation term ∈ in the formula helps to increase the diversity of the algorithm and prevent the algorithm from falling into the local optimal solution too early. By introducing a certain degree of randomness, the algorithm has the opportunity to jump out of the current local optimal solution and continue to search for other possible better solutions. By introducing the final position l of the neighborhood d and the corresponding learning factor c3(t), the algorithm takes into account the information in the neighborhood when updating the particle velocity. This helps particles share information and cooperate with each other, improving the search efficiency of the entire particle swarm. Through the above strategy, the algorithm can find the fault solution that best matches the actual situation more quickly and accurately, thereby improving the efficiency of smart meter fault diagnosis.

[0113] In a preferred embodiment of the present invention, the speed and position of each particle are updated, wherein the updating formula of the position is:

[0114]

[0115] Among them, λ is the parameter that controls the slope of the S-shaped curve, and the sigmoid function is used to map the speed into the interval; Represents the position of particle i at the t+1th iteration in dimension d.

[0116] In an embodiment of the present invention, by introducing the sigmoid function, the velocity of the particle can be mapped to the interval (0, 1), thereby controlling the amplitude of the position update. This helps prevent the particle from making too large a jump during the search process, causing the algorithm to be unstable or fall into a local optimal solution. By adjusting the parameter λ, the slope of the S-shaped curve can be controlled, thereby affecting the sensitivity of the position update. When λ is large, the sigmoid function is close to the step function, and the particle position update is faster; when λ is small, the sigmoid function is close to the linear function, and the particle position update is more stable. This flexibility helps to improve the search efficiency of the algorithm. The introduction of the sigmoid function also enhances the robustness of the algorithm. Since the sigmoid function maps the velocity to the interval (0, 1), even if the velocity of some particles has an abnormal value, it will not have too much impact on the position update, thereby ensuring the stability of the algorithm. By using the sigmoid function, it can be ensured that the particle position update is always carried out within a reasonable range. This avoids the situation where the particle jumps out of the search space or enters the non-feasible solution area during the search process, and improves the feasibility of the algorithm. Through the above strategies, the algorithm can control the position update of particles more precisely, so as to more accurately find the fault solution that best matches the actual situation.

[0117] When specifically applied, this includes:

[0118] Determine the parameter space for fault diagnosis. This step mainly determines the type and location of the fault. In the fault diagnosis of smart meters, it includes current channel fault, voltage channel fault and metering circuit fault. Each fault type also contains multiple specific fault points or fault causes.

[0119] Initialize the particle swarm. Each "particle" in the particle swarm optimization algorithm represents a potential solution. Here, each particle represents a possible fault scenario, including different types of faults and their parameter combinations. For example, a particle may represent a fault scenario such as "the current channel is short-circuited, the voltage channel is normal, and the metering circuit has a deviation."

[0120] A fitness function is constructed to evaluate the degree of match between each fault scenario and the actual fault condition. The function comprehensively considers the error between the actual measured data and the simulated data, the error term related to the fault scenario, and the difference in diagnostic information related to the fault scenario. By adjusting the weight coefficients α, β, and γ, the importance of different error terms can be balanced.

[0121] Iteratively update particle speed and position. In each iteration, the speed of each particle is adjusted according to the speed update formula, which takes into account the influence of the particle's current speed, individual optimal position, global optimal position, and neighborhood optimal position, as well as a random disturbance term.

[0122] Next, the position update formula is used to update the position of the particle. Here, the sigmoid function is used to map the velocity to a reasonable interval to ensure the stability and rationality of the position update. After multiple iterations, the particle swarm will converge to an optimal solution, that is, the fault solution that best matches the actual fault situation. Based on this final solution, the specific fault type can be determined, such as current channel fault, voltage channel fault, or metering circuit fault.

[0123] Specific examples:

[0124] Suppose a smart meter has an error that exceeds the standard and fault diagnosis is required.

[0125] The possible fault types are determined to be current channel fault, voltage channel fault, and metering circuit fault. Assume that a particle swarm containing 10 particles is initialized, and each particle represents a possible fault combination. By collecting the actual measurement data and simulation data of the electric meter, a fitness function is constructed to evaluate the fault scheme. For example, α=0.5, β=0.3, and γ=0.2 are set to balance the influence of different error terms. During the iteration process, the speed and position of the particles are continuously adjusted according to the update formulas of speed and position. For example, assume that after 50 iterations, the particle swarm converges to an optimal solution. According to the final converged particle (fault scheme), the fault type is determined to be "deviation in the current channel". Further inspection revealed that an abnormal resistance value in the current channel caused inaccurate current measurement.

[0126] like Figure 2 As shown, an embodiment of the present invention further provides a smart meter operation error monitoring device 20, comprising:

[0127] The acquisition module 21 is used to periodically collect the operation data of the smart meter, including current value, voltage value, power value and power data;

[0128] A generating module 22, for generating an adjustment factor according to the operation data, usage environment and meter model of the smart meter;

[0129] A calculation module 23, used to calculate the real-time operation error of the smart meter according to the operation data and the adjustment factor;

[0130] A comparison module 24 is used to compare the real-time operation error with a preset error threshold, and when the real-time operation error exceeds the error threshold, trigger an error exceeding threshold warning;

[0131] The recording module 25 is used to record and store the identification, exceeding time, exceeding error value and related operation data of the smart meter that triggers the error exceeding warning;

[0132] The diagnosis module 26 is used to perform fault diagnosis on the smart meter that triggers the error exceeding limit warning to obtain a fault diagnosis result;

[0133] The processing module 27 is used to repair or replace the smart meter according to the fault diagnosis result.

[0134] It should be noted that the system is a system corresponding to the above method, and all implementation methods in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0135] The embodiment of the present invention further provides a computing device, comprising: a processor, a memory storing a computer program, wherein when the computer program is executed by the processor, the method described above is executed. All implementations in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0136] The embodiment of the present invention also provides a computer-readable storage medium storing instructions, which, when executed on a computer, enable the computer to execute the method described above. All implementations in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0137] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for monitoring operation errors of a smart meter, characterized in that: The method comprises the following steps: Periodically collect the operation data of smart meters, including current value, voltage value, power value and power data; Generate an adjustment factor based on the operation data, usage environment and meter model of the smart meter; Calculate the real-time operation error of the smart meter based on the operation data and adjustment factors; Compare the real-time operation error with the preset error threshold, and when the real-time operation error exceeds the error threshold, trigger an error exceeding threshold warning; Record and store the identification, exceeding time, exceeding error value and related operation data of the smart meter that triggers the exceeding error warning; Perform fault diagnosis on the smart meter that triggers the error exceeding limit warning to obtain the fault diagnosis result; Repair or replace the smart meter based on the fault diagnosis results; Generate an adjustment factor based on the smart meter’s operating data, usage environment, and meter model, including: According to the operation data, usage environment and meter model of the smart meter, Calculate adjustment factors; Where AF represents the adjustment factor; a1 and a2 represent the weight coefficients; I c and V c Respectively represent the current effective value of current and voltage; I h and V h Respectively represent the historical current and voltage effective values; b1 and b2 represent weight coefficients; T d Indicates the difference between the current temperature and the standard temperature; T s Indicates standard temperature; H d Indicates the difference between the current humidity and the standard humidity; H s represents standard humidity; c1 and c2 represent weight coefficients; P Fc Indicates the current power factor; P Fh represents the historical power factor; M represents the parameter used to adjust the measurement error of different models of smart meters; d represents the weight coefficient; H Dc Indicates the current harmonic distortion; H Dh Indicates historical harmonic distortion; Based on the operating data and adjustment factors, the real-time operating error of the smart meter is calculated, including: According to the operating data and adjustment factors, Calculate the real-time operation error R of the smart meter; Among them, α, β, γ and δ represent weight coefficients; P c and P h Respectively represent the current power and historical power values; E c and E h Respectively represent the current power and historical power values; Perform fault diagnosis on the smart meter that triggers the error exceeding limit warning to obtain the fault diagnosis results, including: Determine the parameter space for fault diagnosis, including fault type and fault location; Initialize a particle swarm, where each particle in the particle swarm represents a fault scenario, including a parameter combination of a current channel fault, a voltage channel fault, and a metering circuit fault; Construct a fitness function to evaluate how well each failure scenario matches the actual situation; Through iteration, the velocity and position of each particle are updated to obtain the final failure solution; According to the final fault plan, determine the specific fault type, including current channel fault, voltage channel fault and metering circuit fault; The calculation formula of the fitness function is: Among them, α, β and γ are weight coefficients; It is the sum of the absolute errors between the actual measured data and the simulated data; represents the sum of error terms associated with the fault scenario x; represents the sum of the diagnostic information differences associated with fault scenario x; i is the variable used to index the actual measured data points, N is the total number of actual measured data points; j is the variable used to index the error terms associated with fault scenario x; K is the total number of error terms; k is the variable used to index the diagnostic information differences associated with fault scenario x, L is the total number of diagnostic information differences.

2. The method for monitoring the operation error of a smart meter according to claim 1, characterized in that: Update the speed and position of each particle, where the speed update formula is: in, represents the velocity of particle i at the t+1th iteration in dimension d; w(t) is the inertia weight that changes dynamically with time t; represents the velocity of particle i at the tth iteration in dimension d; c1(t) represents the learning factor that changes with time t; r1 represents a random number in the range [0,1]; p id represents the final position of particle i in dimension d; represents the position of particle i at the tth iteration on dimension d; c2(t) represents the learning factor that changes with time t; r2 represents a random number in the range [0,1]; g d represents the global final position of the entire particle swarm in dimension d; c3(t) represents the learning factor that changes with time t; r3 represents a random number in the range [0,1]; l d represents the final position of particle i in the neighborhood; ∈ represents the random perturbation term; in, w max Indicates the maximum value of inertia weight; w min Represents the minimum value of the inertia weight; t represents the current number of iterations; T represents the total number of iterations.

3. The method for monitoring the operation error of a smart meter according to claim 2, characterized in that: Update the speed and position of each particle, where the update formula for the position is: Among them, λ is the parameter that controls the slope of the S-shaped curve, and the sigmoid function is used to map the speed into the interval; Represents the position of particle i at the t+1th iteration in dimension d.

4. A smart meter operation error monitoring device, characterized in that: include: The acquisition module is used to periodically collect the operation data of the smart meter, including current value, voltage value, power value and power data; A generating module, used for generating an adjustment factor according to the operation data, the use environment and the model of the smart meter; A calculation module, used to calculate the real-time operation error of the smart meter according to the operation data and the adjustment factor; A comparison module is used to compare the real-time operation error with a preset error threshold, and when the real-time operation error exceeds the error threshold, an error exceeding threshold warning is triggered; A recording module, used to record and store the identification of the smart meter that triggers the error exceeding limit warning, the exceeding limit time, the exceeding limit error value and related operation data; A diagnosis module, used for performing fault diagnosis on the smart meter that triggers the error exceeding standard warning to obtain a fault diagnosis result; A processing module, used to repair or replace the smart meter according to the fault diagnosis result; Generate an adjustment factor based on the smart meter’s operating data, usage environment, and meter model, including: According to the operation data, usage environment and meter model of the smart meter, Calculate adjustment factors; Where AF represents the adjustment factor; a1 and a2 represent the weight coefficients; I c and V c Respectively represent the current effective value of current and voltage; I h and V h Respectively represent the historical current and voltage effective values; b1 and b2 represent weight coefficients; T d Indicates the difference between the current temperature and the standard temperature; T s Indicates standard temperature; H d Indicates the difference between the current humidity and the standard humidity; H s represents standard humidity; c1 and c2 represent weight coefficients; P Fc Indicates the current power factor; P Fh represents the historical power factor; M represents the parameter used to adjust the measurement error of different models of smart meters; d represents the weight coefficient; H Dc Indicates the current harmonic distortion; H Dh Indicates historical harmonic distortion; Based on the operating data and adjustment factors, the real-time operating error of the smart meter is calculated, including: According to the operating data and adjustment factors, Calculate the real-time operation error R of the smart meter; Among them, α, β, γ and δ represent weight coefficients; P c and P h Respectively represent the current power and historical power values; E c and E h Respectively represent the current power and historical power values; Perform fault diagnosis on the smart meter that triggers the error exceeding limit warning to obtain the fault diagnosis results, including: Determine the parameter space for fault diagnosis, including fault type and fault location; Initialize a particle swarm, where each particle in the particle swarm represents a fault scenario, including a parameter combination of a current channel fault, a voltage channel fault, and a metering circuit fault; Construct a fitness function to evaluate how well each failure scenario matches the actual situation; Through iteration, the velocity and position of each particle are updated to obtain the final failure solution; According to the final fault plan, determine the specific fault type, including current channel fault, voltage channel fault and metering circuit fault; The calculation formula of the fitness function is: Among them, α, β and γ are weight coefficients; It is the sum of the absolute errors between the actual measured data and the simulated data; represents the sum of error terms associated with the fault scenario x; represents the sum of the diagnostic information differences associated with fault scenario x; i is the variable used to index the actual measured data points, N is the total number of actual measured data points; j is the variable used to index the error terms associated with fault scenario x; K is the total number of error terms; k is the variable used to index the diagnostic information differences associated with fault scenario x, L is the total number of diagnostic information differences.

5. A computing device, characterized in that: include: one or more processors; A storage device, used for storing one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program, which, when executed by a processor, implements the method according to any one of claims 1 to 3.

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