CN109358306A - One kind being based on the intelligent electric energy meter health degree trend forecasting method of GM (1,1) - Google Patents
One kind being based on the intelligent electric energy meter health degree trend forecasting method of GM (1,1) Download PDFInfo
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Abstract
The present invention relates to one kind to be based on GM (1,1) intelligent electric energy meter health degree trend forecasting method, the specific step that executes includes: step (1): the health degree for carrying out intelligent electric energy meter is evaluated, and intelligent electric energy meter health degree achievement data is obtained；Step (2): being based on intelligent electric energy meter achievement data, establishes the intelligent electric energy meter health degree trend prediction model for being based on GM (1,1)；Step (3): trend prediction is carried out to intelligent electric energy meter health degree.The present invention knows the holistic health degree situation of intelligent electric energy meter by intelligent electric energy meter health degree comprehensive assessment index, provides effective suggestion and reliable guide for the intelligent electric energy meter health degree anticipation in regional scope.
Description
Technical field
The invention belongs to electrical energy measurement fields, are related to intelligent electric energy meter health degree trend forecasting method, especially a kind of base
In the intelligent electric energy meter health degree trend forecasting method of GM (1,1).
Background technique
In recent years, the overall merit of intelligent electric energy meter health degree is that the forecast of intelligent electric energy meter operating status and fault pre-alarming mention
Supplied certain reference, can first pass through to a batch of intelligent electric energy meter carry out all standing health degree evaluation, then according to
Dynamic maintenance plan is formulated according to the sequence of evaluation score value size, instead of fixed cycle maintenance model before, is mentioned to a certain extent
The high specific aim of intelligent electric energy meter operating maintenance.In short, the health degree evaluation score value of intelligent electric energy meter is higher, operation is got over
Stablize, can skip the field test in next period in the case where sufficiently stable；If intelligent electric energy meter health degree evaluate score value compared with
It is low, then it needs to carry out field test at once or as early as possible, excludes risk in time.However, intelligent electric energy meter health degree evaluation point at present
The critical value of value is determined generally according to management agreement or according to expertise, lacks the means of quantitative analysis；Secondly as real
The limitation of the intelligent electric energy meters O&M such as border personnel, equipment investment, if detected, critical score value is higher to be caused in synchronization needs
The intelligent electric energy meter of site examining and repairing is excessive, and it will cause the anxieties in terms of human resources and verifying attachment；In addition, for difference batch
Secondary, different types of intelligent electric energy meter should be taken different according to the harm size after its significance level and generation failure
Health degree score criteria and inspection dynamics.For this purpose, overhauling for magnanimity intelligent electric energy meter, specific aim is not strong and task is heavy
Problem, effectively to carry out the online O&M detection of accurately intelligent electric energy meter, it is theoretical based on gray model that the invention proposes one kind
Intelligent electric energy meter health degree trend forecasting method.
Summary of the invention
It is an object of the invention in place of overcome the deficiencies in the prior art, provide a kind of intelligent electric energy for being based on GM (1,1)
Table health degree trend forecasting method.
The present invention solves its technical problem and adopts the following technical solutions to achieve:
One kind being based on the intelligent electric energy meter health degree trend forecasting method of GM (1,1), it is characterised in that: specifically executes step
Include:
Step (1): the health degree for carrying out intelligent electric energy meter is evaluated, and obtains intelligent electric energy meter health degree achievement data；
Step (2): being based on intelligent electric energy meter achievement data, establishes the intelligent electric energy meter health degree trend for being based on GM (1,1)
Prediction model；
Step (3): trend prediction is carried out to intelligent electric energy meter health degree.
Moreover, the intelligent electric energy meter health degree comprehensive assessment index system M in the step (1) includes operating status index
M1, configuration mode index M2With operating condition index M3。
Moreover, operating status index M1Equipment qualification rate M including intelligent electric energy meter11, network detection success rate index M12、
Run the standardized rate M of installation environment13With family defect M14, using following formula calculation formula:
M11=U/N*100% (1)
In formula, M11It is equipment qualification rate；U is the successful equipment sum of installation and debugging；N is the equipment of actual installation in engineering
Sum:
M12=B/C*100% (2)
In formula, M12It is the detection success rate that networks；B is that the intelligent electric energy meter of selective examination in a batch is successfully total；C is one
Intelligent electric energy meter sum in a batch:
M13=D/C*100% (3)
In formula, M13It is intelligent electric energy meter operation installation environment standardized rate；D is the intelligent electric energy meter spot-check in a batch
Installation sum is standardized according to intelligent electric energy meter erection code；C is intelligent electric energy meter sum in a batch:
In formula, M14It is the design defect of product itself, is the basic product performance data that each supplier provides, it can be from meter
Amount Production Scheduling System reads the data；With 1 year for assessment cycle, product of the power grid enterprises to electronic mutual inductor supplier
Situation carries out comprehensive marking, and with 100 points for benchmark, previous year occurs product defects and once deducts 10 points, adjusts from metering production
Degree system is read.
Moreover, configuration mode index M2Including Typical Disposition mode rate M21, model matching degree M22With closed performance M23, adopt
With following formula calculation formula:
M21=F/D*100% (5)
In formula, M21It is intelligent electric energy meter Typical Disposition mode rate；F is that there are three types of the above communication modes for same batch configuration
Intelligent electric energy meter sum；D is the sum of same batch intelligent electric energy meter:
M22=X/T*100% (6)
In formula, M22It is model matching degree；X is that the same same model quantity of the same producer of batch is maximum in certain area coverage
Intelligent electric energy meter sum；T is the intelligent electric energy meter sum in certain area coverage:
M23=Y/S*100% (7)
In formula, Y is the intelligent electric energy meter sum that same batch has closed performance in a region；S is in a region
Same batch intelligent electric energy meter sum.
Moreover, operating condition index M3Including running temperature M31, operating humidity M32, operation rate of load condensate M33With operation magnetic
Field intensity M34, wherein M31, M32And M34Related data, M are read from power information acquisition system33Using following formula calculation formula:
M33=V/Z*100% (8)
In formula, M31It is the rate of load condensate of operation；V is that same batch intelligent electric energy meter monthly has normally in certain area coverage
The sum of data；Z is the installation sum of same batch intelligent electric energy meter in certain area coverage.
Moreover, constructing intelligent electric energy meter health degree prediction model, mistake based on GM (1,1) method in the step (2)
Journey includes the following steps:
(1) assume to evaluate to have obtained N number of health degree score value by the health degree of same batch intelligent electric energy meter early period, remember
For h(0)(n), n=1 ..., N, one time cumulative number is classified as
(2) difference equation model of GM (1,1) is
x(0)(n)+az(1)(n)=b (10)
Wherein, a is known as development coefficient, and b is known as grey actuating quantity, sequence z(1)It (n) is h(1)(n) adjacent value formation sequence, and
Have
z(1)(n)=0.5h(1)(n)+0.5h(1)(n-1) (11)
According to existing N number of health degree score value, the estimated value of parameter a and b in GM (1,1) are found out with least square method,
Enable y=[h(0)(2),h(0)(3),...,h(0)(N)]TAnd B=[(- z(1)(2),...,-z(1)(N))T 1N], wherein T indicates to turn
It sets, 1NIndicate that length is complete 1 column vector of N.The estimated value of parameter a and b can be provided by formula (12)
(3) the corresponding albefaction model differential equation of GM (1,1) difference equation model is
The solution of the equation is
It is to obtain the predicted value of one-accumulate ordered series of numbers
Wherein, h(0)(1) first number in the known score value to participate in prediction, a and b are that formula (10) are calculated
Model parameter estimation value.After obtaining the predicted value of one-accumulate ordered series of numbers, it can be calculated according to formula (16) and obtain intelligent electric energy meter health
The predicted value of score value is spent, as follows, so as to carry out dynamic detection reference according to trend prediction value.
Moreover, intelligent electric energy meter health degree carries out trend prediction, according to the actual situation to same in the step (3)
The index of each intelligent electric energy meter health degree comprehensive assessment of batch carries out COMPREHENSIVE CALCULATING and obtains index value, then obtains intelligence
Electric energy meter health degree trend prediction value H.
The advantages and positive effects of the present invention are:
The invention proposes a kind of intelligent electric energy meter health degree trend forecasting methods for being based on GM (1,1), pass through intelligent electricity
Energy table health degree comprehensive assessment index knows the holistic health degree situation of intelligent electric energy meter, is the intelligent electric energy meter in regional scope
Health degree anticipation provides effective suggestion and reliable guide.
Specific embodiment
The invention will be further described below and by specific embodiment, following embodiment be it is descriptive, be not
Limited, this does not limit the scope of protection of the present invention.
One kind being based on GM (1,1) intelligent electric energy meter health degree trend forecasting method, comprising the following steps:
Step 1, the evaluation for carrying out intelligent electric energy meter health degree obtain intelligent electric energy meter health degree achievement data；
It is strong to establish the intelligent electric energy meter based on GM (1,1) for step 2, the intelligent electric energy meter achievement data known based on step 1
Kang Du trend prediction model；
Step 3 carries out trend prediction to intelligent electric energy meter health degree.
Moreover, the intelligent electric energy meter health degree comprehensive assessment index system M of the step 1 includes operating status index M1、
Configuration mode index M2With operating condition index M3, four index systems are described in detail separately below.
The operating status index M of the step 11Equipment qualification rate M including intelligent electric energy meter11, network detection success rate
Index M12, operation installation environment standardized rate M13With family defect M14, using following formula calculation formula.
M11=U/N*100% (1)
In formula, M11It is equipment qualification rate；U is the successful equipment sum of installation and debugging；N is the equipment of actual installation in engineering
Sum.
M12=B/C*100% (2)
In formula, M12It is the detection success rate that networks；B is that the intelligent electric energy meter of selective examination in a batch is successfully total；C is one
Intelligent electric energy meter sum in a batch.
M13=D/C*100% (3)
In formula, M13It is intelligent electric energy meter operation installation environment standardized rate；D is the intelligent electric energy meter spot-check in a batch
Installation sum is standardized according to intelligent electric energy meter erection code；C is intelligent electric energy meter sum in a batch.
In formula, M14It is the design defect of product itself, is the basic product performance data that each supplier provides, it can be from meter
Amount Production Scheduling System reads the data.It is with 1 year for assessment cycle, production of the power grid enterprises to electronic mutual inductor supplier
Product situation carries out comprehensive marking, and with 100 points for benchmark, previous year occurs product defects and once deducts 10 points, can give birth to from metering
Scheduling system is produced to read.
The configuration mode index M of the step 12Including Typical Disposition mode rate M21, model matching degree M22And closed performance
M23, using following formula calculation formula.
M21=F/D*100% (5)
In formula, M21It is intelligent electric energy meter Typical Disposition mode rate；F is that there are three types of the above communication modes for same batch configuration
Intelligent electric energy meter sum；D is the sum of same batch intelligent electric energy meter.
M22=X/T*100% (6)
In formula, M22It is model matching degree；X is that the same same model quantity of the same producer of batch is maximum in certain area coverage
Intelligent electric energy meter sum；T is the intelligent electric energy meter sum in certain area coverage.
M23=Y/S*100% (7)
In formula, Y is the intelligent electric energy meter sum that same batch has closed performance in a region；S is in a region
Same batch intelligent electric energy meter sum.
The operating condition index M of the step 13Including running temperature M31, operating humidity M32, operation rate of load condensate M33With
Run magnetic field strength M34, wherein M31, M32And M34Related data, M are read from power information acquisition system33Using following formula meter
Calculate formula.
M33=V/Z*100% (8)
In formula, M31It is the rate of load condensate of operation；V is that same batch intelligent electric energy meter monthly has normally in certain area coverage
The sum of data；Z is the installation sum of same batch intelligent electric energy meter in certain area coverage.
Moreover, the step 2 constructs intelligent electric energy meter health degree prediction model, process packet based on GM (1,1) method
Include following steps:
Step 2-1: assuming that evaluating to have obtained N number of health degree scoring by the health degree of same batch intelligent electric energy meter early period
Value, is denoted as h(0)(n), n=1 ..., N, one time cumulative number is classified as
The difference equation model of step 2-2:GM (1,1) is
x(0)(n)+az(1)(n)=b (10)
Wherein, a is known as development coefficient, and b is known as grey actuating quantity, sequence z(1)It (n) is h(1)(n) adjacent value formation sequence, and
Have
z(1)(n)=0.5h(1)(n)+0.5h(1)(n-1) (11)
According to existing N number of health degree score value, the estimated value of parameter a and b in GM (1,1) are found out with least square method.
Enable y=[h(0)(2),h(0)(3),...,h(0)(N)]TAnd B=[(- z(1)(2),...,-z(1)(N))T 1N], wherein T indicates to turn
It sets, indicates that length is 1NComplete 1 column vector.The estimated value of parameter a and b can be provided by formula (12)
The corresponding albefaction model differential equation of step 2-3:GM (1,1) difference equation model is
The solution of the equation is
It is to obtain the predicted value of one-accumulate ordered series of numbers
Wherein, h(0)(1) first number in the known score value to participate in prediction, a and b are that formula (10) are calculated
Model parameter estimation value.After obtaining the predicted value of one-accumulate ordered series of numbers, it can be calculated according to formula (16) and obtain intelligent electric energy meter health
The predicted value of score value is spent, as follows, so as to carry out dynamic detection reference according to trend prediction value.
The intelligent electric energy meter health degree of the step 3 carries out trend prediction, according to the actual situation to each intelligence of same batch
The index of energy electric energy meter health degree comprehensive assessment carries out COMPREHENSIVE CALCULATING and obtains index value, then obtains intelligent electric energy meter health degree and becomes
Gesture predicted value H.
Below by taking the intelligent electric energy meter health degree trend prediction Engineering Projects of 10 pieces of some region of Tianjin as an example, to this hair
The intelligent electric energy meter health degree trend forecasting method that bright one kind is based on GM (1,1) is practiced, to verify present invention side
The feasibility and beneficial effect of method.
Selected 10 pieces of some region of Tianjin intelligent electric energy meter health degree trend prediction Engineering Projects is analyzed, intelligence
Electric energy meter health degree predicted value and the basic condition of practical making time are as shown in table 1.
The basic condition of table 1 intelligent electric energy meter health degree predicted value and practical making time
| Serial number | Health degree predicted value (is divided) | The making time time limit (year) |
| 1 | 82 | 1.5 |
| 2 | 73 | 2.5 |
| 3 | 64 | 5.3 |
| 4 | 50 | 7.8 |
| 5 | 90 | 0.5 |
| 6 | 87 | 1.2 |
| 7 | 78 | 2.1 |
| 8 | 64 | 5.3 |
| 9 | 53 | 7.5 |
| 10 | 85 | 1.3 |
It is not difficult to find that intelligent electric energy meter and the time time limit of investment are inversely proportional from table 1, that is, the time of putting into operation is longer, health
Degree trend prediction value is relatively low, this is also to be consistent with practical intelligent electric energy meter；The subsequent lean O&M mistake in intelligent electric energy meter
Cheng Zhong, it should which the preferential selection time longer meter that puts into operation is tested, and specific aim is promoted.
Although disclosing the embodiment of the present invention for the purpose of illustration, it will be appreciated by those skilled in the art that: not
Be detached from the present invention and spirit and scope of the appended claims in, various substitutions, changes and modifications be all it is possible, therefore, this
The range of invention is not limited to the embodiment disclosure of that.
Claims (7)
1. the intelligent electric energy meter health degree trend forecasting method that one kind is based on GM (1,1), it is characterised in that: specific to execute step packet
It includes:
Step (1): the health degree for carrying out intelligent electric energy meter is evaluated, and obtains intelligent electric energy meter health degree achievement data；
Step (2): being based on intelligent electric energy meter achievement data, establishes the intelligent electric energy meter health degree trend prediction for being based on GM (1,1)
Model；
Step (3): trend prediction is carried out to intelligent electric energy meter health degree.
2. the intelligent electric energy meter health degree trend forecasting method according to claim 1 based on GM (1,1), feature exist
In: it include operating status index M in the intelligent electric energy meter health degree comprehensive assessment index system M of the step (1)1, configuration side
Formula index M2With operating condition index M3。
3. the intelligent electric energy meter health degree trend forecasting method according to claim 2 based on GM (1,1), feature exist
In: operating status index M1Equipment qualification rate M including intelligent electric energy meter11, network detection success rate index M12, operation mounting ring
The standardized rate M in border13With family defect M14, using following formula calculation formula:
M11=U/N*100% (1)
In formula, M11It is equipment qualification rate；U is the successful equipment sum of installation and debugging；N is that the equipment of actual installation in engineering is total
Number:
M12=B/C*100% (2)
In formula, M12It is the detection success rate that networks；B is that the intelligent electric energy meter of selective examination in a batch is successfully total；C is one batch
Secondary interior intelligent electric energy meter sum:
M13=D/C*100% (3)
In formula, M13It is intelligent electric energy meter operation installation environment standardized rate；D be the intelligent electric energy meter spot-check in a batch according to
Intelligent electric energy meter erection code is standardized installation sum；C is intelligent electric energy meter sum in a batch:
In formula, M14It is the design defect of product itself, is the basic product performance data that each supplier provides, can be given birth to from metering
It produces scheduling system and reads the data；With 1 year for assessment cycle, product situation of the power grid enterprises to electronic mutual inductor supplier
Comprehensive marking is carried out, with 100 points for benchmark, previous year occurs product defects and once deducts 10 points, from metering production scheduling system
System is read.
4. the intelligent electric energy meter health degree trend forecasting method according to claim 2 based on GM (1,1), feature exist
In: configuration mode index M2Including Typical Disposition mode rate M21, model matching degree M22With closed performance M23, calculated using following formula
Formula:
M21=F/D*100% (5)
In formula, M21It is intelligent electric energy meter Typical Disposition mode rate；F is that there are three types of the intelligence of the above communication mode for same batch configuration
Electric energy meter sum；D is the sum of same batch intelligent electric energy meter:
M22=X/T*100% (6)
In formula, M22It is model matching degree；X is the same same maximum intelligence of model quantity of the same producer of batch in certain area coverage
It can electric energy meter sum；T is the intelligent electric energy meter sum in certain area coverage:
M23=Y/S*100% (7)
In formula, Y is the intelligent electric energy meter sum that same batch has closed performance in a region；S is same in a region
Batch intelligent electric energy meter sum.
5. the intelligent electric energy meter health degree trend forecasting method according to claim 2 based on GM (1,1), feature exist
In: operating condition index M3Including running temperature M31, operating humidity M32, operation rate of load condensate M33With operation magnetic field strength M34,
In, M31, M32And M34Related data, M are read from power information acquisition system33Using following formula calculation formula:
M33=V/Z*100% (8)
In formula, M31It is the rate of load condensate of operation；V is that same batch intelligent electric energy meter monthly has normal data in certain area coverage
Sum；Z is the installation sum of same batch intelligent electric energy meter in certain area coverage.
6. the intelligent electric energy meter health degree trend forecasting method according to claim 1 based on GM (1,1), feature exist
In: based on GM (1,1) method building intelligent electric energy meter health degree prediction model in the step (2), process includes as follows
Step:
(1) assume to evaluate to have obtained N number of health degree score value by the health degree of same batch intelligent electric energy meter early period, be denoted as h(0)(n), n=1 ..., N, one time cumulative number is classified as
(2) difference equation model of GM (1,1) is
x(0)(n)+az(1)(n)=b (10)
Wherein, a is known as development coefficient, and b is known as grey actuating quantity, sequence z(1)It (n) is h(1)(n) adjacent value formation sequence, and have
z(1)(n)=0.5h(1)(n)+0.5h(1)(n-1) (11)
According to existing N number of health degree score value, the estimated value of parameter a and b in GM (1,1) are found out with least square method, enables y=
[h(0)(2),h(0)(3),...,h(0)(N)]TAnd B=[(- z(1)(2),...,-z(1)(N))T 1N], wherein T indicate transposition, 1N
Indicate that length is complete 1 column vector of N.The estimated value of parameter a and b can be provided by formula (12)
(3) the corresponding albefaction model differential equation of GM (1,1) difference equation model is
The solution of the equation is
It is to obtain the predicted value of one-accumulate ordered series of numbers
Wherein, h(0)(1) first number in the known score value to participate in prediction, a and b are the model that formula (10) are calculated
Estimates of parameters.After obtaining the predicted value of one-accumulate ordered series of numbers, acquisition intelligent electric energy meter health degree can be calculated according to formula (16) and is commented
The predicted value of score value, as follows, so as to carry out dynamic detection reference according to trend prediction value.
7. the intelligent electric energy meter health degree trend forecasting method according to claim 1 based on GM (1,1), feature exist
In: in the step (3), intelligent electric energy meter health degree carries out trend prediction, according to the actual situation to a batch of each
The index of a intelligent electric energy meter health degree comprehensive assessment carries out COMPREHENSIVE CALCULATING and obtains index value, then obtains intelligent electric energy meter health
Spend trend prediction value H.
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| US11754429B2 (en) | 2020-11-11 | 2023-09-12 | Honeywell International Inc. | Multifunctional dust trap |
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| CN113538884A (en) * | 2021-05-31 | 2021-10-22 | 宁波三星医疗电气股份有限公司 | Method for improving acquisition success rate of power acquisition terminal and power acquisition terminal |
| CN113538884B (en) * | 2021-05-31 | 2022-04-26 | 宁波三星医疗电气股份有限公司 | Method for improving acquisition success rate of power acquisition terminal and power acquisition terminal |
| CN115408864A (en) * | 2022-09-01 | 2022-11-29 | 国网安徽省电力有限公司电力科学研究院 | Electronic transformer error state self-adaptive prediction method, system and equipment |
| CN115408864B (en) * | 2022-09-01 | 2023-10-31 | 国网安徽省电力有限公司电力科学研究院 | Electronic transformer error state self-adaptive prediction method, system and equipment |
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