CN112684397B - Electric energy meter operation error monitoring method and system based on high-frequency HPLC (high Performance liquid chromatography) data - Google Patents
Electric energy meter operation error monitoring method and system based on high-frequency HPLC (high Performance liquid chromatography) data Download PDFInfo
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Abstract
The application provides an electric energy meter operation error monitoring method and system based on high-frequency HPLC data acquisition, and relates to the technical field of electric power. In the method, firstly, total meter power consumption and sub-meter power consumption obtained by respectively acquiring a total electric energy meter of a station area and a differential electric energy meter of a target station area by a plurality of HPLC devices in a current time period are obtained; secondly, obtaining line loss electric quantity corresponding to the sub-meter electric quantity based on a predetermined electric quantity corresponding relation and the sub-meter electric quantity for each sub-meter electric quantity; then, calculating to obtain total operation error data of the plurality of station-distinguished electric energy meters based on the station area total meter electricity consumption, the sub-meter electricity consumption and the line loss electricity; and finally, determining the operation error data of each station distinguishing electric energy meter based on the total operation error data and the predetermined error proportion information. Based on the method, the problem that the operation error of the station-specific electric energy meter is difficult to effectively monitor in the prior art can be solved.
Description
Technical Field
The application relates to the technical field of electric power, in particular to an electric energy meter operation error monitoring method and system based on high-frequency HPLC (high performance liquid chromatography) data acquisition.
Background
In the field of power technology, a district refers to a power supply range or area of a transformer in a power system, wherein an electric energy meter for measuring total power consumption in the power supply range may be referred to as a district total electric energy meter, and one district may include a plurality of electric energy meters for measuring power consumption of each user, which may be referred to as a district division electric energy meter.
The inventor researches and discovers that each station distinguishing electric energy meter generates certain operation errors after being put into use, and therefore the operation errors need to be monitored. However, the prior art has the problem that the operation error of the station distinguishing electric energy meter is difficult to be effectively monitored.
Disclosure of Invention
In view of this, an object of the present application is to provide a method and a system for monitoring an operation error of an electric energy meter based on HPLC high frequency acquisition data, so as to solve the problem in the prior art that it is difficult to effectively monitor the operation error of a station-specific electric energy meter.
In order to achieve the above purpose, the embodiment of the present application adopts the following technical solutions:
a method and a system for monitoring running errors of an electric energy meter based on high-frequency HPLC data acquisition are applied to a computing platform in an electric energy meter running error monitoring system, wherein the monitoring system further comprises an HPLC device in communication connection with the computing platform, and the method comprises the following steps:
acquiring total table electricity consumption and sub-table electricity consumption obtained by respectively acquiring a total table electricity consumption and a sub-table electricity consumption of a target table area by a plurality of HPLC (high performance liquid chromatography) devices in the current time period, wherein the total table area electricity consumption is one, and the sub-table area electricity consumption is a plurality of;
aiming at the sub-meter power consumption of each station distinguishing electric energy meter, obtaining the line loss electric quantity corresponding to the sub-meter power consumption based on the predetermined electric quantity corresponding relation and the sub-meter power consumption;
calculating to obtain total operation error data of the plurality of station-distinguished electric energy meters based on the station-district general meter electricity consumption, the sub-meter electricity consumption and the line loss electricity;
and determining the operation error data of each station distinguishing electric energy meter based on the total operation error data and the error proportion information determined for each station distinguishing electric energy meter in advance.
On the basis of the above embodiment, the application further provides an electric energy meter operation error monitoring system, which includes an HPLC device and a computing platform in communication connection with the HPLC;
wherein the computing platform comprises:
a memory for storing a computer program;
and the processor is connected with the memory and is used for executing a computer program to realize the electric energy meter operation error monitoring method based on the HPLC high-frequency collected data.
The utility model provides an electric energy meter running error monitoring method and system based on HPLC high frequency data collection, gather district total electric energy meter and platform differentiation electric energy meter respectively through HPLC equipment, can obtain corresponding total table power consumption and branch table power consumption, then, obtain the line loss electric quantity that this branch table power consumption corresponds based on predetermined electric quantity corresponding relation and this branch table power consumption, thereby can combine this total table power consumption to obtain the total running error data that the platform differentiates the electric energy meter, make can be based on the error proportion information of confirming, confirm the running error data that each platform differentiates the electric energy meter. Therefore, the operation error of the power meter in the transformer area can be effectively monitored, and the problem that the operation error of the power meter in the transformer area is difficult to effectively monitor in the prior art is solved.
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
Fig. 1 is a block diagram of a computing platform in an electric energy meter operation error monitoring system according to an embodiment of the present application.
Fig. 2 is a schematic flow chart illustrating steps included in a method for monitoring an operation error of an electric energy meter based on HPLC high-frequency collected data according to an embodiment of the present disclosure.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all the embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The embodiment of the application provides an electric energy meter operation error monitoring system which comprises a plurality of HPLC (high speed power line carrier) devices and a computing platform in communication connection with each HPLC device.
The HPLC equipment is provided with a carrier chip and a main control chip, can be installed on a total electric energy meter and a station distinguishing electric energy meter in a station area, and can be operated independently of the total electric energy meter and the station distinguishing electric energy meter in the station area.
Also, as shown in FIG. 1, a computing platform may include a memory and a processor.
In detail, the memory and the processor are electrically connected directly or indirectly to realize data transmission or interaction. For example, they may be electrically connected to each other via one or more communication buses or signal lines. The memory can have stored therein at least one software function (computer program) which can be present in the form of software or firmware. The processor can be used for executing the executable computer program stored in the memory, so as to implement the electric energy meter operation error monitoring method based on the HPLC high-frequency collected data provided by the embodiment of the application.
Alternatively, the Memory may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like.
Also, the Processor may be a general-purpose Processor including a Central Processing Unit (CPU), a Network Processor (NP), a System on Chip (SoC), and the like; but may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components.
With reference to fig. 2, an embodiment of the present application further provides an electric energy meter operation error monitoring method based on HPLC high-frequency acquired data, which is applicable to a computing platform of the electric energy meter operation error monitoring system. The method steps defined by the relevant flow of the electric energy meter operation error monitoring method based on the HPLC high-frequency collected data can be realized by a computing platform of the electric energy meter operation error monitoring system.
The specific process shown in FIG. 2 will be described in detail below.
Step S110, acquiring total meter electricity consumption and sub meter electricity consumption obtained by respectively acquiring the total electric energy meter of the station area and the differential electric energy meter of the target station area by a plurality of HPLC devices in the current time period.
In this embodiment, the computing platform may acquire, in a current period (that is, a target period from a current time to a previous time, such as a day or a month), a plurality of HPLC devices respectively acquire a target station area (a station area refers to a power supply range or an area of a transformer in an electric power system, an electric energy meter for measuring a total electric energy consumption in the power supply range may be referred to as a station area total electric energy meter, and one station area may further include a plurality of electric energy meters in addition to the station area total electric energy meter, which may be referred to as station distinguishing electric energy meters), and a station area total electric energy meter (which may be periodically corrected or replaced to ensure that measured data has higher accuracy) and a total electric energy consumption meter electric energy consumption obtained by the station distinguishing electric energy meter (that is, an electric energy consumption increment of the station area total electric energy meter in the current period) and a sub-meter electric energy consumption (that is, an electric energy consumption increment of the station area electric energy meter in the current period) may be acquired by the station distinguishing electric energy meter.
The total electric energy meter of the station area is one, and the station area electric energy meters are multiple.
And step S120, distinguishing the sub-meter power consumption of the electric energy meter for each station, and obtaining the line loss electric quantity corresponding to the sub-meter power consumption based on the predetermined electric quantity corresponding relation and the sub-meter power consumption.
In this embodiment, after obtaining the sub-meter power consumption based on step S110, the computing platform may obtain, for each station, the sub-meter power consumption of the electric energy meter, and obtain, based on the predetermined power correspondence and the sub-meter power consumption, the line loss electric quantity corresponding to the sub-meter power consumption (considering that different stations have different line lengths between the different station-specific electric energy meters and the total station electric energy meter, so that the line loss electric quantities may be different even under the same power consumption, and therefore, the line loss electric quantities need to be calculated respectively by combining the corresponding line lengths).
The electric quantity correspondence may be calculated based on simulation (simulating an operating environment of the target distribution area) of the target distribution area.
And step S130, calculating to obtain total operation error data of the plurality of station-specific electric energy meters based on the station-specific total meter electricity consumption, the sub-meter electricity consumption and the line loss electricity quantity.
In this embodiment, after obtaining the line power consumption based on step S120, the computing platform may calculate, based on the line power consumption, and by combining the total power consumption of the distribution area and the power consumption of the sub-meters, total operation error data of the plurality of distribution area electric energy meters.
That is, the total power consumption of the station area, the sum of the power consumptions of the plurality of branch meters, minus the total operation error data of the plurality of line loss power = the plurality of station branch power meters.
And step S140, determining the operation error data of each station distinguishing electric energy meter based on the total operation error data and the error proportion information determined for each station distinguishing electric energy meter in advance.
In this embodiment, after obtaining the total operation error data based on step S130, the computing platform may determine the operation error data of each station-specific electric energy meter based on the total operation error data by combining the error ratio information determined in advance for each station-specific electric energy meter.
Based on the method, the operation error of the electric energy meter can be effectively monitored on the basis of not depending on the actual detection of the detection personnel, so that the problem that the operation error of the electric energy meter is difficult to effectively monitor due to the fact that the detection personnel are required to actually detect the operation error in the prior art is solved.
It should be noted that, in step S140, a specific manner for determining the operation error data of each station for distinguishing the electric energy meters is not limited, and may be selected according to actual application requirements.
For example, in an alternative example, to improve the accuracy of the determined operational error data, step S140 may include the steps of:
a first step of obtaining error ratio history information of each station differentiated electric energy meter in a history time period adjacent to the current time period (for example, if the current time period is 4/18/2020, then an adjacent history time period is 4/17/2020), wherein the time length of the history time period is the same as that of the current time period, the end point of the history time period is the start point of the current time period, and if the history time period is a first history time period, the corresponding error ratio history information is obtained based on measurement;
secondly, segmenting the current time interval according to a preset time length (such as one hour) to obtain a plurality of time segments, and forming a time segment sequence according to the time sequence relation based on the time segments, wherein the end point of the previous time segment in two adjacent time segments is coincided with the start point of the next time segment;
thirdly, acquiring sub-meter sub-electricity consumption of each station distinguishing electric energy meter in the time segment aiming at each time segment in the time segment sequence;
fourthly, distinguishing the electric energy meters for each station, and forming a sub-electric quantity sequence of the distinguishing electric energy meters (such as 0-1 hour of sub-meter sub-electric quantity, 1-2 hours of sub-meter sub-electric quantity, 2-3 hours of sub-meter sub-electric quantity and 3-4 hours of sub-meter sub-electric quantity) according to the time sequence on the basis of a plurality of sub-meter sub-electric quantities corresponding to the distinguishing electric energy meters;
fifthly, for each sub-power consumption sequence, screening each sub-meter sub-power consumption in the sub-power consumption sequence based on a preset abnormal data screening rule (so that the interference of abnormal data can be avoided), and forming a sub-power consumption target sequence corresponding to the sub-power consumption sequence after replacing the screened sub-meter sub-power consumption by the sub-meter preset sub-power consumption;
sixthly, updating the error proportion history information based on each sub-power consumption target sequence (for example, updating the error proportion history information based on proportion information between average values of sub-power consumption of sub-meters included in each sub-power consumption target sequence, where the proportion information between 4 average values is 5%, 30%, 40%, 25%, 4 error proportion history information is 25%, and 25%, respectively, and the corresponding 4 error proportion information is 15%, 27.5%, 32.5%, and 25%, respectively, that is, updating is implemented by means of average value calculation), so as to obtain error proportion information of each electric energy meter for station differentiation;
and seventhly, determining the operation error data of each station distinguishing electric energy meter based on the error proportion information and the total operation error data.
Optionally, in the above example, the specific manner of performing the screening process is not limited, and may be selected according to the actual application requirement.
For example, in an alternative example, the screening process may be performed based on the following steps:
firstly, screening and eliminating sub-meter sub-electricity consumption with preset identification from the sub-electricity consumption sequence to obtain a sub-electricity consumption to-be-interpolated sequence, wherein the preset identification is generated after operation error correction processing is carried out on the distinguishing electric energy meter in a time segment of the corresponding sub-meter sub-electricity consumption (if the sub-meter sub-electricity consumption with the preset identification does not exist, the sub-electricity consumption sequence can be directly used as a sub-electricity consumption target sequence);
secondly, determining the relation between the sub-meter electricity consumption and the (adjacent) former sub-meter electricity consumption and the relation between the sub-meter electricity consumption and the (adjacent) latter sub-meter electricity consumption for each sub-meter electricity consumption which is screened and excluded;
thirdly, for each sub-meter sub-electricity consumption which is screened and eliminated, if the difference value between the sub-meter sub-electricity consumption and the previous sub-meter sub-electricity consumption is not larger than a preset threshold value (which can be set according to the precision requirement, the higher the precision requirement is, the smaller the preset threshold value is), or the difference value between the sub-meter sub-electricity consumption and the next sub-meter sub-electricity consumption is not larger than the preset threshold value, determining a sub-meter preset sub-electricity consumption to replace the sub-meter sub-electricity consumption based on a first preset rule;
fourthly, for each sub-meter sub-electricity consumption which is screened and excluded, if the difference value between the sub-meter sub-electricity consumption and the previous sub-meter sub-electricity consumption is larger than the preset threshold value, and the difference value between the sub-meter sub-electricity consumption and the next sub-meter sub-electricity consumption is larger than the preset threshold value, determining a sub-meter preset sub-electricity consumption to replace the sub-meter sub-electricity consumption based on a second preset rule;
and fifthly, forming a corresponding sub-power consumption target sequence based on each sub-power consumption not screened and excluded in the sub-power consumption sequence and each determined preset sub-power consumption of the sub-tables.
It is understood that, in the above example, the specific manner of determining one sub-table preset sub-power consumption to perform replacement processing on the sub-table sub-power consumption based on the first preset rule is not limited, and may be selected according to actual application requirements.
For example, in an alternative example, the sub-table sub-power usage may be replaced based on determining a sub-table preset sub-power usage by:
firstly, the average power consumption of the previous sub-meter power consumption and the next sub-meter power consumption of the sub-meters power consumption which are screened and excluded can be calculated; and secondly, taking the average power consumption as a sub-table preset sub-power consumption, and replacing the sub-table power consumption which is screened and eliminated through the sub-table preset sub-power consumption.
It is understood that, in the above example, the specific manner of determining one sub-table preset sub-power consumption to perform replacement processing on the sub-table sub-power consumption based on the second preset rule is not limited, and may be selected according to actual application requirements.
For example, in an alternative example, in particular, in order to guarantee the effectiveness of replacement, the sub-table sub-power consumption may be subjected to replacement processing based on the sub-table preset sub-power consumption determined by the following steps:
determining a sequence segment including the sub-meter sub-electricity consumption in the sub-electricity consumption sequence in which the sub-meter sub-electricity consumption which is excluded from the screening is located, wherein the sequence segment includes a first number (which can be configured based on accuracy requirements, and the value of the first number can be larger as the accuracy requirements are higher) of sub-meter sub-electricity consumptions, and the first number of sub-meter sub-electricity consumptions are continuous in time;
secondly, acquiring a sub-power consumption history sequence formed by the station distinguishing electric energy meter corresponding to the sub-power consumption of the selected excluded sub-meter in the adjacent historical time period, wherein the historical time period is divided into a plurality of historical time segments based on the preset time length, and the sub-power consumption history sequence is formed based on the historical sub-meter sub-power consumption of the plurality of historical time segments;
performing sliding window processing on the sub power consumption historical sequences according to the first quantity to obtain a plurality of historical sub sequences, wherein the quantity of the sub power consumption of the historical sub tables included in each historical sub sequence is the first quantity;
fourthly, in the plurality of history subsequences, based on the similarity with the sequence segments (for example, the similarity between the sub-table electricity consumption at the corresponding position and the history sub-table electricity consumption is calculated firstly, and for example, the reciprocal of the difference is used as the similarity, and then the average similarity is calculated), a second number (which can be configured based on the precision requirement, and the higher the precision requirement is, the larger the value of the second number can be) of target history subsequences with the maximum similarity is determined;
fifthly, mapping each historical sub-meter sub-power consumption included by the target historical subsequence based on the historical average power consumption of the historical sub-meter sub-power consumption included by the target historical subsequence to obtain a plurality of historical power consumption identification values included by the target historical subsequence, wherein the historical power consumption identification values corresponding to any two historical sub-meter sub-power consumptions having the same relation with the historical average power consumption (if both the historical sub-meter sub-power consumptions are larger than the historical average power consumption) are the same, and the historical power consumption identification values corresponding to any two historical sub-meter sub-power consumptions having different relations with the historical average power consumption (if one is larger than the historical average power consumption, and the other is not larger than the historical average power consumption) are different;
sixthly, sequencing a plurality of historical power consumption identification values corresponding to the target historical subsequence according to the time sequence relation of the corresponding historical sub-table sub-power consumption for each target historical subsequence to obtain a historical identification value sequence corresponding to the target historical subsequence;
seventhly, calculating a target sequence bit number between each historical identification value sequence and each other historical identification value sequence, wherein the target sequence bit number is a sequence bit number between two historical identification value sequences and has the same historical electricity consumption identification value on a corresponding sequence position (that is, whether the historical electricity consumption identification value of the first position is the same, the historical electricity consumption identification value of the second position is the same, and the historical electricity consumption identification value of the third position is the same or not needs to be determined in each two historical identification value sequences);
an eighth step of, for each of the historical flag sequences, obtaining a variance value for each of the target sequence bit numbers corresponding to the historical flag sequence based on the historical flag sequence (e.g., 3 target sequence bit numbers are 2, and 2, respectively, a corresponding average value is 2, a corresponding variance value is (| 2-2 | plus | 2-2 |)/3 =0, 3 target sequence bit numbers are 1, 2, and 9, respectively, a corresponding average value is 4, and a corresponding variance value is (| 1-4 | plus | 2-4 | plus | 9-4 |)/3 = 3.33);
a ninth step of determining a target historical identification value sequence from the plurality of historical identification value sequences based on the magnitude relation of the discrete degree values, wherein the target historical identification value sequence is the historical identification value sequence with the minimum discrete degree value in the plurality of historical identification value sequences;
tenth, based on the positions (positions in time sequence) of the sub-meter power consumption which is excluded from screening in the sequence segments, determining the historical sub-meter power consumption at the corresponding position in the target historical sub-sequence corresponding to the target historical identification value sequence;
and step eleven, taking the historical sub-meter sub-electricity consumption at the corresponding position as a sub-meter preset sub-electricity consumption, and replacing the sub-meter sub-electricity consumption which is screened and eliminated based on the sub-meter preset sub-electricity consumption.
For another example, in an alternative example, in order to ensure the effectiveness of replacement and take into account the efficiency of the overall calculation, the sub-table sub-power consumption may be replaced based on the following steps:
substep 1, obtaining a plurality of sub-power consumption history sequences formed by a station distinguishing electric energy meter corresponding to the sub-power consumption of the selected and excluded sub-power consumption in a plurality of history periods (which may be a plurality of adjacent history periods), wherein each history period is divided into a plurality of history time segments based on the preset time length, and each sub-power consumption history sequence is formed based on the sub-power consumption of the corresponding history sub-power consumption of the plurality of history time segments;
substep 2, determining a target sub-power consumption history sequence with the maximum similarity to the sub-power consumption sequence where the sub-power consumption of the selected and excluded sub-meter is located in the plurality of sub-power consumption history sequences;
and substep 3, determining a historical sub-meter sub-power consumption as a sub-meter preset sub-power consumption based on a plurality of historical sub-meter sub-power consumptions included in the target sub-power consumption historical sequence, and replacing the sub-meter sub-power consumption which is screened and eliminated based on the sub-meter preset sub-power consumption.
In the above example, the specific manner of determining the historical sub-meter sub-power consumption as the preset sub-power consumption of the sub-meter based on the sub-step 3 is not limited, and may be selected according to the actual application requirement.
For example, in an alternative example, to ensure the efficiency of the overall calculation, sub-step 3 may comprise the following steps:
firstly, determining time segments corresponding to the sub-meter electricity consumption which is excluded by screening;
secondly, determining a corresponding historical time segment based on the time segment (for example, when the time segment is 9-10 of 5, 8 and 7 days of 2020, the historical time segment may be 9-10 of 5, 7 and 7 days of 2020), and obtaining a historical sub-meter sub-electricity consumption corresponding to the historical time segment from a plurality of historical sub-meter sub-electricity consumptions included in the historical sequence of the target sub-electricity consumption;
and then, taking the obtained historical sub-meter electricity consumption as a sub-meter preset sub-electricity consumption, and replacing the sub-meter electricity consumption which is screened and eliminated on the basis of the sub-meter preset sub-electricity consumption.
For another example, in another alternative example, to ensure the validity of the replacement, sub-step 3 may comprise the following steps:
firstly, reordering a plurality of historical sub-table sub-electricity consumptions included in the target sub-electricity consumption historical sequence according to the descending order;
secondly, according to the sequence from big to small, reordering the sub-electricity consumption of the sub-meter included in the sub-electricity consumption sequence where the sub-electricity consumption of the sub-meter which is excluded by screening is located;
then, based on the position of the sorted and excluded sub-meter electricity consumption after reordering, determining the historical sub-meter electricity consumption at the corresponding position in the reordered historical sequence of the target sub-electricity consumption (if the position of the sorted and excluded sub-meter electricity consumption after reordering is the fifth, determining the fifth historical sub-meter electricity consumption in the reordered historical sequence of the target sub-electricity consumption);
and finally, taking the historical sub-meter sub-electricity consumption at the corresponding position as a sub-meter preset sub-electricity consumption, and replacing the sub-meter sub-electricity consumption which is filtered and eliminated on the basis of the sub-meter preset sub-electricity consumption.
Further, considering that in the above example, the maximum similarity needs to be determined when performing sub-step 2, the method may further include the step of calculating the similarity between the historical sequence of the sub power consumption amount and the sequence of the sub power consumption amount where the sub-table sub power consumption amount is excluded from being filtered. Wherein, based on different requirements, the step may include different examples, and in the present embodiment, the following three examples are provided.
In a first example, the following sub-steps may be included:
substep 11, regarding each sub-meter sub-electricity consumption in the sub-electricity consumption sequence in which the sub-meter sub-electricity consumption is excluded from the screening, taking the sub-meter sub-electricity consumption and each sub-meter sub-electricity consumption after the sub-meter sub-electricity consumption as a target comparison sequence of the sub-meter sub-electricity consumption (a plurality of target comparison sequences can be obtained, and the sub-meter sub-electricity consumption included in each target comparison sequence is different in quantity);
substep 12, calculating an electric quantity ratio between the sub-meter sub-electric quantity of the corresponding position (the position is a sequential position in the sequence) in the target comparison sequence and the sub-electric quantity of the sub-meter in the historical sequence of the sub-electric quantity, and obtaining an electric quantity ratio set corresponding to the target comparison sequence;
substep 13, regarding each electric quantity ratio in each electric quantity ratio set, taking the electric quantity ratio and each previous electric quantity ratio of the electric quantity ratio in the corresponding electric quantity ratio set as a ratio sequence corresponding to the electric quantity ratio (each electric quantity ratio set corresponds to at least one ratio sequence);
substep 14, calculating an average value of the electric quantity ratios in each ratio sequence respectively to obtain a ratio average value corresponding to each ratio sequence;
substep 15, determining a maximum ratio average value in the ratio average values of each ratio sequence corresponding to each electric quantity ratio set, and taking the maximum ratio average value as a target ratio average value of the electric quantity ratio set;
substep 16, sorting the target ratio average values according to a sequence of big first and small second to obtain an average value sequence, and obtaining target ratio average values in a preset number (which can be configured according to the precision requirement, and the preset number can be larger if the precision is higher) in the average value sequence;
substep 17, for each target ratio average value of the preset number of target ratio average values, determining the number (e.g. 2, 3, 8, etc.) of the power ratio values in the power ratio set corresponding to the target ratio average value, and determining a first weight coefficient of the target ratio average value based on the number, wherein the number and the first weight coefficient have a positive correlation (that is, the larger the number of the power ratio values in a power ratio set is, the larger the corresponding first weight coefficient is);
substep 18, calculating, for each first weight coefficient, a product of the first weight coefficient and a target ratio average value corresponding to the first weight coefficient, and taking the product as a similarity between a target comparison sequence corresponding to the target ratio average value and the sub-power consumption history sequence;
and a substep 19 of determining a target comparison sequence with the maximum similarity, and taking the similarity of the target comparison sequence as the similarity between the sub power consumption sequence and the sub power consumption history sequence.
In a second example, the following sub-steps may be included:
substep 21, regarding each sub-meter sub-electricity consumption in the sub-electricity consumption sequence where the sub-meter sub-electricity consumption which is excluded by screening is located, taking the sub-meter sub-electricity consumption and each sub-meter sub-electricity consumption behind the sub-meter sub-electricity consumption as a target comparison sequence of the sub-meter sub-electricity consumption;
substep 22, calculating an electric quantity ratio between the sub-meter sub-electric quantity and the historical sub-meter sub-electric quantity at corresponding positions in the target comparison sequence and the sub-electric quantity historical sequence aiming at each target comparison sequence corresponding to the sub-electric quantity sequence, and obtaining an electric quantity ratio set corresponding to the target comparison sequence;
substep 23, regarding each electric quantity ratio in each electric quantity ratio set, taking the electric quantity ratio and each previous electric quantity ratio of the electric quantity ratio in the corresponding electric quantity ratio set as a ratio sequence corresponding to the electric quantity ratio;
substep 24, respectively calculating an average value of the electric quantity ratios in each ratio sequence to obtain a ratio average value corresponding to each ratio sequence;
substep 25, determining a maximum ratio average value in the ratio average values of each ratio sequence corresponding to each electric quantity ratio set, and taking the maximum ratio average value as a target ratio average value of the electric quantity ratio set;
a substep 26 of comparing, for each adjacent (temporal) two of the target comparison sequences, two target ratio averages respectively corresponding to the two target comparison sequences;
a substep 27, if the average difference between the two target ratio averages is smaller than the target difference (configured based on the precision requirement, if the precision requirement is higher, the target difference may be larger), setting a second weight coefficient for the larger one of the two target ratio averages, wherein the second weight coefficient is smaller than 1 and has a negative correlation with the target difference (that is, the larger the target difference, the smaller the second weight coefficient);
substep 28, for each second weight coefficient, performing update processing on the corresponding target ratio average value based on the second weight coefficient (i.e. performing product on the second weight coefficient and the corresponding target ratio average value to realize update processing), so as to obtain an updated target ratio average value;
and a substep 29 of determining a maximum target ratio average value and taking the maximum target ratio average value as the similarity between the sub power consumption sequence and the sub power consumption history sequence.
In the third example, in order to sufficiently ensure that the calculated similarity has high reliability, in particular, the following sub-steps may be included:
substep 30, regarding each sub-meter sub-electricity consumption in the sub-electricity consumption sequence where the sub-meter sub-electricity consumption which is excluded by screening is located, taking the sub-meter sub-electricity consumption and each sub-meter sub-electricity consumption behind the sub-meter sub-electricity consumption as a target comparison sequence of the sub-meter sub-electricity consumption;
substep 31, calculating an electric quantity ratio between the sub-meter sub-electric quantity and the historical sub-meter sub-electric quantity at the corresponding position in the target comparison sequence and the sub-electric quantity historical sequence aiming at each target comparison sequence corresponding to the sub-electric quantity sequence, and obtaining an electric quantity ratio set corresponding to the target comparison sequence;
substep 32, regarding each electric quantity ratio in each electric quantity ratio set, taking the electric quantity ratio and each previous electric quantity ratio of the electric quantity ratio in the corresponding electric quantity ratio set as a ratio sequence corresponding to the electric quantity ratio;
substep 33, calculating an average value of the electric quantity ratios in each ratio sequence respectively to obtain a ratio average value corresponding to each ratio sequence;
substep 34, determining a maximum ratio average value in the ratio average values of each ratio sequence corresponding to each electric quantity ratio set, and taking the maximum ratio average value as a target ratio average value of the electric quantity ratio set;
substep 35, sorting the target ratio average values according to a sequence of first big and second small to obtain an average value sequence, and obtaining the target ratio average values of a preset number in the average value sequence;
substep 36, determining, for each target ratio average value of the preset number of target ratio average values, the number of the power ratio values in the power ratio set corresponding to the target ratio average value, and determining a first weight coefficient of the target ratio average value based on the number, where the number and the first weight coefficient have a positive correlation;
substep 37, calculating, for each first weight coefficient, a product of the first weight coefficient and a target ratio average value corresponding to the first weight coefficient, and taking the product as a similarity between a target comparison sequence corresponding to the target ratio average value and the sub-power consumption history sequence;
substep 38, determining a target comparison sequence with the maximum similarity number;
and a substep 39 of determining a target comparison sequence with the largest quantity of sub-table sub-electricity consumptions included in the target quantity target comparison sequences, and taking the similarity of the target comparison sequence as the similarity between the sub-electricity consumption sequence and the sub-electricity consumption history sequence.
To sum up, the utility model provides an electric energy meter running error monitoring method and system based on HPLC high frequency data collection distinguishes the electric energy meter through HPLC equipment to the total electric energy meter in platform district and platform respectively and gathers, can obtain corresponding total table power consumption and branch table power consumption, then, obtain the line loss electric quantity that this branch table power consumption corresponds based on predetermined electric quantity corresponding relation and this branch table power consumption, thereby can combine this total table power consumption to obtain the total running error data that the platform distinguishes the electric energy meter, make can distinguish the running error data of electric energy meter based on definite error proportion information, confirm each platform. Therefore, the operation error of the power meter in the transformer area can be effectively monitored, and the problem that the operation error of the power meter in the transformer area is difficult to effectively monitor in the prior art is solved.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (9)
1. The electric energy meter operation error monitoring method based on high-frequency HPLC data acquisition is characterized by being applied to a computing platform in an electric energy meter operation error monitoring system, wherein the monitoring system further comprises an HPLC device in communication connection with the computing platform, and the method comprises the following steps:
acquiring total table electricity consumption and sub-table electricity consumption obtained by respectively acquiring a total table electricity consumption and a sub-table electricity consumption of a target table area by a plurality of HPLC (high performance liquid chromatography) devices in the current time period, wherein the total table area electricity consumption is one, and the sub-table area electricity consumption is a plurality of;
aiming at the sub-meter power consumption of each station distinguishing electric energy meter, obtaining the line loss electric quantity corresponding to the sub-meter power consumption based on the predetermined electric quantity corresponding relation and the sub-meter power consumption;
calculating to obtain total operation error data of the plurality of station-distinguished electric energy meters based on the station-district general meter electricity consumption, the sub-meter electricity consumption and the line loss electricity;
determining operation error data of each station distinguishing electric energy meter based on the total operation error data and error proportion information determined for each station distinguishing electric energy meter in advance;
wherein the step of determining the operation error data of each station-specific electric energy meter based on the total operation error data and the error proportion information determined in advance for each station-specific electric energy meter includes:
acquiring error proportion historical information of each station distinguishing electric energy meter in a historical time period adjacent to the current time period, wherein the historical time period is the same as the current time period in duration, the end point of the historical time period is the starting point of the current time period, and if the historical time period is the first historical time period, the corresponding error proportion historical information is obtained based on measurement;
segmenting the current time interval according to a preset time length to obtain a plurality of time segments, and forming a time segment sequence based on the time segments according to the time sequence relation, wherein the end point of the previous time segment in two adjacent time segments is coincided with the start point of the next time segment;
aiming at each time slice in the time slice sequence, obtaining sub-meter sub-electricity consumption of each station distinguishing electric energy meter in the time slice;
aiming at each distinguishing electric energy meter, forming a sub-electricity consumption sequence of the distinguishing electric energy meter based on a plurality of sub-meter sub-electricity consumptions corresponding to the distinguishing electric energy meters according to the time sequence;
aiming at each sub-power consumption sequence, screening each sub-meter sub-power consumption in the sub-power consumption sequence based on a preset abnormal data screening rule, and forming a sub-power consumption target sequence corresponding to the sub-power consumption sequence after replacing the screened sub-meter sub-power consumption by the sub-meter preset sub-power consumption;
updating the error proportion historical information based on each sub-power consumption target sequence to obtain error proportion information of each station distinguishing electric energy meter;
and determining the operation error data of each station distinguishing electric energy meter based on the error proportion information and the total operation error data.
2. The method for monitoring the running error of the electric energy meter based on the high-frequency HPLC collected data according to claim 1, wherein the step of forming the target sequence of the sub-electric energy consumption corresponding to the sub-electric energy consumption sequence after screening each sub-meter sub-electric energy consumption in the sub-electric energy consumption sequence based on the preset abnormal data screening rule and replacing the screened sub-meter sub-electric energy consumption by the preset sub-electric energy consumption of the sub-meter comprises the following steps:
screening and excluding sub-meter sub-electricity consumption with preset identification from the sub-electricity consumption sequence to obtain a sub-electricity consumption to-be-interpolated sequence, wherein the preset identification is generated after operation error correction processing is carried out on the distinguishing electric energy meter based on the time segment of the corresponding sub-meter sub-electricity consumption;
determining the relation between the sub-meter electricity consumption and the former sub-meter electricity consumption and the relation between the sub-meter electricity consumption and the latter sub-meter electricity consumption for each sub-meter electricity consumption which is screened and excluded;
for each sub-meter sub-electricity consumption which is excluded by screening, if the difference value between the sub-meter sub-electricity consumption and the previous sub-meter sub-electricity consumption is not larger than a preset threshold value, or the difference value between the sub-meter sub-electricity consumption and the next sub-meter sub-electricity consumption is not larger than the preset threshold value, determining a sub-meter preset sub-electricity consumption to carry out replacement processing on the sub-meter sub-electricity consumption based on a first preset rule;
for each sub-meter sub-electricity consumption which is excluded by screening, if the difference value between the sub-meter sub-electricity consumption and the former sub-meter electricity consumption is larger than the preset threshold value, and the difference value between the sub-meter sub-electricity consumption and the latter sub-meter electricity consumption is larger than the preset threshold value, determining a sub-meter preset sub-electricity consumption to carry out replacement processing on the sub-meter sub-electricity consumption based on a second preset rule;
and forming a corresponding sub-power consumption target sequence based on each sub-meter sub-power consumption which is not screened and excluded in the sub-power consumption sequence and each determined sub-meter preset sub-power consumption.
3. The method for monitoring the running error of the electric energy meter based on the high-frequency HPLC collected data as claimed in claim 2, wherein the step of determining a sub-meter preset sub-power consumption based on a first preset rule to perform replacement processing on the sub-meter sub-power consumption comprises the following steps:
calculating the average power consumption of the previous sub-meter power consumption and the next sub-meter power consumption of the sub-meters which are screened and excluded;
and taking the average power consumption as a sub-meter preset sub-power consumption, and replacing the sub-meter power consumption which is screened and eliminated through the sub-meter preset sub-power consumption.
4. The method for monitoring the running error of the electric energy meter based on the high-frequency HPLC collected data as claimed in claim 2, wherein the step of determining a sub-meter preset sub-power consumption based on a second preset rule to perform replacement processing on the sub-meter sub-power consumption comprises the following steps:
acquiring a plurality of sub-power consumption historical sequences formed in a plurality of historical time periods by station distinguishing electric energy meters corresponding to the sub-power consumption of the selected and excluded sub-power consumption meters, wherein each historical time period is divided into a plurality of historical time segments based on the preset time length, and each sub-power consumption historical sequence is formed based on the historical sub-power consumption of the corresponding plurality of historical time segments;
determining a target sub-power consumption history sequence with the maximum similarity to the sub-power consumption sequence where the sub-power consumption of the selected and excluded sub-meter is located in the plurality of sub-power consumption history sequences;
and determining a historical sub-meter sub-power consumption as a sub-meter preset sub-power consumption based on a plurality of historical sub-meter sub-power consumptions included in the target sub-power consumption historical sequence, and replacing the sub-meter sub-power consumptions which are screened and eliminated based on the sub-meter preset sub-power consumption.
5. The method for monitoring the running error of the electric energy meter based on the high-frequency HPLC collected data as claimed in claim 4, wherein the step of determining a sub-meter preset sub-power consumption based on a second preset rule to perform replacement processing on the sub-meter sub-power consumption further comprises:
calculating the similarity between the sub-power consumption historical sequence and the sub-power consumption sequence where the sub-power consumption of the selected and excluded sub-meter is located, wherein the step comprises the following steps:
aiming at each sub-meter sub-electricity consumption in the sub-electricity consumption sequence where the sub-meter sub-electricity consumption which is excluded by screening is located, taking each sub-meter sub-electricity consumption after the sub-meter sub-electricity consumption and the sub-meter sub-electricity consumption as a target comparison sequence of the sub-meter sub-electricity consumption;
aiming at each target comparison sequence corresponding to the sub-power consumption sequence, calculating the power ratio between the sub-meter sub-power consumption at the corresponding position in the target comparison sequence and the sub-power consumption in the sub-power consumption historical sequence and the historical sub-power consumption, and obtaining a power ratio set corresponding to the target comparison sequence;
regarding each electric quantity ratio in each electric quantity ratio set, taking each electric quantity ratio of the electric quantity ratio and the front electric quantity ratio of the electric quantity ratio in the corresponding electric quantity ratio set as a ratio sequence corresponding to the electric quantity ratio;
respectively calculating the average value of the electric quantity ratios in each ratio sequence to obtain the average value of the ratio corresponding to each ratio sequence;
determining the maximum ratio average value in the ratio average values of each ratio sequence corresponding to each electric quantity ratio set aiming at each electric quantity ratio set, and taking the maximum ratio average value as the target ratio average value of the electric quantity ratio set;
sequencing the target ratio average values according to the sequence of big first and small second to obtain an average value sequence, and obtaining the target ratio average values of the preset number in the average value sequence;
for each target ratio average value in the preset number of target ratio average values, determining the number of the electric quantity ratios in the electric quantity ratio set corresponding to the target ratio average value, and determining a first weight coefficient of the target ratio average value based on the number, wherein the number and the first weight coefficient have a positive correlation;
calculating the product of the first weight coefficient and the target ratio average value corresponding to the first weight coefficient aiming at each first weight coefficient, and taking the product as the similarity between the target comparison sequence corresponding to the target ratio average value and the historical sequence of the sub power consumption;
and determining a target comparison sequence with the maximum similarity, and taking the similarity of the target comparison sequence as the similarity between the sub power consumption sequence and the sub power consumption historical sequence.
6. The method for monitoring the running error of the electric energy meter based on the high-frequency HPLC collected data as claimed in claim 4, wherein the step of determining a sub-meter preset sub-power consumption based on a second preset rule to perform replacement processing on the sub-meter sub-power consumption further comprises:
calculating the similarity between the sub-power consumption historical sequence and the sub-power consumption sequence where the sub-power consumption of the selected and excluded sub-meter is located, wherein the step comprises the following steps:
aiming at each sub-meter sub-electricity consumption in the sub-electricity consumption sequence where the sub-meter sub-electricity consumption which is excluded by screening is located, taking each sub-meter sub-electricity consumption after the sub-meter sub-electricity consumption and the sub-meter sub-electricity consumption as a target comparison sequence of the sub-meter sub-electricity consumption;
aiming at each target comparison sequence corresponding to the sub-power consumption sequence, calculating the power ratio between the sub-meter sub-power consumption at the corresponding position in the target comparison sequence and the sub-power consumption in the sub-power consumption historical sequence and the historical sub-power consumption, and obtaining a power ratio set corresponding to the target comparison sequence;
regarding each electric quantity ratio in each electric quantity ratio set, taking each electric quantity ratio of the electric quantity ratio and the front electric quantity ratio of the electric quantity ratio in the corresponding electric quantity ratio set as a ratio sequence corresponding to the electric quantity ratio;
respectively calculating the average value of the electric quantity ratios in each ratio sequence to obtain the average value of the ratio corresponding to each ratio sequence;
determining the maximum ratio average value in the ratio average values of each ratio sequence corresponding to each electric quantity ratio set aiming at each electric quantity ratio set, and taking the maximum ratio average value as the target ratio average value of the electric quantity ratio set;
aiming at each two adjacent target comparison sequences, comparing two target ratio average values respectively corresponding to the two target comparison sequences;
if the average difference value between the two target ratio average values is smaller than the target difference value, setting a second weight coefficient for the larger one of the two target ratio average values, wherein the second weight coefficient is smaller than 1 and has a negative correlation with the target difference value;
for each second weight coefficient, updating the corresponding target ratio average value based on the second weight coefficient to obtain an updated target ratio average value;
and determining the maximum target ratio average value, and taking the maximum target ratio average value as the similarity between the sub power consumption sequence and the sub power consumption historical sequence.
7. The method for monitoring the running error of the electric energy meter based on the high-frequency HPLC collected data according to claim 4, wherein the step of determining one historical sub-meter sub-electricity consumption as a sub-meter preset sub-electricity consumption based on a plurality of historical sub-meter sub-electricity consumptions included in the target sub-electricity consumption historical sequence, and performing replacement processing on the sub-meter sub-electricity consumption which is excluded by screening based on the sub-meter preset sub-electricity consumption comprises the following steps:
determining time segments corresponding to the sub-meter electricity consumption which is excluded from screening;
determining a corresponding historical time segment based on the time segment, and acquiring a historical sub-meter sub-power consumption corresponding to the historical time segment from a plurality of historical sub-meter sub-power consumptions included in the historical sequence of the target sub-power consumption;
and taking the obtained historical sub-meter sub-electricity consumption as a sub-meter preset sub-electricity consumption, and replacing the sub-meter sub-electricity consumption which is screened and eliminated based on the sub-meter preset sub-electricity consumption.
8. The method for monitoring the running error of the electric energy meter based on the high-frequency HPLC collected data according to claim 4, wherein the step of determining one historical sub-meter sub-electricity consumption as a sub-meter preset sub-electricity consumption based on a plurality of historical sub-meter sub-electricity consumptions included in the target sub-electricity consumption historical sequence, and performing replacement processing on the sub-meter sub-electricity consumption which is excluded by screening based on the sub-meter preset sub-electricity consumption comprises the following steps:
according to the sequence from big to small, reordering the sub-electricity consumption of the plurality of history sub-tables in the history sequence of the target sub-electricity consumption;
according to the sequence from big to small, reordering the sub-electricity consumption of the sub-meter included in the sub-electricity consumption sequence where the sub-electricity consumption of the sub-meter to be screened and excluded is located;
based on the positions of the sorted sub-meter sub-electricity consumptions, determining historical sub-meter sub-electricity consumptions at corresponding positions in the target sub-electricity consumption historical sequence;
and taking the historical sub-meter sub-electricity consumption at the corresponding position as a sub-meter preset sub-electricity consumption, and replacing the sub-meter sub-electricity consumption which is filtered and eliminated based on the sub-meter preset sub-electricity consumption.
9. An electric energy meter operation error monitoring system, comprising an HPLC device and a computing platform communicatively coupled to the HPLC device, wherein the computing platform comprises:
a memory for storing a computer program;
a processor connected with the memory for executing a computer program to implement the method for monitoring the running error of the electric energy meter based on the high-frequency HPLC data collection of any one of claims 1 to 8.
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