CN113570109B - Distribution transformer weight overload prediction method - Google Patents

Distribution transformer weight overload prediction method Download PDF

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Publication number
CN113570109B
CN113570109B CN202110709128.5A CN202110709128A CN113570109B CN 113570109 B CN113570109 B CN 113570109B CN 202110709128 A CN202110709128 A CN 202110709128A CN 113570109 B CN113570109 B CN 113570109B
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overload
characteristic variable
probability
distribution transformer
heavy overload
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CN113570109A (en
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李绍坚
杨晓燕
王益成
黄霖
莫江婷
侯振华
赖焘焘
李江伟
符华
苏宏宇
李麟
梁卓玲
周平
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Nanning Power Supply Bureau of Guangxi Power Grid Co Ltd
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Nanning Power Supply Bureau of Guangxi Power Grid Co Ltd
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    • 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
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
    • 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—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas 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

Abstract

The invention discloses a distribution transformer heavy overload prediction method, wherein the method comprises the steps of obtaining historical data of heavy overload of a distribution area; determining characteristic variables influencing distribution transformer heavy overload according to the historical data of the transformer area heavy overload; determining the influence proportion of each characteristic variable; analyzing historical data of heavy overload of the transformer area to obtain a danger set value of each characteristic variable; according to the historical data of the heavy overload of the transformer area, the probability of the heavy overload of each characteristic variable reaching a dangerous set value is obtained; determining the probability of heavy overload when each characteristic variable reaches a dangerous set value according to a Bayesian rule; and the probability of the occurrence of the distribution and transformation weight overload is obtained by monitoring each characteristic variable and according to a total probability formula. According to the invention, through a data integration platform based on the big data of the power grid, the real-time distribution transformer weight overload probability is analyzed by using a statistical principle and probability theory, so that the risk of distribution transformer weight overload is prevented, and the operation safety and economic benefit of the power equipment are improved.

Description

Distribution transformer weight overload prediction method
Technical Field
The invention relates to the technical field of electric power big data application, in particular to a distribution transformer weight overload prediction method.
Background
With the mature application of various business systems and the implementation of low-voltage centralized meter reading and full coverage, power supply enterprises have accumulated mass data including the aspects of enterprise operation, production operation, customer service and the like. And these data do not really enable integrated applications across business domains. After the data integration platforms of all power grid companies are on-line, all data assets are sequentially collected to the data integration platforms, data integration application of all business domains is developed, and deep digging of large data value provides help for promoting data asset representation and improving management decisions based on data.
The power distribution network is the last level power grid facing power consumers, and a power distribution station area with a geographic power supply area of a power distribution transformer as a boundary is an important basic unit in operation and maintenance of the power distribution network. The operating state of the distribution transformer determines to a large extent the quality and safety of the power supply in the supply area. When the distribution transformer is in a heavy load or overload state for a long time, the service life of equipment is shortened, equipment failure risks are brought, power failure accidents are caused, and unnecessary economic losses are brought to both the power company and the user. Heavy overloads of distribution transformers are related to the number of active users in the area, the way the users are powered, and the weather. Therefore, the distribution transformer overload is one of a typical big data research demand point and an application scene in a power system, the data required by the user can be obtained through statistics on a data integration platform, the internal data and the external environment data of a power grid are fused based on the power big data concept, the mining and prediction research on the distribution transformer overload influence factors is carried out, and the important practical significance and the economic and social benefits are achieved.
Disclosure of Invention
The invention aims to provide a distribution transformer overload prediction method, which can solve the economic loss caused by distribution transformer overload (distribution transformer) in the prior art through the statistical calculation of power big data.
The purpose of the invention is realized by the following technical scheme:
the invention provides a distribution transformer overload prediction method, which comprises the following steps:
s1, acquiring the heavy overload historical data of a transformer area;
s2, determining characteristic variables influencing distribution transformer heavy overload according to the historical data of the transformer area heavy overload;
s3, determining the influence proportion of each characteristic variable, and classifying the influence proportion into A 1 、A 2 ……A i ；
S4, analyzing the historical data of the heavy overload of the transformer area to obtain the danger set value of each characteristic variable;
s5, obtaining the probability of heavy overload when each characteristic variable reaches a dangerous set value according to the historical data of heavy overload of the transformer area, and marking as P (A) 1 )、P(A 2 )……P(A i )；
S6, determining the probability of heavy overload when each characteristic variable reaches a danger set value according to Bayes rule, and recording as P (B | A) 1 )、P(B|A 2 )……P(B|A i )；
S7, obtaining the probability of the occurrence of distribution and transformation weight overload according to a total probability formula by monitoring each characteristic variable, and marking the probability as P (B), wherein the probability can be expressed as:
P(B)＝P(A 1 )P(B|A 1 )+P(A 2 )P(B|A 2 )+……+P(A i )P(B|A i )
further, the historical data of the station area heavy overload comprises the times of heavy overload counted by year, the times of heavy overload counted by month and characteristic variables causing the heavy overload each time.
Further, the analyzing the historical data of the heavy overload of the transformer area to obtain the danger setting value of each characteristic variable specifically includes:
s101, acquiring historical data of each characteristic variable of a transformer area;
s102, comparing the distribution transformer overload with each characteristic variable under normal operation;
s103, obtaining the dangerous value of each characteristic variable when the distribution and transformation weight is overloaded;
and S104, determining and setting the danger set values of the characteristic variables.
Further, obtaining the probability of heavy overload occurring when each characteristic variable reaches a dangerous set value according to the historical data of heavy overload in the transformer area specifically includes:
s201, defining a statistical time period, recording as S, and taking the unit as day;
s202, acquiring distribution transformer heavy overload data in a station area statistic time period;
s203, counting the times of reaching the dangerous set value of each characteristic variable when heavy overload occurs in the time period, and recording as C 1 、C 2 ……C i ；
S204, calculating to obtain the probability of heavy overload when each characteristic variable reaches a dangerous set value; the calculation formula is as follows:
Figure BDA0003132684310000031
further, the obtaining of the risk value of each characteristic variable during overload of the distribution and transformation weight specifically includes:
s301, acquiring the numerical value of each characteristic variable when the distribution variable is overloaded every time;
s302, based on big data analysis, finding out a danger range value of each characteristic variable with a common interval;
the dangerous range value of the common interval refers to that the characteristic variable causing the distribution transformer overload is found according to the distribution transformer overload condition for multiple times, the value of the characteristic variable at this time is recorded and counted, and after the minimum value and the maximum value are taken from the single characteristic variable value counted for multiple times, the minimum value and the maximum value at this time are the dangerous range value of the common interval of the characteristic variable.
S303, calculating the average value of the dangerous range values of the characteristic variables according to the dangerous range values of the common intervals at each time;
and S304, determining the final risk value of each characteristic variable as an average value of the risk range values.
Further, the characteristic variables include user factors, weather factors, and distribution transformer own factors.
Further, the statistical time period includes annual statistics and monthly statistics.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic representation of the steps of one embodiment of a method of distribution transformer weight overload prediction of the present application;
FIG. 2 is a schematic diagram illustrating steps of another embodiment of a method of distribution transformer overload prediction as claimed in the present application;
FIG. 3 is a schematic diagram illustrating steps of another embodiment of a method of distribution transformer overload prediction as claimed in the present application;
fig. 4 is a schematic step diagram of another embodiment of a method for distribution transformer overload prediction according to the present application.
Detailed Description
The embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.
The embodiments of the present disclosure are described below with specific examples, and other advantages and effects of the present disclosure will be readily apparent to those skilled in the art from the disclosure of the present disclosure. It is to be understood that the described embodiments are merely illustrative of some, and not restrictive, of the embodiments of the disclosure. The disclosure may be embodied or carried out in various other specific embodiments, and various modifications and changes may be made in the details within the description without departing from the spirit of the disclosure. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without making any creative effort, shall fall within the protection scope of the present disclosure.
Referring to fig. 1, one embodiment of a method for distribution transformer overload prediction according to the present application includes:
s1, acquiring historical data of heavy overload of a transformer area;
as we know, the big data technology becomes a trend, and power grid enterprises have mass power data for many years and have sufficient data resource reserves. Electric power data are fused by means of big data technologies such as multi-data fusion, data analysis mining, visualization, data storage and processing and the like. Therefore, the data integration platform can acquire historical data including heavy overload of the transformer area and related data in the power grid enterprise, and the historical data and the related data are easily obtained.
S2, determining characteristic variables influencing distribution transformer heavy overload according to the historical data of the transformer area heavy overload;
in the history of distribution transformer overload in the distribution area, various factors influencing the distribution transformer overload can be analyzed, and the factors form characteristic variables. From experience and data analysis, it is known that various factors affecting distribution transformer overload include customer factors, weather factors, and factors of the distribution transformer itself. The user factors can be classified into power utilization categories, user categories, industry categories, operation capacity, importance levels, operation capacity and the like; weather factors include average temperature, weather conditions, week, quarter, legal holidays, etc.; distribution transformers themselves include varying capacity, date of first shipment, cooling mode, nameplate capacity, protection mode, date of manufacture, etc. We can find some main characteristic variables affecting the distribution transformer in the area in practical analysis to count.
S3, determining the influence proportion of each characteristic variable, and classifying the influence proportion into A 1 、A 2 ……A i ；
Statistical ranking is analyzed from a plurality of influencing factors which influence characteristic variables of distribution transformers, for example, A occurs in a state of heavy distribution overload within one year 1 The most frequent, specific gravity analysis by influenceA is to be 1 The greatest effect is given by weight. The same theory sequentially divides the influence specific gravity into A 1 、A 2 ……A i 。
S4, analyzing the historical data of the heavy overload of the transformer area to obtain the danger set value of each characteristic variable;
s5, obtaining the probability of heavy overload when each characteristic variable reaches a dangerous set value according to the historical data of heavy overload of the transformer area, and marking as P (A) 1 )、P(A 2 )……P(A i )；
S6, determining the probability of heavy overload when each characteristic variable reaches a danger set value according to Bayes rule, and recording as P (B | A) 1 )、P(B|A 2 )……P(B|A i )；
S7, obtaining the probability of the occurrence of distribution and transformation weight overload according to a total probability formula by monitoring each characteristic variable, and marking the probability as P (B), wherein the probability can be expressed as:
P(B)＝P(A 1 )P(B|A 1 )+P(A 2 )P(B|A 2 )+……+P(A i )P(B|A i )
it can be determined that P (B) is a probability value that changes in real time, and this probability value can be used as a predicted value of the distribution transformer access device and related risks, and an early warning value can be set according to the risk level.
Preferably, the historical data of the station area heavy overload comprises the times of heavy overload counted by year, the times of heavy overload counted by month and characteristic variables causing the heavy overload each time. Of course, these statistics may all be derived from the integrated data platform of the power system. The annual statistics may be one year, two years or many years, and the monthly statistics may also be one month, two months or many months, which is not specifically limited herein and may be determined according to actual situations.
Referring to fig. 2, further, in a preferred embodiment of the present application, the analyzing the historical data of the station area heavy overload to obtain the risk set value of each characteristic variable specifically includes:
s101, acquiring historical data of each characteristic variable of a transformer area;
the historical data of each characteristic variable comprises data of the distribution transformer in a normal operation state and a heavy overload state.
S102, comparing the distribution transformer overload with each characteristic variable under normal operation;
s103, obtaining the dangerous value of each characteristic variable when the distribution and transformation weight is overloaded;
and S104, determining and setting the danger set values of the characteristic variables.
Referring to fig. 3, further, in a preferred embodiment of the present application, obtaining the probability of heavy overload occurring when the characteristic variable reaches the set risk value according to the historical data of heavy overload of the distribution room specifically includes:
s201, defining a statistical time period, recording as S, and taking the unit as day;
s202, acquiring distribution transformer heavy overload data in a station area statistic time period;
s203, counting the times of reaching the dangerous set value of each characteristic variable when heavy overload occurs in the time period, and recording as C 1 、C 2 ……C i ；
S204, calculating to obtain the probability of heavy overload of each characteristic variable reaching a dangerous set value; the calculation formula is as follows:
Figure BDA0003132684310000071
for example, when C 1 =10, the statistical time period S is calculated in one year, at which time:
Figure BDA0003132684310000072
referring to fig. 4, further, in a preferred embodiment of the present application, the obtaining the risk value of each characteristic variable when the distribution weight is overloaded specifically includes:
s301, acquiring the numerical value of each characteristic variable when the distribution variable is overloaded every time;
s302, based on big data analysis, finding out a danger range value of each characteristic variable with a common interval;
s303, calculating the average value of the dangerous range values of the characteristic variables according to the dangerous range values of the common intervals at each time;
and S304, determining the final risk value of each characteristic variable as the average value of the risk range values.
Preferably, the characteristic variables include user factors, weather factors, and distribution transformer factors themselves.
The user factors can be further divided into power utilization categories, user categories, industry categories, operation capacity, importance levels, operation capacity and the like; weather factors include average temperature, weather conditions, week, quarter, legal holidays, etc.; distribution transformers themselves include varying capacity, date of first shipment, cooling mode, nameplate capacity, protection mode, date of manufacture, etc. We can find some main characteristic variables affecting the distribution transformer in the area in practical analysis to count.
Preferably, the statistical time period includes a yearly statistic and a monthly statistic. The annual statistics may be one year, two years or many years, and the monthly statistics may also be one month, two months or many months, which is not specifically limited herein and may be determined according to actual situations.
It can be clearly understood by those skilled in the art that, for convenience and simplicity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit may be implemented in the form of hardware, or may also be implemented in the form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and the like.

Claims (7)

1. A method for distribution transformer weight overload prediction, comprising the steps of:
s1, acquiring historical data of heavy overload of a transformer area;
s2, determining characteristic variables influencing distribution transformer heavy overload according to the historical data of the transformer area heavy overload;
s3, determining the influence proportion of each characteristic variable, and classifying the influence proportion into A 1 、A 2 ……A i ；
S4, analyzing the historical data of the heavy overload of the transformer area to obtain the danger set value of each characteristic variable;
s5, obtaining the probability of heavy overload when each characteristic variable reaches a dangerous set value according to the historical data of heavy overload of the transformer area, and marking as P (A) 1 )、P(A 2 )……P(A i )；
S6, determining the probability of heavy overload when each characteristic variable reaches a danger set value according to Bayes rule, and recording as P (B | A) 1 )、P(B|A 2 )……P(B|A i )；
S7, obtaining the probability of the occurrence of distribution and transformation weight overload according to a total probability formula by monitoring each characteristic variable, and marking the probability as P (B), wherein the probability can be expressed as:
P(B)＝P(A 1 )P(B|A 1 )+P(A 2 )P(B|A 2 )+……+P(A i )P(B|A i )
2. the method of distribution transformer heavy overload prediction according to claim 1, wherein the historical data of the station area heavy overload comprises the number of times of heavy overload counted by year, the number of times of heavy overload counted by month and characteristic variables causing the heavy overload each time.
3. The method for predicting distribution transformer overload according to claim 1, wherein the step of analyzing historical data of the distribution transformer area overload to obtain the risk set value of each characteristic variable specifically comprises:
s101, acquiring historical data of each characteristic variable of a transformer area;
s102, comparing the distribution transformer weight overload with each characteristic variable under normal operation;
s103, obtaining the dangerous value of each characteristic variable when the distribution and transformation weight is overloaded;
and S104, determining and setting the danger set values of the characteristic variables.
4. The method for predicting distribution transformer overload according to claim 2, wherein obtaining the probability of overload occurring when the characteristic variables reach the set danger value according to the historical data of station area overload specifically comprises:
s201, defining a statistical time period, recording as S, and taking the unit as day;
s202, acquiring distribution transformer heavy overload data in a station area statistic time period;
s203, obtaining the times of reaching the danger set value of each characteristic variable when heavy overload occurs in the statistical time period, and recording the times as C 1 、C 2 ……C i ；
S204, calculating to obtain the probability of heavy overload when each characteristic variable reaches a dangerous set value; the calculation formula is as follows:
Figure FDA0003132684300000021
5. the method according to claim 3, wherein the obtaining the risk value of each characteristic variable during overload of the distribution transformer specifically comprises:
s301, acquiring the numerical value of each characteristic variable when the distribution variable is overloaded every time;
s302, based on big data analysis, finding out a danger range value of each characteristic variable with a common interval;
s303, calculating the average value of the dangerous range values of the characteristic variables according to the dangerous range values of the common intervals at each time;
and S304, determining the final risk value of each characteristic variable as the average value of the risk range values.
6. The method of distribution transformer weight overload prediction according to claim 1, wherein the characteristic variables include customer factors, weather factors, and distribution transformer factors themselves.
7. The method of distribution transformer weight overload prediction according to claim 4, wherein the statistical time period comprises a yearly statistic and a monthly statistic.
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