CN107239880B - Power grid technical improvement fund demand measurement and calculation model based on equipment life statistical analysis - Google Patents

Power grid technical improvement fund demand measurement and calculation model based on equipment life statistical analysis Download PDF

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CN107239880B
CN107239880B CN201710306242.7A CN201710306242A CN107239880B CN 107239880 B CN107239880 B CN 107239880B CN 201710306242 A CN201710306242 A CN 201710306242A CN 107239880 B CN107239880 B CN 107239880B
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equipment
power grid
time
technical improvement
scrapping
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CN107239880A (en
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张旭东
牛东晓
张彩友
李雅
夏慧聪
宋国超
施婧
王锋华
施永益
张建松
陈浩
陈铁义
王珂
余侃
金程
项丹圆
杨扬
王晓辉
陈俊
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State Grid Zhejiang Electric Power Co Ltd
North China Electric Power University
Zhejiang Huayun Information Technology Co Ltd
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State Grid Zhejiang Electric Power Co Ltd
North China Electric Power University
Zhejiang Huayun Information Technology Co Ltd
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Abstract

The invention discloses a power grid technical improvement fund demand measurement and calculation model based on equipment life statistical analysis. The method calculates the probability that each type of equipment needs technical improvement investment when reaching the end of the service life at a certain time point, and calculates the relation of the technical improvement investment requirement changing along with time. The invention is used for power grid enterprises to measure and calculate technical improvement investment requirements, helps the power grid enterprises to arrange investment plans, and ensures the reliable operation of power equipment.

Description

Power grid technical improvement fund demand measurement and calculation model based on equipment life statistical analysis
Technical Field
The invention relates to a power data analysis technology.
Background
In recent years, with the change of the operating environment of the power grid enterprise and the increasing of the investment of power grid construction, the stock of the fixed assets of the power grid enterprise is greatly increased, and the updating, reforming and repairing expenses of the fixed assets show a trend of rising year by year. Fixed assets such as power equipment and the like are important material guarantees for maintaining and improving the production capacity of power grid enterprises, and account for a large proportion of total assets, wherein the technical improvement and major repair costs are important factors influencing the operation results and cash flow of the enterprises. Most power grid enterprises in China make technical improvement repair decisions and distribution by means of historical experience and basic unit declaration results, the scale of future technical improvement projects is difficult to be accurately grasped or predicted, and the matching condition of investment scale and asset scale and state cannot be effectively measured. Therefore, the method has important practical significance for developing the research on the real asset management of the power grid and improving the asset operation and management level of the power grid enterprise.
From the international advanced electric power enterprise asset management channelIn the experience, intensive capital investment generally leads to large-scale centralized transformation in the future, which brings greater operational reliability and capital pressure, but the research on the demand of centralized transformation of capital for power grid enterprises is less at present. By using a statistical analysis method of the service life of the equipment and combining the rule that the service life of the equipment obeys the exponential distribution[1]And the method can predict the technical improvement scale of the physical assets. By carrying out medium-and-long-term frame calculation on equipment technical improvement requirements, the equipment technical improvement investment plan can be reasonably arranged, the reliable operation of power equipment is guaranteed, the medium-and-long-term overall planning of a power grid enterprise is facilitated, the asset management level is improved, the investment scale is reasonably arranged, and the financial risk of the enterprise is reduced.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a power grid technical improvement fund demand measuring and calculating model based on equipment life statistical analysis, and measuring and calculating technical improvement investment demands according to equipment life statistical analysis data of a power grid enterprise.
In order to solve the technical problems, the invention adopts the following technical scheme: a power grid technical improvement fund demand measurement and calculation model based on equipment life statistical analysis comprises the following steps:
the input module is used for inputting the service life historical data of various devices;
the equipment scrapping time probability distribution calculating module is used for measuring and calculating the probability distribution of scrapping time of various equipment according to the service life historical data of the various equipment;
the equipment scrapping probability distribution function building module is used for building probability distribution functions of scrapping of various equipment at specific time according to probability distribution of scrapping time of various equipment;
the power grid technological improvement fund demand calculation module is used for calculating technological improvement funds required to be input by purchasing replacement equipment after various equipment is scrapped in the time period according to the scrapped probability distribution function of various equipment in specific time;
and the output module outputs the technical improvement fund amount calculated by the power grid technical improvement fund demand calculation module.
Further, the calculation method of the equipment scrapping time probability distribution calculation module comprises the following steps:
when the failure rate of the equipment is constant, the service life of the equipment is considered to be in accordance with exponential distribution regardless of time, and the service life distribution function is
Figure BDA0001285579000000021
A probability density of
Figure BDA0001285579000000031
Expectation of the distribution
Figure BDA0001285579000000032
Variance (variance)
Figure BDA0001285579000000033
The parameter lambda is estimated using a maximum likelihood estimation method,
first, the likelihood function is calculated as
Figure BDA0001285579000000034
Wherein, tiIs historical data of the service life of the equipment, and n is historical observation data tiThe number of the (c) is,
the log-likelihood function is
Figure BDA0001285579000000035
For tiIs more than or equal to 0, the likelihood equation is
Figure BDA0001285579000000036
To obtain
Figure BDA0001285579000000037
Based on the above statistical theory, suppose that at a certain time point T, N devices exist simultaneously, and the lifetime history data of each device is Tij(i 1,2 … N, j 1,2 … N), the exponential distribution parameter for each equipment life is estimated as
Figure BDA0001285579000000041
The lifetime distribution function (t) of N devices can be formedj≥0)
Figure BDA0001285579000000042
Further, the calculation method of the equipment scrapping probability distribution function building module is that the current time is T, N pieces of equipment exist at the same time, and the production year of each piece of equipment is TjThen the probability of each device being scrapped at time T is
Figure BDA0001285579000000043
Wherein, T-TjIndicating the total time elapsed from jth device to the T year.
Furthermore, the calculation method of the power grid technical improvement fund demand calculation module is that the original asset values of N devices are assumed to be X respectivelyj(j ═ 1,2 … N), then at time T, the total need for technical improvement funds would be
Figure BDA0001285579000000044
The technical scheme adopted by the invention calculates the probability that each type of equipment reaches the end of the service life at a certain time point and needs technical improvement investment, and measures and calculates the relation of the technical improvement investment requirement changing along with time.
Compared with the prior art, the invention has the following advantages and beneficial effects:
(1) the method provided by the invention provides a basis for power grid enterprises to measure, calculate, technically improve and invest the demand, helps the power grid enterprises to arrange investment plans, and ensures the reliable operation of power equipment.
(2) The invention introduces a statistical analysis method into the problem of calculating the technical improvement investment requirement of a power grid enterprise, thereby improving the reliability of an analysis result.
Drawings
The invention is further described with reference to the accompanying drawings and the detailed description below:
FIG. 1 is a detailed flow chart of the present invention;
FIG. 2 is a statistical plot of sample data for transformer life;
FIG. 3 is a circuit breaker life sample data statistics graph;
FIG. 4 is a GIS lifetime sample data statistical chart;
fig. 5 is a statistical graph of isolator lifetime sample data.
Detailed Description
The technical solutions of the embodiments of the present invention are explained and illustrated below with reference to the drawings of the embodiments of the present invention, but the following embodiments are only preferred embodiments of the present invention, and not all embodiments. Based on the embodiments in the implementation, other embodiments obtained by those skilled in the art without any creative effort belong to the protection scope of the present invention.
The invention calculates the probability that each kind of equipment at a certain time point reaches the end of the service life and needs technical improvement investment, and calculates the relation of the technical improvement investment requirement changing along with time.
The invention is used for power grid enterprises to measure and calculate technical improvement investment requirements, helps the power grid enterprises to arrange investment plans, and ensures the reliable operation of power equipment.
A power grid technical improvement fund demand measurement and calculation model based on equipment life statistical analysis comprises the following steps:
the input module is used for inputting the service life historical data of various devices;
the equipment scrapping time probability distribution calculating module is used for measuring and calculating the probability distribution of scrapping time of various equipment according to the service life historical data of the various equipment;
the equipment scrapping probability distribution function building module is used for building probability distribution functions of scrapping of various equipment at specific time according to probability distribution of scrapping time of various equipment;
the power grid technological improvement fund demand calculation module is used for calculating technological improvement funds required to be input by purchasing replacement equipment after various equipment is scrapped in the time period according to the scrapped probability distribution function of various equipment in specific time;
and the output module outputs the technical improvement fund amount calculated by the power grid technical improvement fund demand calculation module.
The following further describes the embodiments of the present invention with reference to the drawings.
Referring to fig. 1, a specific embodiment of the present invention is illustrated as follows:
the calculation method of the equipment scrapping time probability distribution calculation module comprises the following steps:
assuming that the service lives of various devices obey a certain index distribution, estimating parameters of the index distribution based on historical data of the service lives of all the assets.
When the failure rate of the equipment is constant, the life of the equipment is considered to be subject to exponential distribution regardless of time. The failure of a device whose lifetime is subject to an exponential distribution is memoryless, independent of the previous case, with a lifetime distribution function of
Figure BDA0001285579000000061
A probability density of
Figure BDA0001285579000000062
Expectation of the distribution
Figure BDA0001285579000000063
Variance (variance)
Figure BDA0001285579000000064
The parameter lambda is estimated using a maximum likelihood estimation method.
First, the likelihood function is calculated as
Figure BDA0001285579000000071
Wherein, tiIs historical data of the service life of the equipment, and n is historical observation data tiThe number of (2).
The log-likelihood function is
Figure BDA0001285579000000072
For tiIs more than or equal to 0, the likelihood equation is
Figure BDA0001285579000000073
To obtain
Figure BDA0001285579000000074
Based on the above statistical theory, suppose that at a certain time point T, N devices exist simultaneously, and the lifetime history data of each device is Tij(i 1,2 … N, j 1,2 … N), the exponential distribution parameter for each equipment life is estimated as
Figure BDA0001285579000000075
The lifetime distribution function (t) of N devices can be formedj≥0)
Figure BDA0001285579000000076
The calculation method of the equipment scrapping probability distribution function building module comprises the following steps:
assuming that the current time is T, N devices exist simultaneously, and the production year of each device is TjThen the probability of each device being scrapped at time T is
Figure BDA0001285579000000081
Wherein, T-TjIndicating the total time elapsed from jth device to the T year.
The calculation method of the power grid technical improvement fund demand calculation module comprises the following steps: suppose that the original assets of N devices are Xj(j ═ 1,2 … N), then at time T, the total need for technical improvement funds would be
Figure BDA0001285579000000082
It should be noted that the data introduced in the above calculation formula may be directly retrieved from the database.
The invention will be further illustrated with reference to application examples.
Assume that sample life data for the main transformer, circuit breaker, GIS and disconnector are shown in fig. 2-5. From this, an exponential distribution parameter estimate for four classes of equipment can be calculated according to step 1 as shown in table 1.
TABLE 1 estimation table of probability distribution parameter lambda of equipment life
Asset classes λj
Main transformer 0.0513
Circuit breaker 0.0697
GIS 0.1086
Isolating switch 0.1274
Assuming that 2017 is the current condition, the assets required by technical improvement in the year comprise a main transformer, a circuit breaker, a GIS and a disconnecting switch, and the original values and the production year parameter settings of the assets are shown in Table 2.
TABLE 2 parameter settings table
Asset classes Time of investment Investment scale (Wanyuan)
Main transformer 2005 3595
Circuit breaker 2010 1394
GIS 2011 192
Isolating switch 2013 456
The year in the table above and the time to measure the investment requirement are both early years.
The calculation result of the technological improvement investment requirement in the early 2017 is 2407.91 ten thousand yuan.
While the invention has been described with reference to specific embodiments thereof, it will be understood by those skilled in the art that the invention is not limited thereto, and may be embodied in many different forms without departing from the spirit and scope of the invention as set forth in the following claims. Any modification which does not depart from the functional and structural principles of the present invention is intended to be included within the scope of the claims.

Claims (3)

1. A power grid technical improvement fund demand measurement model system based on equipment life statistical analysis is characterized by comprising the following steps:
the input module is used for inputting the service life historical data of various devices;
the equipment scrapping time probability distribution calculating module is used for measuring and calculating the probability distribution of scrapping time of various equipment according to the service life historical data of the various equipment;
the equipment scrapping probability distribution function building module is used for building probability distribution functions of scrapping of various equipment at specific time according to probability distribution of scrapping time of various equipment;
the power grid technological improvement fund demand calculation module is used for calculating technological improvement funds required to be input by purchasing replacement equipment after various equipment is scrapped in the time period according to the scrapped probability distribution function of various equipment in specific time;
the output module is used for outputting the technical improvement fund amount calculated by the power grid technical improvement fund demand calculation module;
the calculation method of the equipment scrapping time probability distribution calculation module comprises the following steps:
when the failure rate of the equipment is constant, the service life of the equipment is considered to be in accordance with exponential distribution regardless of time, and the service life distribution function is
Figure FDA0002700576760000011
A probability density of
Figure FDA0002700576760000012
Expectation of the distribution
Figure FDA0002700576760000013
Variance (variance)
Figure FDA0002700576760000021
The parameter lambda is estimated using a maximum likelihood estimation method,
first, the likelihood function is calculated as
Figure FDA0002700576760000022
Wherein, tiIs history data of the service life of the equipment, and n is history data tiThe number of the (c) is,
the log-likelihood function is
Figure FDA0002700576760000023
For tiIs more than or equal to 0, the likelihood equation is
Figure FDA0002700576760000024
To obtain
Figure FDA0002700576760000025
Based on the above statistical theory, suppose that at a certain time point T, N devices exist simultaneously, and the lifetime history data of each device is Tij(i 1,2 … N, j 1,2 … N), the exponential distribution parameter for each equipment life is estimated as
Figure FDA0002700576760000026
Thus, the service life distribution function of N devices can be formed
Figure FDA0002700576760000031
tj≥0。
2. The power grid technological improvement fund demand measurement model system based on equipment life statistical analysis as claimed in claim 1, wherein the equipment scrapping probability distribution function building module is calculated by the following method:
assuming that the current time is T, N devices exist simultaneously, and the production year of each device is TjThen the probability of each device being scrapped at time T is
Figure FDA0002700576760000032
Wherein, T-TjIndicating the total time elapsed from jth device to the T year.
3. The power grid improvement fund demand measurement model system based on equipment life statistical analysis as claimed in claim 2, wherein the calculation method of the power grid improvement fund demand calculation module is as follows: suppose N devicesRespectively has an original asset value of Xj(j ═ 1,2 … N), then at time T, the total need for technical improvement funds would be
Figure FDA0002700576760000033
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CN105678008A (en) * 2016-01-26 2016-06-15 云南电网有限责任公司电力科学研究院 Method and system for estimating economical remaining life of power transformer

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