Power meter over-capacity burning prediction method based on multi-dimensional weighted clustering algorithm

Abstract

The application discloses a power meter over-capacity burning prediction method based on a multi-dimension weighted clustering algorithm, which comprises the following steps: S1, obtaining meter information through an information table of an electric energy meter; S2, obtaining load data of a historical period time of a user through a power load data table; and S3, for the load data in the warehouse, users with a preset number of continuous points or more within a day are over-capacity users, and multi-dimension weighted clustering calculation is performed on the over-capacity users. The power meter over-capacity burning prediction method based on the multi-dimension weighted clustering algorithm considers the over-capacity multiple of the power meter, continuity, heating condition, weather and power consumption change to give a quantitative over-capacity burning risk evaluation; a basic power supply station can replace the meter according to the output data or received short messages, and can replace a suitable meter according to actual needs, so that the meter burning is prevented from occurring, and the equipment utilization is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of electric meter overcapacity and burnout prediction, and specifically relates to an electric meter overcapacity and burnout prediction method based on a multi-dimensional weighted clustering algorithm of an electric power data center. Background Art

[0002] With the improvement of living standards, the addition of new electrical appliances by residential users has led to an increase in electricity consumption. A large number of electricity meters are operating at overcapacity. Prolonged operation can easily cause the meters and their accessories to burn out, not only affecting electricity consumption but even causing fires in the meter box. Conventional overcapacity meter analysis typically extracts overcapacity data, requiring comparison across multiple systems, including marketing and data collection. The data volume and comparison workload are excessive. Due to limited operation and maintenance resources, simply replacing all overcapacity meters is not feasible. Furthermore, the replacement meter capacity often relies on experience, resulting in a mismatch between the replacement capacity and the user's needs. Furthermore, during peak electricity consumption periods in winter and summer, the probability of overcapacity meter burnout increases significantly. A data-based method for locating easily burned meters is urgently needed to guide grassroots power supply stations in targeted meter replacements and reduce the occurrence of meter burnout incidents. Summary of the Invention

[0003] The main purpose of the present invention is to provide a method for predicting overcapacity and burnout of electric meters based on a multi-dimensional weighted clustering algorithm, which considers the overcapacity multiples, continuity, and heating conditions of the electric meters in combination with weather and electricity consumption changes to give a quantitative overcapacity and burnout risk assessment; the grassroots power supply station can replace meters in a targeted manner based on the output data or received text messages, and can replace appropriate meters according to actual needs to prevent the occurrence of meter burnout and improve equipment utilization; the intuitive meter burnout risk value is used to guide the grassroots power supply station to give priority to replacing meters that are prone to burnout.

[0004] To achieve the above objectives, the present invention provides a method for predicting overcapacity and burnout of electric power meters based on a multi-dimensional weighted clustering algorithm, comprising the following methods:

[0005] Step S1: Obtain meter information through the information table (dwd_cst_meter_run) of the electric energy meter, the meter information including rated voltage, rated current, rated capacity (Srated), wiring method and installation date;

[0006] Step S2: Obtain the user's load data for the historical period (96 points per day in the past year) through the electricity load data table (dwd_cst_t_itf_comp_curve). The load data includes voltage, current, and apparent power (S). The load data with apparent power greater than the rated capacity is stored in the database (each phase of the three-phase user is calculated separately);

[0007] Step S3: For the warehoused load data, the user with more than preset number of (preferably 4) consecutive points (points with apparent power greater than rated capacity) is an over-capacity user (three-phase user represents over-capacity phase), and multi-dimensional weighted clustering calculation is performed on the over-capacity user, which is implemented as the following steps:

[0008] Step S3.1: Over-capacity ratio calculation is performed, over-capacity ratio = maximum 5 apparent power average / rated capacity; over-capacity ratio weight R1 = (over-capacity ratio-1)*5;

[0009] Step S3.2: Continuous over-capacity calculation is performed, historical continuous over-capacity weight R2 = maximum continuous over-capacity measurement point number in the past year / 8; recent continuous over-capacity weight R3 = maximum continuous over-capacity measurement point number in the past week / 4;

[0010] Step S3.3: Intraday over-capacity rate calculation is performed, historical intraday over-capacity rate weight R4 = total over-capacity measurement point number of the maximum three days over-capacity in the past year / 72; recent intraday over-capacity rate weight R5 = maximum day over-capacity measurement point number in the past week / 12;

[0011] Step S3.4: Week over-capacity rate calculation is performed, week over-capacity weight R6 = over-capacity days number in the past week / 7*4;

[0012] Step S3.5: Over-heat index calculation is performed, reflecting the heat generated by single over-capacity exceeding a certain ratio,

[0013] Single over-heat index = over-capacity ratio of over-capacity consecutive points greater than preset multiple (preferably 1.12 times) minus threshold ratio corresponding integral value;

[0014]

[0015] In the formula, R7 is over-heat index; It is over-capacity ratio; Imax is threshold ratio;

[0016] Over-heat index weight = maximum single over-heat index in the past week*first value (first value is preferably 2);

[0017] Step S3.6: Aging index calculation is performed, aging base = (current date-installation date) / 365, aging index weight R8 = aging base*historical intraday over-capacity rate;

[0018] Step S3.7: Perform load surge processing, obtain historical weather information from the data center (taking into account the impact of air conditioning load), and use the load conversion factor = user's average summer and winter load / spring and autumn average load for judgment. If the user's average spring and autumn load surges (average load increases by more than 30% year-on-year) and the current spring and autumn average load * load conversion factor is greater than the rated capacity, then increase the seasonal weight R9 during the next seasonal change and indicate the risk of over-capacity burnout during the seasonal change. R9 = (current spring and autumn average load * load conversion factor - rated capacity) / rated capacity * 20;

[0019] Step S3.8: Perform special case processing and obtain weather forecast information from the data center. If the maximum temperature of the next day is above the preset temperature (30 degrees Celsius), increase the overheating index risk value R7. ’ =R7*(maximum temperature-29) to increase the priority of replacing overheating meters;

[0020] Step S3.9: Update the above dimension data (R1-R8) daily and calculate the weighted sum;

[0021]

[0022] By sorting the R total of each electricity meter in reverse order, the over-capacity meters with risk values ​​greater than the total threshold or at the front are the ones that need to be replaced first.

[0023] As a further preferred technical solution of the above technical solution, step S3.9 further includes:

[0024] Step S3.10: Calculate the minimum capacity of the meter recommended for replacement, where the minimum capacity = the average value of the apparent power of the 10 largest collection points throughout the year * 1.2;

[0025] If there is a sudden load increase in step S7, a year-on-year conversion prediction process is performed.

[0026] As a further preferred technical solution of the above technical solution, the user information table (dim_cst_elec_cons_cust) is associated with the substation information table (dim_cst_connection) and the electricity meter information table (dwd_cst_meter_run), and a list of high-risk overcapacity meters is compiled by substation. Through the information of the meter reader (substation maintainer) in the substation information table, a text message is sent to notify the meter so that the meter can be replaced in time, and the minimum capacity information is provided for reference to select the appropriate meter.

[0027] To achieve the above objectives, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes the program, the steps of the method for predicting overcapacity and burnout of electric meters based on a multi-dimensional weighted clustering algorithm are implemented.

[0028] To achieve the above objectives, the present invention also provides a non-transitory computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the power meter overcapacity and burnout prediction method based on a multi-dimensional weighted clustering algorithm are implemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a schematic flow diagram of the present invention. DETAILED DESCRIPTION

[0030] The following description is intended to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are for illustrative purposes only, and those skilled in the art will readily appreciate other obvious variations. The basic principles of the present invention defined in the following description may be applied to other embodiments, variations, improvements, equivalents, and other technical solutions that do not depart from the spirit and scope of the present invention.

[0031] In the preferred embodiment of the present invention, those skilled in the art should note that the power meter and the like involved in the present invention may be regarded as prior art.

[0032] Preferred embodiment.

[0033] The present invention discloses a method for predicting overcapacity and burnout of electric power meters based on a multi-dimensional weighted clustering algorithm, comprising the following methods:

[0034] Step S1: Obtain meter information through the information table (dwd_cst_meter_run) of the electric energy meter, the meter information including rated voltage, rated current, rated capacity (Srated), wiring method and installation date;

[0035] Step S2: Obtain the user's load data for the historical period (96 points per day in the past year) through the electricity load data table (dwd_cst_t_itf_comp_curve). The load data includes voltage, current, and apparent power (S). The load data with apparent power greater than the rated capacity is stored in the database (each phase of the three-phase user is calculated separately);

[0036] Step S3: For the stored load data, users with a preset number (preferably 4) or more consecutive points (points where the apparent power is greater than the rated capacity) in a day are considered overcapacity users (three-phase users reflect the phase of overcapacity), and multi-dimensional weighted clustering calculation is performed on the overcapacity users. The specific implementation is as follows:

[0037] Step S3.1: Calculate the overcapacity ratio, where overcapacity ratio = average value of the five largest apparent powers / rated capacity; overcapacity ratio weight R1 = (overcapacity ratio - 1) * 5;

[0038] Step S3.2: Calculate the continuous overcapacity. The weight of the historical continuous overcapacity is R2 = the maximum number of continuous overcapacity measurement points in the past year / 8; the weight of the recent continuous overcapacity is R3 = the maximum number of continuous overcapacity measurement points in the past week / 4.

[0039] Step S3.3: Calculate the daily over-limit rate. The weight of the historical daily over-limit rate, R4, is equal to the total number of over-limit measurement points of the three largest over-limit days in the past year / 72. The weight of the recent daily over-limit rate, R5, is equal to the maximum number of over-limit measurement points in the past week / 12.

[0040] Step S3.4: Calculate the weekly over-allowance rate, with the weekly over-allowance weight R6 = number of over-allowance days in the past week / 7 * 4;

[0041] Step S3.5: Calculate the overheat index to reflect the single overheating that exceeds a certain ratio.

[0042] Single overheat index = the integral value of the overcapacity ratio of consecutive overcapacity points greater than a preset multiple (preferably 1.12 times) minus the threshold ratio;

[0043]

[0044] Where, R7 is the overheat index; It is the overcapacity ratio; Imax is the threshold ratio;

[0045] Overheat index weight = the largest single overheat index in the past week * the first value (the first value is preferably 2);

[0046] Step S3.6: Calculate the aging index: aging base = (current date - installation date) / 365, aging index weight R8 = aging base * historical daily over-capacity rate;

[0047] Step S3.7: Perform load surge processing, obtain historical weather information from the data center (taking into account the impact of air conditioning load), and use the load conversion factor = user's average summer and winter load / spring and autumn average load to make a judgment. If the user's average spring and autumn load surges (average load increases by more than 30% year-on-year) and the current spring and autumn average load * load conversion factor is greater than the rated capacity, then increase the seasonal weight R9 during the next seasonal change and indicate the risk of over-capacity burnout during the seasonal change: R9 = (current spring and autumn average load * load conversion factor - rated capacity) / rated capacity * 20;

[0048] Step S3.8: Perform special case processing and obtain weather forecast information from the data center. If the maximum temperature of the next day is above the preset temperature (30 degrees Celsius), increase the overheating index risk value R7. ’ =R7*(maximum temperature-29) to increase the priority of replacing overheating meters;

[0049] Step S3.9: Update the above dimension data (R1-R9) daily and calculate the weighted sum;

[0050]

[0051] By sorting the R total of each electricity meter in reverse order, the over-capacity meters with risk values ​​greater than the total threshold or at the front are the ones that need to be replaced first.

[0052] Specifically, after step S3.9, the following steps are further included:

[0053] Step S3.10: Calculate the minimum capacity of the meter recommended for replacement, where the minimum capacity = the average value of the apparent power of the 10 largest collection points throughout the year * 1.2;

[0054] If there is a sudden load increase in step S7, a year-on-year conversion prediction process is performed.

[0055] If it is a three-phase meter, it is the maximum value of the recommended minimum capacity among the three phases ABC (max(recommended minimum capacity A, recommended minimum capacity B, recommended minimum capacity C)).

[0056] More specifically, the user information table (dim_cst_elec_cons_cust) is linked with the substation information table (dim_cst_connection) and the electricity meter information table (dwd_cst_meter_run), and a list of high-risk overcapacity meters is compiled by substation. Through the meter reader (substation maintainer) information in the substation information table, a text message is sent to notify the meter so that it can be replaced in time, and the minimum capacity information is provided for reference to select the appropriate meter.

[0057] It is worth mentioning that the various values ​​in the present invention can be changed according to actual needs and are not fixed.

[0058] The present invention also discloses an electronic device, comprising a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes the program, the steps of the method for predicting overcapacity and burnout of electric meters based on a multi-dimensional weighted clustering algorithm are implemented.

[0059] The present invention also discloses a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the method for predicting overcapacity and burnout of electric meters based on a multi-dimensional weighted clustering algorithm are implemented.

[0060] It is worth mentioning that the technical features such as the electricity meter involved in the patent application of this invention should be regarded as the prior art. The specific structure, working principle and possible control method and spatial layout method of these technical features can be selected by conventional choices in the field, and should not be regarded as the inventive point of the patent of this invention. The patent of this invention will not be further elaborated.

[0061] For those skilled in the art, it is still possible to modify the technical solutions described in the aforementioned embodiments, or to make equivalent replacements for some of the technical features therein. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for predicting overcapacity and burnout of electric power meters based on a multi-dimensional weighted clustering algorithm, characterized in that: This includes the following methods: Step S1: Obtain meter information from the information table of the electric energy meter, where the meter information includes rated voltage, rated current, rated capacity, wiring method, and installation date; Step S2: Obtain the user's load data for historical periods through the power load data table. The load data includes voltage, current, and apparent power. The load data with apparent power greater than the rated capacity is stored in the database. Step S3: For the incoming load data, users with a preset number of consecutive points or more in a day are considered as over-capacity users, and multi-dimensional weighted clustering calculation is performed on the over-capacity users. The specific implementation is as follows: Step S3.1: Calculate the overcapacity ratio, where overcapacity ratio = average value of the five largest apparent powers / rated capacity; overcapacity ratio weight R1 = (overcapacity ratio - 1) * 5; Step S3.2: Calculate the continuous overcapacity. The weight of the historical continuous overcapacity is R2 = the maximum number of continuous overcapacity measurement points in the past year / 8; the weight of the recent continuous overcapacity is R3 = the maximum number of continuous overcapacity measurement points in the past week / 4. Step S3.3: Calculate the daily over-limit rate. The weight of the historical daily over-limit rate, R4, is equal to the total number of over-limit measurement points of the three largest over-limit days in the past year / 72. The weight of the recent daily over-limit rate, R5, is equal to the maximum number of over-limit measurement points in the past week / 12. Step S3.4: Calculate the weekly over-allowance rate, with the weekly over-allowance weight R6 = number of over-allowance days in the past week / 7 * 4; Step S3.5: Calculate the overheat index to reflect the single overheating that exceeds a certain ratio. Single overheat index = the integral value of the overcapacity ratio of consecutive overcapacity points greater than the preset multiple minus the threshold ratio; Where, R7 is the overheat index; It is the overcapacity ratio; Imax is the threshold ratio; Overheat index weight = the largest single overheat index in the past week * the first value; Step S3.6: Calculate the aging index: aging base = (current date - installation date) / 365, aging index weight R8 = aging base * historical daily over-capacity rate; Step S3.7: Perform load surge processing, obtain historical weather information from the data center, and use the load conversion factor = user's average summer and winter load / spring and autumn average load to make a judgment. If the user's average spring and autumn load surges and the current spring and autumn average load * load conversion factor is greater than the rated capacity, increase the seasonal weight R9 during the next seasonal change and indicate the risk of over-capacity burnout during the seasonal change: R9 = (current spring and autumn average load * load conversion factor - rated capacity) / rated capacity * 20; Step S3.8: Handle special cases and obtain weather forecast information from the data center. If the maximum temperature of the next day is above the preset temperature, increase the overheating index risk value R7. ’ =R7*(maximum temperature - 29), in order to increase the priority of replacing the overheating meter; Step S3.9: Update the above dimension data daily and calculate the weighted sum; By sorting the R total of each electricity meter in reverse order, the over-capacity meters with risk values ​​greater than the total threshold or at the front are the ones that need to be replaced first.

2. The method for predicting electric meter overcapacity and burnout based on a multi-dimensional weighted clustering algorithm according to claim 1, characterized in that: Step S3.9 and subsequent steps include: Step S3.10: Calculate the minimum capacity of the meter recommended for replacement, where the minimum capacity = the average value of the apparent power of the 10 largest collection points throughout the year * 1.2; If there is a sudden load increase in step S7, a year-on-year conversion prediction process is performed.

3. The method for predicting electric meter overcapacity and burnout based on a multi-dimensional weighted clustering algorithm according to claim 2, characterized in that: The user information table is linked to the substation information table and the electricity meter information table, and a list of high-risk over-capacity meters is compiled by substation. The meter reader information in the substation information table is used to send SMS notifications so that the meters can be replaced in time, and the minimum capacity information is provided for reference to select the appropriate meter.

4. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the method for predicting overcapacity and burnout of an electric meter based on a multi-dimensional weighted clustering algorithm as described in any one of claims 1 to 3 are implemented.

5. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for predicting overcapacity and burnout of an electric power meter based on a multi-dimensional weighted clustering algorithm as claimed in any one of claims 1 to 3 are implemented.

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