CN116775731B - Calculation method for recovering the amount of electricity lost due to miswiring of a three-phase electric energy meter based on sliding window - Google Patents

Abstract

The invention belongs to the field of power distribution equipment, and particularly relates to a sliding window-based calculation method for the fault wiring trace-back electric quantity of a three-phase electric energy meter, a calculation system and a calculation tool. The method comprises the following steps: s1: identifying the wiring type according to the data uploaded by the electric energy meter by using a wrong wiring research model; s2: and recording historical state data according to the identification result to form a historical data set. S3: and inquiring service data of the abnormal electric energy meter to obtain meter loading time. S4: and analyzing the historical data by adopting a sliding window method to obtain the wrong wiring time. S5: and inquiring the correction coefficient according to the wiring type. S6: in the error wiring time analysis process, error electric quantity of each period is counted by synchronously utilizing metering data, and the tracking and returning electric quantity is calculated by combining correction coefficients. The time of the staggered connection is the time when the staggered connection confidence degrees of the windows on the two sides are different. The invention solves the problems of low efficiency, poor reliability and error of the existing calculation method for the miswiring power tracing and returning quantity of the electric energy meter.

Description

Calculation method for recovering the amount of electricity lost due to miswiring of a three-phase electric energy meter based on sliding window

Technical field

The invention belongs to the field of power distribution equipment, and specifically relates to a sliding window-based calculation method for recovering the amount of electricity lost due to miswiring of a three-phase electric energy meter, as well as a calculation system and accounting tool for recovering the amount of electricity lost due to miswiring of a three-phase electric energy meter.

Background technique

With the development of power systems and the construction of smart grids, electric energy meters, as important equipment for power measurement, are widely used in various fields. The accurate measurement of electric energy meters is crucial to the operation and management of the power system. The electric energy meters used in the existing power supply system have all been upgraded to new electric energy meters. This type of electric energy meters can realize automatic power reporting and remote meter reading without the need for Manually go to the power node site to read meters.

There are 48 and 96 wiring methods for three-phase three-wire electric energy meters and three-phase four-wire electric energy meters respectively. In practical applications, miswiring problems of electric energy meters often occur due to human factors or other reasons. There will be a deviation between the statistical power measured by the electric energy meter and the actual power under certain wrong wiring methods. Since the existing power system does not require frequent manual meter reading and maintenance, the measurement errors caused by this wrong wiring method will continue to accumulate and expand. , and brought certain troubles to the operation and management of the power system.

At present, there are two main solutions to the problem of miswiring of electric energy meters. One is to discover and correct miswiring problems and correct measurement data through manual inspection and testing instruments. This method requires a lot of manual time and effort for inspection, and the cost of using detection instruments is high, which is not economical and practical for large-scale applications. Another approach is to use software algorithms to identify and correct miswiring problems. This method can realize automatic detection and correction, but in the current solutions, the collected meter data is first traversed to find the time point when the miswiring occurred, and then the power value that needs to be recovered during this period is calculated. Strategy. This solution requires traversing all the historical data of the electric energy meter, resulting in low efficiency of troubleshooting, and the reliability of the results of single-point troubleshooting is also relatively low, which ultimately leads to deviations in the identified miswiring moments and large errors in the calculation results of the retroactive power. .

Contents of the invention

In order to solve the problems that the existing calculation method of the electric energy meter to recover the amount of electricity after miswiring is low in efficiency, poor reliability, and errors in the calculation results, the present invention provides a sliding window-based calculation method for the amount of electricity to be recovered after miswiring of a three-phase electric energy meter, and Calculation system and accounting tool for recovering power loss due to incorrect wiring of three-phase energy meters.

The present invention adopts the following technical solutions to achieve:

A sliding window-based calculation method for recovering the amount of electricity lost due to miswiring of a three-phase electric energy meter, which includes the following steps:

S1: Use a wrong wiring analysis model to identify the periodic power statistical data uploaded by the electric energy meter, and obtain the corresponding wiring type Y.

S2: Record the historical status data corresponding to each electric energy meter according to the recognition results to form a historical data set.

Historical status data includes: energy meter asset number ID, data sampling time T, metering data, and wiring type Y.

S3: Query the business data of the electric energy meter with abnormal miswiring type Y according to the electric energy meter asset number ID, and obtain the installation time of the corresponding electric energy meter.

S4: Use the sliding window method to analyze historical data and obtain the miswiring moment. The process is as follows:

S41: Obtain all historical data of any abnormal energy meter ID _i , extract the associated sampling time _Ti and wiring type _Yi for each group, and generate a data pair A _i : A _i = (T _i , Y _i ) .

S42: Arrange the data A _i according to the sampling time to obtain a data queue D:

D={(T ₁ , Y ₁ ), (T ₂ , Y ₂ )…(T _n , Y _n )}.

S43: Find the data pair corresponding in time to the current meter installation time from the data queue, and define it as the starting data pair A _u :

A _u = (T _u , Y _u ).

S45: According to the preset window size j, extend to both sides of the data queue D with the starting data pair A _u as the boundary, and generate the corresponding front window queue B and rear window queue C.

S46: Use the following probability function to calculate the wiring error confidence P _B and P _{C of the front window queue B and the rear window queue C} :

,

In the above formula, C represents the preset probability threshold; F ( Y _i ) is a discriminant function used to distinguish whether the wiring type Yi in the i-th data pair _{A i} is the data miswiring mode, then F ( Y _i )=1, otherwise F ( Y _i )=0. Right now:

,

S47: Judgment P _B P _C =1, whether it is true, then the sampling time corresponding to the starting data pair A _u will be regarded as the miswiring time t0. Otherwise, continue to judge the value of P _B , adjust the starting data pair A _u , and start the next cycle:

(1) When PB=1, move the starting data pair A _u forward by j units, and return to step S46.

(2) When PB=0, move the starting data pair A _u back by j units, and return to step S46.

S5: According to the wiring type Y during the miswiring period, query and obtain the corresponding correction coefficient K.

S6: During the analysis of miswiring time, the metering data in the historical status data are synchronously used to calculate the error power Q in each window period, and the following formula is used to calculate the retroactive power ΔQ:

ΔQ=(K-1)•Q.

As a further improvement of the present invention, in step S1, the electric energy meter uploads power statistical data every 15 minutes; the wrong wiring analysis model generates a corresponding identification result of wiring type Y.

As a further improvement of the present invention, among the wiring types Y identified in step S1, under the three-phase four-wire condition, the wiring type Y is divided into 96 types; among them, 94 types are incorrect wiring and 2 types are correct wiring; under the three-phase three-wire condition Below, the wiring type Y is divided into 48 types; among them, 46 types are incorrect wiring and 2 types are correct wiring.

As a further improvement of the present invention, in step S2, the measurement data in the historical status data includes:

CT, PT, daily frozen forward active electric energy indication (peak, peak, flat, valley), and daily frozen reverse active electric energy indication (peak, peak, flat, valley).

As a further improvement of the present invention, in step S3, for a newly installed power user, the time when the electric energy meter is installed on the power node is used as the meter installation time. For power users with meter replacement records, the time when the power meter was last replaced at that power node will be recorded as the meter installation time.

As a further improvement of the present invention, in step S45, the expressions of the front window queue B and the rear window queue C are as follows:

,

As a further improvement of the present invention, in step S5, the measured power P _Y of each wiring type Y under the test power state is actually measured. Then the calculation formula of the correction coefficient K _Y corresponding to any wiring type Y is as follows:

,

In the above formula, P represents the metering power of the standard wiring method under the test power state.

As a further improvement of the present invention, in step S6, the calculation process of the error electric quantity Q is as follows:

S61: Calculate the daily frozen forward active power q1 (peak, peak, flat, valley) through the following formula:

q1 (peak, peak, flat, valley) = [the daily frozen forward active electric energy indication at the time of wrong wiring (peak, peak, flat, valley) - the daily frozen forward active electric energy indication at the current time (peak, peak, Ping, Gu)]* PT * CT.

S62: Calculate the daily frozen reverse active energy q2 (peak, peak, flat, valley) through the following formula:

q2 (peak, peak, flat, valley) = [the daily frozen reverse active electric energy indication value (peak, peak, flat, valley) at the time of wrong wiring - the daily frozen reverse active electric energy indication value (peak, peak, valley) at the current time Ping, Valley)] * PT * CT.

S63: Calculate the total forward active power Q1 through the following formula:

Q1 = q1(tip) + q1(peak) + q1(flat) + q1(trough).

S64: Calculate the total reverse active energy Q2 through the following formula:

Q2 = q2(tip) + q2(peak) + q2(flat) + q2(trough).

S65: Calculate the error power Q according to the following formula:

Q = Q1 – Q2.

The present invention also provides a calculation system for recovering the amount of electricity lost due to miswiring of a three-phase electric energy meter. It adopts the sliding window-based calculation method for recovering the amount of electricity lost due to miswiring of a three-phase electric energy meter. According to the metering data uploaded by the electric energy meter and the error The wiring type Y output by the wiring research and judgment model is used to generate the retroactive power generated during the current miswiring of the energy meter.

The calculation system of the three-phase electric energy meter for miswiring to recover electricity includes: data collection module, historical database, business data query module, miswiring time analysis module, error electricity statistics module, and calculation module for recovering electricity.

The data acquisition module is used to input the power statistical data uploaded by the electric energy meter into a miswiring research and judgment model, and obtain the identification results of the wiring type output by the miswiring research and judgment model.

The historical database is used to obtain the wiring type output by the data acquisition module and record the historical status data of each electric energy meter according to the asset number of the electric energy meter. Historical status data includes: energy meter asset number, data sampling time, metering data, and wiring type.

The business data query module is used to query the business database of a station based on the asset number of the electric energy meter and retrieve the installation time of the current electric energy meter.

The miswiring time analysis module includes a data queue generation unit, a window queue generation unit, an error confidence calculation unit, and a miswiring time judgment unit. The data queue generation unit is used to extract each group of associated sampling time and wiring type in the historical data of the abnormal electric energy meter and generate a data pair. Then the data pairs are arranged according to time to obtain the data queue. The window queue generation unit is used to take the data pair corresponding to the table loading time as the critical point and extend to both ends of the data queue to obtain the front window queue and the rear window queue. The error confidence calculation unit is used to calculate the wiring error confidence of the front window queue and the rear window queue according to a preset probability function. The miswiring time judgment unit is used to use the sampling time corresponding to the starting data pair as the miswiring time when the wiring error confidence levels of the front window queue and the rear window queue are different. If the wiring error confidence of both is 1, the starting data pair will be moved forward. If the wiring error confidence of both is 0, the starting data pair will be moved backward, and then the window queue will be regenerated.

The wrong power statistics module is used to calculate the wrong power generated during miswiring based on the measurement data of the electric energy meter.

The retroactive power calculation module is used to query and obtain the correction coefficient based on the wiring type, and then calculate the retroactive power generated during the incorrect wiring based on the correction coefficient and the error power.

The invention also provides an accounting tool for recovering electric power due to incorrect wiring of a three-phase electric energy meter, which includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor. When the processor executes the computer program, it executes the steps of the sliding window-based calculation method of the three-phase electric energy meter misconnection recovery amount, and generates the corresponding urging connection recovery amount based on the measurement data uploaded by the electric energy meter.

The technical solution provided by the present invention has the following beneficial effects:

1. The retroactive power calculation method provided by the present invention combines the business data of meter installation and meter replacement. The window can quickly complete the search of massive data by window jumping without having to traverse all historical data in chronological order, which effectively reduces data processing. This improves the efficiency of troubleshooting and recovery power calculations.

2. When troubleshooting miswiring faults, the present invention adopts a dual-window comparison method and uses the probability of confidence to determine the miswiring status at each moment. Compared with conventional single-point data analysis solutions such as miswiring judgment models, The language is more reliable and eliminates the influence of accidental errors in sample data during miswiring troubleshooting.

3. The present invention uses a method of searching and calculating when calculating the amount of energy to be retroactively restored, that is, while using dual windows to search for the starting time point when the miswiring occurs, it also calculates the electric power value that needs to be retroactively restored within the window to avoid distortion of the data. Repeat the traversal; further shortening the time required to calculate the retroactive power.

4. In order to make the calculation of the retroactive power more accurate, the present invention considers the situation of the power at different times of peak, peak, flat and valley. At the same time, in order to reduce the influence of the transformer magnification due to the load change, the window is used as the granularity. The calculation of the power value makes the calculated retroactive power value more consistent with the actual situation.

Description of drawings

FIG. 1 is a step flow chart of a sliding window-based calculation method for recovering the amount of electricity lost due to miswiring of a three-phase electric energy meter provided in Embodiment 1 of the present invention.

FIG. 2 is a flow chart of steps for analyzing miswiring moments using the sliding window method in Embodiment 1 of the present invention.

FIG. 3 is a system architecture diagram of a calculation system for recovering electric power due to miswiring of a three-phase electric energy meter provided in Embodiment 2 of the present invention.

Detailed ways

In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention and are not intended to limit the present invention.

Example 1

This embodiment provides a sliding window-based calculation method for calculating the amount of energy lost due to miswiring of a three-phase energy meter. As shown in Figure 1, it includes the following steps:

S1: Use a wrong wiring analysis model to identify the periodic power statistical data uploaded by the electric energy meter, and obtain the corresponding wiring type Y.

In the existing power system, the electric energy meters installed at the power consumption nodes of each power user have generally adopted smart electric energy meters with communication modules. The smart electric energy meters upload power statistical data to the power consumption information collection system every 15 minutes; every day Upload 96 sets of data packets.

The wrong wiring evaluation model is an online evaluation tool developed by the power grid company to determine the wiring method of the electric energy meter. This tool can determine what type of current wiring method the electric energy meter belongs to based on various power statistical data uploaded by the electric energy meter. A corresponding identification result of wiring type Y, and add a wiring type label to the relevant data.

In the low-voltage power grid with existing power supply methods, there are two types of systems: the neutral point is grounded and the neutral point is not grounded. The three-phase three-wire system is a system in which the low-voltage neutral point is not directly grounded. After the electrical equipment shell is grounded, it is not connected to the change, but is only connected to an independent grounding device. This is called low-voltage protective grounding. This power supply method is called three-phase three-wire. system. The shell of the electrical equipment is grounded and then connected to the change, which is called a protective zero connection. This power supply method is called a three-phase four-wire system.

For the above two energy meters installed with different power supply methods, the wiring methods are also different. For example, under three-phase four-wire conditions, there are two situations of voltage wiring, namely positive phase sequence and reverse phase sequence. There are 6 types of current phase sequence, 3 types of positive phase sequence, and 3 types of reverse phase sequence. Current polarity is divided into 8 situations. Under the combined relationship of voltage wiring, current phase sequence and current polarity, there are 96 types of wiring types for electric energy meters. The wiring comparison table is shown in Table 1.

Table 1: Comparison table of wiring conditions of three-phase four-wire system

Among the 96 wiring methods shown in Table 1, there are only two combinations: (Uabc) + (IaIbIc) + (+i1+i2+i3) and (Uacb) + (IaIcIb) + (+i1+i2+i3) The wiring method is considered as correct wiring, and the rest is defined as incorrect wiring. All, under the three-phase four-wire system, there are 94 types of incorrect wiring and 2 types of correct wiring.

In the three-phase three-wire power supply mode, there are 6 voltage wiring methods, including 1 positive phase sequence, 2 wrong phase sequences, and 3 reverse phase sequences. The current phase sequence is divided into two types, namely one positive phase sequence and one reverse phase sequence, while the current polarity is divided into four types. Under the combined relationship of voltage wiring, current phase sequence and current polarity, there are 48 types of wiring types for electric energy meters. The wiring method comparison table is shown in Table 2.

Table 2: Comparison table of wiring conditions of three-phase three-wire system

Among the 48 wiring methods shown in Table 2, only the wiring methods of the combinations (UaUbUc) + (IaIc) + (Ia+Ic+) and (UcUbUa) + (IcIa) + (Ia+Ic+) are considered correct wiring, the rest are defined as incorrect wiring. Therefore, under the three-phase three-wire system, there are 46 types of incorrect wiring and 2 types of correct wiring.

S2: Record the historical status data corresponding to each electric energy meter according to the recognition results to form a historical data set. Historical status data includes: energy meter asset number ID, data sampling time T, metering data, and wiring type Y.

Among them, the electric energy meter asset number ID can be the IMEI serial number of each electric energy meter, or it can use the internal coding of the power grid system. Each electric energy meter has a unique asset number, so it can be used as the equipment identification number of the electric energy meter. The data sampling time refers to the time when the electric energy meter collects data and uploads the data to the power consumption information collection system. The measurement data in the historical status data include: CT, PT, daily frozen forward active power indication value (peak, peak, flat, valley), and daily frozen reverse active power indication value (peak, peak, flat, valley) ,etc. Among them, CT and PT represent the ratio coefficients of current transformer and voltage transformer respectively.

The database in Taiwan will record the historical status data of each electric energy meter according to the asset number ID of each electric energy meter. The recorded historical status data will be arranged in this order according to the data sampling time T.

S3: Query the business data of the electric energy meter with abnormal miswiring type Y according to the electric energy meter asset number ID, and obtain the installation time of the corresponding electric energy meter.

In this embodiment, the time when the electric energy meter is installed or replaced is used as the initial time for troubleshooting. This time is the most likely time when miswiring occurs. This should be the priority to start troubleshooting, and using it as a starting point can also reduce the amount of historical data processed during the troubleshooting process.

When each electric energy meter is newly installed or replaced, the backend management center will distribute relevant business orders. Therefore, in this embodiment, the meter installation time of the electric energy meter with miswiring abnormality is determined by querying the business data. Among them, for a newly installed power user, the time when the electric energy meter is installed at the power node is regarded as the meter installation time. For power users with meter replacement records, the time when the power meter was last replaced at that power node will be recorded as the meter installation time.

S4: Use the sliding window method to analyze historical data and obtain the miswiring moment of any electric energy meter.

As shown in Figure 2, the analysis process of the miswiring moment in this embodiment is as follows:

S41: Obtain all historical data of any abnormal energy meter ID _i , extract the associated sampling time _Ti and wiring type _Yi for each group, and generate a data pair A _i :

A _i = (T _i , Y _i ).

S42: Arrange the data A _i according to the sampling time to obtain a data queue D:

D={(T ₁ , Y ₁ ), (T ₂ , Y ₂ )…(T _n , Y _n )};

In the above formula, n represents the number of data packets collected in historical data.

S43: Find the data pair corresponding in time to the current meter installation time from the data queue, and define it as the starting data pair A _u :

A _u = (T _u , Y _u ).

S45: According to the preset window size j, extend to both sides of the data queue D with the starting data pair A _u as the boundary, and generate the corresponding front window queue B and rear window queue C.

The expressions of the front window queue B and the rear window queue C are as follows:

.

S46: Use the following probability function to calculate the wiring error confidence P _B and P _{C of the front window queue B and the rear window queue C} :

,

In the above formula, C represents the preset probability threshold; F ( Y _i ) is a discriminant function used to distinguish whether the wiring type Yi in the i-th data pair _{A i} is the data miswiring mode, then F ( Y _i )=1, otherwise F ( Y _i )=0.

S47: Judgment P _B P _C =1, whether it is true, then the sampling time corresponding to the starting data pair A _u will be regarded as the miswiring time t0. Otherwise, continue to judge the value of P _B , adjust the starting data pair A _u , and start the next cycle:

(1) When PB=1, move the starting data pair A _u forward by j units, and return to step S46.

(2) When PB=0, move the starting data pair A _u back by j units, and return to step S46.

S5: According to the wiring type Y during the miswiring period, query and obtain the corresponding correction coefficient K. Different wiring types will produce different errors during measurement. This embodiment compares the measurement result deviation caused by each type of wiring type in advance, and assigns a correction coefficient K to the corresponding wiring method. For normal wiring, the correction factor is 1. When a certain wiring method will cause the electric energy meter to under-count, the corresponding correction coefficient will be greater than 1. When a certain wiring method causes the electric energy meter to over-count, the corresponding correction factor is less than 1.

Actual measurement of the metering power P _Y of each wiring type Y under the test power state, then the correction coefficient K _Y corresponding to any wiring type Y is calculated as follows:

,

In the above formula, P represents the metering power of the standard wiring method under the test power state.

S6: During the analysis of miswiring time, the metering data in the historical status data are synchronously used to calculate the error power Q in each window period, and the following formula is used to calculate the retroactive power ΔQ:

ΔQ=(K-1)•Q.

In this embodiment, the accumulation method of wrong electric power is as follows:

S61: Calculate the daily frozen forward active power q1 (peak, peak, flat, valley) through the following formula:

q1 (peak, peak, flat, valley) = [the daily frozen forward active electric energy indication at the time of wrong wiring (peak, peak, flat, valley) - the daily frozen forward active electric energy indication at the current time (peak, peak, Ping, Gu)]* PT * CT.

S62: Calculate the daily frozen reverse active energy q2 (peak, peak, flat, valley) through the following formula:

q2 (peak, peak, flat, valley) = [the daily frozen reverse active electric energy indication value (peak, peak, flat, valley) at the time of wrong wiring - the daily frozen reverse active electric energy indication value (peak, peak, valley) at the current time Ping, Valley)] * PT * CT.

S63: Calculate the total forward active power Q1 through the following formula:

Q1 = q1(tip) + q1(peak) + q1(flat) + q1(trough).

S64: Calculate the total reverse active energy Q2 through the following formula:

Q2 = q2(tip) + q2(peak) + q2(flat) + q2(trough).

S65: Calculate the error power Q according to the following formula:

Q = Q1 – Q2.

Example 2

Based on the solution in Embodiment 1, this embodiment further provides a calculation system for recovering the amount of electricity due to miswiring of a three-phase electric energy meter. This system is the data processing system that executes the method in Embodiment 1. The system adopts the sliding window-based calculation method for recovering the amount of energy lost due to miswiring of a three-phase electric energy meter as in Embodiment 1. Based on the metering data uploaded by the electric energy meter and the wiring type output by the miswiring research and judgment model, it generates the generated data generated during the current miswiring of the electric energy meter. The battery will be refunded.

As shown in Figure 3, the calculation system of the three-phase electric energy meter for miswiring to recover electricity includes: data collection module, historical database, business data query module, wrong wiring time analysis module, error electricity statistics module, and calculation module for recovering electricity. .

Among them, the data acquisition module is used to input the power statistical data uploaded by the electric energy meter into a miswiring research and judgment model, and obtain the identification results of the wiring type output by the miswiring research and judgment model.

The historical database is used to obtain the wiring type output by the data acquisition module and record the historical status data of each electric energy meter according to the asset number of the electric energy meter. Historical status data includes: energy meter asset number, data sampling time, metering data, and wiring type.

The business data query module is used to query the business database of a station based on the asset number of the electric energy meter and retrieve the installation time of the current electric energy meter.

The miswiring time analysis module includes a data queue generation unit, a window queue generation unit, an error confidence calculation unit, and a miswiring time judgment unit. The data queue generation unit is used to extract each group of associated sampling time and wiring type in the historical data of the abnormal electric energy meter and generate a data pair. Then the data pairs are arranged according to time to obtain the data queue. The window queue generation unit is used to take the data pair corresponding to the table loading time as the critical point and extend to both ends of the data queue to obtain the front window queue and the rear window queue. The error confidence calculation unit is used to calculate the wiring error confidence of the front window queue and the rear window queue according to a preset probability function. The miswiring time judgment unit is used to use the sampling time corresponding to the starting data pair as the miswiring time when the wiring error confidence levels of the front window queue and the rear window queue are different. If the wiring error confidence of both is 1, the starting data pair will be moved forward. If the wiring error confidence of both is 0, the starting data pair will be moved backward, and then the window queue will be regenerated.

The wrong power statistics module is used to calculate the wrong power generated during miswiring based on the measurement data of the electric energy meter.

The retroactive power calculation module is used to query and obtain the correction coefficient based on the wiring type, and then calculate the retroactive power generated during the incorrect wiring based on the correction coefficient and the error power.

Example 3

The invention also provides an accounting tool for recovering electric power due to incorrect wiring of a three-phase electric energy meter, which includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor. When the processor executes the computer program, it creates a calculation system for retrieving the amount of electricity for miswiring of a three-phase electric energy meter as in Embodiment 2, and then executes a calculation system for retrieving electricity for miswiring of a three-phase electric energy meter based on a sliding window as in Embodiment 1. The steps of the method are to generate a corresponding connection prompt to recover the electric power according to the metering data uploaded by the electric energy meter.

This type of three-phase energy meter miswiring recovery calculation tool is essentially a computer device used to implement data processing and instruction generation, which includes a memory, a processor, and data stored in the memory and can be run on the processor. Computer program. The computer device provided in this embodiment may be a smart terminal capable of executing programs, a tablet computer, a notebook computer, a desktop computer, a rack server, a blade server, a tower server or a cabinet server (including independent servers, or multiple A server cluster composed of servers), etc. The computer device in this embodiment at least includes but is not limited to: a memory and a processor that can be communicatively connected to each other through a system bus.

In this embodiment, the memory (i.e., readable storage medium) includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), Read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disks, optical disks, etc. In some embodiments, the memory may be an internal storage unit of the computer device, such as a hard drive or memory of the computer device.

In other embodiments, the memory may also be an external storage device of the computer device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), or a secure digital (SecureDigital, SD) card equipped on the computer device. Flash Card, etc. Of course, memory may also include both internal storage units of the computer device and its external storage devices. In this embodiment, the memory is usually used to store operating systems and various application software installed on the computer device. In addition, the memory can also be used to temporarily store various types of data that have been output or will be output.

In some embodiments, the processor may be a central processing unit (CPU), a graphics processor (GPU), a controller, a microcontroller, a microprocessor, or other data processing chips. The processor is typically used to control the overall operation of a computer device. In this embodiment, the processor is used to run program codes stored in the memory or process data.

The above descriptions are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention shall be included in the protection of the present invention. within the range.

Claims (10)

1. A sliding window-based calculation method for recovering the amount of electricity lost due to miswiring of a three-phase electric energy meter, which is characterized in that it includes the following steps: S1: Use a wrong wiring analysis and judgment model to identify the periodic power statistical data uploaded by the electric energy meter, and obtain the corresponding wiring type Y; S2: Record the historical status data corresponding to each electric energy meter according to the recognition results to form a historical data set; The historical status data includes: electric energy meter asset number ID, data sampling time T, metering data, and wiring type Y; S3: Query the business data of the electric energy meter with abnormal miswiring type Y according to the electric energy meter asset number ID, and obtain the installation time of the corresponding electric energy meter; S4: Use the sliding window method to analyze historical data and obtain the miswiring moment. The process is as follows: S41: Obtain all historical data of any abnormal electric energy meter, extract the associated sampling time _Ti and wiring type _Yi of each group, and generate a data pair A _i : A _i = (T _i , Y _i ); S42: Arrange the data A _i according to the sampling time to obtain a data queue D: D={(T ₁ , Y ₁ ), (T ₂ , Y ₂ )...(T _n , Y _n )}; S43: Find the data pair corresponding in time to the current meter installation time from the data queue, and define it as the starting data pair A _u : A _u = (T _u , Y _u ); S45: According to the preset window size j, extend to both sides of the data queue D with the starting data pair A _u as the boundary, and generate the corresponding front window queue B and rear window queue C; S46: Use the following probability function to calculate the wiring error confidence P _B and P _{C of the front window queue B and the rear window queue C} : In the above formula, C represents the preset probability threshold; F(Y _i ) is a discriminant function used to distinguish whether the wiring type Y _i in the i-th data pair A _i is the wrong data wiring mode, then F(Y _i )=1, otherwise F(Y _i )=0; S47: Judgment , whether it is true, if so, the sampling time corresponding to the starting data pair A _u will be used as the miswiring time t0; otherwise, continue to judge the value of P _B , adjust the starting data pair A _u , and start the next cycle: (1) When PB=1, move the starting data pair A _u forward by j units, and return to step S46; (2) When PB=0, move the starting data pair A _u back by j units, and return to step S46; S5: According to the wiring type Y during the miswiring period, query and obtain the corresponding correction coefficient K; S6: During the analysis of miswiring time, the metering data in the historical status data are synchronously used to calculate the error power Q in each window period, and the following formula is used to calculate the retroactive power ΔQ: ΔQ= (K-1)·Q. 2. The sliding window-based calculation method for recovering the amount of electricity due to miswiring of a three-phase electric energy meter as claimed in claim 1, characterized in that: in step S1, the electric energy meter uploads power statistical data every 15 minutes; a miswiring research and judgment model is generated An identification result corresponding to wiring type Y. 3. The sliding window-based calculation method for recovering the amount of electricity due to wrong wiring of a three-phase electric energy meter as claimed in claim 1, characterized in that: in the wiring type Y identified in step S1, under the three-phase four-wire condition, the The wiring type Y is divided into 96 types; among them, there are 94 types of incorrect wiring and 2 types of correct wiring; under the three-phase three-wire condition, the wiring type Y is divided into 48 types; among them, there are 46 types of incorrect wiring and 2 types of correct wiring. 4. The sliding window-based calculation method for recovering the amount of electricity due to miswiring of a three-phase electric energy meter as claimed in claim 1, characterized in that in step S2, the measurement data in the historical status data includes: CT, PT, daily. Freeze the peak electric energy, peak electric energy, flat electric energy and valley electric energy of the forward active energy indication, and freeze the peak electric energy, peak electric energy, flat electric energy and valley electric energy of the reverse active electric energy indication on a daily basis; Among them, CT and PT represent the ratio coefficients of current transformer and voltage transformer respectively. 5. The sliding window-based calculation method for recovering the amount of electricity due to miswiring of a three-phase electric energy meter as claimed in claim 1, characterized in that: in step S3, for a newly installed electricity user, using it as the electricity node, the electricity The time when the electric energy meter is installed on the node is regarded as the meter installation time; for power users with meter replacement records, which are regarded as power nodes, the time when the electric energy meter was last replaced at the power node is recorded as the meter installation time. 6. The sliding window-based method for calculating the amount of electricity lost due to miswiring of a three-phase electric energy meter as claimed in claim 1, characterized in that in step S45, the expressions of the front window queue B and the rear window queue C are as follows: . 7. The sliding window-based calculation method for recovering the amount of electricity due to incorrect wiring of a three-phase electric energy meter as claimed in claim 1, characterized in that: in step S5, the measurement time of each wiring type Y in the test power state is actually measured. Power P _Y , then the correction coefficient K _Y corresponding to any wiring type Y is calculated as follows: In the above formula, P represents the metering power of the standard wiring method under the test power state. 8. The sliding window-based calculation method for recovering the amount of electricity due to incorrect wiring of a three-phase electric energy meter as claimed in claim 4, characterized in that, in step S6, the calculation process of the error amount Q is as follows: S61: Calculate the daily frozen forward active power q1 (peak), q1 (peak), q1 (flat), q1 (trough) through the following formula: S62: Calculate the daily frozen reverse active power q2 (peak), q2 (peak), q2 (flat), and q2 (trough) through the following formula: S63: Calculate the total forward active power Q1 through the following formula: Q1 = q1(tip) + q1(peak) + q1(flat) + q1(trough); S64: Calculate the total reverse active energy Q2 through the following formula: Q2 = q2(tip) + q2(peak) + q2(flat) + q2(trough); S65: Calculate the error power Q according to the following formula: Q =Q1 - Q2. 9. A calculation system for retrieving the amount of electricity due to miswiring of a three-phase electric energy meter, characterized in that it adopts the method of retrieving the amount of electricity due to miswiring of a three-phase electric energy meter based on a sliding window as described in any one of claims 1-8. The calculation method is based on the measurement data uploaded by the electric energy meter and the wiring type Y output by the miswiring judgment model to generate the retroactive power generated during the current miswiring period of the electric energy meter. ΔQ; The calculation system for recovering the amount of electricity lost due to miswiring of a three-phase electric energy meter includes: A data acquisition module, which is used to input the power statistical data uploaded by the electric energy meter into a miswiring research and judgment model, and obtain the identification results of the wiring type output by the miswiring research and judgment model; A historical database, which is used to obtain the wiring type output by the data acquisition module, and record the historical status data of each electric energy meter according to the asset number of the electric energy meter. The historical status data includes: the electric energy meter asset number, and the data sampling time. T, metering data, and wiring type; The business data query module is used to query the business database of a station based on the asset number of the electric energy meter and retrieve the installation time of the current electric energy meter; Wrong wiring time analysis module, which includes a data queue generation unit, a window queue generation unit, an error confidence calculation unit, and a miswiring time judgment unit; the data queue generation unit is used to extract each group of historical data of abnormal electric energy meters Correlate the sampling time and wiring type and generate a data pair; then arrange the data pairs according to time to obtain a data queue; the window queue generation unit is used to use the data pair corresponding to the meter installation time as the critical point and extend to both ends of the data queue , obtain the front window queue and the rear window queue; the error confidence calculation unit is used to calculate the wiring error confidence of the front window queue and the rear window queue according to the preset probability function; the miswiring moment judgment unit is used to calculate the wiring error confidence in the front window queue and the rear window queue. When the wiring error confidence levels of the window queue and the rear window queue are different, the sampling time corresponding to the starting data pair is regarded as the miswiring time; if the wiring error confidence levels of the two are the same as 1, the starting data pair will be moved forward. If the wiring error confidence of both is 0, the starting data pair will be moved back, and then the window queue will be regenerated; Error power statistics module, the error power statistics module is used to count the erroneous power generated during the miswiring period based on the metering data of the electric energy meter; The retroactive power calculation module is used to query and obtain the correction coefficient based on the wiring type, and then calculate the retroactive power generated during the incorrect wiring based on the correction coefficient and the error power. 10. An accounting device for recovering electricity due to miswiring of a three-phase electric energy meter, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the The computer program executes the steps of the sliding window-based calculation method for miswiring traceback of a three-phase electric energy meter as described in any one of claims 1 to 8, and generates a corresponding miswiring traceback based on the metering data uploaded by the electric energy meter. Refund amount.