CN109767051B - Transformer power failure planning method based on big data analysis - Google Patents
Transformer power failure planning method based on big data analysis Download PDFInfo
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Abstract
The invention discloses a transformer power failure plan arrangement method based on big data analysis, which belongs to the technical field of power grid systems and comprises the following steps: step one, extracting relevant data of transformer power failure; step two, fitting a main transformer N-1 characteristic curve by using a K-means method; thirdly, clustering and fitting the load data of the main transformer N-1 for many years by using a K-means algorithm in Matlab software to obtain a fitting curve of the main transformer N-1 of the transformer substation; step four, extracting a suitable transformer station power failure maintenance time section; step five, customizing an index 'maintenance plan reasonable rate' to quantify and represent the planned work execution condition of transformer power failure; step six, optimizing the power failure time length of typical overhaul work of the main transformer by utilizing a particle swarm algorithm; and seventhly, optimizing scheduling of the transformer power failure plan.
Description
Technical Field
The invention belongs to the technical field of power grid systems, and particularly relates to a transformer power failure plan arrangement method based on big data analysis.
Background
In recent years, with the continuous deepening of the construction of 'digital roads', the requirement of people on the power supply quality of a large-scale urban power distribution network is increasingly improved, because the network frame of 35kV and above is perfect, direct load loss is rarely caused by planned maintenance and power failure of main network equipment, and the power grid operation safety risk generated by maintenance becomes the most main evaluation standard. How to optimize the planned scheduling period of transformer power failure and comprehensively reduce the safety risk of transformer power failure becomes the key problem of power grid safety control.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a transformer power failure plan arrangement method based on big data analysis. The method comprises the steps of performing clustering curve fitting on transformer load data in a dispatching automation system by adopting a K-means algorithm to obtain an annual characteristic curve of a transformer substation main transformer N-1, selecting a trough section as a proper maintenance section, considering long-scale safety of power failure, optimizing typical power failure operation execution time in a historical maintenance plan by utilizing a particle swarm algorithm to obtain a typical maintenance work suggested time length, considering short-scale safety of power failure of the transformer, finally comprehensively obtaining a proper maintenance period and power failure time length of the transformer substation, guiding relevant departments of operation inspection, infrastructure construction, marketing and the like to reasonably arrange engineering progress, changing a traditional transformer power failure plan arrangement mode, and realizing digital and intelligent management and control on the power failure safety of the transformer.
One of the purposes of the invention is to provide a transformer power failure planning method based on big data analysis, which comprises the following steps:
step one, extracting relevant data of transformer power failure;
step two, fitting a main transformer N-1 characteristic curve by using a K-means method; wherein: n-1 represents the load rate of other main transformers after one main transformer in the transformer substation is powered off;
thirdly, clustering and fitting the load data of the main transformer N-1 for many years by using a K-means algorithm in Matlab software to obtain a fitting curve of the main transformer N-1 of the transformer substation;
step four, extracting a section suitable for the transformer station transformer power failure maintenance time; the method comprises the following specific steps:
according to the rule of power failure maintenance of a transformer line, after obtaining a load fitting characteristic curve of a transformer substation in N-1 year, selecting a wave trough area with smooth waveform as a suitable maintenance time section, wherein the section is not more than 60 days, and if the time is less than 60 days, selecting other wave trough sections for filling;
step five, customizing an index 'maintenance plan reasonable rate' to quantify and represent the execution condition of the plan work of the transformer power failure, wherein the higher the reasonable rate is, the more reasonable the power failure duration is;
the overhaul plan equity rate = actual working time/planned application time 100%;
step six, optimizing the power failure time length of typical overhaul work of the main transformer by utilizing a particle swarm algorithm; the method comprises the following specific steps:
extracting power failure plan time and actual working time data of test type work in a historical main transformer power failure plan, and calculating typical time by utilizing a particle swarm algorithm to obtain the typical plan time, so that the historical main transformer power failure maintenance plan has the highest reasonable rate, the reasonable rate is not more than 100%, and the optimized time is taken as the typical power failure time of the typical maintenance type;
step seven, optimizing scheduling of the transformer power failure plan;
and integrating the suitable transformer power-off section obtained by using the K-means algorithm and the typical transformer overhaul power-off duration obtained by using the particle swarm algorithm to obtain the optimized scheduling result of the transformer power-off plan.
Further: in the first step: the transformer power failure related data comprises main transformer power failure plan data and a main transformer N-1, wherein: acquiring periodic T1 data within five years from an OMS (operation management system) by adopting an online collection mode for main transformer power failure plan data, wherein the periodic T1 data takes a month as a unit; the main transformer N-1 acquires periodic T2 data in five years from a dispatching automation system in an online collection mode, and the periodic T1 data take days as units. The OMS refers to a scheduling management application business process of a national power grid company.
Further: the second step is specifically as follows:
firstly, initializing, inputting a gene expression matrix as an object set X, inputting a specified clustering number N, randomly selecting N objects in the object set X as initial clustering centers, and setting an iteration stopping condition, wherein the iteration stopping condition is the maximum cycle number or the convergence error tolerance of the clustering centers;
secondly, iteration is carried out, and the data objects are distributed to the nearest clustering centers according to a similarity criterion, so that a class is formed; initializing a membership matrix, updating a clustering center, taking an average vector of each class as a new clustering center, and redistributing data objects;
and thirdly, repeatedly executing the second step until the stopping condition is met.
The second purpose of the invention is to provide a computer program for realizing a transformer power failure planning method based on big data analysis.
The invention further aims to provide an information data processing terminal for realizing the transformer power failure planning method based on big data analysis.
It is a further object of the present invention to provide a computer-readable storage medium comprising instructions which, when executed on a computer, cause the computer to perform a transformer outage scheduling method based on big data analysis.
In summary, the advantages and positive effects of the invention are as follows:
the invention utilizes a big data tool method to mine the operation and maintenance data rule and value of the transformer, and comprehensively obtains a power failure plan optimization scheduling method for comprehensively optimizing the power failure period and duration of the transformer. The method comprises the steps of performing clustering curve fitting on transformer load data in a dispatching automation system by adopting a K-means algorithm to obtain an annual characteristic curve of a transformer substation main transformer N-1, selecting a trough section as a proper maintenance section, considering long-scale safety of power failure, optimizing typical power failure operation execution time in a historical maintenance plan by utilizing a particle swarm algorithm to obtain a typical maintenance work suggested time length, considering short-scale safety of power failure of the transformer, finally comprehensively obtaining a proper maintenance period and power failure time length of the transformer substation, guiding relevant departments of operation inspection, infrastructure construction, marketing and the like to reasonably arrange engineering progress, changing a traditional transformer power failure plan arrangement mode, and realizing digital and intelligent management and control on the power failure safety of the transformer.
Drawings
FIG. 1 is a K-means algorithm clustering in a preferred embodiment of the present invention;
FIG. 2 is a main transformer N-1 load fitting characteristic curve in the preferred embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and do not limit the invention.
Referring to fig. 1 to 2, a transformer power failure planning method based on big data analysis includes the following steps:
1. and extracting relevant data of the power failure of the transformer. The extraction requirements are mainly given in table 1 below:
TABLE 1 extraction requirement Table
2. Raw data processing
The method comprises the steps of compiling a formula by utilizing excel software to process main transformer circuit load data, and realizing the verification of the integrity, uniqueness, authority, legality and consistency of the data through correcting errors, deleting repeated items, unifying specifications, correcting logics, converting structures, compressing the data, complementing incomplete/empty values and discarding data/variables.
3. Main transformer N-1 characteristic curve fitting by using K-means method
(1) K-means principle:
first, initialization. Inputting a gene expression matrix as an object set X, inputting a specified clustering number N, and randomly selecting N objects in the X as initial clustering centers. Iteration stop conditions are set, such as maximum loop times or cluster center convergence error margins.
And secondly, iteration is carried out. Data objects are assigned to the closest cluster centers according to a similarity criterion, thereby forming a class. And initializing a membership matrix. And updating the clustering center. The data objects are then reassigned with the average vector of each class as the new cluster center.
And thirdly, repeatedly executing the second step until the stopping condition is met.
(2) And (3) performing cluster fitting on the load data of the main transformer N-1 for many years by utilizing a self-contained K-means algorithm in Matlab software to obtain a fitting curve of the main transformer N-1 of the transformer substation.
(3) And extracting a suitable transformer station transformer power failure maintenance time section.
According to the law of the power failure maintenance of the transformer line, a power failure window period of about 60 days is needed within one year, and accounts for about 16.7% of the whole year. And after obtaining the N-1 annual load fitting characteristic curve of the main transformer of the transformer substation, selecting a wave trough area with smooth waveform as a suitable maintenance time section, wherein the section is not more than 60 days. If the number of days is less than 60, other trough sections can be selected for filling.
And meanwhile, designing limiting conditions, if the N-1 values of valley areas are all smaller than 90%, determining the conditions to be proper, screening the results by utilizing boundary conditions of winter, summer, major power protection and the like to obtain a proper overhaul period of each main transformer of the transformer substation, and compiling into a transformer overhaul calendar. .
4. Power failure duration optimization of transformer
4.1, a self-defined index 'maintenance plan reasonable rate' is used for quantifying and representing the plan work execution condition of the transformer power failure, and the higher the reasonable rate is, the more reasonable the power failure duration is. (in principle not more than 100%, if exceeding represents too short a planning time)
Maintenance schedule equity = actual working time/scheduled application time 100%
TABLE 2 rational grading of service plans
4.2 Particle Swarm Optimization (PSO) iterative optimization
(1) Basic principle
The basic idea of the PSO algorithm is to randomly initialize a group of particles without volume and mass, take the position of each particle as the solution of the optimization problem, and measure the quality of the particles by a fitness function reflecting the optimization target. The particles are searched in a set feasible solution space at a certain speed, and the optimal solution is finally obtained through multiple iterative search through memory and learning. In each iteration process, the particles can 'memorize' the self historical optimal solution and 'learn' the historical optimal solution of the whole population.
The basic principle of the particle swarm algorithm is as follows:
in the target search space of N dimensions, m particles fly at a certain speed, and at time t, the operating state of particle i can be set as follows:
position: x i ＝(x i1 ,,...x id ) T i＝1,2,...,m
Speed: v i ＝(v i1 ,,...v id ) T i＝1,2,...,m
The adaptation values are: fitness i ＝f(X 1 )
The D-dimension component (D is more than or equal to 1 and less than or equal to D) varies according to the following formula:
w t ＝w max -(w max -w min )t/t max
in the formula:
is the d-dimensional component of the velocity vector of particle i at the t-th iteration;
r 1 ，r 2 is randomly and uniformly distributed between 0 and 1; c. C 1 ，c 2 For a particle learning factor, generally take c 1 ＝c 2 ＝2。w t Is the inertial weight;
in the optimizing process of the particle swarm, on one hand, the experience accumulation of the particle swarm is memorized, on the other hand, the experience of other particles is learned, when the information value of a certain particle is better, the particle swarm is learned by other particles and correspondingly adjusted, so that the optimizing capability of the particle is improved, and the whole particle swarm continuously evolves in a good direction through cooperation and competition among the particles.
(2) And performing optimization calculation on the power failure time of the typical overhaul work of the main transformer by using a particle swarm algorithm.
Extracting the power failure plan time length and the actual working time length data of the test type work in the historical main transformer power failure plan, calculating the typical time length by utilizing a particle swarm algorithm, and obtaining the typical plan time length, so that the reasonable rate of the historical main transformer power failure maintenance plan is the highest, the reasonable rate value is not more than 100%, and the optimized time length is used as the typical power failure time length of the typical maintenance type.
5. Optimized scheduling period of transformer power failure plan
And integrating the suitable transformer power-off section obtained by using the K-means algorithm and the typical transformer overhaul power-off duration obtained by using the particle swarm algorithm to obtain the optimized scheduling result of the transformer power-off plan. Therefore, related departments such as operation and inspection, capital construction, marketing and the like are guided to reasonably arrange the project progress, the traditional transformer power failure plan arrangement mode is changed, and the digital and intelligent management and control on the transformer power failure safety are realized.
A computer program for implementing the above preferred embodiment, namely a transformer power failure planning method based on big data analysis.
An information data processing terminal for realizing the preferred embodiment, namely a transformer power failure planning method based on big data analysis.
A computer-readable storage medium comprising instructions which, when executed on a computer, cause the computer to perform the preferred embodiment described above, namely a transformer outage scheduling method based on big data analysis.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When used in whole or in part, is implemented in a computer program product that includes one or more computer instructions. When loaded or executed on a computer, cause the flow or functions according to embodiments of the invention to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center via wire (e.g., coaxial cable, fiber optic, digital Subscriber Line (DSL), or wireless (e.g., infrared, wireless, microwave, etc.)). The computer readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that includes one or more available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid State Disk (SSD)), among others.
The above description is intended to be illustrative of the preferred embodiment of the present invention and should not be taken as limiting the invention, but rather, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the invention.
Claims (4)
1. A transformer power failure plan arrangement method based on big data analysis is characterized by comprising the following steps: at least comprises the following steps:
step one, extracting relevant data of transformer power failure;
step two, fitting a main transformer N-1 characteristic curve by using a K-means method;
thirdly, performing cluster fitting on the load data of the multi-year main transformer N-1 by using a K-means algorithm in Matlab software to obtain a fitting curve of the main transformer N-1 of the transformer substation;
step four, extracting a section suitable for the transformer station transformer power failure maintenance time; the method specifically comprises the following steps:
according to the power failure maintenance rule of a transformer line, after obtaining a load fitting characteristic curve of a transformer substation main transformer in N-1 year, selecting a wave trough area with smooth waveform as a suitable maintenance time section, wherein the section is not more than 60 days, and if the section is less than 60 days, selecting other wave trough sections for filling;
step five, customizing an index 'maintenance plan reasonable rate' to quantify and represent the execution condition of the plan work of the transformer power failure, wherein the higher the reasonable rate is, the more reasonable the power failure duration is;
the reasonable rate of the maintenance plan = actual working time/planned application time 100%;
step six, optimizing the power failure time length of typical overhaul work of the main transformer by utilizing a particle swarm algorithm; the method specifically comprises the following steps:
extracting power failure plan time and actual working time data of test type work in a historical main transformer power failure plan, and calculating typical time by using a particle swarm algorithm to obtain typical plan time, so that the reasonable rate of the historical main transformer power failure maintenance plan is the highest, the reasonable rate is not more than 100%, and the optimized time is used as the typical power failure time of the typical maintenance type;
seventhly, optimizing scheduling of the transformer power failure plan;
the method comprises the steps of integrating a suitable transformer power failure section obtained by using a K-means algorithm and a typical transformer overhaul power failure duration obtained by using a particle swarm algorithm to obtain a transformer power failure plan optimization scheduling result;
the second step is specifically as follows:
firstly, initializing, inputting a gene expression matrix as an object set X, inputting a specified clustering number N, randomly selecting N objects in the object set X as initial clustering centers, and setting an iteration stopping condition, wherein the iteration stopping condition is the maximum cycle number or the convergence error tolerance of the clustering centers;
secondly, iteration is carried out, and the data objects are distributed to the closest clustering centers according to a similarity criterion, so that a class is formed; initializing a membership matrix, updating a clustering center, taking an average vector of each class as a new clustering center, and redistributing data objects;
and thirdly, repeatedly executing the second step until the stopping condition is met.
2. The transformer power failure planning method based on big data analysis of claim 1, wherein: in the first step: the transformer power failure related data comprises main transformer power failure plan data and a main transformer N-1, wherein: acquiring cycle T1 data within five years from a scheduling management application business process of a national power grid company by adopting an online collection mode for main transformer power failure plan data, wherein the cycle T1 data takes a month as a unit; the main transformer N-1 acquires periodic T2 data within five years from a dispatching automation system in an online collection mode, and the periodic T1 data take days as units.
3. An information data processing terminal for implementing the transformer power failure planning method based on big data analysis according to any one of claims 1-2.
4. A computer-readable storage medium comprising instructions that, when executed on a computer, cause the computer to perform the big data analysis-based transformer outage scheduling method of any one of claims 1-2.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104252646A (en) * | 2013-06-29 | 2014-12-31 | 贵州黔驰信息股份有限公司 | Method for automatically formulating daily power grid maintenance plans |
| CN105740977A (en) * | 2016-01-28 | 2016-07-06 | 福州大学 | Multi-target particle swarm-based power outage management optimization method |
| CN105913177A (en) * | 2016-04-08 | 2016-08-31 | 江苏省电力公司苏州供电公司 | Scheduling power failure plan information processing method based on cloud |
| WO2017071230A1 (en) * | 2015-10-30 | 2017-05-04 | 南京南瑞集团公司 | Method for short-term optimal scheduling of multi-agent hydropower station group |
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| US9977787B2 (en) * | 2016-02-02 | 2018-05-22 | Sap Se | Machine maintenance optimization with dynamic maintenance intervals |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN104252646A (en) * | 2013-06-29 | 2014-12-31 | 贵州黔驰信息股份有限公司 | Method for automatically formulating daily power grid maintenance plans |
| WO2017071230A1 (en) * | 2015-10-30 | 2017-05-04 | 南京南瑞集团公司 | Method for short-term optimal scheduling of multi-agent hydropower station group |
| CN105740977A (en) * | 2016-01-28 | 2016-07-06 | 福州大学 | Multi-target particle swarm-based power outage management optimization method |
| CN105913177A (en) * | 2016-04-08 | 2016-08-31 | 江苏省电力公司苏州供电公司 | Scheduling power failure plan information processing method based on cloud |
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