CN110428166A - Power grid physical assets evaluation method based on regressing fitting model - Google Patents
Power grid physical assets evaluation method based on regressing fitting model Download PDFInfo
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Abstract
The power grid physical assets evaluation method based on regressing fitting model that the present invention relates to a kind of, comprising: the history PMS data and ERP data of power grid physical assets to be evaluated are obtained, to establish physical assets data set；It builds including the structure of size, utilization efficiency, the general level of the health and the retired asset evaluation index system for scrapping totally four level-one evaluation indexes；In conjunction with physical assets data set, calculate separately to obtain each evaluation index numerical value；It is fitted the relationship of equipment deficiency rate and equipment enlistment age, to obtain equipment deficiency rate predicted value, and compares main transformer ratio of defects and Rate of average load, to obtain the healthy Rate of average load section of main transformer using homing method according to evaluation index numerical value.Compared with prior art, the present invention establishes the effectively reliable physical assets assessment indicator system of data, can predict equipment deficiency rate based on regressing fitting model, while having widened main transformer load analysis content, powerful support is provided for subsequent maintenance work, is conducive to optimize physical assets configuration.
Description
Technical field
The present invention relates to grid equipment assessment technology fields, real more particularly, to a kind of power grid based on regressing fitting model
Goods and materials produce evaluation method.
Background technique
With the rapid development of China's power grid, the quantity and value of power grid asset are consequently increased, the extensive electricity of tradition
There are certain deficiencies for power plant asset management mode, are unfavorable for the operation and maintenance, update and transformation of power grid.To power grid enterprises
For, physical assets management is still a relatively weak link, although by prelimi nary work, the electricity of State Grid Corporation of China
Net assets have been carried out the consistent of card object of settling a debt or an account, but be only it is in kind consistent, for integral status (including the money of current assets
The operation conditions of production and the economic data of assets), lack real time information and effective monitoring, it is not quantitative to asset management risk
Data analysis.Every asset management policy generally all based on qualitative analysis, lacks quantitative analysis and data supporting；Asset management
Technical tactic is difficult to estimate the financial pressure corresponding to it and the economic impact to company operation.
Currently, having carried out a degree of research to physical assets management both at home and abroad, certain management has also been carried out
Practice, analytic hierarchy process (AHP) are widely used in the evaluation of power grid physical assets, such as a kind of quantitative method with qualitative combination
Chinese patent CN108537437A discloses a kind of power grid physical assets evaluation method based on analytic hierarchy process (AHP), from failure, lacks
Four aspects of sunken, stoppage in transit and operational efficiency establish technical indicator system, and power grid material object money is evaluated according to the weight of each index
The health status of production, the patent is when the defect to grid equipment is evaluated, after the statistical shortcomings duration
It is calculated, not can guarantee the reliability and accuracy of grid equipment ratio of defects, may cause evaluation result and physical presence
Deviation it is larger.
In existing research achievement, physical assets is evaluated and is managed from assets life cycle management angle research compared with
Horn of plenty, but it is concentrated mainly on the application of Life Cycle Cost method and the research of asset management theory, for asset management
The research of the specific targets system construction of middle asset evaluation is less: having research according to the assets feature of State Grid Corporation of China and management
Status constructs State Grid Corporation of China's assets life-cycle from management strategy, workflow, evaluation, security mechanism etc.
Cycle management frame system has shallowly been stated and how to have constructed index system in terms of safety, efficiency, Life Cycle Cost three, but
The specific composition of index system is not furtherd investigate, the frame of index system is only proposed；There is research from safety, efficiency, Quan Shou
The life aspect of period expense three devises index calculating method, but index is single and not system, does not have composing indexes system, as in
State patent CN109711683A discloses security effectiveness appraisal procedure and device in a kind of power grid asset life cycle management, the patent
According to life cycle theory, to the safety of power grid asset in terms of initial cost index, efficiency index and safety index three
Efficiency is assessed, and is lacked for the power grid asset structure of size, the general level of the health and the retired assessment for scrapping aspect；Separately grind
Study carefully and construct the asset management index system of power supply enterprise in terms of security effectiveness, efficiency, Life Cycle Cost three, but refers to
Target setting is still unsatisfactory for the asset management actual needs of grid company.
In addition, the index system for being currently used in the evaluation of power grid physical assets generally includes structure of size index, the general level of the health
Index, utilization efficiency index and retired scrap indicator, purpose predominantly predicts O&M technological transformation risk, without lacking to equipment
It falls into and is predicted with enlistment age relationship, lack the analysis to main transformer Rate of average load, be unfavorable for optimizing physical assets configuration, Wu Fati
The lean of high power grid physical assets management is horizontal.
Summary of the invention
It is an object of the present invention to overcome the above-mentioned drawbacks of the prior art and provide one kind to be based on regression fit
The power grid physical assets evaluation method of model.
The purpose of the present invention can be achieved through the following technical solutions: a kind of power grid based on regressing fitting model is in kind
Asset evaluation method, comprising the following steps:
S1, obtain power grid physical assets to be evaluated history PMS data and ERP data, and uniform data collect bore with
Range, to establish physical assets data set；
S2, it builds including the structure of size, utilization efficiency, the general level of the health and the retired money for scrapping totally four level-one evaluation indexes
Produce assessment indicator system, wherein the structure of size includes asset size index, assets newness rate index and exceedes age assets accounting and refer to
Mark, asset size index include that assets value scale merit, amount of assets scale merit and assets increase scale merit newly；
Utilization efficiency includes Unit Assets electricity sales amount index, assets in fortune rate index, the average annual debt ratio index of main transformer and standby
Product spare part turnover rate index；
The general level of the health includes equipment deficiency rate index and normal condition accounting index；
It is retired to scrap including dead assets newness rate index, dead assets net value accounting index and dead assets average life span
Index；
S3, in conjunction with physical assets data set, calculate separately to obtain each evaluation index numerical value in step S2；
S4, the relationship of equipment deficiency rate and equipment enlistment age is fitted using homing method according to evaluation index numerical value, with
To equipment deficiency rate predicted value, and compare main transformer ratio of defects and Rate of average load, to obtain the healthy Rate of average load area of main transformer
Between.
Further, the assets value scale merit is specifically and is obtained from ERP data according to different voltages grade
End of term initial asset value and end of term Net asset value；
The amount of assets scale merit specifically:
The end of term obtained from PMS data each route class amount of assets (unit: item/kilometer), wherein route class assets packet
Include overhead transmission line, cable transmission line, distribution overhead line, distribution cable route and communication line；
The end of term obtained from ERP data and PMS data each equipment class amount of assets (unit: MVA/ platform/set), wherein
Equipment class assets include transformer (MVA), converter, electrical general device, automated system and equipment, relay protection and peace
Full-automatic device, instrument and meter and test equipment, production management Work tool, transporting equipment and auxiliary production equipment and utensil；
The end of term house and building amount of assets (unit: square metre) obtained from ERP data；
The assets increase scale merit newly specifically:
The current year obtained from ERP data all kinds of assets increase initial value (Wan Yuan) newly；
The current year obtained from ERP data all kinds of assets increase net value (Wan Yuan) newly；
The current year obtained from PMS data each route class new assets quantity (unit: item/kilometer)；
The current year obtained from ERP data and PMS data each equipment class new assets quantity (unit: MVA/ platform/set)；
The current year house and building new assets quantity (unit: square metre) obtained from ERP data；
The assets newness rate index specifically:
Assets newness rate=end of term Net asset value/end of term initial asset value × 100%；
It is described to exceed age assets accounting index specifically:
Exceeding age assets value accounting=end of term exceedes age initial asset value/end of term initial asset value × 100%
Exceeding age amount of assets accounting=end of term exceedes age amount of assets/end of term number of devices × 100%
Wherein, exceed age assets and refer to that the net value of the assets is its initial value 5% and following.
Further, the Unit Assets electricity sales amount index specifically:
Unit Assets electricity sales amount=current year total electricity sales amount/total net value × 100% of end of term assets
Unit Assets electricity sales amount increment rate=(current year Unit Assets electricity sales amount-last year Unit Assets electricity sales amount)
/ last year Unit Assets electricity sales amount；
The assets are in fortune rate index specifically:
Assets are in fortune rate=end of term in the fortune total initial value of assets/total initial value × 100% of end of term assets；
The average annual load factor of main transformer specifically:
Main transformer annual load=(separate unit main transformer whole year active energy/separate unit main transformer whole year pot life
/ separate unit main transformer rated capacity/power factor) × 100%
Wherein, separate unit main transformer whole year active energy (kilowatt hour) directly provides data by dispatching, and separate unit main transformer whole year is available
Time (hour) is that data are directly acquired from scheduling, and separate unit main transformer rated capacity (kilowatt) is to directly acquire data from scheduling, is lacked
It loses data to obtain from PMS data, power factor is to directly acquire data from scheduling；
The standby redundancy turnover rate index specifically:
Standby redundancy quantity/total standby redundancy quantity × 100% within standby redundancy turnover rate=N
Wherein, N >=3.
Further, the equipment deficiency rate index specifically:
Certain enlistment age equipment sum/100 of defect number/current year occur for certain enlistment age equipment of equipment deficiency rate=current year
Wherein, it is that the enlistment age current year equipment obtained from PMS data occurs that defect number, which occurs, for certain enlistment age equipment of current year
Major defect, the number summation of critical defect, current year, certain enlistment age equipment sum was divided into the length of route and the number of equipment；
The normal condition accounting index specifically:
Equipment normal condition accounting=current year state evaluation result is normal number of devices/current year equipment sum × 100%
Wherein, current year state evaluation result be in PMS data current year the equipment last time state evaluation as a result, being
The equipment state evaluation result that year counts.
Further, the dead assets newness rate specifically:
The total net value of the dead assets newness rate=end of term dead assets/total initial value of end of term dead assets；
The dead assets net value accounting specifically:
The dead assets net value accounting=total net value of end of term dead assets/total net value of end of term assets；
The dead assets average life span specifically:
Dead assets average life=dead assets service life over the years total value/dead assets over the years sum.
Further, the homing method in the step S4 includes linear regression, polynomial regression, index return, logarithm
It returns and power function returns.
Further, be fitted equipment deficiency rate and the relationship of enlistment age in the step S4 specifically includes the following steps:
S41, linear regression, polynomial regression, index return, logarithm regression and power function homing method are successively used, point
The other relationship to equipment deficiency rate and enlistment age is fitted, and obtains the first to the 5th regressing fitting model；
S42, variance verification is carried out to the first to the 5th regressing fitting model respectively, chooses variance yields and is less than preset threshold
The predicted value of equipment deficiency rate can be obtained as prediction model, bonding apparatus enlistment age in regressing fitting model.
Further, main transformer ratio of defects compared with main transformer ratio of defects is specifically with Rate of average load is compared in the step S4 to exist
The distribution relation in main transformer Rate of average load section, to obtain the healthy Rate of average load section of main transformer.
Compared with prior art, the invention has the following advantages that
One, the present invention collects bore and range by uniform data, relies on account card object to administer achievement, from PMS data and ERP
The data set of physical assets evaluation index is directly acquired in data, and the consistency, accuracy and total constitution of data can be effectively ensured
Amount, while the analysis by carrying out four dimensions to physical assets, establish physical assets assay index system, can be abundant
Understand the value scale and quantity size of total assets, the utilization state current to assets, health status and scraps situation and have one
It is a clearly to recognize, to efficiently provide data supporting for the objective of physical assets evaluation.
Two, the present invention combines the data characteristics of each equipment in physical assets, using a variety of homing methods to equipment deficiency rate
It is fitted with enlistment age relationship, and is verified by variance, to verify the validity of regressing fitting model, pass through regressing fitting model
The predicted value of equipment deficiency rate is obtained, provides reliable basis for future maintenance work.
Three, the present invention obtains the healthy Rate of average load of main transformer by comparing main transformer ratio of defects and main transformer Rate of average load
Main transformer load analysis content has not only been widened in section, while being conducive to subsequent optimization physical assets configuration, further increases power grid
The lean of physical assets management is horizontal.
Detailed description of the invention
Fig. 1 is method flow schematic diagram of the invention；
Fig. 2 is power grid physical assets assessment indicator system structure chart of the invention；
Fig. 3 is the fit correlation figure of six kinds of equipment deficiency rates and its enlistment age in embodiment:
Fig. 3 (a) is the fit correlation figure of breaker ratio of defects and its enlistment age in embodiment；
Fig. 3 (b) is the fit correlation figure of disconnecting switch ratio of defects and its enlistment age in embodiment；
Fig. 3 (c) is the fit correlation figure of current transformer ratio of defects and its enlistment age in embodiment；
Fig. 3 (d) is the fit correlation figure of capacitance type potential transformer ratio of defects and its enlistment age in embodiment；
Fig. 3 (e) is the fit correlation figure that wall bushing and its enlistment age are exchanged in embodiment；
Fig. 3 (f) is the fit correlation figure of switchgear and its enlistment age in embodiment；
Fig. 4 is the fit correlation figure of main transformer ratio of defects and Rate of average load in embodiment.
Specific embodiment
The present invention is described in detail with specific embodiment below in conjunction with the accompanying drawings.
As shown in Figure 1, a kind of power grid physical assets evaluation method based on regressing fitting model, comprising the following steps:
S1, obtain power grid physical assets to be evaluated history PMS data and ERP data, and uniform data collect bore with
Range, to establish physical assets data set；
S2, it builds including the structure of size, utilization efficiency, the general level of the health and the retired money for scrapping totally four level-one evaluation indexes
Produce assessment indicator system, as shown in Figure 2, wherein the structure of size include asset size index, assets newness rate index and exceed age money
Accounting index is produced, asset size index includes that the newly-increased scale of assets value scale merit, amount of assets scale merit and assets refers to
Mark；
Utilization efficiency includes Unit Assets electricity sales amount index, assets in fortune rate index, the average annual debt ratio index of main transformer and standby
Product spare part turnover rate index；
The general level of the health includes equipment deficiency rate index and normal condition accounting index；
It is retired to scrap including dead assets newness rate index, dead assets net value accounting index and dead assets average life span
Index；
S3, in conjunction with physical assets data set, calculate separately to obtain each evaluation index numerical value in step S2；
S4, the relationship of equipment deficiency rate and equipment enlistment age is fitted using homing method according to evaluation index numerical value, with
To equipment deficiency rate predicted value, and compare main transformer ratio of defects and Rate of average load, to obtain the healthy Rate of average load area of main transformer
Between.
In step s 4, the present invention is fitted equipment deficiency rate and the relationship of equipment enlistment age using homing method, returns
Returning analysis is a kind of statistical analysis technique of complementary quantitative relationship between two or more determining variable, using very
Extensively, regression analysis according to the variable being related to how much, be divided into simple regression and multiple regression analysis；
According to the number of dependent variable, simple regression analysis and multiple regression analysis can be divided into；
According to the relationship type between independent variable and dependent variable, linear regression analysis and nonlinear regression analysis can be divided into.
If only including an independent variable and a dependent variable, and the relationship of the two can be with one directly in regression analysis
Line approximate representation, this regression analysis are known as simple linear regression analysis；
If in regression analysis including two or more independents variable, and there is linear correlation between independent variable, then
Referred to as multiple linear regression analysis；
In statistics, regression analysis refers to determining complementary quantitative relationship between two or more variable
A kind of statistical analysis technique；
In big data analysis, regression analysis is a kind of modeling technique of predictability, and what it was studied is dependent variable and becomes certainly
Relationship between amount is commonly used in forecast analysis.
In real work, between variable may not all wired sexual intercourse, curve matching refers to selection curve type appropriate to intend
Observation data are closed, and analyze the relationship between two variables with the curvilinear equation of fitting, present invention is mainly applicable to Regression and song
Line approximating method model:
(1) linear regression
It is most for one of modeling technique known to people, and linear regression is usually that people are preferred when learning prediction model
One of technology, in this technique, dependent variable are continuously that independent variable, which can be, is continuously also possible to discrete, the tropic
Property be it is linear, linear regression using optimal fitting a straight line (the namely tropic) in dependent variable (Y) and one or more
A kind of relationship is established between a independent variable (X)；
Multiple linear regression is represented by Y=a+b1*X+b2*X2+ e, wherein a indicates intercept, and b indicates the slope of straight line,
E is error term, and multiple linear regression can predict the value of target variable according to given predictive variable s.
(2) polynomial regression
For a regression equation, if the index of independent variable is greater than 1, it is exactly Polynomical regressive equation, as follows
Shown in equation:
Y=a+b*X2
In this regression technique, line of best fit not instead of straight line, one is used for the curve of fitting data point.
(3) exponential function
The normal formula form of exponential function are as follows:
Y=aebX
Natural logrithm is taken to above formula both sides, is obtained:
Ln Y=ln a+b*X
When b > 0, Y increases with X and is increased；When b < 0, Y increases with X and is reduced.When the scatter plot drawn with lnY and X is in straight
When line trend, it is contemplated that describe the non-linear relation between Y and X using exponential function, lna and b are respectively intercept and slope.
More common exponential function are as follows:
Y=aebX+k
In formula, k is constant.
(4) logarithmic function
The normal formula form of logarithmic function are as follows:
Y=logaX (a > 0, a ≠ 1)
Using refooting, formula can be obtained:
General type are as follows:
Y=b ln X+a
When a > 1, Y increases with X and is increased, first quick and back slow；When 0 < a < 1, Y increases with X and is reduced, first quick and back slow；When with Y and
When the linear trend of scatter plot that lnX is drawn, it is contemplated that the non-linear relation between Y and X is described using logarithmic function, in formula
B and a is respectively slope and intercept.
(5) power function
The normal formula form of power function are as follows:
Y=axb
In formula when b > 0, Y increases with X and is increased；When b < 0, Y increases with X and is reduced；
Logarithm is taken to above formula both sides, is obtained:
Lg Y=lg a+blg X
So when trend linear with the scatter plot of lnY and lnX drafting, it is contemplated that describe Y and X using power function
Between non-linear relation, lna and b are intercept and slope respectively.
The present embodiment establishes linear relationship to the ratio of defects of 35kV and the above three categories main equipment and its enlistment age, ratio of defects
Formula is as follows:
Herein, what Y was indicated is to indicate that ratio of defects occurs for every class assets different enlistment ages, and defect number and sample number correspond to money
The different enlistment ages are produced, specific as follows:
The annual ratio of defects of certain class assets is enumerated, establishes the relationship of ratio of defects (secondary/hundred) and assets enlistment age, such as
Shown in table 1:
Table 1
Through analyzing, it is known that all kinds of assets put into operation the time more than that can enter defect rapid increase area behind a certain region, according to song
Line analysis finds the enlistment age section for entering defect rising area, existing utilization index regression model, linear regression model (LRM), polynomial regression
This five kinds of mathematical models of model, power function, logarithmic function are fitted various capital equipment ratio of defects and the relationship of enlistment age, give
It is most suitable for model out, judges whether have visible trend relationship.
Breaker: in a variety of mathematical models, the smallest model of variance test is index return mathematical model, but R2=0
< 0.05 illustrates that variance test passes through, and 35kV and the above breaker ratio of defects and the relationship of its enlistment age are as shown in Figure 3a, ratio of defects
It is fluctuated at 3 times/about hundred；
Disconnecting switch: in a variety of mathematical models, the smallest model of variance test is index return mathematical model, but R2=
0 < 0.05, illustrates that variance test passes through, and 35kV and the above disconnecting switch ratio of defects and the relationship of its enlistment age are as shown in Figure 3b, lacks
The rate of falling into is fluctuated at 3 times/about hundred；
Current transformer: in a variety of mathematical models, the smallest model of variance test is linear regression mathematical model, but R2
=0.0022 < 0.05, illustrates that variance test passes through, and trend is it is obvious that as shown in Figure 3c, can be predicted electric current using Trendline
Mutual inductor ratio of defects, formula y=0.0023x+0.3459；
Capacitance type potential transformer: using linear regression, logarithm, Polynomial Method to capacitance type potential transformer ratio of defects
It is fitted with the equipment enlistment age, show that most suitable model is linear regression, variance test is minimum, R2=0.0036 <
0.05, by variance test, illustrate that linear trend is obvious, minimum 0, peak 0.37, average value 0.07, ratio of defects is with respect to it
His equipment is lower, and the ratio of defects predicted value of capacitance type potential transformer is as shown in Figure 3d: y=0.0015x+0.0868；
It exchanges wall bushing: exchange wall bushing ratio of defects and equipment being used as a servant using linear regression, logarithm, Polynomial Method
Age is fitted, and show that most suitable model is logarithmic model, variance test is minimum, R2=0.0442 < 0.05, passes through variance
It examines, illustrates that linear trend is obvious, exchange wall bushing ratio of defects and the relationship of its enlistment age is as shown in Figure 3 e, average defect rate is
8 times/hundred, be the ratio of defects of predictable exchange wall bushing by the mathematical formulae in Fig. 3 e；
Switchgear: using linear regression, logarithm, polynomial method to exchange wall bushing ratio of defects and the equipment enlistment age into
Row fitting show that most suitable model is logarithmic model, and variance test is minimum, R2=0.005 < 0.05, by variance test,
Illustrating that linear trend is obvious, as illustrated in figure 3f, ratio of defects is general for the relationship of switchgear ratio of defects and its enlistment age, and high point is 3.5 times/
Hundred, the 6th year in putting equipment in service occurs, average defect rate is 1.29 times/hundred, and the calculating of switchgear ratio of defects predicted value is such as
In Fig. 3 f shown in formula.
The reason of the present embodiment is occurred by analyzing defect, it is known that main defects liability reason is equipment reason itself,
In terms of ratio of defects, it is O&M from now on that incidence is higher the defects of transformer, overhead transmission line, shunt capacitor, exchange wall bushing
Work the emphasis paid close attention to.
Ratio of defects and equipment enlistment age relationship are analyzed, Exponential Regression Model, linear regression model (LRM), multinomial is taken to return
Return a variety of mathematical models such as model, power function, logarithmic model, show that (index returns for breaker (Exponential Regression Model), disconnecting switch
Return model), current transformer (linear regression model (LRM)), capacitance type potential transformer (linear regression model (LRM)), exchange wall bushing
There are obvious relation between persistence relationships with the enlistment age for the ratio of defects of (linear regression model (LRM)), switchgear (logarithmic function model) this 6 kinds of equipment.
The present embodiment be inquire into main transformer ratio of defects and Rate of average load relationship, to 2016 occur defect 110kV and
Totally 328 main transformers carry out analysis load factor analysis to above, obtain statistical data as shown in Table 2:
Table 2
It compares each voltage class transformer sum and each voltage class defect transformer occurs in different Rate of average load areas
Between distribution, it is known that the two distribution is consistent, and high point appears in the section 20%-30%, reaches 27.93%, low spot is in 50%-60%
Section is 12.5%.
220kV the and 110kV voltage class independent analysis more to number of devices again, obtains 220kV main transformer and exists
The equipment deficiency rate highest in the section 20%-30% is 35.45%, average in several Rate of average load sections of data with existing
The transformer ratio of defects that load factor is in 50%-60% is minimum, is 15.38%；
Similarly in the distribution of each Rate of average load section, spikes/low- points also appear in together 110kV main transformer ratio of defects
In sample section.
To further illustrate problem, as shown in figure 4, either 220kV transformer or 110kV transformer, when equipment is flat
When equal load factor is in the section 20%-30%, the probability that defect occurs for equipment is maximum, when equipment Rate of average load is in 50%-
When 60% section, the probability that defect occurs for equipment is minimum.
By to 2016 occur defect 110kV and above totally 328 main transformers carry out analysis learn,
Transformer is in the section Rate of average load 0%-60%, wherein the main transformer in 20%-30% Rate of average load lacks
Incidence highest is fallen into, the main transformer defect incidence in 50%-60% Rate of average load section is minimum, and as main transformer is strong
Health Rate of average load section.
Claims (8)
1. a kind of power grid physical assets evaluation method based on regressing fitting model, which comprises the following steps:
S1, the history PMS data and ERP data for obtaining power grid physical assets to be evaluated, and uniform data collects bore and range,
To establish physical assets data set；
S2, it builds and is commented including the structure of size, utilization efficiency, the general level of the health and the retired assets for scrapping totally four level-one evaluation indexes
Valence index system, wherein the structure of size includes asset size index, assets newness rate index and exceedes age assets accounting index, money
Producing scale merit includes that assets value scale merit, amount of assets scale merit and assets increase scale merit newly；
Utilization efficiency includes that Unit Assets electricity sales amount index, assets are standby in fortune rate index, the average annual debt ratio index of main transformer and spare unit
Part turnover rate index；
The general level of the health includes equipment deficiency rate index and normal condition accounting index；
Retired scrap refers to including dead assets newness rate index, dead assets net value accounting index and dead assets average life span
Mark；
S3, in conjunction with physical assets data set, calculate separately to obtain each evaluation index numerical value in step S2；
S4, the relationship of equipment deficiency rate and equipment enlistment age is fitted, using homing method to be set according to evaluation index numerical value
Standby ratio of defects predicted value, and compare main transformer ratio of defects and Rate of average load, to obtain the healthy Rate of average load section of main transformer.
2. a kind of power grid physical assets evaluation method based on regressing fitting model according to claim 1, feature exist
In, the assets value scale merit be specifically according to different voltages grade, the end of term initial asset value that is obtained from ERP data and
End of term Net asset value；
The amount of assets scale merit specifically:
The end of term obtained from PMS data each route class amount of assets, wherein route class assets include overhead transmission line, electricity
Cable transmission line of electricity, distribution overhead line, distribution cable route and communication line；
The end of term obtained from ERP data and PMS data each equipment class amount of assets, wherein equipment class assets include transformer,
Converter, electrical general device, automated system and equipment, relay protection and automatic safety device, instrument and meter and test
Equipment, production management Work tool, transporting equipment and auxiliary production equipment and utensil；
The end of term house and building amount of assets obtained from ERP data；
The assets increase scale merit newly specifically:
The current year obtained from ERP data all kinds of assets increase initial value newly；
The current year obtained from ERP data all kinds of assets increase net value newly；
The current year obtained from PMS data each route class new assets quantity；
The current year obtained from ERP data and PMS data each equipment class new assets quantity；
The current year house and building new assets quantity obtained from ERP data；
The assets newness rate index specifically:
Assets newness rate=end of term Net asset value/end of term initial asset value × 100%；
It is described to exceed age assets accounting index specifically:
Exceeding age assets value accounting=end of term exceedes age initial asset value/end of term initial asset value × 100%
Exceeding age amount of assets accounting=end of term exceedes age amount of assets/end of term number of devices × 100%
Wherein, exceed age assets and refer to that the net value of the assets is its initial value 5% and following.
3. a kind of power grid physical assets evaluation method based on regressing fitting model according to claim 2, feature exist
In the Unit Assets electricity sales amount index specifically:
Unit Assets electricity sales amount=current year total electricity sales amount/total net value × 100% of end of term assets
Unit Assets electricity sales amount increment rate=(current year Unit Assets electricity sales amount-last year Unit Assets electricity sales amount)/last year unit
Assets electricity sales amount；
The assets are in fortune rate index specifically:
Assets are in fortune rate=end of term in the fortune total initial value of assets/total initial value × 100% of end of term assets；
The average annual load factor of main transformer specifically:
Main transformer annual load=[separate unit main transformer whole year active energy/separate unit main transformer whole year pot life/specified appearance of separate unit main transformer
Amount/power factor] × 100%
Wherein, separate unit main transformer whole year active energy directly provides data by dispatching, and separate unit main transformer whole year pot life is from scheduling
Data are directly acquired, separate unit main transformer rated capacity is that data are directly acquired from scheduling, and missing data is obtained from PMS data, function
Rate factor is to directly acquire data from scheduling；
The standby redundancy turnover rate index specifically:
Standby redundancy quantity/total standby redundancy quantity × 100% within standby redundancy turnover rate=N
Wherein, N >=3.
4. a kind of power grid physical assets evaluation method based on regressing fitting model according to claim 3, feature exist
In the equipment deficiency rate index specifically:
Certain enlistment age equipment sum/100 of defect number/current year occur for certain enlistment age equipment of equipment deficiency rate=current year
Wherein, it is that the enlistment age current year equipment obtained from PMS data occurs seriously that defect number, which occurs, for certain enlistment age equipment of current year
Defect, the number summation of critical defect, current year, certain enlistment age equipment sum was divided into the length of route and the number of equipment；
The normal condition accounting index specifically:
Equipment normal condition accounting=current year state evaluation result is normal number of devices/current year equipment sum × 100%
Wherein, current year state evaluation result be in PMS data current year the equipment last time state evaluation as a result, be year
The equipment state evaluation result of statistics.
5. a kind of power grid physical assets evaluation method based on regressing fitting model according to claim 4, feature exist
In the dead assets newness rate specifically:
The total net value of the dead assets newness rate=end of term dead assets/total initial value of end of term dead assets；
The dead assets net value accounting specifically:
The dead assets net value accounting=total net value of end of term dead assets/total net value of end of term assets；
The dead assets average life span specifically:
Dead assets average life=dead assets service life over the years total value/dead assets over the years sum.
6. a kind of power grid physical assets evaluation method based on regressing fitting model according to claim 2, feature exist
In the homing method in the step S4 includes that linear regression, polynomial regression, index return, logarithm regression and power function are returned
Return.
7. a kind of power grid physical assets evaluation method based on regressing fitting model according to claim 6, feature exist
In, be fitted equipment deficiency rate and the relationship of enlistment age in the step S4 specifically includes the following steps:
S41, linear regression, polynomial regression, index return, logarithm regression and power function homing method are successively used, it is right respectively
Equipment deficiency rate and the relationship of enlistment age are fitted, and obtain the first to the 5th regressing fitting model；
S42, variance verification is carried out to the first to the 5th regressing fitting model respectively, chooses the recurrence that variance yields is less than preset threshold
The predicted value of equipment deficiency rate can be obtained as prediction model, bonding apparatus enlistment age in model of fit.
8. a kind of power grid physical assets evaluation method based on regressing fitting model according to claim 2, feature exist
In comparing compared with main transformer ratio of defects and Rate of average load are specifically main transformer ratio of defects in the step S4 in main transformer Rate of average load
The distribution relation in section, to obtain the healthy Rate of average load section of main transformer.
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