CN113267692B - Low-voltage transformer area line loss intelligent diagnosis and analysis method and system - Google Patents
Low-voltage transformer area line loss intelligent diagnosis and analysis method and system Download PDFInfo
- Publication number
- CN113267692B CN113267692B CN202110534648.7A CN202110534648A CN113267692B CN 113267692 B CN113267692 B CN 113267692B CN 202110534648 A CN202110534648 A CN 202110534648A CN 113267692 B CN113267692 B CN 113267692B
- Authority
- CN
- China
- Prior art keywords
- line loss
- analysis
- intelligent
- abnormal
- data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Remote Monitoring And Control Of Power-Distribution Networks (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The application discloses low-voltage transformer area line loss intelligent diagnosis analysis method and system, line loss data are collected through additionally installing intelligent hardware, line loss calculation is carried out based on a distributed framework, whether line loss is abnormal or not is judged according to low-voltage transformer area line loss indexes, the line loss abnormal type is further judged, the line loss abnormal reason of the low-voltage transformer area is automatically judged based on an abnormal judgment rule, the number of abnormal items is determined, intelligent diagnosis analysis is further carried out on the low-voltage transformer area line loss by utilizing intelligent power utilization big data and a data mining analysis technology, a transformer area physical examination report is generated, and field personnel are assisted in processing. The application carries out the distribution transformation to current centralized architecture system of line loss, utilizes data mining and analytic technique to carry out automatic analysis location to the unusual reason of low pressure platform district line loss, has promoted the line loss and has administered and manage and control ability, promotes platform district line loss management level, has reached the purpose that reduces the loss and improve the efficiency.
Description
Technical Field
The application relates to the field of power systems, in particular to a low-voltage transformer area line loss intelligent final diagnosis and analysis method and system.
Background
With the popularization and application of high-speed power line carrier (HPLC) in an acquisition system, the acquired data items are gradually expanded to voltage, current, power factors, power, events and the like from electric energy indication values and demand, the acquisition frequency is from 1 time per day to 1 time per 15 minutes, and the acquired data volume shows explosive increase. The centralized master station system cannot meet the power utilization acquisition service development requirements in the form of an energy internet, the traditional theoretical line loss calculation has limitations, the reason for the traditional theoretical line loss calculation is difficult to accurately judge, and the line loss reason analysis is not intelligent enough. Meanwhile, the line loss responsible person can only search for the function of supporting analysis through personal experience in the analysis process, and in the whole analysis process, no guiding type guidance problem solving idea exists, so that a great deal of time is consumed for analyzing the line loss reason.
Disclosure of Invention
Object of the application
Based on this, in order to promote the management and control ability of abnormal management and refinement of the line loss, and achieve the purpose of reducing the loss and improving the efficiency, the application discloses the following technical scheme.
(II) technical scheme
The application discloses a low-voltage transformer area line loss intelligent diagnosis and analysis method, which comprises the following steps:
acquiring line loss data by using additionally-installed intelligent hardware, and calculating line loss based on a distributed framework;
judging whether the line loss is abnormal according to the line loss index of the low-voltage transformer area, and further judging the type of the line loss abnormality;
automatically judging the line loss abnormal reason of the low-voltage transformer area based on an abnormal judgment rule, and determining the number of abnormal items;
and further carrying out intelligent diagnosis and analysis on the line loss of the low-voltage transformer area by using intelligent power utilization big data and a data mining analysis technology to generate a physical examination report.
In a possible implementation, the intelligent hardware includes intelligent management terminal and intelligent monitoring terminal, wherein, intelligent management terminal installs on near transformer's major loop, and intelligent monitoring terminal divide into branch intelligent monitoring terminal and table case intelligent monitoring terminal, and wherein, branch intelligent monitoring terminal installs near the feeder pillar, and table case intelligent monitoring terminal installs near user's table case.
In a possible embodiment, the abnormality determination rule includes: collecting an abnormal judgment rule, a file abnormal judgment rule, a power utilization abnormal judgment rule and a metering abnormal judgment rule.
In a possible implementation manner, the collected line loss data includes real-time line loss data and offline line loss data.
In one possible embodiment, the content of the intelligent diagnostic analysis includes: the method comprises the steps of clock error analysis, user variable relation verification, line loss fluctuation analysis, terminal collected data abnormity analysis, curve similarity analysis and line loss split-phase branch subsection analysis.
As a second aspect of the present application, the present application further discloses a low-voltage transformer area line loss intelligent final diagnosis analysis system, including:
the intelligent management terminal is used for determining a power supply relation through signal transmission with the intelligent monitoring terminal, acquiring and calculating bus loss data of the low-voltage transformer area, and analyzing and processing part of line loss abnormity of the low-voltage transformer area;
the intelligent monitoring terminal is used for determining a power supply relation through signal transmission with the intelligent management terminal, and acquiring and calculating line loss data of each branch of the low-voltage transformer area;
and the big data analysis platform is used for intelligently diagnosing and analyzing the line loss of the low-voltage transformer area through a distributed real-time calculation framework constructed by real-time calculation, distributed storage and intelligent analysis functions.
In one possible implementation, the big data analytics platform comprises:
the line loss calculation module is used for acquiring line loss data by using additionally-installed intelligent hardware and performing line loss calculation based on a distributed framework;
the abnormity judgment module is used for judging whether the line loss is abnormal according to the line loss index of the low-voltage transformer area so as to judge the line loss abnormal type;
the abnormal item determining module is used for automatically judging the line loss abnormal reason of the low-voltage transformer area based on an abnormal judgment rule and determining the number of abnormal items;
and the intelligent analysis module is used for carrying out intelligent diagnosis and analysis on the line loss of the low-voltage transformer area by further utilizing intelligent power utilization big data and a data mining analysis technology to generate a physical examination report.
In a possible embodiment, the abnormality determination rule includes: collecting an abnormity judgment rule, an archive abnormity judgment rule, an electricity utilization abnormity judgment rule and a metering abnormity judgment rule.
In one possible implementation, the collected line loss data includes real-time line loss data and offline line loss data.
In one possible embodiment, the content of the intelligent diagnostic analysis includes: the method comprises the steps of clock error analysis, user variable relation verification, line loss fluctuation analysis, terminal collected data abnormity analysis, curve similarity analysis and line loss split-phase branch subsection analysis.
(III) advantageous effects
According to the intelligent diagnosis and analysis method and system for the line loss of the low-voltage transformer area, the intelligent diagnosis and analysis platform built by the distributed real-time line loss calculation framework is utilized, the intelligent and refined management and control capacity for abnormal line loss is improved, and the purposes of reducing loss and improving efficiency are achieved.
Drawings
The embodiments described below with reference to the drawings are exemplary and intended to be used for explaining and illustrating the present application and should not be construed as limiting the scope of protection of the present application.
Fig. 1 is a flowchart of a low-voltage transformer area line loss intelligent diagnosis and analysis method disclosed in the present application.
FIG. 2 is a diagram of a smart diagnostic analysis platform architecture as disclosed herein.
FIG. 3 is a functional diagram of the intelligent diagnostic analysis platform disclosed in the present application.
Fig. 4 is a diagram of the architecture of the intelligent low-voltage transformer area line loss diagnosis and analysis system disclosed in the present application.
Detailed Description
In order to make the implementation objects, technical solutions and advantages of the present application clearer, the technical solutions in the embodiments of the present application will be described in more detail below with reference to the drawings in the embodiments of the present application.
Referring to fig. 1, an embodiment of the intelligent line loss diagnosis and analysis method for a low voltage transformer area according to the present application is described in detail below, and as shown in fig. 1, the method disclosed in this embodiment mainly includes the following steps 100 to 400.
And step 100, acquiring line loss data by using the additionally-installed intelligent hardware, and calculating line loss based on a distributed framework.
In order to promote the intelligent, refined management and control ability of low-voltage distribution room, increase the data volume of gathering, realize the collection and the distributed line loss calculation of real-time power consumption data, adopt the mode cooperation software of installing intelligent hardware additional, intelligent hardware includes intelligent monitoring terminal and intelligent management terminal, and intelligent management terminal installs near the transformer, sends power frequency distortion signal through intelligent management terminal, installs the intelligent monitoring terminal received signal at each branch node, according to the characteristic of signal, confirms the power supply relation, utilizes broadband power line carrier communication to transmit corresponding data, realizes the topological complete structure of distribution room, reaches the purpose of accurate discernment distribution room topology. The intelligent management terminal monitors the state of the main loop in real time and is responsible for scheduling the low-voltage intelligent management system. The intelligent management terminal is used as a low-voltage transformer area power utilization information sub data center, is communicated with the management main station through 4G in an uplink mode, and is communicated with the monitoring terminals through HPLC in a downlink mode. The intelligent monitoring terminal is divided into a branch intelligent monitoring terminal and a meter box intelligent monitoring terminal, and is mainly installed at the positions of various levels of switches such as a busbar, a pi junction box and a corridor unit main switch, and the intelligent management terminal is matched with the intelligent monitoring terminal. The real-time topology of the power supply line can be dynamically analyzed and generated. The line loss index is calculated by additionally arranging intelligent hardware, and segmented line loss calculation is carried out based on the collected data of an intelligent terminal, a branch box, a meter box and a low-voltage user and in combination with the branch line topology and phase recognition condition of a transformer area.
Further, generating the real-time topology specifically includes the steps of:
a. the intelligent management terminal sends a power frequency distortion signal;
b. the intelligent monitoring terminal receives the power frequency distortion signal, analyzes the signal, automatically identifies the position of the intelligent monitoring terminal, and further determines the electrical connection relation;
c. the intelligent management terminal and the intelligent monitoring terminal automatically identify abnormal conditions such as line loss, impedance, three-phase imbalance, household sales, new addition and the like, and the topological structure is adaptively changed and adjusted according to the abnormal conditions.
The whole topology identification process is automatically executed, and a real-time topology structure diagram is presented through APP.
In at least one embodiment, the line loss calculation is divided into real-time line loss calculation and offline line loss calculation, and an original centralized line loss calculation framework is changed into a distributed calculation framework, so that higher-frequency and larger-amount line loss data acquisition can be realized, and more accurate and timely line loss calculation can be realized. Real-time line loss calculates and to carry out real-time supervision to the key factor that influences the line loss, analyzes the unusual district of line loss every day, utilizes collection system data to call the function of surveying and carries out real-time data acquisition analysis to the user that has data anomaly automatically, obtains the collection abnormal conditions that causes by the communication reason. And for the abnormal problems caused by non-communication reasons, the abnormal conditions of the data are further analyzed and identified by adopting big data, and the data are recalled or corrected in time. The line loss abnormity can be timely found and timely processed, and the offline line loss calculation can be used for calculating the offline data such as daily and monthly line loss.
Further, the real-time line loss specific calculation process comprises the following steps:
step a, acquiring a real-time line loss calculation basic value;
specifically, the line loss calculation basic value is obtained by using an intelligent monitoring terminal additionally installed at each node, the real-time line loss calculation basic value comprises a voltage value, a current value and a power value, and the power value comprises an active power value and a reactive power value.
Step b, forming an algebraic equation set and calculating a voltage phase angle;
specifically, a balance node is selected for m +1 extreme systems according to the line loss calculation basic value obtained in the step a, and the voltage equations of the other m nodes are as follows:
in the formula of U i Is the voltage of the node(s) and,
wherein, U' i Is the magnitude of the voltage, theta i Is the phase angle of voltage, I i In order to be the node current,
I′ i is the magnitude of the current and is,
is the voltage current phase difference of the node.
The node voltage current phase difference may be expressed as:
substituting it into the voltage equation yields:
the formula is collated to obtain:
when P is present i ，Q i ，U i ，I i I is 1, 2, … m is measured value, let
Solving for x according to the above equation i Further obtain the voltage phase angle theta i 。
And c, calculating inflow power, and further calculating real-time line loss.
Specifically, according to the voltage value measured in real time and the voltage phase angle obtained in the step b, calculating the real-time line loss of the line connected with the node:
in the formula (I), the compound is shown in the specification,
the incoming complex power at terminal i for branch ij connected to node i,
for the incoming complex power at terminal i, y, of branch ij connected to node j ij0 Is the node i ground admittance.
The line loss power is:
the line loss is:
and 200, judging whether the line loss is abnormal according to the line loss reasonable index of the low-voltage transformer area, and further judging the line loss abnormal type.
After line loss calculation is carried out in the step 100, whether line loss on the same day and the same month is abnormal or not is judged according to line loss assessment standards of national network companies, and abnormality classification is carried out by combining historical day and month line loss data, wherein line loss abnormal types mainly comprise high loss, negative loss, non-calculable line loss and the like. The high loss is long-term high loss and sudden high loss, the negative loss is long-term negative loss and sudden negative loss, the non-calculable line loss is the conditions that the power supply quantity is zero or null, the power consumption quantity is null and the like, and the reason of the abnormal line loss is analyzed according to the abnormal type of the line loss.
And 300, automatically judging the line loss abnormal reason of the low-voltage transformer area based on the abnormal judgment rule, and determining the number of abnormal items.
When low-voltage transformer district line loss is unusual, based on unusual judgement rule automatic positioning low-voltage transformer district line loss unusual reason, unusual judgement rule is the decision rule of classifying the key factor that influences the line loss and generating for location transformer district line loss unusual reason makes the judgement to the general problem such as whether the line loss data gathers completely, provides the basis for subsequent intelligent diagnosis, and unusual judgement rule includes:
file exception judgment rule: the file exception is to analyze the marketing file based on the rule, such as error attribute, inconsistent data and authenticity. And through a marketing data platform, consistency analysis of a production system, a marketing system and a collection system or consistency analysis of each system and field data is carried out, and line loss abnormal reasons are positioned, wherein the line loss abnormal reasons comprise the consistency analysis of a station-user relationship, the consistency of the multiplying power of an ammeter, the rationality analysis of configuration, the state analysis of a user metering point and the like.
And (3) a measurement abnormity judgment rule: the metering abnormity is to analyze the integrity, the rationality, the compliance and the logicality of the collected data and judge whether abnormity exists. Whether conditions such as no meter code, backward running, flying running, stop running, three-phase current unbalance, voltage loss, phase failure, reverse phase sequence, electric energy meter fault replacement and the like exist in the distribution room general meter and the user electric energy meter for 7 continuous days can be analyzed through the acquisition system, and then the line loss abnormal reason of the distribution room is determined.
Collecting an abnormity judgment rule: the abnormal collection comprises the conditions of communication abnormality in the data collection process and data collection errors caused by clock errors, wherein the abnormal collection analysis can be performed to determine whether the communication port setting of the general meter of the analysis station area is correct or not, determine whether the communication port setting of the electric energy meter of a user is correct or not, determine whether the serial number of a system is consistent with the site serial number, the protocol of the system is consistent with the site protocol or not and determine whether the communication address of the system is consistent with the site communication address or not; the abnormal acquisition caused by the clock error can be analyzed through statistics on the clock deviation between the master and sub meters and the calendar clock, the clock deviation between the master meter clock of the distribution area and the electric energy meter of the user under the distribution area, whether the clock of the electric energy meter is over-deviated or not, so that the abnormal acquisition data is caused, and the reason of the abnormal line loss of the distribution area is further determined.
And (3) judging the power utilization abnormity rule: due to abnormal electricity utilization caused by illegal behaviors of users, the line loss abnormal reason of the transformer area can be determined through whether electricity stealing, electricity quantity comparison analysis, electricity consumption condition analysis of adjacent transformer areas and the like occur or not, such as abnormal uncovering events, electric energy meter opening button box events and the like.
And judging the abnormal reason according to the abnormal rule, and determining the abnormal quantity under the related abnormal items. And determining some basic conventional problems according to the judgment result of the abnormality judgment rule, and assisting field personnel to solve the problems, but if some abnormal reasons can not be embodied to users or are difficult to dig deeper reasons, further analysis needs to be carried out by means of an intelligent diagnosis platform.
Step 400, intelligent diagnosis and analysis are further performed on the line loss of the low-voltage transformer area by using intelligent power utilization big data and a data mining analysis technology, and a physical examination report is generated.
Step 300, after determining abnormal items by judging abnormal reasons, if the abnormal items cannot be directly solved, further performing intelligent diagnosis and analysis, where the intelligent diagnosis and analysis is implemented based on a big data intelligent diagnosis and analysis platform, a platform system architecture is shown in fig. 2, and system services include: micro-services, distributed big data task scheduling services, distributed big data processing services, distributed computing services and message queue Kafka services.
Micro-service: and the task management service can manually trigger the task through the page.
Distributed big data task scheduling service: a distributed task management service is built based on the ElasticJob + Zookeeper technology, and comprises the functions of task configuration, task fragmentation, task monitoring, task exception handling and the like.
Distributed big data processing service: the Kafka specified theme is monitored and consumed, tasks are processed according to the type of the computing task, the data volume is small, the computing logic is simple, the tasks can be processed in a mode of querying a relational database and Java, the data volume is large, and the logic is responsible for the computing task and needs to call distributed computing services for processing.
Distributed computing service: and calculating a calculation task based on an Hbase + Spark Sql mode. The complex calculation task considers the split and step-by-step calculation modes.
Message queue Kafka service: inter-service decoupling.
The big data support platform supports big data distributed computation, has the capabilities of visual analysis, statistical analysis, mining analysis and the like, meets the analysis and mining requirements of real-time and off-line application, and provides basic platform support for line loss analysis decision application construction. The power failure event analysis, the transformer area load analysis, the electricity larceny prevention analysis, the metering on-line monitoring, the meter reading fault analysis, the line loss real-time calculation and the like are completed based on mining analysis, the conventional statistics can be completed by adopting statistical analysis, and meanwhile, the final result can be displayed based on visual analysis.
The big data intelligent diagnosis analysis functional architecture is shown in fig. 3, and the intelligent diagnosis analysis comprises:
line loss fluctuation analysis: when the station area monthly line loss rate is normal, but the daily line loss rate fluctuates greatly, big data analysis is carried out on historical daily and monthly line loss data of the station area, the fluctuation regularity of specific line loss is analyzed, whether the daily line loss is abnormal due to the reason of clock out-of-tolerance is determined, and a station area with an aggravated fluctuation trend is mainly reminded of line loss responsible persons.
Further, the line loss fluctuation specific algorithm is as follows:
the specific steps of the daily line loss fluctuation rate calculation are as follows:
1) acquiring the line loss rate of 180 days before the interval to be calculated;
2) clustering the line loss rate of 180 days by using a K-means clustering algorithm to obtain a clustering result;
specifically, the line loss rate of 180 days, that is, 180 points are respectively clustered into cluster clusters with different numbers, for example, the 180 points are clustered into four cases of 2 types, 3 types, 4 types, and 5 types, and different clustering results are respectively obtained.
3) Respectively calculating overall contour coefficients according to the clustering results to obtain a target K value;
specifically, the overall contour coefficient is obtained by averaging the contour coefficients of each point, which is as follows:
a. for the ith point, X i . Calculating X i The average distance from other points in the set, denoted as disMean in ；
b. For each relative to X i Respectively computing X i Average distance to each point in the outer set. Thus, if N sets exist in the clustering result, N-1 average distances are obtained, the minimum value of the N-1 average distances is taken to obtain the shortest average distance which is recorded as a discoman out ；
c. Computing
The result is point X i The contour coefficient of (a);
d. and respectively calculating the contour coefficient of each point by using the above method, and solving an average value to obtain the overall contour coefficient of the current cluster.
And respectively calculating the overall contour coefficient of each clustering result, wherein the classification number corresponding to one clustering result closest to 1 in all the overall contour coefficients is the target K value.
4) Clustering again by using the target K value and calculating the maximum fluctuation rate and the minimum fluctuation rate according to a clustering result;
specifically, the maximum and minimum fluctuation rates are calculated as follows:
a. if the number of the sets of the clustering results is N, finding out the point number larger than the total point number from the N sets
(possibly resulting in multiple sets, here assumed to be X).
b. For the X sets, all the points in each set are sorted in an ascending order, and the fluctuation rate between every two points is calculated (formula)
) And find the maximum and minimum values from them. Thus, X sets have X pairs of maximum and minimum fluctuation rates.
c. And taking the X pairs as a union set to finally obtain a pair of maximum and minimum fluctuation rates.
5) Using formulas
And calculating the line loss fluctuation rate of the query date.
The specific steps of the monthly line loss fluctuation rate calculation are as follows:
1) obtaining the monthly line loss rate of 36 months before the interval to be calculated and calculating the average value
2) Screening the monthly line loss rate;
specifically, the monthly line loss rate value should exclude negative numbers and 0, and the line loss rate value should be in a reasonable range (the current standard is more than 0 and less than 40).
3) Let the line loss rate per month in the sample be x i Calculating the standard deviation
Where n represents the number of months of the query.
4) And acquiring the fluctuation rate of the monthly line loss.
Specifically, the monthly line loss rate in the query time interval is set to y i Average value of
The monthly line loss fluctuation rate is calculated by the formula
Upper limit of fluctuation ratio: 2sd (a straight line as the upper limit of the fluctuation rate), and the monthly line loss fluctuation result is displayed as a fluctuation rate broken line graph.
Analyzing abnormity of terminal collected data: the method is suitable for abnormal distribution areas with daily line loss caused by abnormal freezing indication reasons of the terminal-collected electric energy meter, whether metering users with electric quantity collected in two-day or three-day regular distribution areas exist in each distribution area is judged every day, if yes, the electric quantity of the users is converted into single-day electric quantity, the daily line loss is recalculated, and whether line loss caused by abnormal collection is abnormal or not is judged.
And (3) curve similarity analysis: when the low-voltage transformer area has long-term high loss and negative loss phenomena, and after various possible reasons are checked by technical means, specific reasons are still difficult to find, the relevance of the total table electricity quantity and voltage curve of the transformer area and a user curve is analyzed by using a curve similarity analysis method, and users with abnormal electricity utilization are screened out.
Further, the curve similarity analysis specific algorithm is as follows:
1) calculating the similarity of the daily electric quantity of the user and the cell table;
specifically, assume that the cell list is T, and the user set U under the cell is { U ═ U 1 ,u 2 ,…,u n }，u i Representing the ith user, and the total n users; d is taken as D, D takes 7 days, 15 days or 30 days, and the similarity of the two electric quantities S in D is calculated as cos θ, assuming that in D,
representing user u i The daily amount of electricity for the j-th day,
indicating the day of the cell table TQuantity, where j e {1, …, D }, i e {1, …, n }, then,
and when the number of days for which the daily electricity is collected in the natural month is day, calculating the period Count which is day/D. The final similarity is S a : calculate S in each time period from 1 to Count k ：
And for all users U, judging the similarity Sa of each user and the gateway T of the transformer area, and judging that a problem exists when the Sa is less than 0.5 from small to large.
2) Calculating the voltage similarity between the user and the table;
specifically, D is taken as D, and the value of D is taken as 7 days, and the similarity S of both voltages in D days is calculated as cos θ. It is assumed that within the D days,
representing user representation user u i The average value of the voltage at the j-th day,
represents the voltage average value of the jth day of the cell table T, j belongs to {1, …, D }, i belongs to {1, …, n },
when the number of days of collecting the daily voltage in the natural month is day, the calculation period Count is day/D. Final degree of similarityIs S a : calculate S in each time period from 1 to Count k
For all users U, judging the similarity S between each user and the gateway T of the transformer area a According to the order of small to large, S a <0.5, it was judged that there was a problem.
3) And (4) obtaining the intersection of the result 1) and the result 2) to obtain the final user record with the line loss problem.
And (3) analyzing clock errors: when the monthly line loss of the low-voltage transformer area has high loss and negative loss, a clock error analysis method can be used, the time of the intelligent management terminal is taken as the standard, the electric parameter data of each branch switch of the transformer area are collected, and the electric parameter data of the transformer area general table are collected and stored. Calling the electric energy meter clock in the user electricity information acquisition system, comparing the electric energy meter clock with system time, showing that the data of the frozen power supply and consumption amount are different, firstly, the data of the total meter of the transformer area is frozen before the data of the electric energy meter of the user, and the power supply amount is less. The transformer area presents negative line loss symptoms; secondly, the electric energy indication value is frozen in advance by the user electric energy meter, so that the power supply and consumption are different, and the transformer area shows a high loss condition.
The line loss phase splitting, branching and segmenting analysis method comprises the following steps: when the high loss and the negative loss cannot be positioned at specific positions, a line loss phase splitting, branching and segmentation analysis method is adopted, and the segmented line loss is accurately calculated by additionally arranging equipment such as an intelligent management terminal and a monitoring terminal. The method comprises the steps of carrying out sectional line loss calculation based on collected data of an intelligent terminal, a branch box, a meter box and a low-voltage user and combining branch line topology and phase recognition conditions of a transformer area, and carrying out grading line loss calculation on a comprehensive power distribution cabinet, a distribution transformer low-voltage outgoing line, the branch box, a low-voltage branch line and the meter box. Based on the statistical result of the electric energy loss of each section of the transformer area, the specific positions of high loss and negative loss are accurately positioned by combining the analysis of the line loss occupation ratio of each stage, the line loss distribution of each stage and the like in the transformer area.
And (4) checking the user variable relationship: the phenomenon that the household variable relation of a low-voltage transformer area is inaccurate is mainly screened, and the problems that a certain low-voltage transformer area is high in loss and an adjacent transformer area is negative in loss are solved. And checking the specific time of the user when the user has the user change relationship aiming at the data such as the corresponding electric quantity of the user and the corresponding electric quantity of the two transformer areas, and outputting the user change relationship data.
Further, according to the technology of correlation analysis of the electricity consumption of the user and the line loss rate of the transformer area, a correlation coefficient is obtained, and whether the problem exists in the user variable relation is judged through the correlation coefficient. The specific steps of the user variable relationship verification are as follows:
1) acquiring actual data and statistical data of the power loss of the source station area with the wrong user variable relationship;
specifically, suppose that there are n +1 users in a certain area, and there is a user variable relationship error, and suppose that the serial number of the user is i * . By definition,
the actual data of the power supply amount of the station area is:
the statistical data of the power supply amount of the transformer area are as follows:
in the formula, epsilon t Is the real loss electric quantity, epsilon' t To account for lost power, x i (t) is the daily power of the ith user, y t The daily electric quantity of the transformer area.
2) Preliminarily determining users with possible errors in the user variable relationship;
specifically, the statistical line loss rate satisfies:
in the formula, epsilon t Is the real loss electric quantity, epsilon' t To count the loss of electricity, k' t To count the line loss rate, k t To true line loss rate, x i Error of variable relationElectricity consumption of user, y t The daily electric quantity of the transformer area is.
Under the assumption of linear line loss, the true line loss rate k t Is a constant, and the line loss rate k 'is counted' t Will be engaged with
Becoming negative correlation, calculating the correlation coefficient between the user power consumption ratio and the statistical line loss rate, wherein the correlation coefficient is between-1 and-0.8]The users in the interval of (2) can be preliminarily determined as wrong users not belonging to the local area.
3) And determining whether the user variable relation error occurs, and manually checking output data.
For the target station area, the following equation can also be derived by similar calculation:
ξ t +x i* (t)＝ξ′ t
in the formula, xi t Is actual loss electric quantity of the target station area, xi' t The power loss is counted in the platform region,
therefore, there are:
wherein y is t * Electric quantity k of the beacon zone at the t day t ′ * Is the statistical line loss rate, k, of the t day of the target station area t * The real line loss rate of the target station area at the t day is shown. Calculating the correlation coefficient of the electricity consumption of the user and the statistical line loss rate of the target area, if the correlation coefficient is between 0.8 and 1]And if so, determining that the user variable relation error occurs, and further manually checking the output data.
4) And calculating a correlation coefficient, and checking the user-variable relationship according to the correlation coefficient.
The calculation of the correlation coefficient mainly comprises a Pearson correlation coefficient, a Spireman correlation coefficient and a Kendall correlation coefficient, and the specific calculation formula is as follows:
where ρ is 1 ，ρ 2 ，ρ 3 Respectively, Pearson's correlation coefficient, Spireman's correlation coefficient and Kendall's correlation coefficient, X i ，Y i The ith numbers of variables X and Y,
is the average number, n is the total number of X, Y; di means that at the same point i, for example, the third X and the third Y, two groups of variables are simultaneously sorted from large to small (or from small to large), and the point is the difference (for example, 2) between the ranking number (for example, 1) at X and the ranking number (for example, 3) at Y; and P is the logarithm of a statistical object with the consistent arrangement size relationship of the two attribute values.
For the discrimination of the correlation coefficient, there are the following discrimination according to statistics:
TABLE 1 correlation coefficient discrimination Table
The abnormal items obtained in step 300 may have enlightenment effect on the intelligent diagnosis and analysis, and it may be determined directly what kind of analysis method is to be performed according to some abnormal items, and when the abnormal items do not prompt the analysis method, several analysis methods should be operated separately to finally determine the cause of the abnormality and perform abnormality processing.
And the intelligent diagnosis and analysis platform generates an anomaly analysis report after completing anomaly analysis. The abnormal analysis report comprises the basic information of the transformer area, the theoretical line loss rate of the transformer area, the statistical line loss rate of the transformer area, the abnormal type of the line loss of the transformer area, the abnormal reason of the line loss of the transformer area and the like.
The embodiment of the intelligent line loss diagnosis and analysis system for low-voltage transformer area of the present application is described in detail below with reference to fig. 4, and includes:
and the intelligent management terminal is used for determining a power supply relation through signal transmission with the intelligent monitoring terminal, acquiring and calculating bus loss data of the low-voltage transformer area, and analyzing and processing part of line loss abnormity of the low-voltage transformer area.
And the intelligent monitoring terminal is used for determining the power supply relation through signal transmission between the intelligent monitoring terminal and the intelligent management terminal, and acquiring and calculating the line loss data of each branch of the low-voltage transformer area.
And the big data analysis platform is used for intelligently diagnosing and analyzing the line loss of the low-voltage transformer area through a distributed real-time calculation framework constructed by real-time calculation, distributed storage and intelligent analysis functions.
In at least one embodiment, the big data analytics platform comprises:
the line loss calculation module is used for acquiring line loss data by using additionally-installed intelligent hardware and performing line loss calculation based on a distributed framework;
the abnormity judgment module is used for judging whether the line loss is abnormal according to the line loss index of the low-voltage transformer area so as to judge the line loss abnormal type;
the abnormal item determining module is used for automatically judging the line loss abnormal reason of the low-voltage transformer area based on an abnormal judgment rule and determining the number of abnormal items;
and the intelligent analysis module is used for carrying out intelligent diagnosis and analysis on the line loss of the low-voltage transformer area by further utilizing intelligent power utilization big data and a data mining analysis technology to generate a physical examination report.
In at least one embodiment, the abnormality determination rule includes: collecting an abnormity judgment rule, an archive abnormity judgment rule, an electricity utilization abnormity judgment rule and a metering abnormity judgment rule.
In at least one embodiment, the collected line loss data includes real-time line loss data and offline line loss data.
In at least one embodiment, the content of the intelligent diagnostic analysis includes: the method comprises the steps of clock error analysis, user variable relation verification, line loss fluctuation analysis, terminal collected data abnormity analysis, curve similarity analysis and line loss split-phase branch subsection analysis.
The division of the modules herein is merely a division of logical functions, and other divisions may be possible in actual implementation, for example, a plurality of modules may be combined or integrated in another system. Modules described as separate components may or may not be physically separate.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present application should be covered within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (7)
1. The intelligent line loss diagnosis and analysis method for the low-voltage transformer area is characterized by comprising the following steps of:
acquiring line loss data by using additionally-installed intelligent hardware, and calculating line loss based on a distributed framework;
judging whether the line loss is abnormal according to the line loss index of the low-voltage transformer area, and further judging the type of the line loss abnormality;
automatically judging the line loss abnormal reason of the low-voltage transformer area based on an abnormal judgment rule, and determining the number of abnormal items;
the line loss of the low-voltage transformer area is intelligently diagnosed and analyzed by further utilizing intelligent power utilization big data and a data mining analysis technology, and a physical examination report is generated;
the intelligent diagnostic analysis comprises the following contents: clock error analysis, user variable relation verification, line loss fluctuation analysis, terminal acquired data abnormity analysis, curve similarity analysis and line loss split-phase branch segmentation analysis;
the line loss fluctuation analysis comprises a daily line loss analysis and a monthly line loss analysis, wherein the daily line loss analysis comprises the following steps:
acquiring the line loss rate 180 days before the date to be inquired;
clustering the line loss rate of 180 days by using a K-means clustering algorithm to obtain a clustering result;
respectively calculating overall contour coefficients according to the clustering results to obtain a target K value;
clustering again by using the target K value and calculating the maximum fluctuation rate and the minimum fluctuation rate according to a clustering result; the maximum and minimum fluctuation rates are calculated as follows: setting the number of the sets of the clustering results to be N, and finding out points greater than the total points from the N sets
X in the set of (a); for the X sets, all points in each set are sorted in an ascending order, and a formula is utilized
Calculating the fluctuation rate between every two points, and finding out the maximum value and the minimum value from the fluctuation rate; the X sets have X pairs of maximum and minimum fluctuation rates, and the X pairs of maximum and minimum fluctuation rates are subjected to union set to finally obtain a pair of maximum and minimum fluctuation rates;
using formulas
And calculating the line loss fluctuation rate of the query date based on the maximum fluctuation rate and the minimum fluctuation rate.
2. The method of claim 1, wherein the intelligent hardware comprises an intelligent management terminal and an intelligent monitoring terminal, the intelligent management terminal is installed on the main loop near the transformer, the intelligent monitoring terminal is divided into a branch intelligent monitoring terminal and a meter box intelligent monitoring terminal, the branch intelligent monitoring terminal is installed near a branch box, and the meter box intelligent monitoring terminal is installed near a user meter box.
3. The method of claim 1, wherein the anomaly determination rule comprises: collecting an abnormal judgment rule, a file abnormal judgment rule, a power utilization abnormal judgment rule and a metering abnormal judgment rule.
4. The method of claim 1, wherein the collected line loss data comprises real-time line loss data and offline line loss data.
5. The utility model provides a low pressure platform district line loss intelligent diagnosis analytic system which characterized in that includes:
the intelligent management terminal is used for determining a power supply relation through signal transmission with the intelligent monitoring terminal, acquiring and calculating bus loss data of the low-voltage transformer area, and analyzing and processing part of abnormal line loss of the low-voltage transformer area;
the intelligent monitoring terminal is used for determining a power supply relation through signal transmission with the intelligent management terminal, and acquiring and calculating line loss data of each branch of the low-voltage distribution room;
the big data analysis platform is used for intelligently diagnosing and analyzing the line loss of the low-voltage transformer area through a distributed real-time calculation framework constructed by real-time calculation, distributed storage and intelligent analysis functions;
the big data analysis platform comprises:
the line loss calculation module is used for acquiring line loss data by using additionally-installed intelligent hardware and performing line loss calculation based on a distributed framework;
the abnormity judgment module is used for judging whether the line loss is abnormal according to the line loss index of the low-voltage transformer area so as to judge the line loss abnormal type;
the abnormal item determining module is used for automatically judging the line loss abnormal reason of the low-voltage transformer area based on an abnormal judgment rule and determining the number of abnormal items;
the intelligent analysis module is used for carrying out intelligent diagnosis and analysis on the line loss of the low-voltage transformer area by further utilizing intelligent power utilization big data and a data mining analysis technology to generate a physical examination report;
the intelligent diagnostic analysis comprises the following contents: analyzing clock errors, verifying a user variable relation, analyzing line loss fluctuation, analyzing terminal acquired data abnormity, analyzing curve similarity and analyzing line loss split-phase branch segmentation;
the line loss fluctuation analysis comprises a daily line loss analysis and a monthly line loss analysis, wherein the daily line loss analysis comprises:
the line loss rate acquisition unit is used for acquiring the line loss rate 180 days before the date to be inquired;
the clustering unit is used for clustering the line loss rate of 180 days by using a K-means clustering algorithm and obtaining a clustering result;
a K value obtaining unit, configured to respectively calculate overall contour coefficients according to the clustering results, and obtain a target K value;
the maximum and minimum fluctuation rate calculation unit is used for clustering again by using the target K value and calculating the maximum fluctuation rate and the minimum fluctuation rate according to a clustering result; the maximum and minimum fluctuation rates are calculated as follows: setting the number of the sets of the clustering results to be N, and finding out points greater than the total points from the N sets
X in the set of (a); for the X sets, all points in each set are sorted in an ascending order, and a formula is utilized
Calculating the fluctuation rate between every two points, and finding out the maximum value and the minimum value from the fluctuation rate; the X sets have X pairs of maximum and minimum fluctuation rates, and the X pairs of maximum and minimum fluctuation rates are collected to finally obtain a pair of maximum and minimum fluctuation rates;
a sun-ray loss rate calculating unit using a formula
Based on the maximumAnd calculating the line loss fluctuation rate of the query date by the fluctuation rate and the minimum fluctuation rate.
6. The system of claim 5, wherein the anomaly determination rule comprises: collecting an abnormal judgment rule, a file abnormal judgment rule, a power utilization abnormal judgment rule and a metering abnormal judgment rule.
7. The system of claim 5, wherein the collected line loss data comprises real-time line loss data and offline line loss data.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110534648.7A CN113267692B (en) | 2021-05-17 | 2021-05-17 | Low-voltage transformer area line loss intelligent diagnosis and analysis method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110534648.7A CN113267692B (en) | 2021-05-17 | 2021-05-17 | Low-voltage transformer area line loss intelligent diagnosis and analysis method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113267692A CN113267692A (en) | 2021-08-17 |
| CN113267692B true CN113267692B (en) | 2022-09-06 |
Family
ID=77231299
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110534648.7A Active CN113267692B (en) | 2021-05-17 | 2021-05-17 | Low-voltage transformer area line loss intelligent diagnosis and analysis method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113267692B (en) |
Families Citing this family (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113447749B (en) * | 2021-08-31 | 2021-11-23 | 江苏数能电力技术有限公司 | Method for judging abnormal line loss of transformer area |
| CN113985339B (en) * | 2021-09-22 | 2023-11-24 | 北京市腾河科技有限公司 | Error diagnosis method and system for intelligent ammeter, equipment and storage medium |
| CN114139862A (en) * | 2021-10-28 | 2022-03-04 | 国网江苏省电力有限公司连云港供电分公司 | Large data-based distribution room line loss analysis monitoring system and analysis method thereof |
| CN114236283B (en) * | 2021-12-15 | 2024-02-13 | 广东电网有限责任公司 | Method and device for determining line loss reason of power supply network |
| CN114285162B (en) * | 2021-12-21 | 2023-08-08 | 青岛鼎信通讯股份有限公司 | Metering anomaly analysis method based on low-voltage transformer area acquisition data |
| CN114441892A (en) * | 2021-12-27 | 2022-05-06 | 国网江西省电力有限公司电力科学研究院 | 10kV line abnormal line loss distribution transformer positioning method |
| CN114386630A (en) * | 2022-01-10 | 2022-04-22 | 广东电网有限责任公司 | Electric power spot transaction data monitoring and analyzing method and device and computer equipment |
| CN115267323B (en) * | 2022-08-01 | 2023-11-03 | 合肥顺帆信息科技有限公司 | Line loss analysis management system |
| CN115392648A (en) * | 2022-08-03 | 2022-11-25 | 中国电力科学研究院有限公司 | Transformer area line loss fusion diagnosis system and diagnosis method thereof |
| CN115792370B (en) * | 2023-02-08 | 2023-05-26 | 北京清众神州大数据有限公司 | Intelligent ammeter-based energy diagnosis method, device and equipment |
| CN117110795B (en) * | 2023-10-18 | 2024-01-30 | 国网安徽省电力有限公司合肥供电公司 | Transformer area line fault positioning system |
Family Cites Families (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109086950A (en) * | 2018-10-11 | 2018-12-25 | 国网上海市电力公司 | A kind of user's coulometric analysis prediction technique based on big data analysis |
| CN110134708A (en) * | 2019-03-03 | 2019-08-16 | 云南电网有限责任公司信息中心 | Electric net platform region line loss abnormal cause diagnostic method, device, computer equipment and storage medium |
| CN109977535B (en) * | 2019-03-22 | 2023-05-02 | 南方电网科学研究院有限责任公司 | Line loss abnormality diagnosis method, device, equipment and readable storage medium |
| CN110109771A (en) * | 2019-05-07 | 2019-08-09 | 北京恒泰实达科技股份有限公司 | The abnormal key diagnostic method of a kind of area's line loss |
| CN110472871B (en) * | 2019-08-16 | 2023-07-07 | 广东电网有限责任公司 | Investigation method for managing line loss abnormal reasons |
| CN111781463A (en) * | 2020-06-25 | 2020-10-16 | 国网福建省电力有限公司 | Auxiliary diagnosis method for abnormal line loss of transformer area |
| CN112598234A (en) * | 2020-12-14 | 2021-04-02 | 广东电网有限责任公司广州供电局 | Low-voltage transformer area line loss abnormity analysis method, device and equipment |
-
2021
- 2021-05-17 CN CN202110534648.7A patent/CN113267692B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN113267692A (en) | 2021-08-17 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113267692B (en) | Low-voltage transformer area line loss intelligent diagnosis and analysis method and system | |
| CN110097297B (en) | Multi-dimensional electricity stealing situation intelligent sensing method, system, equipment and medium | |
| CN113159339B (en) | One-area one-index line loss management method and system based on big data | |
| CN107340492A (en) | Electric power meter failure analysis methods with scene anticipation are excavated based on big data | |
| CN110222991B (en) | Metering device fault diagnosis method based on RF-GBDT | |
| CN112307003B (en) | Power grid data multidimensional auxiliary analysis method, system, terminal and readable storage medium | |
| CN110276511A (en) | A kind of line change relationship anomalous discrimination method based on electricity and line loss relevance | |
| CN111339491A (en) | Evaluation method for urban power distribution network transformation scheme | |
| CN112688428A (en) | 10kV power grid synchronous line loss treatment method based on Internet of things technology | |
| CN114519514B (en) | Low-voltage transformer area reasonable line loss value measuring and calculating method, system and computer equipment | |
| CN107730399B (en) | Theoretical line loss evaluation method based on wind power generation characteristic curve | |
| CN110826228B (en) | Regional power grid operation quality limit evaluation method | |
| CN112288303A (en) | Method and device for determining line loss rate | |
| CN115905319B (en) | Automatic identification method and system for abnormal electricity fees of massive users | |
| CN111476397B (en) | Bus load prediction method and system applied to medium and long term electric quantity safety check | |
| CN115293257A (en) | Detection method and system for abnormal electricity utilization user | |
| Hartmann et al. | Suspicious electric consumption detection based on multi-profiling using live machine learning | |
| CN113642933A (en) | Power distribution station low-voltage diagnosis method and device | |
| CN112734274A (en) | Mining and comprehensive evaluation method for low-carbon power grid operation leading influence factors | |
| CN116521760A (en) | Electrical energy metering device exception handling evaluation system based on multisource information fusion | |
| CN116450625A (en) | Metering abnormal data screening device based on electricity consumption information acquisition system | |
| CN107832928B (en) | Equivalent comprehensive line loss evaluation method based on wind power generation characteristic curve | |
| CN113902485B (en) | Special power user industry identification method, device and equipment | |
| CN114168662A (en) | Power distribution network problem combing and analyzing method and system based on multiple data sources | |
| CN114662994A (en) | Whole county type photovoltaic partitioning method, storage device and terminal |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |