CN116775731B - Sliding window-based calculation method for fault-connection trace-back electric quantity of three-phase electric energy meter - Google Patents
Sliding window-based calculation method for fault-connection trace-back electric quantity of three-phase electric energy meter Download PDFInfo
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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
Technical Field
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 for the fault wiring trace-back electric quantity of the three-phase electric energy meter and an accounting tool.
Background
Along with the development of a power system and the construction of a smart grid, an electric energy meter is used as important equipment for electric power metering and is widely applied to various fields. The accurate metering of the electric energy meter is crucial to the operation and management of the power system, the electric energy meter used in the existing power supply system is completely updated into a novel electric energy meter, the electric energy meter can automatically report electric quantity and remotely meter, and the meter reading is not needed to be performed manually to a power node site.
In practical application, the problem of wrong wiring of the electric energy meter often occurs due to human factors or other reasons. The electric energy meter has deviation between the statistical electric quantity and the actual electric quantity measured in some wrong wiring modes, and because the existing electric power system is not subjected to frequent meter reading and maintenance by people, the measurement errors generated in the wrong wiring modes can be accumulated and enlarged continuously, and certain trouble is brought to the operation and management of the electric power system.
At present, two main solutions to the problem of wrong wiring of the electric energy meter exist. One is to find and correct the misconnection problem by manual inspection and detection instruments and correct the metering data. The method requires a lot of time and effort to carry out inspection, and the use cost of the detection instrument is high, so that the method is not economical and practical for large-scale application. Another approach is to identify and correct the miswire problem by a software algorithm. The method can realize automatic detection and correction, but in the existing scheme, the method adopts a strategy of firstly traversing the collected ammeter data to find the time point of wrong wiring and then calculating the electric quantity value needing to be tracked and backed up in the time. According to the scheme, the historical data of the electric energy meter is required to be traversed in a full quantity, so that the investigation efficiency is low, the reliability of the single-point investigation result is relatively low, and finally, the deviation exists in the identified wrong wiring moment and the error of the tracking and returning electric quantity calculation result is large.
Disclosure of Invention
The invention provides a sliding window-based three-phase electric energy meter misconnection tracking and annealing electric quantity calculation method, a three-phase electric energy meter misconnection tracking and annealing electric quantity calculation system and a calculation tool, and aims to solve the problems that an existing electric energy meter misconnection tracking and annealing electric quantity calculation method is low in efficiency, poor in reliability and error in calculation result.
The invention is realized by adopting the following technical scheme:
a calculation method for the fault-connection trace-back electric quantity of a three-phase electric energy meter based on a sliding window comprises the following steps:
s1: and identifying the periodic electric power statistical data uploaded by the electric energy meter by using a wrong wiring research model to obtain a corresponding wiring type Y.
S2: and recording historical state data corresponding to each electric energy meter according to the identification result to form a historical data set.
The historical state data includes: the electric energy meter asset number ID, the data sampling time T, metering data and the wiring type Y.
S3: and inquiring service data of the electric energy meter with the wrong wiring type Y as an abnormality according to the asset number ID of the electric energy meter, and acquiring the meter loading time of the corresponding electric energy meter.
S4: the historical data is analyzed by adopting a sliding window method to obtain the wrong wiring time, and the process is as follows:
s41: obtaining any one of the abnormal electric energy meter IDs i Extracting the sampling time T associated with each group i And wiring type Y i And generates a data pair A i ：A i =（T i ，Y i ）。
S42: pair A of data according to sampling time i And (3) performing arrangement to obtain a data queue D:
D={（T 1 ，Y 1 ），（T 2 ，Y 2 ）…（T n ，Y n ）}。
s43: finding a data pair corresponding to the loading time of the current electric energy meter in time from the data queue, and defining the data pair as a starting data pair A u ：
A u =（T u ，Y u ）。
S45: according to the preset window size j, respectively using the initial data pair A u And (5) generating a front window queue B and a rear window queue C corresponding to the boundary extending to two sides of the data queue D.
S46: calculating the wiring error confidence P of the front window queue B and the rear window queue C by using the following probability function B And P C ：
，
In the above formula, C represents a preset probability threshold;F（Y i ) Is a method for distinguishing the ith data pair A i Type Y of connection in (C) i Judging whether the data is miswired, if yesF（Y i ) =1, otherwiseF（Y i ) =0. Namely:
，
s47: judging P B P C =1, if true, pair a of initial data u The corresponding sampling instant is taken as the misconnection instant t0. Otherwise, continuing to judge P B Adjust the value of the initial data pair A u The next cycle is started:
(1) When pb=1, the initial data pair a u Advancing by j units, returning to step S46.
(2) When pb=0, the initial data pair a u J units are moved back and the process returns to step S46.
S5: and inquiring to obtain a corresponding correction coefficient K according to the wiring type Y in the fault wiring period.
S6: in the miswiring time analysis process, calculating the wrong electric quantity Q of each window period by synchronously utilizing metering data in historical state data, and calculating the follow-up electric quantity delta Q by utilizing the following formula:
ΔQ=（K-1）• Q 。
as a further improvement of the invention, in step S1, the electric energy meter uploads the electric power statistics data once every 15 minutes; the miswiring research model generates a corresponding recognition result of the 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 types Y are divided into 96 types; 94 kinds of wires are wrongly connected, and 2 kinds of wires are correctly connected; under the three-phase three-wire condition, the wiring type Y is divided into 48 types; of these, 46 were wired incorrectly and 2 were wired correctly.
As a further improvement of the present invention, in step S2, the metering data in the history state data includes:
CT, PT, daily freezing forward active energy indication (tip, peak, flat, valley), and daily freezing reverse active energy indication (tip, peak, flat, valley).
As a further improvement of the present invention, in step S3, for a newly installed power consumer, the time for installing the electric energy meter at the power node is taken as the meter installation time. And for the power users with the meter changing records, the time of the power node for changing the electric energy meter last time is recorded as the meter loading 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 development of the invention, in step S5, the power P at the time of metering in the test power consumption state is actually measured for each wiring type Y Y The correction coefficient K corresponding to any wiring type Y Y The calculation formula of (2) is as follows:
，
in the above formula, P represents the power of the standard wiring mode at the time of measurement in the test electricity consumption state.
As a further improvement of the present invention, in step S6, the calculation process of the erroneous electric quantity Q is as follows:
s61: the daily frozen forward active power q1 (peak, flat, valley) was calculated by:
q1 (tip, peak, flat, valley) = [ forward active power indication of day frozen at wrong junction moment (tip, peak, flat, valley) -forward active power indication of day frozen at current moment (tip, peak, flat, valley) ]ptct.
S62: the reverse active power q2 (peak, flat, valley) of day-frozen was calculated by:
q2 (peak, flat, valley) = [ reverse active power indication of day frozen at wrong wiring time (peak, flat, valley) -reverse active power indication of day frozen at current time (peak, flat, valley) ]ptct.
S63: the total forward active power Q1 is calculated by:
q1=q1 (tip) +q1 (peak) +q1 (flat) +q1 (valley).
S64: the total reverse active power Q2 is calculated by:
q2=q2 (tip) +q2 (peak) +q2 (flat) +q2 (valley).
S65: the erroneous electric quantity Q is calculated by:
Q = Q1 – Q2 。
the invention also provides a computing system for the three-phase electric energy meter fault-connection line-tracing and power-returning quantity, which adopts the computing method for the three-phase electric energy meter fault-connection line-tracing and power-returning quantity based on the sliding window, and generates the current fault-connection line-tracing and power-returning quantity during the fault-connection period of the electric energy meter according to the metering data uploaded by the electric energy meter and the wiring type Y output by the fault-connection line-studying and judging model.
The calculation system for the fault connection trace-back electric quantity of the three-phase electric energy meter comprises the following components: the system comprises a data acquisition module, a historical database, a service data query module, a fault wiring moment analysis module, a fault electric quantity statistics module and a tracking electric quantity calculation module.
The data acquisition module is used for inputting the electric power statistical data uploaded by the electric energy meter into a wrong wiring research model and acquiring the recognition result of the wiring type output by the wrong wiring research model.
The historical database is used for acquiring the wiring type output by the data acquisition module and classifying and recording the historical state data of each electric energy meter according to the asset number of the electric energy meter. The historical state data includes: the electric energy meter asset number, the data sampling time, the metering data and the wiring type.
The business data query module is used for querying a business database of a platform area according to the asset number of the electric energy meter and retrieving the loading time of the current electric energy meter.
The miswiring time analysis module comprises a data queue generating unit, a window queue generating unit, an error confidence calculating unit and a miswiring time judging unit. The data queue generating unit is used for extracting each group of associated sampling time and wiring type in the historical data of the abnormal electric energy meter and generating a data pair. The data pairs are then arranged in time to form a data queue. The window queue generating unit is used for extending to two ends of the data queue by taking the data pair corresponding to the table loading time as a critical point to obtain a front window queue and a rear window queue. The error confidence calculating unit is used for calculating the wiring error confidence of the front window queue and the rear window queue according to a preset probability function. The error wiring time judging unit is used for taking the sampling time corresponding to the initial data pair as the error wiring time when the wiring error confidence degrees of the front window queue and the rear window queue are different. If the wiring error confidence coefficient of the two is 1, the initial data pair is moved forward, and if the wiring error confidence coefficient of the two is 0, the initial data pair is moved backward, and then the window queue is regenerated.
The error electric quantity statistics module is used for counting the error electric quantity generated during the error wiring according to the metering data of the electric energy meter.
The tracking power quantity calculation module is used for inquiring and obtaining a correction coefficient according to the wiring type, and then calculating the tracking power quantity generated during error wiring according to the correction coefficient and the error power quantity.
The invention also provides an accounting tool for the fault connection trace-back electric quantity of the three-phase electric energy meter, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor. When the processor executes the computer program, the steps of the method for calculating the fault line tracking and returning electric quantity of the three-phase electric energy meter based on the sliding window are executed, and the corresponding forced line tracking and returning electric quantity is generated according to the metering data uploaded by the electric energy meter.
The technical scheme provided by the invention has the following beneficial effects:
1. the method for calculating the tracking and returning electric quantity combines the business data of the meter loading and changing, and the window can quickly complete the search of mass data in a window jumping mode without traversing all historical data according to a time sequence, so that the data processing quantity is effectively reduced, and the working efficiency of fault detection and tracking and returning electric quantity calculation is improved.
2. When the fault line is detected, the invention adopts a double-window comparison mode, judges the fault line state at each moment by utilizing the probability of the confidence coefficient, is more reliable compared with the conventional single-point data analysis schemes such as a fault line judging model and the like, and eliminates the influence of accidental errors of sample data in the fault line detection process.
3. The method adopts a method of searching and calculating at the same time when the chase-back electric quantity is calculated, namely, the electric quantity value needing chase-back in a window is calculated while the starting time point of the occurrence of error wiring is searched by utilizing a double window, so that repeated traversal of data is avoided; further shortening the time required for calculating the chase-back electric quantity.
4. In order to enable the calculation of the tracking and returning electric quantity to be more accurate, the situation that the electric quantity is at different moments of a peak, a flat and a valley is considered, and meanwhile, in order to reduce the influence of the multiplying power of the transformer due to load change, the electric quantity value calculation with a window as granularity is adopted, so that the calculated tracking and returning electric quantity value is more in line with the actual situation.
Drawings
Fig. 1 is a flowchart of a method for calculating the trace-back electric quantity of a three-phase electric energy meter fault line based on a sliding window according to embodiment 1 of the present invention.
Fig. 2 is a flowchart showing steps for analyzing the miswiring time by using the sliding window method in embodiment 1 of the present invention.
Fig. 3 is a system architecture diagram of a computing system for tracking and returning electric quantity of a fault wiring of a three-phase electric energy meter provided in embodiment 2 of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Example 1
The embodiment provides a sliding window-based calculation method for the trace-back electric quantity of a three-phase electric energy meter fault-connection line, which comprises the following steps as shown in fig. 1:
s1: and identifying the periodic electric power statistical data uploaded by the electric energy meter by using a wrong wiring research model to obtain a corresponding wiring type Y.
In the existing power system, an intelligent electric energy meter with a communication module is commonly adopted for an electric energy meter installed on an electric power consumption node of each power consumer, and the intelligent electric energy meter uploads electric power statistical data to an electric power consumption information acquisition system every 15 minutes; 96 sets of data packets are uploaded every day.
The miswiring research model is an online assessment tool developed by a power grid company and used for judging the wiring mode of the electric energy meter, the tool can judge which type the current wiring mode of the electric energy meter belongs to according to various electric power statistical data uploaded by the electric energy meter, then a corresponding recognition result of the wiring type Y is obtained, and a tag of the wiring type is added to related data.
In the existing low-voltage power grid of the power supply mode, two systems of neutral point grounding and neutral point ungrounded exist. In the system with the low-voltage neutral point indirectly grounded, the electrical equipment shell is not connected with change after being grounded, but is only connected with an independent grounding device, namely the low-voltage protection grounding, and the power supply mode is called as the three-phase three-wire system. The shell of the electrical equipment is grounded and then connected with change, which is called protection zero connection, and the power supply mode is called three-phase four-wire system.
For the electric energy meter installed by adopting different power supply modes, the existing wiring modes are different. For example, under three-phase four-wire conditions, there are 2 cases of voltage wiring modes, namely, a positive phase sequence and a negative phase sequence. The current phase sequence has 6 conditions, the positive phase sequence has 3 conditions, and the negative phase sequence has 3 conditions. The polarity of the current is divided into 8 cases. The connection types of the electric energy meter are provided with 96 types under the combined relation of voltage connection, current phase sequence and current polarity, and the connection situation comparison table is shown in table 1.
Table 1: three-phase four-wire wiring condition comparison table
Of the 96 wiring schemes shown in table 1, only the two combinations of (Uabc) + (IaIbIc) +(+i1+i2+i3) and (Uacb) + (IaIcIb) +(+i1+i2+i3) were considered to be correctly wired, and the rest were defined as faulty wires. All, 94 kinds of wires are wrongly wired and 2 kinds of wires are correctly wired under the three-phase four-wire system condition.
Under the three-phase three-wire system power supply mode, the voltage wiring mode comprises 6 types, namely 1 type of positive phase sequence, 2 types of wrong phase sequence and 3 types of negative phase sequence. The current phase sequence is divided into 2 types, namely 1 type of positive phase sequence and 1 type of negative phase sequence, and the current polarity is divided into 4 types. Under the combined relation of voltage wiring, current phase sequence and current polarity, 48 wiring types of the electric energy meter are provided, and a wiring mode comparison table is shown in table 2.
Table 2: three-phase three-wire system wiring condition comparison table
Of the 48 wiring schemes shown in Table 2, only the wiring scheme of the combination of (UaUbUc) + (Iaic) + (Ia+ic+) and (UcUbUa) + (IcIa) + (Ia+ic+) was considered to be the correct wiring, and the remainder were defined as the wrong wiring. Therefore, under the three-phase three-wire system condition, 46 kinds of wires are wrongly wired, and 2 kinds of wires are correctly wired.
S2: and recording historical state data corresponding to each electric energy meter according to the identification result to form a historical data set. The historical state data includes: the electric energy meter asset number ID, the data sampling time T, metering data and the wiring type Y.
The electric energy meter asset number ID can be an IMEI serial number of each electric energy meter, or can be coded in a power grid system, and each electric energy meter has a unique asset number, so that the electric energy meter can be used as an equipment identification number of the electric energy meter. The data sampling time refers to the time when the electric energy meter collects data each time and uploads the data to the electricity consumption information collection system. Metering data in the historical state data includes: CT, PT, daily freeze forward active power indication (peak, flat, valley), daily freeze reverse active power indication (peak, flat, valley), and so forth. Wherein CT and PT represent transformation ratio coefficients of the current transformer and the voltage transformer respectively.
The database of the station area can record the historical state data of each electric energy meter in a classified mode according to the asset number ID of each electric energy meter, and the recorded historical state data are arranged according to the sequence of the data sampling time T.
S3: and inquiring service data of the electric energy meter with the wrong wiring type Y as an abnormality according to the asset number ID of the electric energy meter, and acquiring the meter loading time of the corresponding electric energy meter.
In this embodiment, the time at which the electric energy meter is mounted or replaced is taken as the initial time of the investigation, and this time is the time at which miswiring is most likely to occur. In this way, the investigation should be started with priority, and the data amount of the history data processed by the investigation process can be reduced by taking it as a starting point.
When each electric energy meter is newly assembled or replaced, the related service list is distributed by the background management center, so that the embodiment selects the meter assembling time of the electric energy meter with the wrong wiring abnormality by inquiring service data. And for a newly installed power consumer, taking the time for installing the electric energy meter of the power node as the meter installation time. And for the power users with the meter changing records, the time of the power node for changing the electric energy meter last time is recorded as the meter loading time.
S4: and analyzing the historical data by adopting a sliding window method to obtain the wrong wiring time of any electric energy meter.
As shown in fig. 2, the analysis process of the wrong wiring time in this embodiment is as follows:
s41: obtaining any one of the abnormal electric energy meter IDs i Extracting the sampling time T associated with each group i And wiring type Y i And generates a data pair A i ：
A i =（T i ，Y i ）。
S42: pair A of data according to sampling time i And (3) performing arrangement 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 the history data.
S43: finding out the data pair corresponding to the loading time of the current electric energy meter in time from the data queue, and defining the data pair asInitial data pair A u ：
A u =（T u ，Y u ）。
S45: according to the preset window size j, respectively using the initial data pair A u And (5) generating a front window queue B and a rear window queue C corresponding to the boundary extending to two sides of the data queue D.
The expressions of the front window queue B and the rear window queue C are as follows:
。
s46: calculating the wiring error confidence P of the front window queue B and the rear window queue C by using the following probability function B And P C ：
，
In the above formula, C represents a preset probability threshold;F（Y i ) Is a method for distinguishing the ith data pair A i Type Y of connection in (C) i Judging whether the data is miswired, if yesF（Y i ) =1, otherwiseF（Y i ）=0。
S47: judging P B P C =1, if true, pair a of initial data u The corresponding sampling instant is taken as the misconnection instant t0. Otherwise, continuing to judge P B Adjust the value of the initial data pair A u The next cycle is started:
(1) When pb=1, the initial data pair a u Advancing by j units, returning to step S46.
(2) When pb=0, the initial data pair a u J units are moved back and the process returns to step S46.
S5: and inquiring to obtain a corresponding correction coefficient K according to the wiring type Y in the fault wiring period. Different wiring types can generate different errors in measurement, and the embodiment compares measurement result deviation caused by each type of wiring type in advance and endows a correction coefficient K for a corresponding wiring mode. For a normal wiring scheme, the correction factor is 1. When a certain wiring mode can lead to a meter with less electric quantity, the corresponding correction coefficient is larger than 1. When a certain wiring mode can lead to multiple electric meters of the electric energy meter, the corresponding correction coefficient is smaller than 1.
Actually measuring the power P at the time of measurement of each wiring type Y in the test power consumption state Y The correction coefficient K corresponding to any wiring type Y Y The calculation formula of (2) is as follows:
，
in the above formula, P represents the power of the standard wiring mode at the time of measurement in the test electricity consumption state.
S6: in the miswiring time analysis process, calculating the wrong electric quantity Q of each window period by synchronously utilizing metering data in historical state data, and calculating the follow-up electric quantity delta Q by utilizing the following formula:
ΔQ=（K-1）• Q 。
in this embodiment, the method for accumulating the error electric quantity is as follows:
s61: the daily frozen forward active power q1 (peak, flat, valley) was calculated by:
q1 (tip, peak, flat, valley) = [ forward active power indication of day frozen at wrong junction moment (tip, peak, flat, valley) -forward active power indication of day frozen at current moment (tip, peak, flat, valley) ]ptct.
S62: the reverse active power q2 (peak, flat, valley) of day-frozen was calculated by:
q2 (peak, flat, valley) = [ reverse active power indication of day frozen at wrong wiring time (peak, flat, valley) -reverse active power indication of day frozen at current time (peak, flat, valley) ]ptct.
S63: the total forward active power Q1 is calculated by:
q1=q1 (tip) +q1 (peak) +q1 (flat) +q1 (valley).
S64: the total reverse active power Q2 is calculated by:
q2=q2 (tip) +q2 (peak) +q2 (flat) +q2 (valley).
S65: the erroneous electric quantity Q is calculated by:
Q = Q1 – Q2 。
example 2
On the basis of the scheme in the embodiment 1, the embodiment further provides a computing system for the fault wiring trace-back electric quantity of the three-phase electric energy meter. The system is a data processing system that performs the method of embodiment 1. The system adopts the calculation method of the three-phase electric energy meter fault wiring follow-up electric quantity based on the sliding window as in the embodiment 1, and the follow-up electric quantity generated during the current electric energy meter fault wiring is generated according to the metering data uploaded by the electric energy meter and the wiring type output by the fault wiring research model.
As shown in fig. 3, the computing system for the fault-connection trace-back electric quantity of the three-phase electric energy meter comprises: the system comprises a data acquisition module, a historical database, a service data query module, a fault wiring moment analysis module, a fault electric quantity statistics module and a tracking electric quantity calculation module.
The data acquisition module is used for inputting the electric power statistical data uploaded by the electric energy meter into a wrong wiring research model and acquiring a recognition result of the wiring type output by the wrong wiring research model.
The historical database is used for acquiring the wiring type output by the data acquisition module and classifying and recording the historical state data of each electric energy meter according to the asset number of the electric energy meter. The historical state data includes: the electric energy meter asset number, the data sampling time, the metering data and the wiring type.
The business data query module is used for querying a business database of a platform area according to the asset number of the electric energy meter and retrieving the loading time of the current electric energy meter.
The miswiring time analysis module comprises a data queue generating unit, a window queue generating unit, an error confidence calculating unit and a miswiring time judging unit. The data queue generating unit is used for extracting each group of associated sampling time and wiring type in the historical data of the abnormal electric energy meter and generating a data pair. The data pairs are then arranged in time to form a data queue. The window queue generating unit is used for extending to two ends of the data queue by taking the data pair corresponding to the table loading time as a critical point to obtain a front window queue and a rear window queue. The error confidence calculating unit is used for calculating the wiring error confidence of the front window queue and the rear window queue according to a preset probability function. The error wiring time judging unit is used for taking the sampling time corresponding to the initial data pair as the error wiring time when the wiring error confidence degrees of the front window queue and the rear window queue are different. If the wiring error confidence coefficient of the two is 1, the initial data pair is moved forward, and if the wiring error confidence coefficient of the two is 0, the initial data pair is moved backward, and then the window queue is regenerated.
The error electric quantity statistics module is used for counting the error electric quantity generated during the error wiring according to the metering data of the electric energy meter.
The tracking power quantity calculation module is used for inquiring and obtaining a correction coefficient according to the wiring type, and then calculating the tracking power quantity generated during error wiring according to the correction coefficient and the error power quantity.
Example 3
The invention also provides an accounting tool for the fault connection trace-back electric quantity of the three-phase electric energy meter, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor. When the processor executes the computer program, a computing system of the three-phase electric energy meter miswiring power tracing and withdrawing quantity as in the embodiment 2 is created, and then the steps of the computing method of the three-phase electric energy meter miswiring power tracing and withdrawing quantity based on the sliding window as in the embodiment 1 are executed, and corresponding forced wiring power tracing and withdrawing quantity is generated according to the metering data uploaded by the electric energy meter.
The accounting tool for miswiring power tracing and back-off of the three-phase electric energy meter is essentially a computer device for realizing data processing and instruction generation, and comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor. The computer device provided in this embodiment may be an intelligent terminal, a tablet computer, a notebook computer, a desktop computer, a rack-mounted server, a blade server, a tower server, or a rack-mounted server (including an independent server or a server cluster formed by a plurality of servers) capable of executing a program, or the like. The computer device of the present embodiment includes at least, but is not limited to: a memory, a processor, and the like, which may be communicatively coupled to each other via a system bus.
In this embodiment, the memory (i.e., readable storage medium) includes flash memory, hard disk, multimedia card, card memory (e.g., 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 disk, optical disk, etc. In some embodiments, the memory may be an internal storage unit of a computer device, such as a hard disk or memory of the computer device.
In other embodiments, the memory may also be an external storage device of a computer device, such as a plug-in hard disk, smart Media Card (SMC), secure Digital (SD) Card, flash Card (Flash Card) or the like, which are provided on the computer device. Of course, the memory may also include both internal storage units of the computer device and external storage devices. In this embodiment, the memory is typically used to store an operating system and various application software installed on the computer device. In addition, the memory can be used to temporarily store various types of data that have been output or are to be output.
The processor may be a central processing unit (Central Processing Unit, CPU), an image processor GPU (Graphics Processing Unit), a controller, a microcontroller, a microprocessor, or other data processing chip in some embodiments. The processor is typically used to control the overall operation of the computer device. In this embodiment, the processor is configured to execute the program code stored in the memory or process the data.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the invention.
Claims (10)
1. A calculation method for the fault-connection trace-back electric quantity of a three-phase electric energy meter based on a sliding window is characterized by comprising the following steps:
s1: identifying periodic electric power statistical data uploaded by the electric energy meter by using a wrong wiring research model to obtain a corresponding wiring type Y;
s2: recording historical state data corresponding to each electric energy meter according to the identification result to form a historical data set;
the historical state data includes: the electric energy meter asset number ID, the data sampling time T, the metering data and the wiring type Y;
s3: inquiring electric energy meter service data with abnormal fault wiring type Y according to the asset number ID of the electric energy meter, and acquiring meter loading time of the corresponding electric energy meter;
s4: the historical data is analyzed by adopting a sliding window method to obtain the wrong wiring time, and the process is as follows:
s41: acquiring all historical data of any abnormal electric energy meter, and extracting the associated sampling time T of each group i And wiring type Y i And generates a data pair A i ：A i =（T i ，Y i ）；
S42: pair A of data according to sampling time i And (3) performing arrangement to obtain a data queue D: d= { (T) 1 ，Y 1 ），（T 2 ，Y 2 ）…（T n ，Y n ）}；
S43: finding a data pair corresponding to the loading time of the current electric energy meter in time from the data queue, and defining the data pair as a starting data pair A u ：A u =（T u ，Y u ）；
S45: according to the preset window size j, respectively using the initial data pair A u The boundary extends to two sides of the data queue D to generate a front window queue B and a rear window queue C which correspond to the boundary;
s46: by means ofThe following probability function calculates the wiring error confidence P of the front window queue B and the rear window queue C B And P C ：
In the above formula, C represents a preset probability threshold; f (Y) i ) Is a method for distinguishing the ith data pair A i Type Y of connection in (C) i If the discriminant function of the data miswiring mode is F (Y i ) =1, otherwise F (Y i )=0；
S47: judgingIf true, the initial data pair A u The corresponding sampling time is taken as the staggered wiring time t0; otherwise, continuing to judge P B Adjust the value of the initial data pair A u The next cycle is started:
(1) When pb=1, the initial data pair a u Advancing j units, and returning to the execution step S46;
(2) When pb=0, the initial data pair a u J units are moved backwards, and the step S46 is executed in a return mode;
s5: inquiring to obtain a corresponding correction coefficient K according to the wiring type Y in the fault wiring period;
s6: in the miswiring time analysis process, calculating the wrong electric quantity Q of each window period by synchronously utilizing metering data in historical state data, and calculating the follow-up electric quantity delta Q by utilizing the following formula:
ΔQ= (K-1)·Q。
2. the sliding window-based calculation method for the fault line follow-up electric quantity of the three-phase electric energy meter is characterized by comprising the following steps of: in the step S1, the electric energy meter uploads the electric power statistical data once every 15 minutes; the miswiring research model generates a corresponding recognition result of the wiring type Y.
3. The sliding window-based calculation method for the fault line follow-up electric quantity of the three-phase electric energy meter is characterized by comprising the following steps of: in the wiring types Y identified in the step S1, under the three-phase four-wire condition, the wiring types Y are divided into 96 types; 94 kinds of wires are wrongly connected, and 2 kinds of wires are correctly connected; under the three-phase three-wire condition, the wiring type Y is divided into 48 types; of these, 46 were wired incorrectly and 2 were wired correctly.
4. The method for calculating the fault-connection trace-back electric quantity of the three-phase electric energy meter based on the sliding window according to claim 1, wherein in the step S2, the metering data in the historical state data includes: CT, PT, peak section electric quantity, flat section electric quantity and valley Duan Dianliang of the daily freezing forward active electric energy indication value, peak section electric quantity, flat section electric quantity and valley Duan Dianliang of the daily freezing reverse active electric energy indication value; wherein CT and PT represent transformation ratio coefficients of the current transformer and the voltage transformer respectively.
5. The sliding window-based calculation method for the fault line follow-up electric quantity of the three-phase electric energy meter is characterized by comprising the following steps of: in step S3, for a newly installed power consumer, taking the newly installed power consumer as a power node, and taking the time for installing the electric energy meter on the power node as the meter installation time; and for the power user with the meter change record, taking the power user as a power node, and recording the time of the last meter change of the power node as the meter loading time.
6. The method for calculating the fault-connection trace-back electric quantity of the three-phase electric energy meter based on the sliding window according to claim 1, wherein in the 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 the fault line follow-up electric quantity of the three-phase electric energy meter is characterized by comprising the following steps of: step by stepIn step S5, the power P at the time of measurement in the test power consumption state is actually measured for each wiring type Y Y The correction coefficient K corresponding to any wiring type Y Y The calculation formula of (2) is as follows:
in the above formula, P represents the power of the standard wiring mode at the time of measurement in the test electricity consumption state.
8. The method for calculating the fault line-following power consumption of the three-phase electric energy meter based on the sliding window according to claim 4, wherein in step S6, the calculation process of the fault power consumption Q is as follows:
s61: the daily frozen forward active power q1 (tip), q1 (peak), q1 (plateau), q1 (valley) were calculated by:
s62: the reverse active power q2 (tip), q2 (peak), q2 (flat), q2 (valley) of daily freezing was calculated by:
s63: the total forward active power Q1 is calculated by:
q1=q1 (tip) +q1 (peak) +q1 (flat) +q1 (valley);
s64: the total reverse active power Q2 is calculated by:
q2=q2 (tip) +q2 (peak) +q2 (flat) +q2 (valley);
s65: the erroneous electric quantity Q is calculated by:
Q =Q1 - Q2。
9. a computing system for the tracking and the return electric quantity of a three-phase electric energy meter fault line according to any one of claims 1-8, wherein the computing method for the tracking and the return electric quantity of the three-phase electric energy meter fault line based on a sliding window is adopted, and the tracking and the return electric quantity generated during the current electric energy meter fault line is generated according to metering data uploaded by the electric energy meter and a wiring type Y output by a fault line judging model
Δq; the calculation system for the miswiring power tracing and returning quantity of the three-phase electric energy meter comprises:
the data acquisition module is used for inputting the electric power statistical data uploaded by the electric energy meter into a wrong wiring research model and acquiring a recognition result of the wiring type output by the wrong wiring research model;
the historical database is used for acquiring the wiring type output by the data acquisition module and classifying and recording the historical state data of each electric energy meter according to the asset number of the electric energy meter, and the historical state data comprises: the electric energy meter asset number, the data sampling time T, the metering data and the wiring type;
the business data query module is used for querying a business database of a platform area according to the asset number of the electric energy meter and retrieving the loading time of the current electric energy meter;
the miswiring time analysis module comprises a data queue generating unit, a window queue generating unit, an error confidence calculating unit and a miswiring time judging unit; the data queue generating unit is used for extracting each group of associated sampling time and wiring type in the historical data of the abnormal electric energy meter and generating a data pair; then, arranging the data pairs according to time to obtain a data queue; the window queue generating unit is used for extending to two ends of the data queue by taking a data pair corresponding to the meter loading time as a critical point to obtain a front window queue and a rear window queue; the error confidence coefficient calculating unit is used for calculating wiring error confidence coefficients of the front window queue and the rear window queue according to a preset probability function; the error wiring time judging unit is used for taking the sampling time corresponding to the initial data pair as error wiring time when the wiring error confidence degrees of the front window queue and the rear window queue are different; if the wiring error confidence coefficient of the two is 1, the initial data pair is moved forward, and if the wiring error confidence coefficient of the two is 0, the initial data pair is moved backward, and then a window queue is regenerated;
the error electric quantity statistics module is used for counting the error electric quantity generated during the error wiring according to the metering data of the electric energy meter;
and the tracking and returning electric quantity calculation module is used for inquiring and obtaining a correction coefficient according to the wiring type, and then calculating the tracking and returning electric quantity generated during the error wiring according to the correction coefficient and the error electric quantity.
10. The utility model provides a three-phase electric energy meter misconnection chasing and withdrawing electric quantity accounting device, its includes memory, treater and stores on the memory and can run on the treater computer program, characterized in that, when the treater is carried out computer program, carry out the step of the three-phase electric energy meter misconnection chasing and withdrawing electric quantity based on sliding window's calculation method according to any one of claims 1-8, the metering data that uploads according to the electric energy meter generates corresponding misconnection chasing and withdrawing electric quantity.
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