CN109581269A - A kind of electronic mutual inductor error characteristics fast appraisement method and system - Google Patents
A kind of electronic mutual inductor error characteristics fast appraisement method and system Download PDFInfo
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- G01R35/02—Testing or calibrating of apparatus covered by the other groups of this subclass of auxiliary devices, e.g. of instrument transformers according to prescribed transformation ratio, phase angle, or wattage rating
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Abstract
The present invention relates to a kind of electronic mutual inductor error characteristics fast appraisement method and systems, pre-process to the data in raw data base, judge really reflect the abnormal data of mutual inductor operation characteristic, and delete the abnormal data；For the data in the raw data base after deletion, dimension-reduction treatment is carried out to the impact factor for influencing electronic mutual inductor error, data dimension is reduced to two dimension or three-dimensional, retains the high factor of weight and carries out subsequent analysis.The present invention is not only advantageous to improve the treatment effeciency of data, and advantageously ensures that the Stability and veracity of operation.
Description
Technical field
The present invention relates to the technical fields of electrical energy measurement, refer in particular to a kind of electronic mutual inductor error characteristics Fast Evaluation
Method and system.
Background technique
Electric energy is national economy and the particularly important energy of people's lives, and electrifing degree and management modernization level are weighing apparatuses
Measure important symbol whether country's prosperity.In recent years, as the management of commercial operation is in the utilization of electric system, and
Along with the development of market economy and the popularization of State Power Corporation's internal simulative electricity market, electric system and society are to electric energy meter
Amount accuracy is increasingly paid attention to, the reliable and stable economic effect for being directly related to electric system of electric system electric energy metered system
Benefit.With the reform of China's electric system, to electrical energy measurement work, more stringent requirements are proposed, especially passes through from traditional plan
The transformation helped to market economy, Electric Energy Metering Technology is even more important, needs to pay attention to its stabilization and accuracy.
With the popularization and application of intelligent substation, electronic mutual inductor, combining unit and digitalized electrical energy meter are formed
Digital metering system be widely applied.In intelligent substation, electronic mutual inductor is entire digital metering system
The key equipment of system, conversion accuracy, stability play a leading role in entire metering system control errors.Become from current intelligence
In power station from the point of view of the operating condition of a large amount of digital metering systems, generally existing accuracy, stability problem, or even event occurs
Barrier, it is difficult to meet the requirement of legality measurement, one of them important reason is exactly that the accuracy and reliability shortage of its own has
The evaluation method of effect.
Discovery is investigated to the detection case of the distribution electronic mutual inductor to put into operation in system in State Grid Metering Center: wherein
16% electronic mutual inductor is not detected before putting into operation, for example, carrying out cycle detection after putting into operation not to mutual inductor, not examining
Survey whether mutual inductor metering performance is qualified, does not judge whether method used by the qualified mutual inductor of detection is reasonable, and how
Running quality of transformer performance is evaluated, these problems all can bring hidden danger to the safe operation of system, and restrict it on a large scale
The critical issue of application.
Summary of the invention
For this purpose, technical problem to be solved by the present invention lies in overcome in the prior art accuracy it is poor, and treatment effeciency is low
The problem of, to provide, a kind of accuracy is good, and the high electronic mutual inductor error characteristics fast appraisement method for the treatment of effeciency and
System.
In order to solve the above technical problems, a kind of electronic mutual inductor error characteristics fast appraisement method of the invention, including
Following steps: pre-processing the data in raw data base, judges really reflect the different of mutual inductor operation characteristic
Regular data, and delete the abnormal data；For the data in the raw data base after deletion, missed to electronic mutual inductor is influenced
The impact factor of difference carries out dimension-reduction treatment, data dimension is reduced to two dimension or three-dimensional, the high factor of reservation weight carries out subsequent
Analysis.
In one embodiment of the invention, the data in raw data base pre-process, and judging cannot
The method of the abnormal data of true reflection mutual inductor operation characteristic are as follows: calculated under the same conditions according to a series of measurements flat
Mean value；According to the mean value calculation experimental standard deviation；Abnormal number is determined according to the testing standard difference and the average value
According to.
In one embodiment of the invention, abnormal data is determined according to the testing standard difference and the average value
Method are as follows: judge whether the absolute value of the difference of data and average value is greater than or equal to the setting multiple of experimental standard deviation, if described
Data are greater than or equal to the setting multiple of the experimental standard deviation, then judge the data for abnormal data；Conversely, not being then different
Regular data.
In one embodiment of the invention, a series of test results include survey of the electronic mutual inductor than difference
The test result of test result and angular difference.
In one embodiment of the invention, dimension-reduction treatment is carried out to the impact factor for influencing electronic mutual inductor error
Method are as follows: calculate the information gain value of each impact factor.
In one embodiment of the invention, the calculation method of the information gain value are as follows: calculate electronic mutual inductor ratio
The information summation of difference or angular difference；The attribute of each impact factor is divided into multiple attributes, calculates the information content of each attribute；Root
According to the information summation of each attribute under each impact factor of information computing of each attribute；According to described than difference or angular difference
Information summation and each impact factor under the information summation of each attribute obtain the information gain value.
In one embodiment of the invention, the information gain value be it is described than poor or angular difference information summation with it is described
The difference of the information summation of each attribute under each impact factor.
In one embodiment of the invention, the raw data base is by randomly selecting electronic type mutual inductance in live pilot
Operation data composition in device.
In one embodiment of the invention, the impact factor include in following at least one of: it is load, temperature, wet
Degree, vibration, magnetic field.
The present invention also provides a kind of electronic mutual inductor error characteristics RES(rapid evaluation system), including preprocessing module and
Processing module, wherein the preprocessing module is for pre-processing the data in raw data base, judging cannot be true
Reflect the abnormal data of mutual inductor operation characteristic, and deletes the abnormal data；For the number in the raw data base after deletion
According to the processing module is used to carry out dimension-reduction treatment to the impact factor for influencing electronic mutual inductor error, and data dimension is dropped
As low as two dimension or three-dimensional, the high factor progress subsequent analysis of reservation weight.
The above technical solution of the present invention has the following advantages over the prior art:
Electronic mutual inductor error characteristics fast appraisement method of the present invention and system, to the number in raw data base
According to being pre-processed, judges really reflect the abnormal data of mutual inductor operation characteristic, and delete the abnormal data, pick
Except the exceptional value in measurement data, retain the data deviateed farther out but be not belonging to exceptional value for objectively responding error characteristics, to just
Really analysis obtains electronic mutual inductor error characteristics and is of great significance；To influence electronic mutual inductor error influence because
Son carries out dimension-reduction treatment, and data dimension is reduced to two dimension or three-dimensional, retains the high factor of weight and carries out subsequent analysis, not only has
Conducive to the treatment effeciency of raising data, and advantageously ensure that the Stability and veracity of operation.
Detailed description of the invention
In order to make the content of the present invention more clearly understood, it below according to specific embodiments of the present invention and combines
Attached drawing, the present invention is described in further detail, wherein
Fig. 1 is the flow chart of electronic mutual inductor error characteristics fast appraisement method of the present invention.
Specific embodiment
Embodiment one
As shown in Figure 1, the present embodiment provides a kind of electronic mutual inductor error characteristics fast appraisement methods, including walk as follows
Rapid: step S1: pre-processing the data in raw data base, judges really reflect the different of mutual inductor operation characteristic
Regular data, and delete the abnormal data；Step S2: for the data in the raw data base after deletion, to influence electronic type
The impact factor of transformer error carries out dimension-reduction treatment, and data dimension is reduced to two dimension or three-dimensional, the high factor of reservation weight
Carry out subsequent analysis.
Electronic mutual inductor error characteristics fast appraisement method described in the present embodiment, in the step S1, to initial data
Data in library are pre-processed, and judge really reflect the abnormal data of mutual inductor operation characteristic, and delete described different
Regular data will necessarily impact analysis if being mixed with exceptional value in the sample data analyzed as electronic mutual inductor error characteristics
As a result, reservation objectively responds the deviation of error characteristics farther out but is not belonging to exceptional value so rejecting the exceptional value in measurement data
Data, to Correct Analysis obtain electronic mutual inductor error characteristics be of great significance；In the step S2, for deleting
The data in raw data base after removing, since a large amount of high dimensional data can be generated in electronic mutual inductor operational process, if
Directly carry out operation, the treatment effeciency of data will be greatly reduced, and by influence the influence of electronic mutual inductor error because
Son carries out dimension-reduction treatment, and data dimension is reduced to two dimension or three-dimensional, retains the high factor of weight and carries out subsequent analysis, not only has
Conducive to the treatment effeciency of raising data, and advantageously ensure that the Stability and veracity of operation.
The data in raw data base pre-process, and judge really reflect mutual inductor operation characteristic
The method of abnormal data are as follows: average value is calculated according to a series of measurements under the same conditions, its calculation formula is:The wherein x1, x2…xnRepresent measurement result；According to the mean value calculation experimental standard deviation, the experiment
The calculation formula of standard deviation s are as follows:Abnormal number is determined according to the testing standard difference and the average value
According to.The method for determining abnormal data according to the testing standard difference and the average value are as follows: judge the difference of data and average value
Absolute value whether be greater than or equal to the setting multiple of experimental standard deviation, if the data are greater than or equal to the experimental standard deviation
Setting multiple, then judge the data for abnormal data；Conversely, not being then abnormal data.Specifically, if some dubious value xd
With the average value of n resultAbsolute value of the difference be greater than or equal to 3s when, determine xdFor exceptional value, i.e.,
A series of test results include the electronic mutual inductor than the test result of difference and the test result of angular difference.
The method that dimension-reduction treatment is carried out to the impact factor for influencing electronic mutual inductor error are as follows: calculate each impact factor
Information gain value.The calculation method of the information gain value are as follows: calculate the electronic mutual inductor than difference or the information of angular difference
Summation, that is, the gross information content of initial data, the wherein calculation formula of information summation Info (D) are as follows:Wherein, if often
The data amount check of a classification is defined as x·j, N is the number of all data in data acquisition system, and the probability of appearance of all categories can define
For pj=x·j/ N, it is-log that information of all categories, which can be obtained, according to information theory (Information Theory)2pj, wherein Info
(D) it is also known as entropy (Entropy) to commonly use to measure data discrete degree or unrest degree, Info (D) can be used as assessment training data
The expectation information of all categories under set D, when the probability of appearance of all categories is equal, then entropy is 1, indicates the information of the classification
Mixed and disorderly degree highest；The attribute of each impact factor is divided into multiple attributes, calculates the information content of each attribute, it is specifically, false
If data acquisition system D will be split according to attribute A, generates total L data and divide set Di, wherein xiFor each attribute value AiUnder
Segmentation data total number, xijFor attribute value AiIt down and is classification CjNumber therefore can computation attribute AiUnder information content
Info(Ai), whereinThen according to described
The information summation of each attribute under each impact factor of the information computing of each attribute, that is, the gross information content after branch,
The information of attribute A then according under each attribute value data amount check number determine,According to described than difference or angular difference
Information summation and each impact factor under the information summation of each attribute obtain the information gain value.Specifically,
The information gain value is described more total than difference or the information summation of angular difference and the information of each attribute under each impact factor
The difference of sum, Gain (A)=Info (D)-InfoA(D), total information of the information gain since initial data can be expressed as
Amount subtracts the gross information content after branch, indicates using attribute A as fork attribute to the percentage contribution of information, and so on can calculate
Institute's energy bring information contribution degree using each attribute as branching variable out, can find out point with best information gain after comparing
Branch attribute.
It is illustrated for randomly selecting the operation data in live pilot in electronic mutual inductor below.Wherein, institute
Stating electronic mutual inductor includes current transformer and voltage transformer, the impact factor of error include in following at least one of it is negative
Lotus, temperature, humidity, magnetic field, vibration；It measures to probe into each impact factor to electronic mutual inductor than poor, angular difference influence degree,
The information gain for calculating impact factor carries out dimension-reduction treatment to them.
Such as by taking the weighing factor to electronic current mutual inductor as an example: randomly selecting 6 groups (every group 12 in one-year age
Data), each impact factor is subjected to interval division first: basic, normal, high three sections are divided into for load (4A-25A), for
Temperature (- 5 DEG C -35 DEG C) is divided into basic, normal, high three sections, is divided into basic, normal, high three areas for humidity (20%RH-70%RH)
Between, weak, strong two sections are divided into for magnetic field (0.02Gs-0.3Gs), vibration (0.02g-0.2g) are divided into two weak, strong
Section, the data after being converted are as shown in table 3:
It is brought under five attribute to be brought than poor information content summation Info (D), each attribute value for first group of data
Information content Info (Ai) and information measurement pointer Gain (A), wherein in the poor column of the ratio of the electronic current mutual inductor,
The quantity of " overproof " is 4, and the quantity of " not overproof " is 8, and sum is 12, then the gross information content of initial data calculates such as
Under:
If selecting load (A) as fork attribute, wherein in the load (A), when underload is 7, the electronic type
The ratio difference of current transformer is that the quantity of " overproof " is 2, is 5 than the quantity that difference is " not overproof ", under the underload attribute
Information content beIt is described when middle load is 2 in the load (A)
The ratio difference of electronic current mutual inductor is that the quantity of " overproof " is 2, is 0 than the quantity that difference is " not overproof ", load in this
Information content under attribute isIn the load (A), when high load capacity is 3,
The ratio difference of the electronic current mutual inductor is that the quantity of " overproof " is 0, is 3 than the quantity that difference is " not overproof ", the height
Information content under load attribute isGross information content after branch at this timeThen its information gain value are as follows: Gain (A)=0.918-0.503=0.415.
Similarly, if selecting temperature (B) as fork attribute, the calculating of information gain is as follows: if wherein selecting temperature
(B) it is used as fork attribute, wherein when low temperature is 3, the ratio difference of the electronic current mutual inductor is " super in the temperature
The quantity of difference " is 1, is 2 than the quantity that difference is " not overproof ", the information content under the low temperature attribute isWhen middle temperature is 7, the ratio of the electronic current mutual inductor is poor
Quantity for " overproof " is 2, is 5 than the quantity that difference is " not overproof ", the information content under the medium temperature attribute isWhen high-temperature is 2, the ratio difference of the electronic current mutual inductor is
The quantity of " overproof " is 1, is 1 than the quantity that difference is " not overproof ", the information content under the high temperature properties isGross information content after branch at this timeThen its information gain value are as follows: Gain (B)=0.918-
0.900=0.018.
Similarly, if selecting humidity (C) as fork attribute, the calculating of information gain is as follows: if wherein selecting humidity
(C) it is used as fork attribute, wherein when low humidity is 3, the ratio difference of the electronic current mutual inductor is " super in the humidity
The quantity of difference " is 0, is 3 than the quantity that difference is " not overproof ", the information content under the low humidity attribute isWhen middle humidity is 2, the ratio difference of the electronic current mutual inductor is " super
The quantity of difference " is 0, is 2 than the quantity that difference is " not overproof ", the information content in this under humidity attribute isWhen high humility is 7, the ratio difference of the electronic current mutual inductor is " super
The quantity of difference " is 4, is 3 than the quantity that difference is " not overproof ", the information content under the high temperature properties isGross information content after branch at this timeThen its information gain value are as follows: Gain (C)=0.918-0.575=0.343.
Similarly, if selecting vibration (D) as fork attribute, the calculating of information gain is as follows: if wherein selection vibration
(D) it is used as fork attribute, wherein when weak vibration is 10, the ratio difference of the electronic current mutual inductor is " super in the vibration
The quantity of difference " is 4, is 6 than the quantity that difference is " not overproof ", and the information content under the weak vibration attribute isWhen strong vibration is 2, the ratio of the electronic current mutual inductor
Difference is that the quantity of " overproof " is 0, is 2 than the quantity that difference is " not overproof ", the information content under the strong vibration attribute isGross information content after branch at this timeThen its information gain value are as follows: Gain (D)=0.918-0.809=0.109.
Similarly, if selecting magnetic field (E) as fork attribute, the calculating of information gain is as follows: if wherein selecting magnetic field
(E) it is used as fork attribute, wherein when low-intensity magnetic field is 12, the ratio difference of the electronic current mutual inductor is " super in the magnetic field
The quantity of difference " is 4, is 8 than the quantity that difference is " not overproof ", the information content under the low-intensity magnetic field attribute isWhen high-intensity magnetic field is 0, the ratio of the electronic current mutual inductor
Difference is that the quantity of " overproof " is 0, is 0 than the quantity that difference is " not overproof ", and the information content under the high-intensity magnetic field attribute is Info
(EBy force)=0；Gross information content after branch at this timeThen its information gain value are as follows: Gain (E)
=0.
For first group of data, brought angular difference information content summation Info (D), each attribute value are brought under five attribute
Information content Info (Ai) and information measurement pointer Gain (A), wherein in one column of angular difference of the electronic current mutual inductor,
The quantity of " overproof " is 4, and the quantity of " not overproof " is 8, and sum is 12, then the gross information content of initial data calculates such as
Under:
If selecting load (A) as fork attribute, information gain calculates as follows: low negative wherein in the load (A)
When lotus is 7, the ratio difference of the electronic current mutual inductor is that the quantity of " overproof " is 3, than the quantity that difference is " not overproof "
It is 4, the information content under the underload attribute isThe load (A)
In, when middle load is 2, the ratio difference of the electronic current mutual inductor is that the quantity of " overproof " is 1, is " not overproof " than difference
Quantity be 1, the information content in this under load attribute isThe load
(A) in, when high load capacity is 3, the ratio difference of the electronic current mutual inductor is that the quantity of " overproof " is 0, " is not surpassed than difference
The quantity of difference " is 3, and the information content under the high load capacity attribute isAt this time
Gross information content after branchThen its information gain value are as follows: Gain (A)=
0.918-0.741=0.177.
Similarly, if selecting temperature (B) as fork attribute, information gain calculates as follows: if wherein selecting temperature (B)
As fork attribute, wherein when low temperature is 3, the ratio difference of the electronic current mutual inductor is " overproof " in the temperature
Quantity be 0, than difference be " not overproof " quantity be 3, the information content under the low temperature attribute isWhen middle temperature is 7, the ratio difference of the electronic current mutual inductor is " super
The quantity of difference " is 2, is 5 than the quantity that difference is " not overproof ", the information content under the medium temperature attribute isWhen high-temperature is 2, the ratio difference of the electronic current mutual inductor is
The quantity of " overproof " is 2, is 0 than the quantity that difference is " not overproof ", the information content under the high temperature properties isGross information content after branch at this time
Then its information gain value are as follows: Gain (B)=0.918-0.503=0.415.
Similarly, if selecting humidity (C) as fork attribute, information gain calculates as follows: if wherein selecting humidity (C)
As fork attribute, wherein when low humidity is 3, the ratio difference of the electronic current mutual inductor is " overproof " in the humidity
Quantity be 0, than difference be " not overproof " quantity be 3, the information content under the low humidity attribute isWhen middle humidity is 2, the ratio difference of the electronic current mutual inductor is " super
The quantity of difference " is 2, is 0 than the quantity that difference is " not overproof ", the information content in this under humidity attribute isWhen high humility is 7, the ratio difference of the electronic current mutual inductor is " super
The quantity of difference " is 2, is 5 than the quantity that difference is " not overproof ", the information content under the high temperature properties isGross information content after branch at this timeThen its information gain value are as follows: Gain (C)=0.918-0.503=0.415.
Similarly, if selecting vibration (D) as fork attribute, information gain calculates as follows: if wherein selection vibration (D)
As fork attribute, wherein when weak vibration is 10, the ratio difference of the electronic current mutual inductor is " overproof " in the vibration
Quantity be 3, than difference be " not overproof " quantity be 7, this it is weak vibrate attribute under information content beWhen strong vibration is 2, the ratio of the electronic current mutual inductor
Difference is that the quantity of " overproof " is 1, is 1 than the quantity that difference is " not overproof ", the information content under the strong vibration attribute isGross information content after branch at this timeThen its information gain value are as follows: Gain (D)=0.918-0.901=
0.017。
Similarly, if selecting magnetic field (E) as fork attribute, information gain calculates as follows: if wherein selecting magnetic field (E)
As fork attribute, wherein when low-intensity magnetic field is 12, the ratio difference of the electronic current mutual inductor is " overproof " in the magnetic field
Quantity be 4, than difference be " not overproof " quantity be 8, the information content under the low-intensity magnetic field attribute isWhen high-intensity magnetic field is 0, the ratio of the electronic current mutual inductor
Difference is that the quantity of " overproof " is 0, is 0 than the quantity that difference is " not overproof ", the information content Info under the high-intensity magnetic field attribute
(EBy force)=0；Gross information content after branch at this timeThen its information gain value are as follows: Gain (E)
=0.
From last time: the information gain (0.415) than load in difference is maximum, followed by humidity (0.343)；And according to same
The method of sample, which is calculated, finds that the information gain of temperature and humidity is maximum (0.415) with the angular difference in group data, followed by load
(0.177)；According to the above same steps, obtained maximum information gain value appears in three kinds of load, temperature, humidity factors
In, therefore by the pairs of load of analysis dimensionality reduction of five kinds of impact factors, the analysis of temperature, humidity these three leading factor, not only have
Conducive to the treatment effeciency of raising data, and advantageously ensure that the Stability and veracity of operation.
Embodiment two
The present embodiment provides a kind of electronic mutual inductor error characteristics RES(rapid evaluation system)s, utilize side described in embodiment one
Method evaluates electronic mutual inductor error characteristics, including preprocessing module and processing module, wherein the pretreatment mould
Block judges the abnormal number that cannot really reflect mutual inductor operation characteristic for pre-processing to the data in raw data base
According to, and delete the abnormal data；For the data in the raw data base after deletion, the processing module is used for electric to influencing
The impact factor of minor transformer error carries out dimension-reduction treatment, and data dimension is reduced to two dimension or three-dimensional, it is high to retain weight
Factor carries out subsequent analysis.
Electronic mutual inductor error characteristics RES(rapid evaluation system) described in the present embodiment, including preprocessing module and processing mould
Block, wherein the preprocessing module is judged really reflect mutually for pre-processing the data in raw data base
The abnormal data of sensor operation characteristic, and the abnormal data is deleted, the exceptional value in measurement data is rejected, reservation objectively responds
The data of error characteristics deviateed farther out but be not belonging to exceptional value, obtaining electronic mutual inductor error characteristics to Correct Analysis has ten
Divide important meaning；For the data in the raw data base after deletion, the processing module is used for influence electronic type mutual inductance
The impact factor of device error carries out dimension-reduction treatment, and data dimension is reduced to two dimension or three-dimensional, the high factor progress of reservation weight
Subsequent analysis is not only advantageous to improve the treatment effeciency of data, and advantageously ensures that the Stability and veracity of operation.
Obviously, the above embodiments are merely examples for clarifying the description, does not limit the embodiments.For
For those of ordinary skill in the art, other different form variations can also be made on the basis of the above description or are become
It is dynamic.There is no necessity and possibility to exhaust all the enbodiments.And obvious variation extended from this or change
It moves still within the protection scope of the invention.
It should be understood by those skilled in the art that, embodiments herein can provide as method, system or computer program
Product.Therefore, complete hardware embodiment, complete software embodiment or reality combining software and hardware aspects can be used in the application
Apply the form of example.Moreover, it wherein includes the computer of computer usable program code that the application, which can be used in one or more,
The computer program implemented in usable storage medium (including but not limited to magnetic disk storage, CD-ROM, optical memory etc.) produces
The form of product.
The application is referring to method, the process of equipment (system) and computer program product according to the embodiment of the present application
Figure and/or block diagram describe.It should be understood that every one stream in flowchart and/or the block diagram can be realized by computer program instructions
The combination of process and/or box in journey and/or box and flowchart and/or the block diagram.It can provide these computer programs
Instruct the processor of general purpose computer, special purpose computer, Embedded Processor or other programmable data processing devices to produce
A raw machine, so that being generated by the instruction that computer or the processor of other programmable data processing devices execute for real
The device for the function of being specified in present one or more flows of the flowchart and/or one or more blocks of the block diagram.
These computer program instructions, which may also be stored in, is able to guide computer or other programmable data processing devices with spy
Determine in the computer-readable memory that mode works, so that it includes referring to that instruction stored in the computer readable memory, which generates,
Enable the manufacture of device, the command device realize in one box of one or more flows of the flowchart and/or block diagram or
The function of being specified in multiple boxes.
These computer program instructions also can be loaded onto a computer or other programmable data processing device, so that counting
Series of operation steps are executed on calculation machine or other programmable devices to generate computer implemented processing, thus in computer or
The instruction executed on other programmable devices is provided for realizing in one or more flows of the flowchart and/or block diagram one
The step of function of being specified in a box or multiple boxes.
Claims (10)
1. a kind of electronic mutual inductor error characteristics fast appraisement method, which comprises the steps of:
Step S1: pre-processing the data in raw data base, judges really reflect mutual inductor operation characteristic
Abnormal data, and delete the abnormal data；
Step S2: for the data in the raw data base after deletion, to influence the impact factor of electronic mutual inductor error into
Data dimension is reduced to two dimension or three-dimensional, the high factor progress subsequent analysis of reservation weight by row dimension-reduction treatment.
2. electronic mutual inductor error characteristics fast appraisement method according to claim 1, it is characterised in that: described to original
Data in database are pre-processed, the method for judging really reflect the abnormal data of mutual inductor operation characteristic are as follows:
Average value is calculated according to a series of measurements under the same conditions；According to the mean value calculation experimental standard deviation；According to institute
It states testing standard difference and the average value determines abnormal data.
3. electronic mutual inductor error characteristics fast appraisement method according to claim 2, it is characterised in that: according to described
The method that testing standard difference and the average value determine abnormal data are as follows: judge data and average value absolute value of the difference whether
More than or equal to the setting multiple of experimental standard deviation, if the data are greater than or equal to the setting multiple of the experimental standard deviation,
Then judge the data for abnormal data；Conversely, not being then abnormal data.
4. electronic mutual inductor error characteristics fast appraisement method according to claim 2, it is characterised in that: a system
Column test result includes the electronic mutual inductor than the test result of difference and/or the test result of angular difference.
5. electronic mutual inductor error characteristics fast appraisement method according to claim 1, it is characterised in that: electric to influencing
The method that the impact factor of minor transformer error carries out dimension-reduction treatment are as follows: calculate the information gain value of each impact factor.
6. electronic mutual inductor error characteristics fast appraisement method according to claim 5, it is characterised in that: the information
The calculation method of yield value are as follows: calculate electronic mutual inductor than difference or the information summation of angular difference；By the attribute of each impact factor
Multiple attributes are divided into, the information content of each attribute is calculated；According to each impact factor of information computing of each attribute
Under each attribute information summation；According to each category under the information summation and each impact factor than difference or angular difference
The information summation of property obtains the information gain value.
7. electronic mutual inductor error characteristics fast appraisement method according to claim 6, it is characterised in that: the information
Yield value is the difference of the information summation of each attribute under the information summation and each impact factor than difference or angular difference.
8. electronic mutual inductor error characteristics fast appraisement method according to claim 1, it is characterised in that: described original
Database is formed by randomly selecting the operation data in live pilot in electronic mutual inductor.
9. electronic mutual inductor error characteristics fast appraisement method according to claim 1, it is characterised in that: the influence
The factor includes at least one in following: load, temperature, humidity, vibration, magnetic field.
10. a kind of electronic mutual inductor error characteristics RES(rapid evaluation system), it is characterised in that: including preprocessing module and processing
Module, wherein the preprocessing module is judged really to reflect for pre-processing the data in raw data base
The abnormal data of mutual inductor operation characteristic, and delete the abnormal data；For the data in the raw data base after deletion, institute
Processing module is stated for carrying out dimension-reduction treatment to the impact factor for influencing electronic mutual inductor error, data dimension is reduced to two
Dimension is three-dimensional, retains the high factor of weight and carries out subsequent analysis.
Priority Applications (1)
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