CN111080474A - Distribution network reliability analysis method based on big data visualization technology - Google Patents
Distribution network reliability analysis method based on big data visualization technology Download PDFInfo
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- CN111080474A CN111080474A CN201911222386.XA CN201911222386A CN111080474A CN 111080474 A CN111080474 A CN 111080474A CN 201911222386 A CN201911222386 A CN 201911222386A CN 111080474 A CN111080474 A CN 111080474A
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- 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
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Abstract
The invention belongs to an analysis method, and particularly relates to a distribution network reliability analysis method based on a big data visualization technology. It includes: the method comprises the following steps: data acquisition, namely acquiring and storing public variable operation data, and the step two is as follows: data processing, calculating the common variable acquisition rate and the integrity rate, and the third step: reliability analysis, namely performing reliability analysis on the public transformer acquisition rate and the integrity rate; step four: visual output, namely outputting the common variable acquisition rate and the integrity rate on output equipment; displaying the credible public variable acquisition rate by using green numbers, or displaying the public variable acquisition rate by using red numbers; and displaying the credible integrity rate by using green figures, or displaying the integrity rate by using red figures. The invention has the following remarkable effects: by increasing reliability analysis, closed-loop judgment is carried out on whether the public transformer acquisition rate and the integrity rate are reliable or not, so that the reliability of the two results is increased; by adding the visualization link, the operator can visually see the judgment result, and the workload of the operator is reduced.
Description
Technical Field
The invention belongs to an analysis method, and particularly relates to a distribution network reliability analysis method based on a big data visualization technology.
Background
The distribution network is at the end of the whole power grid and is a window facing the society of power enterprises, the operation management of the distribution network is directly related to thousands of households, and the social responsibility and influence are huge. With the continuous development of society, higher and higher requirements are put forward on lean management of distribution networks. The distribution network has the characteristics of a plurality of points, long lines and wide area, along with the development of a power utilization information system, the acquisition device is increasingly advanced, most distribution network public distribution transformers have the conditions of acquiring current, voltage and power, public transformer operation data are effectively utilized to carry out statistical analysis on indexes, and the method has important practical significance for finding out public transformer operation abnormity, data acquisition quality and transmission channel problems as soon as possible.
At present, the conventional index statistical analysis of public transformer three-phase unbalance, low voltage, heavy overload and the like is provided, so that the running condition of the public transformer of the power distribution network can be better reflected, and the method can be used for carrying out remediation work in time. However, with the gradual accumulation of the public variable operation data, the traditional analysis method is gradually lack of strength, so that an analysis method which can control the overall situation from the data source and is more beneficial to the development of the improvement work and the designation of the improvement measures is needed.
In order to solve the above problems, CN201810834235.9 in the prior art provides a method and a system for analyzing indexes of a power distribution network based on big data, which combines running data such as common variable current, voltage, and power acquired by a power consumption information acquisition system, and relies on a big data technology-distributed parallel computing framework to perform fast and effective analysis on the common variable running data according to models such as common variable access timeliness rate, integrity rate, and acquisition rate, and through the control of key indexes such as integrity rate, acquisition rate, and timeliness rate, not only can macroscopically control the running health condition of a transformer, but also can discover defects of an acquisition device and a data transmission channel, thereby having important practical significance for discovering potential failure hidden dangers of equipment and ensuring safe and stable running of a power system.
However, the above-mentioned techniques also have drawbacks: it is impossible to judge whether the acquisition rate and the integrity rate of the common transformer are reliable or not. Therefore, a method for judging whether the acquisition rate and the integrity rate of the common variable are credible is needed.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a distribution network reliability analysis method based on a big data visualization technology.
The invention is realized by the following steps: a distribution network reliability analysis method based on big data visualization technology comprises the following steps:
the method comprises the following steps: collecting data;
collecting and storing the public change operation data,
step two: processing data;
calculating according to the following formula;
the number of the common variables of the collected common variable operation data is the data collected in the first step; the total public variable quantity of the distribution network public transformer is externally given data; the number of the actually collected common-variation operation data points is the sum of the current actual collection point, the voltage actual collection point and the power actual collection point; the number of common variable operating data points that should be collected is externally given data,
step three: analyzing reliability;
carrying out reliability analysis on the common variation acquisition rate and the integrity rate;
step four: visual output;
outputting a common variable acquisition rate and a integrity rate on output equipment; if the result of the judgment in the third step is that the public variable acquisition rate is credible, displaying the public variable acquisition rate by using green numbers, otherwise, displaying the public variable acquisition rate by using red numbers; and if the judgment result of the third step is that the integrity rate is credible, displaying the integrity rate by using green figures, otherwise displaying the integrity rate by using red figures.
The distribution network reliability analysis method based on the big data visualization technology, wherein the common variable operation data in the first step includes: the current, voltage and power of the common variations,
the voltage, current and power are respectively Vij、Iij、PijWherein i represents the number of users and j represents the acquisition time.
The distribution network reliability analysis method based on the big data visualization technology is characterized in that the common-transformer operation data preferably include common-transformer three-phase current, three-phase voltage and four-phase power.
The distribution network reliability analysis method based on the big data visualization technology comprises the following steps of judging whether the following inequality is true;
where j is the acquisition time, PjIs the total power of the system at the current moment, i is the number of users, N is the total number of users, the symbol | S | represents the absolute value of S,
if the inequality is true, judging that the public variable acquisition rate calculated in the step two is credible; otherwise, the common variable acquisition rate is judged to be not credible,
judging whether the following inequality is true;
where j is the acquisition time, K is an externally given threshold, i is the number of users, N is the total number of users, the symbol | S | represents the absolute value of S,
if the inequality is established, judging that the integrity rate is credible; otherwise, judging that the integrity rate is not credible.
The invention has the following remarkable effects: by increasing reliability analysis, closed-loop judgment is carried out on whether the public transformer acquisition rate and the integrity rate are reliable or not, so that the reliability of the two results is increased; by adding the visualization link, the operator can visually see the judgment result, and the workload of the operator is reduced.
Detailed Description
A distribution network reliability analysis method based on big data visualization technology comprises the following steps:
the method comprises the following steps: collecting data;
collecting and storing the operation data of the public transformer, wherein the operation data of the public transformer comprises: the current, voltage and power of the common transformer are preferably common transformer three-phase current, three-phase voltage and four-phase power.
The voltage, current and power are respectively Vij、Iij、PijWherein i represents the number of users and j represents the acquisition time.
Step two: processing data;
calculating according to the following formula;
the number of the common variables of the collected common variable operation data is the data collected in the first step; the total public variable quantity of the distribution network public transformer is externally given data; the number of the actually collected common-variation operation data points is the sum of the current actual collection point, the voltage actual collection point and the power actual collection point; the number of common variable operating data points that should be collected is externally given data.
Step three: analyzing reliability;
judging whether the following inequality is true;
where j is the acquisition time, PjThe total power of the system at the current moment, i is the number of users, and N is the total number of users. The symbol | S | represents taking the absolute value of S.
If the inequality is true, judging that the public variable acquisition rate calculated in the step two is credible; otherwise, the common variable acquisition rate is judged to be unreliable.
Judging whether the following inequality is true;
where j is the acquisition time, K is an externally given threshold, i is the number of users, and N is the total number of users. The symbol | S | represents taking the absolute value of S.
If the inequality is established, judging that the integrity rate is credible; otherwise, judging that the integrity rate is not credible.
Step four: visual output;
outputting a common variable acquisition rate and a integrity rate on output equipment; if the result of the judgment in the third step is that the public variable acquisition rate is credible, displaying the public variable acquisition rate by using green numbers, otherwise, displaying the public variable acquisition rate by using red numbers; and if the judgment result of the third step is that the integrity rate is credible, displaying the integrity rate by using green figures, otherwise displaying the integrity rate by using red figures.
Claims (4)
1. A distribution network reliability analysis method based on big data visualization technology is characterized by comprising the following steps:
the method comprises the following steps: collecting data;
collecting and storing the public change operation data,
step two: processing data;
calculating according to the following formula;
the number of the common variables of the collected common variable operation data is the data collected in the first step; the total public variable quantity of the distribution network public transformer is externally given data; the number of the actually collected common-variation operation data points is the sum of the current actual collection point, the voltage actual collection point and the power actual collection point; the number of common variable operating data points that should be collected is externally given data,
step three: analyzing reliability;
carrying out reliability analysis on the common variation acquisition rate and the integrity rate;
step four: visual output;
outputting a common variable acquisition rate and a integrity rate on output equipment; if the result of the judgment in the third step is that the public variable acquisition rate is credible, displaying the public variable acquisition rate by using green numbers, otherwise, displaying the public variable acquisition rate by using red numbers; and if the judgment result of the third step is that the integrity rate is credible, displaying the integrity rate by using green figures, otherwise displaying the integrity rate by using red figures.
2. The distribution network reliability analysis method based on the big data visualization technology as claimed in claim 1, wherein: the common variable operation data in the first step comprises: the current, voltage and power of the common variations,
the voltage, current and power are respectively Vij、Iij、PijWherein i represents the number of users and j represents the acquisition time.
3. The distribution network reliability analysis method based on the big data visualization technology as claimed in claim 2, wherein: the public transformation operation data are preferably public transformation three-phase current, three-phase voltage and four-phase power.
4. The distribution network reliability analysis method based on the big data visualization technology as claimed in claim 3, wherein: the third step comprises the following steps of judging whether the inequality is true or not;
where j is the acquisition time, PjIs the total power of the system at the current moment, i is the number of users, N is the total number of users, the symbol | S | represents the absolute value of S,
if the inequality is true, judging that the public variable acquisition rate calculated in the step two is credible; otherwise, the common variable acquisition rate is judged to be not credible,
judging whether the following inequality is true;
where j is the acquisition time, K is an externally given threshold, i is the number of users, N is the total number of users, the symbol | S | represents the absolute value of S,
if the inequality is established, judging that the integrity rate is credible; otherwise, judging that the integrity rate is not credible.
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Address after: 730030 Anju community, Yannan Road, Chengguan District, Lanzhou City, Gansu Province Applicant after: Jiang Heshun Address before: 230022 State Grid Anhui Electric Power Co., Ltd., No. 9 Huangshan Road, Hefei, Anhui Applicant before: Jiang Heshun |
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