CN111490540B - Distribution transformer area index big data generation method based on three-phase unbalance degree judgment - Google Patents
Distribution transformer area index big data generation method based on three-phase unbalance degree judgment Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00001—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by the display of information or by user interaction, e.g. supervisory control and data acquisition systems [SCADA] or graphical user interfaces [GUI]
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00002—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by monitoring
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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Abstract
The invention discloses a distribution transformer area index big data generation method based on three-phase unbalance degree judgment, which comprises the steps of accessing original data sources operated by each distribution transformer in a distribution transformer area every day, and cleaning the data sources to obtain cleaned data sources; calculating three-phase unbalance and load rate of the cleaned data source, and judging according to a three-phase unbalance judgment rule to obtain a data source unbalance interval; performing application index calculation on the unbalanced region of the data source to obtain index data of each distribution transformer in the distribution transformer area every day; forming index data for each month and each year of the correspondence; and storing the daily index data, the monthly index data and the annual index data of each distribution transformer to an oracle database by a big data platform for foreground display. The invention can rapidly extract important relevant and useful data resources of the distribution transformer area, realize the big data fusion monitoring of the distribution transformer area and ensure the development of the big data construction of the power grid.
Description
Technical Field
The invention relates to the technical field of power grid data processing, in particular to a distribution transformer area index big data generation method based on three-phase unbalance degree judgment.
Background
In recent years, industrial Internet strategy, internet +' action plan and Internet enterprises represented by BAT are put forward at home and abroad, and the Internet enterprises firstly apply new technologies such as big data, cloud computing and the like in the fields of business, finance and the like to generate subversion influence. The national grid company is actively developing innovative action plans. Under the wide application of big data, cloud computing, internet of things and mobile interconnection technology, the work of the ultra-high voltage power grid (across continents, countries and regions), the ubiquitous smart power grid (source network coordination, production control and user interaction), the clean energy source, the three-integration five-major intensification and the like is fully developed.
Along with the continuous deep information construction, the big data characteristics of the information data are increasingly obvious, and the application requirements on the big data are increasingly urgent. Big data is the key of improving core competitiveness and preempting market precedent for enterprises, and is an important thrust for the enterprises to change from 'business driving' to 'data driving'. The operation monitoring center takes the operation monitoring center as a trigger, and aims at mass data of the operation of the distribution network, under the conditions that the traditional storage and calculation cannot meet the requirements and the technical disadvantages are more obvious, the actual value of the data is mined by taking a big data platform as a basis and taking big data analysis as a means, the reference and the assistance are provided for the construction and the planning of the power grid, and the economic benefit of the operation of enterprises is improved.
In order to promote the development of various monitoring analysis works, a series of problems of storage bottlenecks, calculation performance bottlenecks, real-time bottlenecks, advanced analysis mining bottlenecks and the like of massive cross-professional data in the normal monitoring are solved. By adopting a big data technology, a business development path from a point, a line to a surface is broken through, a unified data resource pool is established, comprehensive monitoring of power grid operation is realized, the breadth and depth of monitoring analysis are expanded, cross-major and cross-type data association analysis and prediction are realized, inherent trends and rules of company management are excavated, the capability of effectively positioning and managing difficulties and problem sources is improved, standardization and lean of business management are continuously promoted, and further the improvement of the operation technical level and the innovation of management modes of enterprises are promoted.
Along with the rapid application of big data technology in the power grid industry, the distribution transformer area is the terminal and the user side of the distribution transformer network and is responsible for all-round power supply and utilization services for residents of the area and industrial and commercial users. For a long time, the operation monitoring and management of the distribution transformer area is responsible for a plurality of departments of the power grid, is uncoordinated and has a defect, and is a short board for the operation management of the distribution transformer area. In recent years, with the trial and promotion of intelligent construction of distribution transformer areas, the national network and south network distribution transformer, marketing/metering departments sequentially release technical specifications of intelligent distribution transformer terminals, and terminal manufacturers continuously release new terminal products with different functions of intelligent distribution transformer. However, the large data integration of the distribution transformer area still has many defects at present, and important relevant and useful data resources of the distribution transformer area cannot be rapidly extracted, so that blind spots exist in the large data monitoring of the distribution transformer area, and in order to accelerate the development of large data technology in the power grid industry, how to rapidly and effectively generate data indexes of the distribution transformer area is a problem which needs to be solved at present.
Disclosure of Invention
In order to overcome the defects of the prior art on big data integration of a distribution transformer area, the invention aims to provide a distribution transformer area index big data generation method based on three-phase unbalance judgment, which is to access original data sources operated by each distribution transformer in the distribution transformer area every day and clean the data sources to obtain cleaned data sources; calculating three-phase unbalance and load rate of the cleaned data source, and judging according to a three-phase unbalance judgment rule to obtain a data source unbalance interval; performing application index calculation on the unbalanced region of the data source to obtain index data of each distribution transformer in the distribution transformer area every day; forming index data for each month and each year of the correspondence; and storing the daily index data, the monthly index data and the annual index data of each distribution transformer into an oracle database by a big data platform for foreground display to realize the distribution transformer area index based on three-phase unbalance degree judgment. The method can rapidly extract important relevant and useful data resources of the distribution transformer area, realize the large data fusion monitoring of the distribution transformer area, ensure the progress of large data construction of the power grid, and has novel method, ingenious structure and good application prospect.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
a distribution transformer area index big data generation method based on three-phase unbalance degree judgment is characterized by comprising the following steps:
step (A), the original data sources operated by each distribution transformer in the distribution transformer area every day are accessed and data source cleaning is carried out, and the cleaned data sources are obtained;
step (B), calculating three-phase unbalance and load factor of the cleaned data source, and judging according to a three-phase unbalance judgment rule to obtain a data source unbalance interval;
step (C), performing application index calculation on the unbalanced region of the data source to obtain index data of each distribution transformer in the distribution transformer area every day;
step (D), repeating the step (A) -the step (C), and gathering the index data of each distribution transformer every day layer by layer to form index data of each month and each year of the distribution transformer;
and (E) storing the daily index data, the month index data and the year index data of each distribution transformer into an oracle database by a big data platform for foreground display to realize the distribution transformer area index based on three-phase unbalance degree judgment.
Further, the step (A) includes the steps of,
(A1) Deleting the whole repeated data under the condition of the same measuring point ID and the same service identification on the same date;
(A2) And (3) preparing: deleting the whole piece of data, namely one-day data, when the current has negative numbers;
(A3) Setting the data points with the distribution voltage greater than 0V and smaller than 110V or greater than 330V as empty, and deleting the whole empty data;
(A4) Deleting the distribution transformer with the voltage transformation ratio not being 1, and deleting the station transformation point data with the voltage and current items less than 6 in the three-phase transformation day;
step (B) comprises the steps of,
(B1) For the cleaned data source, according to the formulas (1) and (2), respectively obtaining three-phase unbalance degree and load rate,
wherein I is a ,I b ,I c Three-phase current values corresponding to the respective pairs of the current sensors; u (U) a ,U b ,U c Three-phase voltage values corresponding to the respective matches; c is the rated capacity of the distribution transformer, and t is the multiplying power of the distribution transformer;
(B2) The three-phase unbalance degree and the load rate of each distribution transformer form a single-day three-phase unbalance data set and a single-day load rate data set;
(B3) Carrying out unbalance interval judgment on the single-day three-phase unbalance data set and the single-day load rate data set according to a three-phase unbalance judgment rule, wherein the three-phase unbalance degree is judged point by point, the point meeting the condition is marked as 1, and the point not meeting the condition is marked as 0; judging the load rate point by point, wherein the point meeting the condition is marked as 1, and the point not meeting the condition is marked as 0;
(B4) The three-phase unbalance degree and the load rate are logically and-d point by point to obtain a 0,1 data source unbalance interval sequence;
(B5) Searching for 8 continuous intervals of 1 and above according to the data source unbalanced interval sequence to obtain a data source unbalanced interval;
in the step (E), the daily index data, the month index data and the year index data of each distribution transformer are stored into an oracle database by a big data platform to be displayed in the foreground.
In the foregoing method for generating big data of distribution transformer area index based on three-phase imbalance determination, (B3), the imbalance interval determination criterion is imbalance > =30% and lasts for two hours or more; load ratio > =20% and lasting two hours or more, the unbalance interval determination is not carried out by the type of the matching transformer with load ratio <20%, and the unbalance degree of the type of the matching transformer is set to be zero.
The step (C) of the method for generating the index big data of the distribution transformer area based on the three-phase unbalance degree judgment is to calculate the application index of each distribution transformer in the distribution transformer area to obtain the index data of each distribution transformer day, wherein the application index comprises the unbalance times, the unbalance duration, the average unbalance degree, the maximum unbalance degree, the average load rate and the maximum load rate, and the application index is the index data of each distribution transformer day.
In the method for generating the distribution transformer area index big data based on the three-phase unbalance degree judgment, step (E), the daily index data, the month index data and the year index data of each distribution transformer are stored into the oracle database by a big data platform, and foreground display is carried out based on the oracle database.
According to the distribution transformer area index big data generation method based on the three-phase unbalance degree judgment, the step (A) is that 24 points of each distribution transformer in the distribution transformer area are accessed to an original data source operated every day and are subjected to data source cleaning, so that a cleaned data source is obtained; and (D) repeating the steps (A) - (C), and gathering the index data of each distribution transformer layer by layer, wherein 24 points at the bottom of each month form index data of each month of the distribution transformer, and 24 points at the bottom of each year form index data of each year of the distribution transformer.
The beneficial effects of the invention are as follows: according to the distribution transformer area index big data generation method based on three-phase unbalance degree judgment, original data sources of each distribution transformer running every day in a distribution transformer area are accessed and data source cleaning is carried out, and a cleaned data source is obtained; calculating three-phase unbalance and load rate of the cleaned data source, and judging according to a three-phase unbalance judgment rule to obtain a data source unbalance interval; performing application index calculation on the unbalanced region of the data source to obtain index data of each distribution transformer in the distribution transformer area every day; forming index data for each month and each year of the correspondence; and storing the daily index data, the monthly index data and the annual index data of each distribution transformer to an oracle database by a big data platform for foreground display.
According to the invention, the index data of each distribution transformer is obtained by cleaning and calculating the data source, so that the distribution transformer area index based on three-phase imbalance judgment is conveniently realized by statistics, important relevant and useful data resources of the distribution transformer area can be rapidly extracted, the big data fusion monitoring of the distribution transformer area is realized, the progress of the big data construction of a power grid is ensured, the method is novel, the structure is ingenious, and the application prospect is good.
Drawings
Fig. 1 is a flowchart of a distribution transformer area index big data generation method based on three-phase unbalance degree judgment according to the present invention.
Detailed Description
The invention will be further described with reference to the drawings.
As shown in fig. 1, a distribution transformer area index big data generation method based on three-phase unbalance degree judgment comprises the following steps,
step (A), the original data sources operated by each distribution transformer in the distribution transformer area every day are accessed and data source cleaning is carried out, and the cleaned data sources are obtained; comprises the following procedures of the method,
(A1) Deleting the whole repeated data under the condition of the same measuring point ID and the same service identification on the same date;
(A2) And (3) preparing: deleting the whole piece of data, namely one-day data, when the current has negative numbers;
(A3) Setting the data points with the distribution voltage greater than 0V and smaller than 110V or greater than 330V as empty, and deleting the whole empty data;
(A4) Deleting the distribution transformer with the voltage transformation ratio not being 1, and deleting the station transformation point data with the voltage and current items less than 6 in the three-phase transformation day;
step (B), calculating the three-phase unbalance and the load factor of the cleaned data source, judging according to the three-phase unbalance judgment rule to obtain a data source unbalance interval, comprising the following steps,
(B1) For the cleaned data source, according to the formulas (1) and (2), respectively obtaining three-phase unbalance degree and load rate,
wherein I is a ,I b ,I c Three-phase current values corresponding to the respective pairs of the current sensors; u (U) a ,U b ,U c Three-phase voltage values corresponding to the respective matches; c is the rated capacity of the distribution transformer, and t is the multiplying power of the distribution transformer;
(B2) The three-phase unbalance degree and the load rate of each distribution transformer form a single-day three-phase unbalance data set and a single-day load rate data set;
(B3) Carrying out unbalance interval judgment on the single-day three-phase unbalance data set and the single-day load rate data set according to a three-phase unbalance judgment rule, wherein the three-phase unbalance degree is judged point by point, the point meeting the condition is marked as 1, and the point not meeting the condition is marked as 0; the load rate is judged point by point, the point meeting the condition is marked as 1, the point not meeting the condition is marked as 0, and the unbalance interval judging criterion is unbalance degree > =30% and lasts for two hours or more; load rate > =20% and lasting for two hours or more, the configuration of load rate <20% does not make imbalance interval judgment, and the imbalance degree of the configuration of the type is set to zero;
(B4) The three-phase unbalance degree and the load rate are logically and-d point by point to obtain a 0,1 data source unbalance interval sequence;
(B5) Searching for 8 continuous intervals of 1 and above according to the data source unbalanced interval sequence to obtain a data source unbalanced interval;
step (C), calculating application indexes of each distribution transformer in the data source unbalanced zone to obtain index data of each distribution transformer in the distribution transformer zone, wherein the application indexes comprise unbalanced times, unbalanced time length, average unbalanced degree, maximum unbalanced degree, average load rate and maximum load rate, and the application indexes are the index data of each distribution transformer every day;
step (D), repeating the step (A) -the step (C), and gathering the index data of each distribution transformer every day layer by layer to form index data of each month and each year of the distribution transformer, wherein in the step (A), 24 points of each distribution transformer in a distribution transformer area are accessed to an original data source operated every day and are subjected to data source cleaning, so that a cleaned data source is obtained; and (D) repeating the step (A) -step (C), gathering the index data of each distribution transformer every day layer by layer, forming index data of each month corresponding to the distribution transformer at 24 points at the month end and forming index data of each year corresponding to 24 points at the year end, generating index big data of each year of a complete distribution transformer area, rapidly extracting important relevant and useful data resources of the distribution transformer area, realizing the big data fusion monitoring of the distribution transformer area, and ensuring the development of the construction of big data of a power grid.
In summary, according to the distribution transformer area index big data generation method based on three-phase unbalance degree judgment, original data sources operated by each distribution transformer in the distribution transformer area every day are accessed and data source cleaning is carried out, so that cleaned data sources are obtained; calculating three-phase unbalance and load rate of the cleaned data source, and judging according to a three-phase unbalance judgment rule to obtain a data source unbalance interval; performing application index calculation on the unbalanced region of the data source to obtain index data of each distribution transformer in the distribution transformer area every day; forming index data for each month and each year of the correspondence; daily index data, month index data and year index data of each distribution transformer are stored into an oracle database by a big data platform to be displayed on a foreground, so that the distribution transformer area index based on three-phase unbalance judgment is realized, important relevant and useful data resources of the distribution transformer area can be rapidly extracted, big data fusion monitoring of the distribution transformer area is realized, the progress of big data construction of a power grid is ensured, the method is novel, the construction is ingenious, and the method has good application prospect.
The foregoing has outlined and described the basic principles, features, and advantages of the present invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and that the above embodiments and descriptions are merely illustrative of the principles of the present invention, and various changes and modifications may be made without departing from the spirit and scope of the invention, which is defined in the appended claims. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (5)
1. A distribution transformer area index big data generation method based on three-phase unbalance degree judgment is characterized by comprising the following steps:
step (A), the original data sources operated by each distribution transformer in the distribution transformer area every day are accessed and data source cleaning is carried out, and the cleaned data sources are obtained;
step (B), calculating three-phase unbalance and load factor of the cleaned data source, and judging according to a three-phase unbalance judgment rule to obtain a data source unbalance interval;
step (C), performing application index calculation on the unbalanced region of the data source to obtain index data of each distribution transformer in the distribution transformer area every day;
step (D), repeating the step (A) -the step (C), and gathering the index data of each distribution transformer every day layer by layer to form index data of each month and each year of the distribution transformer;
step (E), storing the daily index data, the monthly index data and the annual index data of each distribution transformer into an oracle database by a big data platform for foreground display to realize the distribution transformer area index based on three-phase unbalance degree judgment;
the step (A) is specifically as follows:
(A1) Deleting the whole repeated data under the condition of the same measuring point ID and the same service identification on the same date;
(A2) And (3) preparing: deleting the whole piece of data, namely one-day data, when the current has negative numbers;
(A3) Setting the data points with the distribution voltage greater than 0V and smaller than 110V or greater than 330V as empty, and deleting the whole empty data;
(A4) Deleting the distribution transformer with the voltage transformation ratio not being 1, and deleting the station transformation point data with the voltage and current items less than 6 in the three-phase transformation day;
the step (B) is specifically as follows:
(B1) For the cleaned data source, according to the formulas (1) and (2), respectively obtaining three-phase unbalance degree and load rate,
wherein I is a ,I b ,I c Three-phase current values corresponding to the respective pairs of the current sensors; u (U) a ,U b ,U c Three-phase voltage values corresponding to the respective matches; c is the rated capacity of the distribution transformer, and t is the multiplying power of the distribution transformer;
(B2) The three-phase unbalance degree and the load rate of each distribution transformer form a single-day three-phase unbalance data set and a single-day load rate data set;
(B3) Carrying out unbalance interval judgment on the single-day three-phase unbalance data set and the single-day load rate data set according to a three-phase unbalance judgment rule, wherein the three-phase unbalance degree is judged point by point, the point meeting the condition is marked as 1, and the point not meeting the condition is marked as 0; judging the load rate point by point, wherein the point meeting the condition is marked as 1, and the point not meeting the condition is marked as 0;
(B4) The three-phase unbalance degree and the load rate are logically and-d point by point to obtain a 0,1 data source unbalance interval sequence;
(B5) And searching for 8 continuous intervals of 1 and above according to the data source unbalanced interval sequence to obtain the data source unbalanced interval.
2. The distribution transformer area index big data generation method based on three-phase unbalance degree judgment according to claim 1, wherein the method comprises the following steps: (B3) Wherein the imbalance interval criterion is imbalance > =30% and lasts for two hours or more; load rate > =20% and lasts for two hours or more; the unbalance interval judgment is not carried out on the distribution transformer with the load ratio of <20%, and the unbalance degree of the three types of distribution transformers is set to be zero.
3. The distribution transformer area index big data generation method based on three-phase unbalance degree judgment according to claim 1, wherein the method comprises the following steps: in the step (C), the application indexes include unbalance times, unbalance duration, average unbalance degree, maximum unbalance degree, average load rate and maximum load rate, and the application indexes are index data of each distribution transformer every day.
4. The distribution transformer area index big data generation method based on three-phase unbalance degree judgment according to claim 1, wherein the method comprises the following steps: in the step (E), the daily index data, the month index data and the year index data of each distribution transformer are stored into an oracle database by a big data platform, and foreground display is carried out based on the oracle database.
5. The distribution transformer area index big data generation method based on three-phase unbalance degree judgment according to claim 1, wherein the method comprises the following steps: step (A), the 24 points of each distribution transformer in the distribution transformer area are accessed to an original data source operated every day and cleaned to obtain a cleaned data source; and (D) repeating the steps (A) - (C), and gathering the index data of each distribution transformer layer by layer, wherein 24 points at the bottom of each month form index data of each month of the distribution transformer, and 24 points at the bottom of each year form index data of each year of the distribution transformer.
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| CN103729682A (en) * | 2014-01-21 | 2014-04-16 | 国家电网公司 | Three-phase imbalance judgment method |
| CN106505593A (en) * | 2016-10-14 | 2017-03-15 | 国网信通亿力科技有限责任公司 | A kind of method of the analysis of distribution transforming three-phase imbalance and load adjustment based on big data |
| CN107478917A (en) * | 2017-07-17 | 2017-12-15 | 国网江西省电力公司电力科学研究院 | A kind of decision method and device of taiwan area degree of unbalancedness |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN103729682A (en) * | 2014-01-21 | 2014-04-16 | 国家电网公司 | Three-phase imbalance judgment method |
| CN106505593A (en) * | 2016-10-14 | 2017-03-15 | 国网信通亿力科技有限责任公司 | A kind of method of the analysis of distribution transforming three-phase imbalance and load adjustment based on big data |
| CN107478917A (en) * | 2017-07-17 | 2017-12-15 | 国网江西省电力公司电力科学研究院 | A kind of decision method and device of taiwan area degree of unbalancedness |
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