CN114878961A - Method and device for monitoring abnormal line loss of distribution room based on service center - Google Patents
Method and device for monitoring abnormal line loss of distribution room based on service center Download PDFInfo
- Publication number
- CN114878961A CN114878961A CN202210441250.3A CN202210441250A CN114878961A CN 114878961 A CN114878961 A CN 114878961A CN 202210441250 A CN202210441250 A CN 202210441250A CN 114878961 A CN114878961 A CN 114878961A
- Authority
- CN
- China
- Prior art keywords
- line loss
- transformer area
- monitored
- area
- abnormal
- 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.)
- Pending
Links
- 230000002159 abnormal effect Effects 0.000 title claims abstract description 107
- 238000000034 method Methods 0.000 title claims abstract description 52
- 238000009826 distribution Methods 0.000 title claims abstract description 26
- 238000012544 monitoring process Methods 0.000 title claims abstract description 17
- 238000005259 measurement Methods 0.000 claims abstract description 40
- 238000013507 mapping Methods 0.000 claims abstract description 7
- 238000004364 calculation method Methods 0.000 claims description 20
- 238000007781 pre-processing Methods 0.000 claims description 17
- 238000003860 storage Methods 0.000 claims description 14
- 238000004422 calculation algorithm Methods 0.000 claims description 9
- 238000004590 computer program Methods 0.000 claims description 9
- 238000012806 monitoring device Methods 0.000 claims description 3
- 238000007726 management method Methods 0.000 description 12
- 238000004458 analytical method Methods 0.000 description 11
- 238000012545 processing Methods 0.000 description 10
- 230000008859 change Effects 0.000 description 5
- 238000010586 diagram Methods 0.000 description 5
- 230000008901 benefit Effects 0.000 description 4
- 238000004891 communication Methods 0.000 description 3
- 238000012423 maintenance Methods 0.000 description 3
- 238000005065 mining Methods 0.000 description 3
- 238000003672 processing method Methods 0.000 description 3
- 238000010276 construction Methods 0.000 description 2
- 239000006185 dispersion Substances 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 230000005611 electricity Effects 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 230000009467 reduction Effects 0.000 description 2
- 230000004044 response Effects 0.000 description 2
- 230000005856 abnormality Effects 0.000 description 1
- 238000007792 addition Methods 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 230000000295 complement effect Effects 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000000354 decomposition reaction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000004134 energy conservation Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 230000014509 gene expression Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 230000005055 memory storage Effects 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 238000012797 qualification Methods 0.000 description 1
- 238000005215 recombination Methods 0.000 description 1
- 230000006798 recombination Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 230000001960 triggered effect Effects 0.000 description 1
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
- G01R31/08—Locating faults in cables, transmission lines, or networks
- G01R31/081—Locating faults in cables, transmission lines, or networks according to type of conductors
- G01R31/086—Locating faults in cables, transmission lines, or networks according to type of conductors in power transmission or distribution networks, i.e. with interconnected conductors
-
- 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
-
- 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
- G01R31/08—Locating faults in cables, transmission lines, or networks
- G01R31/088—Aspects of digital computing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- 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)
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Economics (AREA)
- Theoretical Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Public Health (AREA)
- Mathematical Physics (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Human Resources & Organizations (AREA)
- Marketing (AREA)
- Primary Health Care (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Remote Monitoring And Control Of Power-Distribution Networks (AREA)
Abstract
The invention discloses a method and a device for monitoring abnormal line loss of a distribution room based on a service center. The method comprises the following steps: acquiring the ID of transformer equipment in a monitored transformer area according to a general query interface under the service of a power grid resource center in a power grid resource service center; acquiring all low-voltage user information IDs in a monitored transformer area by using a low-voltage transformer area mapping topology interface of a power grid topology center; acquiring measurement point information of all equipment by using query measurement point query service of a metering application center through the ID of the transformer equipment and the IDs of all low-voltage user information; searching measurement service by using measurement query service of a measurement point management center according to measurement point information of all equipment to obtain measurement information of all equipment in a monitored station area; calculating a real-time line loss value in the monitored transformer area based on the measurement information of all the equipment in the monitored transformer area to obtain transformer area line loss data of the monitored transformer area; and judging whether the monitored transformer area has abnormal line loss based on the transformer area line loss data.
Description
Technical Field
The invention relates to the technical field of power grid operation management, in particular to a method and a device for monitoring abnormal line loss of a distribution room based on a service center.
Background
Loss reduction of a power distribution network is one of main targets of energy-saving work, loss of electric energy in the power distribution network is closely related to factors such as an operation mode, a network structure and a voltage grade of the whole power distribution network and is influenced by management levels such as scheduling, operation and maintenance, and theoretical calculation and analysis of line loss have guiding significance for loss reduction, energy conservation, subdivision and accurate line loss management. At present, the line loss of most power supply companies is basically in a service block management state, a production marketing system lacks a uniform information exchange platform, information circulation is difficult to be in a controllable and in-control state, and sometimes, the phenomena of untimely information circulation, information loss and the like exist, so that line loss statistics and analysis are real, the line loss management is not strong in pertinence and low in effectiveness, line loss statistical results are difficult to truly reflect a 'short board' of line loss management, and therefore the basic management level of the line loss is urgently required to be improved through fine management.
The power grid resource service center integrates and disperses core resources and service support capabilities of equipment, topology and the like which are respectively maintained in equipment, marketing and scheduling, a power grid resource center and a power grid asset center are constructed, sharing services are provided for cross-professional service through of equipment management professional core service processing, scheduling, marketing, finance, planning and the like, model unification, resource collection, homologous maintenance and co-construction sharing from a power supply, a power grid to a user are realized, and a power grid map is formed.
At present, a plurality of service centers such as a power grid asset center, a power grid resource center and a power grid topology center are arranged under a power grid resource service center, so that service support is provided for foreground application.
At present, the common abnormal manifestations of the line loss of the transformer area are mainly as follows: 1. the line loss rate of a certain cell area is increased abnormally for several months continuously, meanwhile, the line loss rate of the adjacent cell areas is obviously lower and even has a negative value, and the line loss rates between the cell areas show the complementary phenomenon of 'this length of cancellation'. 2. The original line loss rate in the distribution room is normal and stable for a long time, the line loss rate has large change in a short time, and then the line loss rate is reset around a certain value in a long time. 3. The line loss rate within a certain period of time in the transformer area has large change range, the line loss rate is overall unstable, and abnormal fluctuation frequently occurs.
The current main method for judging the abnormal line loss of the transformer area is to derive the sum of the electricity consumption of all users in the transformer area and the electricity consumption of the outlet side of a transformer from a metering system, calculate the difference between the sum and the difference, calculate the line loss value of the transformer area in a certain time period, set a threshold value of the line loss qualification rate, trigger an alarm once the line loss exceeds the threshold value, manually compare data and screen the abnormal line loss value.
The disadvantages of these methods are: 1. simple threshold judgment, and alarm can be triggered by accidental line loss data exceeding caused by metering device or communication error, so that false alarm is caused. 2. The workload of line loss abnormity analysis is greatly increased by manual judgment, a large amount of manpower is required to be invested to analyze and process massive alarm data, and the efficiency is low. 3. When the service center station is not used, information such as station area power consumption and user power consumption is non-real-time information, and real-time study and judgment of abnormal line loss cannot be achieved.
Therefore, the problems that the abnormal line loss judgment of the transformer area easily causes false alarm, the efficiency is low, and the real-time study and judgment of the abnormal line loss cannot be achieved are problems which need to be solved at present.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a method and a device for monitoring abnormal line loss of a distribution room based on a service center.
According to one aspect of the present invention, a method for monitoring abnormal line loss of a distribution room based on a service center is provided, which includes:
acquiring the ID of transformer equipment in a monitored transformer area according to a general query interface under the service of a power grid resource center in a power grid resource service center;
acquiring all low-voltage user information IDs in the monitored transformer area by using a low-voltage transformer area mapping topology interface of a power grid topology center through the equipment IDs;
acquiring measurement point information of all equipment by using query measurement point query service of a metering application center through a transformer equipment ID and all low-voltage user information IDs in a monitored transformer area;
measuring point information of all equipment is obtained, measuring inquiry service of a measuring point management center is utilized, measuring service is searched according to resource ID, and measuring information of all equipment in a monitored station area is obtained;
calculating to obtain a real-time line loss value in the monitored transformer area based on the measurement information of all the equipment in the monitored transformer area, and obtaining transformer area line loss data of the monitored transformer area;
and judging whether the monitored transformer area has abnormal line loss based on the transformer area line loss data.
Optionally, judging whether there is a situation of abnormal line loss in the monitored distribution room according to the distribution room line loss data includes:
preprocessing the line loss data of the transformer area;
clustering the preprocessed transformer area line loss data;
calculating discrete characteristic values of the clustered line loss data of the transformer area;
and judging whether the monitored transformer area has abnormal line loss or not based on the discrete characteristic value.
Optionally, the preprocessing the line loss data of the transformer area includes:
calculating the average value of the line loss data of the transformer area;
and deleting redundant data and blank data in the line loss data of the transformer area according to the calculated average value and a preset average value threshold.
Optionally, the clustering operation is performed on the preprocessed distribution room line loss data, and includes:
calculating a fixed threshold value of a clustering algorithm according to the average value obtained in the preprocessing calculation and the characteristics of the line loss data of the transformer area;
performing primary clustering on the line loss data of the transformer area, and dividing the line loss data of the transformer area into corresponding clustering categories according to a clustering result of the primary clustering, wherein the clustering categories are divided into low-level abnormity and high-level abnormity, and the abnormal degree of the line loss of the transformer area with the low-level abnormity is greater than that of the line loss of the transformer area with the high-level abnormity;
and according to the abnormal conditions of different levels, adopting quadratic clustering calculation of different degrees, and according to the clustering result of the quadratic clustering and a fixed threshold value, determining the abnormal degree of the area line loss of the monitored area.
Optionally, the discrete characteristic value of the clustered station area line loss data is calculated by using the following formula:
wherein, the y discrete characteristic value is n is the line loss rate data quantity of the maximum class of the clustering center; t is t u For each time corresponding to the line loss rate data.
Optionally, based on the discrete characteristic value, whether the monitored transformer area has a transformer area abnormal line loss is determined by the following formula:
wherein x is a discrete characteristic coefficient of abnormal line loss of the transformer area and y is a discrete characteristic value;
when x is less than 0.5, determining that the abnormal state of the line loss of the monitored transformer area is good, and the abnormal line loss of the transformer area does not exist in the monitored transformer area; when x is larger than 0.5, the abnormal state of the line loss of the monitored transformer area is poor, and the abnormal line loss of the transformer area exists in the monitored transformer area.
According to another aspect of the present invention, there is provided a station area abnormal line loss monitoring device based on a station in service, including:
the first acquisition module is used for acquiring the ID of the transformer equipment of the monitored station area according to a general query interface under the service of a power grid resource center in the power grid resource service;
the second acquisition module is used for acquiring all low-voltage user information IDs in the monitored transformer area by using a low-voltage transformer area mapping topological interface of the power grid topological center through the equipment IDs;
the first query module is used for acquiring measurement point information of all equipment by utilizing query measurement point query service of the metering application center through the ID of the transformer equipment and the ID of all low-voltage user information in the monitored transformer area;
the second query module is used for searching the measurement service according to the resource ID by using the measurement query service of the measurement point management center through the measurement point information of all the equipment to obtain the measurement information of all the equipment in the monitored station area;
the real-time line loss value calculation module is used for calculating to obtain a real-time line loss value in the monitored transformer area based on the measurement information of all the equipment in the monitored transformer area, and obtaining transformer area line loss data of the monitored transformer area;
and the abnormal line loss judging module is used for judging whether the monitored transformer area has the abnormal line loss condition of the transformer area based on the transformer area line loss data.
Optionally, the abnormal line loss determining module is specifically configured to:
preprocessing the line loss data of the transformer area;
clustering the preprocessed transformer area line loss data;
calculating discrete characteristic values of the clustered line loss data of the transformer area;
and judging whether the monitored transformer area has abnormal line loss or not based on the discrete characteristic value.
According to a further aspect of the invention, there is provided a computer readable storage medium having stored thereon a computer program for executing the method of any of the above aspects of the invention.
According to still another aspect of the present invention, there is provided an electronic apparatus including: a processor; a memory for storing the processor-executable instructions; the processor is configured to read the executable instructions from the memory and execute the instructions to implement the method according to any one of the above aspects of the present invention.
Therefore, the method for monitoring the abnormal line loss of the station area based on the service center station is a real-time method, and compared with the traditional method, the real-time data acquisition capability of the service center station has incomparable rapid response advantage. Compared with the traditional method for setting the abnormal data threshold value to give an alarm, the large data processing method reduces data redundancy and errors, reduces workload of manual judgment and system false alarm caused by random errors caused by unstable data, and improves accuracy of line loss abnormal monitoring of the transformer area.
Drawings
A more complete understanding of exemplary embodiments of the present invention may be had by reference to the following drawings in which:
fig. 1 is a schematic flowchart of a method for monitoring abnormal line loss in a distribution room based on a service center according to an exemplary embodiment of the present invention;
fig. 2 is a schematic flow chart of processing line loss data of a distribution room according to an exemplary embodiment of the present invention;
fig. 3 is a schematic structural diagram of a station area abnormal line loss monitoring device based on a service center according to an exemplary embodiment of the present invention;
fig. 4 is a structure of an electronic device according to an exemplary embodiment of the present invention.
Detailed Description
Hereinafter, example embodiments according to the present invention will be described in detail with reference to the accompanying drawings. It is to be understood that the described embodiments are merely a subset of embodiments of the invention and not all embodiments of the invention, with the understanding that the invention is not limited to the example embodiments described herein.
It should be noted that: the relative arrangement of the components and steps, the numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention unless specifically stated otherwise.
It will be understood by those of skill in the art that the terms "first," "second," and the like in the embodiments of the present invention are used merely to distinguish one element, step, device, module, or the like from another element, and do not denote any particular technical or logical order therebetween.
It should also be understood that in embodiments of the present invention, "a plurality" may refer to two or more and "at least one" may refer to one, two or more.
It is also to be understood that any reference to any component, data, or structure in the embodiments of the invention may be generally understood as one or more, unless explicitly defined otherwise or stated to the contrary hereinafter.
In addition, the term "and/or" in the present invention is only one kind of association relationship describing the associated object, and means that there may be three kinds of relationships, for example, a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. In the present invention, the character "/" generally indicates that the preceding and following related objects are in an "or" relationship.
It should also be understood that the description of the embodiments of the present invention emphasizes the differences between the embodiments, and the same or similar parts may be referred to each other, so that the descriptions thereof are omitted for brevity.
Meanwhile, it should be understood that the sizes of the respective portions shown in the drawings are not drawn in an actual proportional relationship for the convenience of description.
The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.
Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail, but are intended to be part of the specification where appropriate.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, further discussion thereof is not required in subsequent figures.
Embodiments of the invention are operational with numerous other general purpose or special purpose computing system environments or configurations, and with numerous other electronic devices, such as terminal devices, computer systems, servers, etc. Examples of well known terminal devices, computing systems, environments, and/or configurations that may be suitable for use with electronic devices, such as terminal devices, computer systems, servers, and the like, include, but are not limited to: personal computer systems, server computer systems, thin clients, thick clients, hand-held or laptop devices, microprocessor-based systems, set-top boxes, programmable consumer electronics, networked personal computers, minicomputer systems, mainframe computer systems, distributed cloud computing environments that include any of the above, and the like.
Electronic devices such as terminal devices, computer systems, servers, etc. may be described in the general context of computer system-executable instructions, such as program modules, being executed by a computer system. Generally, program modules may include routines, programs, objects, components, logic, data structures, etc. that perform particular tasks or implement particular abstract data types. The computer system/server may be practiced in distributed cloud computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed cloud computing environment, program modules may be located in both local and remote computer system storage media including memory storage devices.
Exemplary method
Fig. 1 is a schematic flow chart of a method for monitoring abnormal line loss in a distribution room based on a service center according to an exemplary embodiment of the present invention. The embodiment can be applied to electronic equipment, such as but not limited to a financial electronic bill management platform system, the processed data volume of which is far smaller than the large data demand, and the improvement is made on the basis of the system to realize low resource consumption and high processing speed. As shown in fig. 1, a method 100 for monitoring abnormal line loss in a cell based on a station in service includes the following steps:
102, acquiring information IDs of all low-voltage users in a monitored transformer area by using a low-voltage transformer area mapping topology interface of a power grid topology center through the equipment IDs;
103, acquiring measuring point information of all equipment by using a query measuring point query service of a metering application center through a transformer equipment ID and all low-voltage user information IDs in a monitored transformer area;
104, searching measurement service according to the resource ID by using measurement inquiry service of a measurement point management center through measurement point information of all equipment to obtain measurement information of all equipment in the monitored transformer area;
105, calculating to obtain a real-time line loss value in the monitored transformer area based on the measurement information of all the devices in the monitored transformer area, and obtaining transformer area line loss data of the monitored transformer area;
and step 106, judging whether the monitored transformer area has abnormal transformer area line loss or not based on the transformer area line loss data.
In the embodiment of the present invention, the obtained line loss data of the monitored transformer area needs to determine whether there is an abnormal line loss of the transformer area by the following data processing method. A flow chart of the method for processing the line loss data of the transformer area is shown in fig. 2.
Optionally, judging whether there is a situation of abnormal line loss in the monitored distribution room according to the distribution room line loss data includes: preprocessing the line loss data of the transformer area; clustering the preprocessed transformer area line loss data; calculating discrete characteristic values of the clustered line loss data of the transformer area; and judging whether the monitored transformer area has abnormal line loss or not based on the discrete characteristic value.
Optionally, the preprocessing the line loss data of the transformer area includes: calculating the average value of the line loss data of the transformer area; and deleting redundant data and blank data in the line loss data of the transformer area according to the calculated average value and a preset average value threshold.
In the embodiment of the invention, the line loss data acquisition and preprocessing operation of the transformer area is performed by performing data preprocessing on low-voltage line loss data called by the transformer in service. In order to ensure the authenticity of the analysis data, the average value of the low-voltage line loss data acquired by the service center station is calculated, and redundant data and blank data in the data are deleted, so that the analysis and processing can be reduced, and the accuracy of abnormal line loss can be improved. In the data preprocessing process, the calculated data average value takes 3% as a demarcation point, once the average value is less than 3%, the fluctuation of the line loss value of the transformer area is small, the threshold value of the subsequent clustering algorithm is 1.5%, the data analysis loses significance, and if the data average value is less than 3%, the transformer area line loss is a low line loss rate area, and further calculation and analysis are not needed; if the data mean is greater than 3%, then further data processing calculations are required.
Optionally, the clustering operation is performed on the preprocessed distribution room line loss data, and includes: calculating a fixed threshold value of a clustering algorithm according to the average value obtained in the preprocessing calculation and the characteristics of the line loss data of the transformer area; performing primary clustering on the line loss data of the transformer area, and dividing the line loss data of the transformer area into corresponding clustering categories according to a clustering result of the primary clustering, wherein the clustering categories are divided into low-level abnormity and high-level abnormity, and the abnormal degree of the line loss of the transformer area with the low-level abnormity is greater than that of the line loss of the transformer area with the high-level abnormity; and according to the abnormal conditions of different levels, adopting quadratic clustering calculation of different degrees, and according to the clustering result of the quadratic clustering and a fixed threshold value, determining the abnormal degree of the area line loss of the monitored area.
In the embodiment of the invention, after the data preprocessing operation, the acquired data is calculated through a data clustering algorithm, the clustering algorithm firstly preprocesses the characteristics of the average value and the station area line loss data during calculation, and calculates the fixed threshold of the clustering algorithm, wherein the threshold is the evaluation limit calculated by the clustering algorithm.
The auxiliary calculation formula is as follows:
wherein s is a clustering center distance threshold; a is i The average value of the line loss rate a of the station area is obtained.
When the data mean is greater than 3% of the value, it makes sense to perform clustering calculations. When the value is more than 3% and less than 10%, the threshold value of the transformer area line loss is set to be 3%, which means that the clustering difference value between data in the transformer area line loss is not more than 3%. And if the average value of the data is more than 10%, the line loss rate of the transformer area is too large, the transformer area is judged to be an abnormal area, discrete analysis is carried out, and the transformer area line loss abnormal area is analyzed.
In order to more accurately judge the level of the line loss abnormity of the transformer area, two times of clustering analysis can be carried out on the clustering analysis, firstly, primary clustering is carried out on the line loss data of the transformer area, two clustering categories, namely low-level abnormity and high-level abnormity, are set for dividing the abnormal degree of the line loss of the transformer area, and the data are divided into corresponding clustering categories according to clustering results. Adopting quadratic clustering calculation of different degrees according to the anomalies of different levels, and if the numerical value of the quadratic clustering is smaller than a set threshold value, judging that the line loss of the low-level transformer area is abnormal; if greater than the threshold, this region is a high-level exception region.
Optionally, the discrete characteristic value of the clustered distribution room line loss data is calculated by using the following formula:
wherein, the y discrete characteristic value is n is the line loss rate data quantity of the maximum class of the clustering center; t is t u For each time corresponding to the line loss rate data.
In the embodiment of the invention, the discrete analysis is to take the time points corresponding to the secondary clustering result and the line loss rate data in the line loss rate data of the abnormal distribution area as research objects, calculate the average value of data fluctuation in the time point intervals, and take the variable as an index for measuring the abnormal degree of the line loss. The calculation formula is as follows:
wherein n is the line loss rate data quantity of the maximum class of the clustering center; t is t u For each time corresponding to the line loss rate data.
The discrete calculation result represents the abnormal condition of the line loss of the transformer area, and if the discrete result is larger, the abnormal data dispersion degree is obvious, and the abnormal condition is not serious; if the smaller the discrete result data is, the lower the discrete degree of the data is, the abnormal condition of the line loss of the transformer area is serious.
Optionally, based on the discrete characteristic value, whether the monitored transformer area has a transformer area abnormal line loss is determined by the following formula:
wherein x is a discrete characteristic coefficient of abnormal line loss of the transformer area and y is a discrete characteristic value;
when x is less than 0.5, determining that the abnormal state of the line loss of the monitored transformer area is good, and the abnormal line loss of the transformer area does not exist in the monitored transformer area; when x is larger than 0.5, the abnormal state of the line loss of the monitored transformer area is poor, and the abnormal line loss of the transformer area exists in the monitored transformer area.
In the embodiment of the invention, the distribution condition of the abnormal state of the line loss of the transformer area can be effectively analyzed by discrete calculation, the characteristic value of the discrete state is calculated according to the dispersion of the clustering center value, and the level of the abnormal state of the line loss of the transformer area is evaluated in a body mode, wherein the specific formula is as follows:
and judging the characteristic value of the discrete state, wherein x is a line loss abnormal discrete characteristic coefficient of the transformer area. The actual line loss operation rule of the transformer area specifies that the abnormal state of the line loss of the transformer area is good when x is less than 0.5; when x is larger than 0.5, the line loss abnormal state of the transformer area is poor, the system immediately sends out a transformer area line loss abnormal alarm, and the cause analysis needs to be carried out on the transformer area with abnormal line loss to eliminate the abnormality.
In the embodiment of the invention, the enterprise-level power grid resource service middlebox is based on a unified information model standard and a sharing service framework, the integration and sharing service of power grid resources on each service line of a company are realized, the service is enabled in a service mode, different service departments are opened, a power grid resource management maintenance order is constructed, the core competitiveness of the enterprise is improved, the front-end service is decoupled from the stable and common middlebox service through the changeable and customized front-end service, the application of the front-end service is lighter, faster and more flexible, and the problems of unsmooth collaboration, inconsistent data, difficult application construction, poor user experience and the like of related services of the current power grid resources are solved. On the distribution network line loss layer, the line loss management work can be refined and real-time through the application of the service center.
Therefore, the method for monitoring the abnormal line loss of the station area based on the service center station is a real-time method, and compared with the traditional method, the real-time data acquisition capability of the service center station has incomparable rapid response advantage. Compared with the traditional method for setting the abnormal data threshold value to give an alarm, the large data processing method reduces data redundancy and errors, reduces workload of manual judgment and system false alarm caused by random errors caused by unstable data, and improves accuracy of line loss abnormal monitoring of the transformer area.
Exemplary devices
Fig. 3 is a schematic structural diagram of a device for monitoring an abnormal line loss in a distribution room based on a service center according to an exemplary embodiment of the present invention. As shown in fig. 3, the apparatus 300 includes:
the first acquisition module is used for acquiring the ID of the transformer equipment of the monitored station area according to a general query interface under the service of a power grid resource center in the power grid resource service;
the second acquisition module is used for acquiring all low-voltage user information IDs in the monitored transformer area by using a low-voltage transformer area mapping topological interface of the power grid topological center through the equipment IDs;
the first query module is used for acquiring the measuring point information of all equipment by utilizing query measuring point query service of the metering application center through the transformer equipment ID and all low-voltage user information IDs in the monitored transformer area;
the second query module is used for searching the measurement service according to the resource ID by using the measurement query service of the measurement point management center through the measurement point information of all the equipment to obtain the measurement information of all the equipment in the monitored station area;
the real-time line loss value calculation module is used for calculating to obtain a real-time line loss value in the monitored transformer area based on the measurement information of all the equipment in the monitored transformer area, and obtaining transformer area line loss data of the monitored transformer area;
and the abnormal line loss judging module is used for judging whether the monitored transformer area has the abnormal line loss condition of the transformer area based on the transformer area line loss data.
Optionally, the abnormal line loss determining module is specifically configured to:
preprocessing the line loss data of the transformer area;
clustering the preprocessed transformer area line loss data;
calculating discrete characteristic values of the clustered line loss data of the transformer area;
and judging whether the monitored transformer area has abnormal line loss or not based on the discrete characteristic value.
Optionally, the abnormal line loss determining module is further specifically configured to:
calculating the average value of the line loss data of the transformer area;
and deleting redundant data and blank data in the line loss data of the transformer area according to the calculated average value and a preset average value threshold.
Optionally, the abnormal line loss determining module is further specifically configured to:
calculating a fixed threshold value of a clustering algorithm according to the average value obtained in the preprocessing calculation and the characteristics of the line loss data of the transformer area;
performing primary clustering on the line loss data of the transformer area, and dividing the line loss data of the transformer area into corresponding clustering categories according to a clustering result of the primary clustering, wherein the clustering categories are divided into low-level abnormity and high-level abnormity, and the abnormal degree of the line loss of the transformer area with the low-level abnormity is greater than that of the line loss of the transformer area with the high-level abnormity;
and (4) calculating by adopting secondary clustering of different degrees according to the anomalies of different levels, and determining the abnormal degree of the area line loss of the monitored transformer area according to the clustering result of the secondary clustering and a fixed threshold value.
Optionally, the discrete characteristic value of the clustered station area line loss data is calculated by using the following formula:
wherein, the y discrete characteristic value is n is the line loss rate data quantity of the maximum class of the clustering center; t is t u For each time corresponding to the line loss rate data.
Optionally, the abnormal line loss determining module is further specifically configured to: judging whether the monitored transformer area has abnormal line loss through the following formula:
wherein x is a discrete characteristic coefficient of abnormal line loss of the transformer area and y is a discrete characteristic value;
when x is less than 0.5, determining that the abnormal state of the line loss of the monitored transformer area is good, and the abnormal line loss of the transformer area does not exist in the monitored transformer area; when x is larger than 0.5, the abnormal state of the line loss of the monitored transformer area is poor, and the abnormal line loss of the transformer area exists in the monitored transformer area.
The device 300 for monitoring abnormal line loss of a station area based on a middle service station in the embodiment of the present invention corresponds to the method 100 for monitoring abnormal line loss of a station area based on a middle service station in another embodiment of the present invention, and is not described herein again.
Exemplary electronic device
Fig. 4 is a structure of an electronic device according to an exemplary embodiment of the present invention. The electronic device may be either or both of the first device and the second device, or a stand-alone device separate from them, which stand-alone device may communicate with the first device and the second device to receive the acquired input signals therefrom. FIG. 4 illustrates a block diagram of an electronic device in accordance with an embodiment of the present invention. As shown in fig. 4, electronic device 40 includes one or more processors 41 and memory 42.
The processor 41 may be a Central Processing Unit (CPU) or other form of processing unit having data processing capabilities and/or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.
The input device 43 may also include, for example, a keyboard, a mouse, and the like.
The output device 44 can output various kinds of information to the outside. The output devices 44 may include, for example, a display, speakers, a printer, and a communication network and its connected remote output devices, among others.
Of course, for simplicity, only some of the components of the electronic device that are relevant to the present invention are shown in fig. 4, omitting components such as buses, input/output interfaces, and the like. In addition, the electronic device may include any other suitable components, depending on the particular application.
Exemplary computer program product and computer-readable storage Medium
In addition to the above-described methods and apparatus, embodiments of the present invention may also be a computer program product comprising computer program instructions that, when executed by a processor, cause the processor to perform the steps in the method of information mining of historical change records according to various embodiments of the present invention described in the "exemplary methods" section above of this specification.
The computer program product may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages, for carrying out operations according to embodiments of the present invention. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server.
Furthermore, embodiments of the present invention may also be a computer-readable storage medium having stored thereon computer program instructions which, when executed by a processor, cause the processor to perform the steps in the method of information mining of historical change records according to various embodiments of the present invention described in the "exemplary methods" section above of this specification.
The computer-readable storage medium may take any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may include, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The basic principles of the present invention have been described above with reference to specific embodiments, but it should be noted that the advantages, effects, etc. mentioned in the present invention are only examples and are not limiting, and the advantages, effects, etc. must not be considered to be possessed by various embodiments of the present invention. Furthermore, the foregoing disclosure of specific details is for the purpose of illustration and description and is not intended to be limiting, since the invention is not limited to the specific details described above.
In the present specification, the embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same or similar parts in the embodiments are referred to each other. For the system embodiment, since it basically corresponds to the method embodiment, the description is relatively simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The block diagrams of devices, systems, apparatuses, and systems involved in the present invention are merely illustrative examples and are not intended to require or imply that the devices, systems, apparatuses, and systems must be connected, arranged, or configured in the manner shown in the block diagrams. These devices, systems, apparatuses, systems may be connected, arranged, configured in any manner, as will be appreciated by those skilled in the art. Words such as "including," "comprising," "having," and the like are open-ended words that mean "including, but not limited to," and are used interchangeably therewith. The words "or" and "as used herein mean, and are used interchangeably with, the word" and/or, "unless the context clearly dictates otherwise. The word "such as" is used herein to mean, and is used interchangeably with, the phrase "such as but not limited to".
The method and system of the present invention may be implemented in a number of ways. For example, the methods and systems of the present invention may be implemented in software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order for the steps of the method is for illustrative purposes only, and the steps of the method of the present invention are not limited to the order specifically described above unless specifically indicated otherwise. Furthermore, in some embodiments, the present invention may also be embodied as a program recorded in a recording medium, the program including machine-readable instructions for implementing a method according to the present invention. Thus, the present invention also covers a recording medium storing a program for executing the method according to the present invention.
It should also be noted that in the systems, apparatus and methods of the present invention, the various components or steps may be broken down and/or re-combined. These decompositions and/or recombinations are to be regarded as equivalents of the present invention. The previous description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects without departing from the scope of the invention. Thus, the present invention is not intended to be limited to the aspects shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
The foregoing description has been presented for purposes of illustration and description. Furthermore, the description is not intended to limit embodiments of the invention to the form disclosed herein. While a number of example aspects and embodiments have been discussed above, those of skill in the art will recognize certain variations, modifications, alterations, additions and sub-combinations thereof.
Claims (10)
1. A method for monitoring abnormal line loss of a distribution room based on a service center station is characterized by comprising the following steps:
acquiring the ID of transformer equipment in a monitored transformer area according to a general query interface under the service of a power grid resource center in a power grid resource service center;
acquiring all low-voltage user information IDs in the monitored transformer area by using a low-voltage transformer area mapping topology interface of a power grid topology center through the equipment IDs;
acquiring measurement point information of all equipment by using query measurement point query service of a metering application center through a transformer equipment ID and all low-voltage user information IDs in a monitored transformer area;
measuring point information of all equipment is obtained, measuring inquiry service of a measuring point management center is utilized, measuring service is searched according to resource ID, and measuring information of all equipment in a monitored station area is obtained;
calculating to obtain a real-time line loss value in the monitored transformer area based on the measurement information of all the equipment in the monitored transformer area, and obtaining transformer area line loss data of the monitored transformer area;
and judging whether the monitored transformer area has abnormal line loss based on the transformer area line loss data.
2. The method of claim 1, wherein determining whether the monitored block has abnormal line loss according to the block line loss data comprises:
preprocessing the line loss data of the transformer area;
clustering the preprocessed transformer area line loss data;
calculating discrete characteristic values of the clustered line loss data of the transformer area;
and judging whether the monitored transformer area has abnormal line loss or not based on the discrete characteristic value.
3. The method of claim 2, wherein preprocessing the line loss data of the distribution area comprises:
calculating the average value of the line loss data of the transformer area;
and deleting redundant data and blank data in the line loss data of the transformer area according to the calculated average value and a preset average value threshold.
4. The method of claim 2, wherein clustering the preprocessed platform region line loss data comprises:
calculating a fixed threshold value of a clustering algorithm according to the average value obtained in the preprocessing calculation and the characteristics of the line loss data of the transformer area;
performing primary clustering on the line loss data of the transformer area, and dividing the line loss data of the transformer area into corresponding clustering categories according to a clustering result of the primary clustering, wherein the clustering categories are divided into low-level abnormity and high-level abnormity, and the abnormal degree of the line loss of the transformer area with the low-level abnormity is greater than that of the line loss of the transformer area with the high-level abnormity;
and according to the abnormal conditions of different levels, adopting quadratic clustering calculation of different degrees, and according to the clustering result of the quadratic clustering and a fixed threshold value, determining the abnormal degree of the area line loss of the monitored area.
5. The method of claim 2, wherein the discrete eigenvalues of the clustered station line loss data are calculated using the following formula:
wherein, the y discrete characteristic value is n is the line loss rate data quantity of the maximum class of the clustering center; t is t u For each time corresponding to the line loss rate data.
6. The method of claim 5, wherein based on the discrete eigenvalues, whether the monitored transformer area has abnormal line loss is determined by the following formula:
wherein x is a discrete characteristic coefficient of abnormal line loss of the transformer area and y is a discrete characteristic value;
when x is less than 0.5, determining that the abnormal state of the line loss of the monitored transformer area is good, and the abnormal line loss of the transformer area does not exist in the monitored transformer area; when x is larger than 0.5, the abnormal state of the line loss of the monitored transformer area is poor, and the abnormal line loss of the transformer area exists in the monitored transformer area.
7. The utility model provides a platform district unusual line loss monitoring devices based on platform in the business which characterized in that includes:
the first acquisition module is used for acquiring the ID of the transformer equipment of the monitored station area according to a general query interface under the service of a power grid resource center in the power grid resource service;
the second acquisition module is used for acquiring all low-voltage user information IDs in the monitored transformer area by using a low-voltage transformer area mapping topological interface of the power grid topological center through the equipment IDs;
the first query module is used for acquiring the measuring point information of all equipment by utilizing query measuring point query service of the metering application center through the transformer equipment ID and all low-voltage user information IDs in the monitored transformer area;
the second query module is used for searching the measurement service according to the resource ID by using the measurement query service of the measurement point management center through the measurement point information of all the equipment to obtain the measurement information of all the equipment in the monitored station area;
the real-time line loss value calculation module is used for calculating to obtain a real-time line loss value in the monitored transformer area based on the measurement information of all the equipment in the monitored transformer area, and obtaining transformer area line loss data of the monitored transformer area;
and the abnormal line loss judging module is used for judging whether the monitored transformer area has the abnormal line loss condition of the transformer area based on the transformer area line loss data.
8. The apparatus of claim 7, wherein the abnormal line loss determining module is specifically configured to:
preprocessing the line loss data of the transformer area;
clustering the preprocessed transformer area line loss data;
calculating discrete characteristic values of the clustered line loss data of the transformer area;
and judging whether the monitored transformer area has abnormal line loss or not based on the discrete characteristic value.
9. A computer-readable storage medium, characterized in that the storage medium stores a computer program for performing the method of any of the preceding claims 1-6.
10. An electronic device, characterized in that the electronic device comprises:
a processor;
a memory for storing the processor-executable instructions;
the processor is configured to read the executable instructions from the memory and execute the instructions to implement the method of any one of claims 1-6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210441250.3A CN114878961A (en) | 2022-04-25 | 2022-04-25 | Method and device for monitoring abnormal line loss of distribution room based on service center |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210441250.3A CN114878961A (en) | 2022-04-25 | 2022-04-25 | Method and device for monitoring abnormal line loss of distribution room based on service center |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114878961A true CN114878961A (en) | 2022-08-09 |
Family
ID=82671069
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210441250.3A Pending CN114878961A (en) | 2022-04-25 | 2022-04-25 | Method and device for monitoring abnormal line loss of distribution room based on service center |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114878961A (en) |
-
2022
- 2022-04-25 CN CN202210441250.3A patent/CN114878961A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111984499B (en) | Fault detection method and device for big data cluster | |
| US11449315B2 (en) | Systems and methods for utilizing machine learning to identify non-technical loss | |
| CN102081622B (en) | Method and device for evaluating system health degree | |
| CN110097297A (en) | A kind of various dimensions stealing situation Intellisense method, system, equipment and medium | |
| CN106886485A (en) | Power system capacity analyzing and predicting method and device | |
| CN110046744A (en) | Energy consumption data method for early warning and relevant device based on trend prediction | |
| CN114878934A (en) | Electric energy consumption data abnormity early warning method | |
| KR102269647B1 (en) | Server performance monitoring apparatus | |
| CN114862109A (en) | Power utilization abnormity monitoring method and device, electronic equipment and storage medium | |
| CN116820767A (en) | Cloud resource management method and device, electronic equipment and storage medium | |
| CN111190790A (en) | Cloud computing cluster monitoring method and system based on peak prediction | |
| CN114878961A (en) | Method and device for monitoring abnormal line loss of distribution room based on service center | |
| CN108205761A (en) | A kind of multi-layer power sales data analysis monitors system | |
| CN114077977B (en) | Building intelligent management method and system based on big data and readable storage medium | |
| Loboz | Cloud resource usage: extreme distributions invalidating traditional capacity planning models | |
| CN114565324A (en) | Transformer area line loss evaluation method and device, electronic equipment and storage medium | |
| Daraghmi et al. | Accurate and time‐efficient negative binomial linear model for electric load forecasting in IoE | |
| CN115081893A (en) | User electricity consumption data analysis method and device, electronic equipment and readable storage medium | |
| CN111429257B (en) | Transaction monitoring method and device | |
| CN113656452A (en) | Method and device for detecting abnormal index of call chain, electronic equipment and storage medium | |
| CN112633692A (en) | Acquisition method and device for electricity stealing checking threshold value, and electricity stealing checking device and method | |
| Hu et al. | Adaptive threshold modeling algorithm for monitoring indicators of power network server based on Chebyshev inequality | |
| CN118467180B (en) | Multi-tenant data management method | |
| CN112783637A (en) | Resource regulation and control method and device | |
| CN117391261B (en) | AI intelligent water service system of internet of things based on low-power consumption ultrasonic measurement |
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 |