CN114814402A - Abnormal electricity utilization analysis method based on integrated electricity quantity and line loss system big data - Google Patents
Abnormal electricity utilization analysis method based on integrated electricity quantity and line loss system big data Download PDFInfo
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Abstract
The abnormal power utilization analysis method of the large data of the integrated power and line loss system is based on the integrated power and line loss management system, and performs targeted processing analysis on the data collected in the system by combining with the real-time data in the D5000 and marketing acquisition system. The method starts from theoretical line loss and contemporaneous line loss, performs abnormal power utilization analysis causing line loss, provides analysis and confirmation of reasons from line loss change to abnormal power utilization through structural analysis of an abnormal power utilization analysis model, and verifies through actual cases.
Description
Technical Field
The invention relates to an abnormal power utilization analysis method, in particular to an abnormal power utilization analysis method based on integrated electric quantity and line loss system big data, and belongs to the technical field of intelligent power networks.
Background
At present, China has a large population and a huge electricity utilization base number, and the electric energy line loss caused by abnormal electricity utilization is increasingly serious. The electric energy line loss is generated in each link of power transmission, power transformation, power distribution and power selling and serves as an important comprehensive economic index of an electric power enterprise, and the line loss rate can reflect the economic efficiency of the electric power enterprise and the profitability of the enterprise. Subsequently, the information technologies such as cloud computing, internet of things, mobile internet, social network and the like are rapidly developed, and big data technologies represented by distributed storage, distributed computing, massive data mining and the like are unprecedentedly developed and are becoming mature.
At present, the abnormal power consumption analysis of the power distribution network needs to be analyzed and compared by means of data collected in automation equipment and a management system, and the collected mass data have typical 4V big data characteristics, namely Volume, Velocity, Variety and Value, namely, huge data Volume, high generation speed, multiple data types and low Value density.
In the face of increasingly huge data, most of the data processing is still in the process of collection, induction and statistics by adopting the traditional storage and processing technology, the effective utilization of the data cannot be realized, the deep value of the data cannot be mined, for example, the classification and analysis of abnormal data of electric quantity still can depend on manual verification, the efficiency is low, the accuracy is low, and a new management mode is urgently needed to effectively process the data. Meanwhile, an integrated electric quantity and line loss management system emphasizing electric quantity and line loss data calculation and statistical display exists, but the deep mining of abnormal data and the analysis and identification of abnormal electricity consumption are still in a starting stage. Therefore, an integrated power and line loss management system and a big data technology are needed to construct an effective and direct analysis model aiming at abnormal data in the mass power and line loss data stored in the system, so as to realize accurate evaluation and classification of abnormal power utilization.
Disclosure of Invention
The invention aims to overcome the defects and provide an abnormal electricity utilization analysis method based on the big data of the integrated electricity quantity and line loss system, which is used for carrying out targeted processing and analysis on the data collected in the system by combining with the real-time data in the D5000 and marketing acquisition system. The abnormal power utilization analysis which causes the line loss is carried out starting from the theoretical line loss and the synchronous line loss, the analysis and confirmation from the line loss change to the abnormal power utilization reason are provided through the framework analysis of the abnormal power utilization analysis model, and the verification is carried out through an actual case.
The purpose of the invention is realized as follows:
an abnormal electricity utilization analysis method for large data of an integrated electricity quantity and line loss system comprises the following steps:
firstly, constructing a large abnormal electricity utilization analysis database and an abnormal electricity utilization analysis model:
abnormal electricity consumption analysis big database: the server extracts line account information which comprises historical data structures of installed capacity, load, electric quantity, power consumption information, working conditions and abnormal event records of a user; simultaneously, the server collects the following data: line gateway electric quantity, public and private variable daily electric quantity, public and private variable monthly electric quantity, line loss statistical curve, line electric quantity detail and metering device data in the synchronous line loss statistical data; line loss calculation values, common and special variable copper loss and iron loss data and a line topological graph in line loss calculation data of the line loss system; active and reactive electric quantity, active and reactive power, real-time power factor, terminal calendar clock and terminal parameter in the power consumption information data acquisition system; d5000 real-time line voltage, line current and load curve data in the system;
the server constructs an abnormal electricity utilization analysis model: the abnormal electricity consumption analysis model comprises two parts of electricity quantity and line loss data access and abnormal electricity consumption analysis, and as shown in fig. 2, the integrated electricity quantity and line loss system big data comprise line loss data, user load data, user electricity consumption information data, line gateway electricity quantity and the like. The abnormal electricity utilization analysis and identification based on the big data comprises the following steps: comparing homologous homogeneous data and homologous heterogeneous data. Wherein the comparison of homologous and homogeneous data comprises: user load comparison, daily electric quantity comparison, current comparison and active power comparison; the homologous heterogeneous data comprises voltage and current comparison, current and active power comparison. Various comparative techniques are shown. The method mainly compares the abnormal electricity utilization data characteristic values in the database with the target of abnormal electricity utilization.
And then selecting an abnormal electricity utilization characteristic value: typical abnormal electricity utilization data including technical faults such as line breakage, power supply faults, line faults, meter errors and data transmission errors and electricity stealing behaviors are selected, big data comparison is carried out on the data and the abnormal electricity utilization data, then data conversion is carried out, and the characteristic value of the abnormal electricity utilization is marked.
Then, analysis is carried out according to an abnormal electricity utilization analysis model: and analyzing and evaluating the abnormal power utilization based on an abnormal power utilization comparison algorithm, and distinguishing the abnormal power utilization from the normal power utilization. And extracting abnormal data in the system, comparing the abnormal data with the abnormal power utilization characteristic value, extracting comparison data, and finally importing a generated comparison result into an abnormal power utilization analysis model to finally realize diagnosis and classification of abnormal power utilization.
And finally, exporting abnormal power utilization diagnosis.
Compared with the prior art, the invention has the beneficial effects that:
the system is based on an integrated electric quantity and line loss management system, and performs targeted processing and analysis on data collected in the system by combining with real-time data in a D5000 and marketing acquisition system. The method starts from theoretical line loss and synchronous line loss, performs abnormal electricity utilization analysis causing line loss, provides analysis confirmation from line loss change to abnormal electricity utilization reasons through framework analysis of an abnormal electricity utilization analysis model, and verifies through actual cases.
Drawings
Fig. 1 is a flow chart of abnormal electricity consumption analysis according to the present invention.
Fig. 2 is an architecture diagram of an abnormal electricity consumption analysis model.
FIG. 3 is a schematic diagram of a single interconnection line model according to the present invention.
FIG. 4 is a diagram of a single line loss calculation model according to the present invention.
FIG. 5 is a model diagram of the line loss of a 10kV JiangB line fault.
FIG. 6 is a diagram of a model for analyzing electricity stealing according to the present invention.
Fig. 7 is a 10kV certain line topology diagram of the invention.
Fig. 8 is a detailed view of the invention.
Fig. 9 is a three-phase current unbalance current diagram of a user according to the invention.
FIG. 10 is a current diagram of a measuring point of the ammeter of the present invention.
Detailed Description
Referring to fig. 1-10, the invention relates to an abnormal electricity consumption analysis method based on integrated electricity and line loss system big data;
the term is defined as:
line loss: the line loss can be classified into statistical line loss, theoretical line loss, and management line loss according to its characteristics.
And (4) line loss statistics: the actual line loss of the power grid is the difference value of the power supply quantity and the electricity selling quantity measured and counted by the electricity meter, and the actual loss condition of the power grid is reflected.
Theoretical line loss: the line loss is also called technical line loss and is obtained through theoretical calculation according to parameters of power supply equipment and real-time load data of power grid operation. The theoretical line loss reflects the amount of power that the grid should theoretically lose under a particular grid structure and mode of operation.
Managing line loss: and the other losses except the theoretical line loss are the actual loss of the power grid, and the value of the other losses is the difference value between the statistical line loss and the theoretical line loss. The theoretical line loss research mainly utilizes actual measurement data such as a power grid topological structure and power flow, and adopts a theoretical calculation method to calculate based on power system knowledge or adopts a machine learning algorithm to predict a line loss value by utilizing historical data. The management line loss comprises electricity stealing, meter error, electric leakage and the like.
Line loss system: the method is an important component in an integrated electric quantity and line loss management platform. The system integrates data of various on-line lines, distribution transformers, pole transformers, interconnection switches, station rooms and the like, and displays the electricity quantity for sale on the platform. With these integration data acquisition, utilize big data technology and with adopt, D5000 etc. in the system data combine the analysis, will realize the accurate discernment of unusual power consumption, greatly improve the analytic efficiency of unusual power consumption, provide powerful support for the safe operation of distribution network.
And (4) theoretically supporting:
a: and (3) abnormal electricity utilization analysis:
according to the electric power calculation method, a power P metering formula in the electric energy meter is as follows:
P＝UIcosα……(1-1)；
wherein, U is the voltage value of the measuring element, I is the current value of the measuring element, and alpha is the power factor angle.
As can be seen from the formula (1-1), the magnitude of the power measured by the electric energy meter is related to three variables, namely voltage, current and the phase relation between the voltage and the current. The abnormal electricity utilization analysis analyzes abnormal electricity utilization behaviors, judges whether electricity stealing and metering faults exist and the like by monitoring and analyzing whether electrical data such as voltage, current, power factor (phase angle), line loss, electric quantity and the like are normal or not and the variation trend of the parameters.
The abnormal electricity utilization behavior is caused by technical faults such as line breakage, power supply faults, line faults, errors of a meter and a data transmission, electricity stealing behavior, external interference and the like.
B: abnormal electricity utilization analysis and identification process under data background:
under the background of large electric power data, a large amount of electricity utilization data in the integrated electric quantity and line loss management system are collected and integrated, and related original data are purposefully selected according to actual demands to form an abnormal electricity utilization database. And secondly, analyzing and comparing abnormal data in the system with the characteristic values to determine the type of the abnormal electricity utilization.
The invention discloses an abnormal electricity consumption analysis method based on integrated electricity and line loss system big data, which comprises the following steps:
firstly, constructing a large abnormal electricity utilization analysis database and an abnormal electricity utilization analysis model:
abnormal electricity consumption analysis big database: the server extracts line account information which comprises historical data structures of installed capacity, load, electric quantity, power consumption information, working conditions and abnormal event records of a user; simultaneously, the server collects the following data: line gateway electric quantity, public and private variable daily electric quantity, public and private variable monthly electric quantity, line loss statistical curve, line electric quantity detail and metering device data in the synchronous line loss statistical data; line loss calculation values, common and special variable copper loss and iron loss data and a line topological graph in line loss calculation data of the line loss system; active and reactive electric quantity, active and reactive power, real-time power factor, terminal calendar clock and terminal parameter in the power consumption information data acquisition system; d5000 real-time line voltage, line current and load curve data in the system;
the server constructs an abnormal electricity utilization analysis model: the abnormal electricity consumption analysis model includes two parts, namely, electricity quantity and line loss data access and abnormal electricity consumption analysis, as shown in fig. 2, the integrated electricity quantity and line loss system big data includes line loss data, user load data, user electricity consumption information data, line gateway electricity quantity and the like (the existing data can be obtained from internal systems such as a server and a D5000 system). The abnormal electricity utilization analysis and identification based on the big data comprises the following steps: comparing homologous homogeneous data and homologous heterogeneous data. Wherein the comparison of homologous and homogeneous data comprises: user load comparison, daily electric quantity comparison, current comparison and active power comparison; the homologous heterogeneous data comprises voltage and current comparison, current and active power comparison. Various comparative techniques are shown. The method mainly compares the abnormal electricity utilization data characteristic values in the database with the target of abnormal electricity utilization.
And then selecting an abnormal electricity utilization characteristic value: typical abnormal electricity utilization data including technical faults such as line breakage, power supply faults, line faults, meter errors and data transmission errors and electricity stealing behaviors are selected, big data comparison is carried out on the data and the abnormal electricity utilization data, then data conversion is carried out, and the characteristic value of the abnormal electricity utilization is marked.
Then, analysis is carried out according to an abnormal electricity utilization analysis model: and analyzing and evaluating the abnormal power utilization based on an abnormal power utilization comparison algorithm, and distinguishing the abnormal power utilization from the normal power utilization. And extracting abnormal data in the system, comparing the abnormal data with the abnormal power utilization characteristic value, extracting comparison data, and finally importing a generated comparison result into an abnormal power utilization analysis model to finally realize diagnosis and classification of abnormal power utilization.
And finally, exporting abnormal power utilization diagnosis.
The following description is given by way of a specific example:
analyzing and establishing a power distribution network line loss model:
the topological structure in the distribution network is complex, the coverage area of distribution network equipment is wide, the operation mode is frequently changed, and the conditions have great influence on the distribution network branching line loss calculation result. Taking a single connection line as an example, a distribution network distribution diagram is shown in fig. 3. Based on the current state, in the analysis and calculation of the line loss model, in order to reduce the influence of planned power failure, temporary power supply, operation mode change and the like on line loss calculation, a power grid topology change and operation mode change real-time updating model is established, line change relation change, change time and electric quantity change caused by topology change and operation mode change are brought into the line loss calculation, and accurate calculation of line loss in the model is realized.
For the convenience of calculation and expression, the daily average load of each part of the 10kV river A line and the 10kV river B line is assumed to be completely consistent and the load is uniformly distributed within 24 hours.
1. Single line loss model
As shown in fig. 3, when the 10kV river a line and the 10kV river B line work independently, it can be known that the line loss W1 and the line loss rate η 1 of the 10kV river a line are:
W 1 ＝W A -W 1a -W 2a (2-1)
η 1 ＝W 1 ÷W A (2-2)
wherein WA is the input electric quantity of a 10kV river A line gateway, W1a is the electric quantity collected by a public transformer A metering device, and W2a is the electric quantity collected by a special transformer A metering device.
The line loss W2 and the line loss rate eta 2 of the line A of the 10kV river are as follows:
W 2 ＝W B -W 1b -W 2b (2-3)
η 2 ＝W 2 ÷W B (2-4)
among them, WB is the input electric quantity of 10kV river B line pass, W1B is public transformer A metering device collection electric quantity, W2B is special transformer A metering device collection electric quantity. The computational model is shown in fig. 4.
2. 10kV river B line fault line loss model
If the outgoing cable of the transformer substation in the river B of 10kV has a fault, the rush-repair time is T, namely, in the T time, when the contact switch K2001 is closed, the breaker of the B line of 10kV is disconnected. Because the user metering devices in the line and the line correspond to each other, the electric quantity of the 10kV river A line is supplied to the 10kV river B line, the electric quantity cannot be counted by the river A line gateway metering device, and the electric quantity is classified into line loss. When the 10kV river A line is provided with a 10kV river B line, the line gate input electric quantity WA-B of the 10kV river A line is expressed as follows:
W A-B ＝W A +W B ×T/24 (2-5)
the line loss ratio η a-B at this time is expressed as:
η 2 ＝(W A-B -W 1a -W 2a )÷W A (2-6)
as can be seen from equations (2-2) and (2-6), the line a is in a high loss state.
The input electric quantity WB-A of the 10kV river B line at the closing time is
W B-A ＝W B ×T/24 (2-7)
The line loss amount W2 and the line loss rate η 2 are expressed as:
W 2 ＝W B-A -W 1b -W 2b (2-8)
η B-A ＝W 1 ÷W A (2-9)
as can be seen from equations (2-7), (2-8), and (2-9), the line a is in a negative loss state at this time.
The calculation model is shown in fig. 5 with the 10kV river a line as the reference object.
3. Power line loss model for abnormity of certain user
Supposing that when a public transformer A user in a 10kV river B line breaks down, the public transformer A user is isolated through a user switch B, the fault time is T1, the interconnection switch K2001 is disconnected at the moment, and the line loss WB-B and the line loss rate eta B-B of the 10kV river B line are as follows:
W B-b ＝W B -W 1a -W 2a ×T 1 /24 (2-10)
η 1 ＝W B-b ÷W B (2-11)
at this time, the fault of the public transformer user causes the protection action of the outgoing line side of the transformer substation, namely, the 10kV river B breaker is switched off, the 10kV river B line section switch K2101 is switched off, the interconnection switch K2001 is switched on, and the 10kV river A line is provided for the rear section of the 10kV river B line within the time T1.
In this case, the daily line loss WA-Bb and the line loss rate eta A-Bb of the 10kV Jiang A line are represented as follows:
W A-Bb ＝W 1 +W Ba ×T 1 /24 (2-12)
η A-Bb ＝W A-Bb ÷W A (2-13)
wherein WBa is the daily electric quantity of public transformer A in 10kV river B line.
The daily line loss WA-Bb of the 10kV Jiang A line is expressed as:
W B-Bb ＝W 2 +W Ba ×T 1 /24 (2-12)
the line loss rate eta B-Bb is represented by the formulas (2-3), (2-4) and (2-12)
η A-Bb ＝W B-Bb ÷W B (2-13)
And (3) abnormal electricity utilization analysis:
firstly, comparing line loss characteristic values, analyzing and evaluating abnormal power consumption, distinguishing abnormal power consumption and normal power consumption, and then classifying line loss: communication line loss and power stealing line loss.
1. Loss of communication line
The communication line loss in the power distribution network is generally caused by communication limitation and faults of the metering device, data cannot be synchronized within a certain period of time, after the communication faults are repaired, data transmission of the metering device is recovered, the communication data is uploaded and repaired, and therefore line loss abnormity caused by the communication faults is corrected.
And the other is mainly caused by permanent faults of the metering device, such as lightning damage, human damage, electricity stealing modification and the like, and such fault data are lost to cause abnormal electricity utilization of the line. The operation and maintenance personnel are required to analyze the abnormal reasons, carry out targeted repair and restore normal metering and communication. The metering device uploads data on average once in 15 minutes and the communication system updates every 30 minutes. And when the data cannot be synchronized, selecting the data at the previous moment of the fault and the data at the same moment of the previous day for mean value weighting to serve as the metering data of the communication fault moment. Based on the data, the line loss value is calculated and compared with the actually presented line loss value, and the part which is consistent with the abnormal electricity utilization characteristic value is found out, so that the reason of the abnormal electricity utilization is obtained.
2. Line loss of electricity stealing
According to the data of the synchronous line loss system, the daily electric quantity of a certain user or a distribution area is greatly different from historical data, the line loss rate is obviously increased, the electric quantity variation trend difference with the same user is large, and meanwhile, the deviation value from the theoretical line loss exceeds a set threshold value. For the electricity utilization customers, the electricity utilization customers are classified as electricity stealing key attention objects, an independent data model is built by utilizing a big data technology, the electricity utilization rules of the individual data model are summarized, and the individual data model is analyzed in a targeted mode. The specific electricity stealing analysis model is shown in FIG. 6.
After the users with abnormal line loss are determined, the public and special variables are subjected to reason analysis respectively. Wherein the communal transformation can be divided into residential and non-residential users. The users of the special transformer can be divided into users with three-phase imbalance, metering abnormality, load fluctuation and the like. Therefore, the abnormal electricity utilization type is further determined, and the range is narrowed.
At present, users who steal electricity often change the structure and wiring mode of the electric energy meter, or adjust the current and voltage to achieve the purpose of metering less and metering less electric quantity. The abnormal electricity consumption of the user terminal can cause the line loss to be in a high loss state. By comparing the line loss data and the electric quantity data with the electricity utilization abnormity characteristic value, users with abnormal electricity utilization and the abnormal electricity utilization types can be quickly determined.
The following is analyzed in a practical application case:
according to the monitoring of a synchronous line loss system in 2018, 7, month and 2 days, the line loss of a 10kV certain line in 2018, 6 months and 6 months is 8.69 percent, the line loss exceeds a set 6 percent month line loss high loss threshold value, the line loss of the line in 7 months and 2 days is 10.05 percent, relevant data are collected and input into a model for comparative analysis and evaluation, and the initial judgment is that a certain user steals electricity, and the electricity stealing characteristic value is three-phase voltage unbalance. Derived by the system, the line topology is shown in fig. 7. The line public and private specification is shown in fig. 8.
The integrated electric quantity and line loss system derived data are shown in fig. 8, the line loss of the line on the same day increases from 2016, 12, month and 5 days, increases from 1.2% to 5.4%, and then stabilizes at about 6%, the daily average line loss electric quantity increases from 1282 kilowatt hours to 7050 kilowatt hours, and the same time increases to 5768 kilowatt hours. However, the set solar line loss and high loss threshold is 10%, the monthly line loss is 6%, the high set loss threshold is not triggered all the time in 2016 and 2017, the electricity stealing users cannot be found, and the electricity stealing amount of the users is increased by 7 months in 2018 and summer, so that the abnormal high loss of the line is caused. The actual check confirms the correctness of the model output conclusion.
And (3) verification:
through marketing and system inspection, the three-phase imbalance of the metering current of the household meter is found from 2016, 12, 5 months. As shown in fig. 9. 13 pm on 7/18/2018: about 00, more than ten people can be detected on site from the Tianlian textile company Limited in Jiangyun city by the power execution method. On-site inspection finds that the metering is inaccurate as the user conducts short circuit shunting on the metering transformer by using the current short-circuit wire, and violates the power supply business rule, namely the first hundred: the power consumption metering device of the power supply enterprise is deliberately inaccurate or invalid, and belongs to the behavior of electricity stealing. Fig. 10 shows a current curve of a measurement point of an on-site electricity meter.
In addition: it should be noted that the above-mentioned embodiment is only a preferred embodiment of the present patent, and any modification or improvement made by those skilled in the art based on the above-mentioned conception is within the protection scope of the present patent.
Claims (3)
1. The abnormal power utilization analysis method of the large data of the integrated power and line loss system is characterized by comprising the following steps of: the method comprises the following steps:
step one, establishing a large abnormal power utilization analysis database and an abnormal power utilization analysis model:
abnormal electricity consumption analysis big database: extracting line ledger information from a server;
the server constructs an abnormal electricity utilization analysis model: the abnormal electricity consumption analysis model comprises: the electric quantity and line loss data access and abnormal electricity utilization analysis module;
step two, the comparison mode of leading in the abnormal electricity utilization analysis module is as follows: comparing homologous homogeneous data and homologous heterogeneous data;
step three, selecting an abnormal electricity utilization characteristic value: selecting typical abnormal electricity utilization data from a historical database, wherein the typical abnormal electricity utilization data comprise technical faults such as line breakage, power supply faults, line faults, meter errors, data transmission errors and electricity stealing behavior data, comparing the typical abnormal electricity utilization data with normal electricity utilization data and abnormal electricity utilization data, then performing data conversion, and labeling characteristic values of the abnormal electricity utilization;
step four, analyzing according to the abnormal electricity utilization analysis model constructed in the step one: based on an abnormal electricity utilization comparison algorithm, after the electric quantity and the line loss data are accessed, the abnormal electricity utilization and the normal electricity utilization are distinguished after being evaluated according to an abnormal electricity utilization analysis module;
meanwhile, extracting abnormal data in the large abnormal electricity utilization analysis database, comparing the abnormal data with the abnormal electricity utilization characteristic value, extracting comparison data, and diagnosing and classifying the abnormal electricity utilization according to the generated comparison result;
and fifthly, exporting abnormal power utilization diagnosis.
2. The abnormal power consumption analysis method for the big data of the integrated power and line loss system according to claim 1, wherein the abnormal power consumption analysis method comprises the following steps: in the first step, the server collects and stores the following data: line gateway electric quantity, public and private variable daily electric quantity, public and private variable monthly electric quantity, line loss statistical curve, line electric quantity detail and metering device data in the synchronous line loss statistical data; line loss calculation values, common and special variable copper loss and iron loss data and a line topological graph in line loss calculation data of the line loss system; active and reactive electric quantity, active and reactive power, real-time power factor, terminal calendar clock and terminal parameter in the power consumption information data acquisition system; and D5000 real-time line voltage, line current and load curve data in the system.
3. The abnormal power consumption analysis method for the big data of the integrated power and line loss system according to claim 1, wherein the abnormal power consumption analysis method comprises the following steps: in the second step: the homologous and homogeneous data comparison comprises the following steps: user load comparison, daily electric quantity comparison, current comparison and active power comparison;
the homologous heterogeneous data comprises voltage and current comparison, current and active power comparison.
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| CN115575754B (en) * | 2022-11-21 | 2023-05-02 | 浙江万胜智能科技股份有限公司 | Intelligent industrial park electricity consumption anomaly identification method and system |
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