CN112101798A - Power equipment service life management method based on big data technology - Google Patents
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Abstract
The invention relates to a service life management method of electric power equipment based on big data technology, which extracts the related structured data of the electric power equipment from a big data platform to a database; preprocessing the data extracted into the database, obtaining basic information of the equipment at the same time, performing Boolean transformation on the preprocessed data except the equipment running time, and performing year division on the equipment running time to obtain wide table data; and establishing a service life management model of the power equipment according to a distributed Apriori association analysis algorithm. The invention utilizes big data technology to establish the service life management method of the power equipment, provides scientific judgment and early warning for the running state of the power equipment, and provides support for updating and upgrading the power equipment.
Description
Technical Field
The invention relates to the field of data processing of big data technology, in particular to a service life management method of power equipment based on big data technology.
Background
With the continuous development of social economy, the requirements of people on the power supply quality of power enterprises are higher and higher. The power equipment is an important component for the development of the power enterprise and also a foundation for supporting the normal operation of the power enterprise, and the development of the power enterprise is directly influenced by the operating state of the power equipment. Any power equipment has a certain service life, and the aging speed of the power equipment is also changed according to the condition of using the power equipment rightly or not. At present, the proportion of power equipment entering an aging stage in power enterprises in China is getting larger and larger, and in actual work, the equipment is often replaced when the equipment fails, and the replacement of the equipment is from a standing item to an implementation short, namely 1 year long and 2 years or longer. Under the condition, if the equipment is operated in an overrun way and is not updated in time, the paralysis of a certain service of the power grid can be caused, and the major economic loss of an enterprise can be directly caused. If the equipment running beyond the limit is not updated in time, paralysis of certain service of the power grid can be caused, and the major economic loss of enterprises can be directly caused. Taking the communication network as an example, the operating time of the exchange for dispatching and administrative telephone in a certain area is about 15 years, and the time of the renewal is reached, and the replacement of the equipment may take about 2 years for various reasons. During the period, the switch is in an over-age working state, faults can occur at any time, and scheduling and administrative telephone calls in large areas are all unavailable due to serious reasons. Therefore, the application of the big data technology to real-time monitoring of the operating state of the power equipment and early warning are of great importance to improving the operating performance of the power grid.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a power equipment service life management method based on a big data technology, which solves the problem of tedious work progress caused by that equipment needs to be replaced when the equipment fails in the prior art, fully applies big data analysis and processing technology to calculate all equipment data, performs classified statistics on the running years and running states of the power equipment, finds meaningful service rules among equipment variables by using an association analysis algorithm, is finally used for updating the equipment and can give reasonable and intuitive early warning.
The technical scheme adopted by the invention for realizing the purpose is as follows:
a power equipment service life management method based on big data technology comprises the following steps:
step 1: extracting the structural data related to the electric power equipment from the big data platform to a database;
step 2: preprocessing the data extracted into the database, obtaining basic information of the equipment at the same time, performing Boolean transformation on the preprocessed data except the equipment running time, and performing year division on the equipment running time to obtain wide table data;
and step 3: and establishing a service life management model of the power equipment according to a distributed Apriori association analysis algorithm.
The power equipment related structured data comprises: equipment base information, equipment maintenance records, equipment fault records, blackout events, cost investments, bid procurement, warehouse inventory, and supplier data.
The equipment basic information comprises equipment running time, running state and defect handling condition
The pretreatment comprises the following steps: and removing error data which cannot be matched in the equipment data, processing null values and irrelevant data index field values, and storing the filtered data into a newly-built base table.
The year division includes dividing the device operation time into 4 year intervals in 10 years, 20 years and 30 years.
The establishing of the power equipment service life management model according to the distributed Apriori association analysis algorithm comprises the following processes:
a) scanning wide table data, and generating a first candidate set C required by a model according to the equipment type1Then from the first candidate set C1Through support degree filtering, a first frequent item set L is generated1；
b) Set L of the first frequent item1Are spliced pairwise into a second candidate set C2；
c) From the second candidate set C2Initially, a second frequent item set L is generated by support degree filtering2And splicing into a third candidate set C according to Apriori principle3Third candidate set C3Generating a third frequent item set L by support filtering3And so on until the kth frequent item set LkOnly one or no data item in the list is generated, and a frequent item set list is generated;
d) and deducing association rules according to the frequent item set list, calculating corresponding confidence, setting a minimum confidence value, and screening to obtain strong association rules. The strong association rule is an association rule obtained through confidence deletion.
The invention has the following beneficial effects and advantages:
the method has the advantages that the big data technology is utilized, the service life management method of the power equipment is established, scientific judgment and early warning are given to the operation state of the power equipment, and support is provided for updating and upgrading of the power equipment.
Drawings
FIG. 1 is a flow chart of a method of the present invention;
FIG. 2 is a flow chart of the present invention for discovering a frequent itemset.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples.
In order to make the aforementioned objects, features and advantages of the present invention more comprehensible, embodiments accompanying the drawings are described in detail below. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein, but rather should be construed as modified in the spirit and scope of the present invention as set forth in the appended claims.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. Fig. 1 shows a flow chart of the method of the present invention.
The method comprises the following steps:
step 1, the data acquisition component extracts structured data related to the power equipment to a large data platform distributed database Hbase through a large data platform sqoop component.
And 2, compiling a script program by the data processing component through a Hive SQL (structured query language) HQL (schema language) and processing the related structured data of the power equipment acquired by the data acquisition component.
And 2.1, matching the specific equipment details according to the equipment ID, removing error data which cannot be matched in the equipment data, processing null values and irrelevant data index field values, and storing the filtered data into a newly-built base table.
Step 2.2, according to the requirement of subsequent modeling analysis, basic information such as running time, running state, defect processing condition and the like of each device, device maintenance records, device fault records, power failure events, cost investment, bid procurement, warehouse inventory and supplier data are counted through a distributed data warehouse component Hive;
and 2.3, because the Apriori algorithm is based on an algorithm for mining a frequent item set of Boolean variable association rules, the data format and the data granularity need to be converted and regulated. The data format adjustment is to convert all variables into Boolean values, the operation time is converted into the age limit, the age limit is divided (within 10 years, 10-20 years, 20-30 years and more than 30 years), the divided attributes are converted into independent variables, the rest variables are processed according to the method, the data granularity adjustment mainly ensures the uniqueness of the equipment, and finally, wide-table data required by the analysis model is formed.
step 3.1, scanning all the equipment wide table data, and generating a candidate set C required by the model according to the equipment type1Then from C1Through support degree filtering, a frequent item set L is generated1。
Step 3.2, mixing L1Are spliced into C2。
Step 3.3 Slave candidate set C2Initially, L is generated by support filtering2。L2Splicing candidate item set C according to Apriori principle3；C3Generating L by support filtering3.. until LkOnly one or no data items.
Step 3.4, deriving association rules according to the frequent item set list generated in step 3.3, and calculating corresponding confidence; the confidence coefficient calculation method is that under the condition that the precondition X occurs, the probability of Y is deduced by an association rule of 'X → Y'; that is, in the item set containing X, the probability of containing Y is set with the minimum confidence value, and after screening, the strong association rule is obtained.
And 3.5, finding out the intrinsic relation among the equipment variables according to the candidate item list, and finding out the common characteristics of the equipment before updating. The invention also includes a power equipment life management module, which includes: the device comprises a data acquisition component, a data processing component and a data calculation component; the data acquisition component extracts the relevant data of the power equipment to a large data platform distributed database Hbase through a large data platform sqoop component, the sqoop is an open source tool and is mainly used for data transmission, and the HBase database is a distributed storage system with high reliability, high performance, column orientation and scalability;
the data processing component performs data extraction statistics through a Hive SQL-like query language HQL, wherein Hive is a distributed data warehouse component which defines a simple SQL-like query language HQL and allows users familiar with SQL to perform data query and processing;
the data calculation component realizes the service life management of the power equipment through the application of an Apriori correlation analysis technology, an Apriori algorithm is an algorithm for finding frequent item sets, the algorithm can find meaningful relations among services in a large data set, the calculation cost can be greatly reduced, and the search speed is greatly improved.
FIG. 2 is a flow chart of the frequent item set discovery method of the present invention.
Example data for one device type (transformer, circuit breaker, arrester, current transformer, disconnector, etc.) is chosen:
1) scanning all the equipment data to generate a candidate set C1＝{(x1)，(x2)，…，(x5)}；
2) Calculating the support degree of the item set, namely calculating the number of items in the candidate set, namely the total number 6,7,6,2,2 of the item set with the median of each variable being 1, wherein the support degree calculation method is the proportion of records containing the item set in the candidate set, so that the support degree of each item set is respectively as follows: 67%, 78%, 67%, 22%, 22%, and a minimum support of 20%, can yield a frequent 1-item set L1-{(x1)，(x2)，…，(x5)}；
3) According to L1Combining every two to generate a candidate frequent 2-item set, calculating the support degree of each item set, and obtaining a frequent 2-item set L according to the minimum support degree of 20 percent2(ii) a According to the rule, continuously iterating until L4Until there is no data item, the detailed iterative process is as shown in fig. 2;
4) judging a frequent item set through the steps, and reserving the frequent item set { x1，x2，x3}，{x1，x2，x5}；
5) And generating an association rule according to the frequent item set list, and calculating a corresponding confidence coefficient. The confidence calculation method is to deduce the probability of Y from the association rule "X → Y" in the case of the occurrence of the precondition X. That is, in the item set containing X, the probability of Y is set to have a minimum confidence of 70%, and after screening, the strong association rule is set to be { X1，x0}→{x2}、{x2，x0}→{x1}、{x0}→{x1，x2}；
6) And finding out the internal relation among the device variables according to the strong association rule list, and finding out the common characteristics of the devices before updating.
Claims (7)
1. A method for managing the service life of electric power equipment based on big data technology is characterized in that: the method comprises the following steps:
step 1: extracting the structural data related to the electric power equipment from the big data platform to a database;
step 2: preprocessing the data extracted into the database, obtaining basic information of the equipment at the same time, performing Boolean transformation on the preprocessed data except the equipment running time, and performing year division on the equipment running time to obtain wide table data;
and step 3: and establishing a service life management model of the power equipment according to a distributed Apriori association analysis algorithm.
2. The electrical equipment life management method based on big data technology according to claim 1, characterized in that: the power equipment related structured data comprises: equipment basic information, equipment maintenance records, equipment fault records, power failure event records, cost input data, bid procurement data, warehouse inventory data and supplier data.
3. The power equipment life management method based on big data technology according to claim 1 or 2, characterized in that: the device basic information comprises device running time, running state and defect handling condition.
4. The electrical equipment life management method based on big data technology according to claim 1, characterized in that: the pretreatment comprises the following steps: and removing error data which cannot be matched in the equipment data, processing null values and irrelevant data index field values, and storing the filtered data into a newly-built base table.
5. The electrical equipment life management method based on big data technology according to claim 1, characterized in that: the year division includes dividing the device operation time into 4 year intervals in 10 years, 20 years and 30 years.
6. The electrical equipment life management method based on big data technology according to claim 1, characterized in that: the establishing of the power equipment service life management model according to the distributed Apriori association analysis algorithm comprises the following processes:
a) scanning wide table data, and generating a first candidate set C required by a model according to the equipment type1Then from the first candidate set C1Through support degree filtering, a first frequent item set L is generated1；
b) Set L of the first frequent item1Are spliced pairwise into a second candidate set C2；
c) From the second candidate set C2Initially, a second frequent item set L is generated by support degree filtering2And splicing into a third candidate set C according to Apriori principle3Third candidate set C3Generating a third frequent item set L by support filtering3And so on until the kth frequent item set LkOnly one or no data item in the list is generated, and a frequent item set list is generated;
d) and deducing association rules according to the frequent item set list, calculating corresponding confidence, setting a minimum confidence value, and screening to obtain strong association rules.
7. The electrical equipment life management method based on big data technology as claimed in claim 6, characterized in that: the strong association rule is an association rule obtained through confidence deletion.
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| CN115442421A (en) * | 2022-07-22 | 2022-12-06 | 云南电网有限责任公司 | Equipment replacement method and device, computer equipment and storage medium |
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| CN111143428A (en) * | 2019-11-30 | 2020-05-12 | 贵州电网有限责任公司 | Protection abnormity alarm processing method based on correlation analysis method |
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| CN103871003A (en) * | 2014-03-31 | 2014-06-18 | 国家电网公司 | Power distribution network fault diagnosis method utilizing historical fault data |
| CN108629528A (en) * | 2018-06-20 | 2018-10-09 | 国网江苏省电力有限公司电力科学研究院 | Quality of Transformer problem analysis method based on Apriori algorithm |
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