CN104076319B - Online error analysis system of digitized electric energy metering device - Google Patents

Online error analysis system of digitized electric energy metering device Download PDF

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CN104076319B
CN104076319B CN201410183998.3A CN201410183998A CN104076319B CN 104076319 B CN104076319 B CN 104076319B CN 201410183998 A CN201410183998 A CN 201410183998A CN 104076319 B CN104076319 B CN 104076319B
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China
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electric energy
energy metering
sequence
error
metering error
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CN201410183998.3A
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Chinese (zh)
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CN104076319A (en
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张秋雁
杨爱冰
肖监
欧家祥
徐宏伟
王路
李红斌
程含渺
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贵州电力试验研究院
贵阳供电局
华中科技大学
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Abstract

The invention discloses an online error analysis system of a digitized electric energy metering device and belongs to the field of online monitoring of smart power grid devices. The online error analysis system continuously obtains error influence factor relevant data of the digitized electric energy metering device and operation data of the device through sensors installed at key positions of a transformer substation in an online mode and adopts a correlation analysis and related algorithm combined error analysis method to analyze the electric energy metering error influence degree of relevant influence factors. The online error analysis system comprises a current-voltage sensing unit, an environment sensing unit and a computer. The online error analysis system provides actual operation data for systematic study of digitized electric energy metering, well masters an error change rule of the digitized electric energy metering device and lays the theoretical and practical foundation for popularization and application of the digitized electric energy metering.

Description

A kind of online error analytical system of digitalized electric energy measuring apparatus
Technical field
The invention belongs to intelligent grid equipment on-line monitoring field, more specifically, relate to a kind of online error analytical system of digitalized electric energy measuring apparatus.
Background technology
Along with the progress of science and technology, electric system is towards digitizing and intelligent direction development, and digital transformer substation is the important component part of intelligent grid.In digital transformer substation, electric energy metrical no longer adopts traditional electric power meter, but adopts totally digitilized electric power meter, and typical three-phase digital electric power meter composition as shown in Figure 1.Be positioned on high-tension side combined electronic transformer 1-3 and be respectively A combined formula electric mutual inductor, B combined formula electric mutual inductor and the combined formula electric mutual inductor of C, wherein, combined electronic transformer comprises the electronic current mutual inductor and electronic type voltage transformer that fit together, synchronous through synchronous clock 4, gather electric current and the voltage of A phase bus 5, B phase bus 6 and C phase bus 7 respectively, the data of collection with certain form as FT3 to send to the merge cells 8 of low-pressure side through optical fiber.The sampled data of reception is sent to digital electric energy meter 9 with IEC61850-9-2 frame format by merge cells 8 again.Digital electric energy meter 9 adopts certain electric energy algorithm to calculate electric energy.Digital electric energy meter 9 has RS485 or infrared interface, for communicating with other equipment.
Although digital transformer substation has had certain operating experience, digitalized electric energy measuring apparatus has also been in the immature stage at present on the whole.Compare electronic mutual inductor and merge cells has been in the relative maturity stage, digital electric energy meter is substantially also in conceptual phase.On the one hand, relevant criterion is not yet sound; On the other hand, theoretical research is abundant all not enough with practice.At theoretical side, digital electric energy meter has the error link more less than electronic electric energy meter from principle, but sampling to isolate due to digital electric energy meter and front end analogue comes, and causes the adaptability of electric energy algorithm to actual condition poorer on the contrary.Such as, reply harmonic wave, m-Acetyl chlorophosphonazo and direct current etc., different manufacturers has algorithms of different, may directly cause energy calculate error.In putting into practice, mainly two large key equipments, i.e., there is outstanding accuracy and integrity problem in electronic mutual inductor and digital electric energy meter in operational process.Investigational data shows, after the electronic mutual inductor of fortune puts into operation a period of time, ubiquity Stability and veracity problem, off-gage phenomenon is general, even breaks down, critical point electric energy metrical may be caused thus to there is comparatively big error even mistake.
Along with the development of optical fiber technology and optical communication technology, and the owned excellent electromagnetism interference performance of optical fiber communication, the information in digital electric power metering system all adopts optical fiber with the format transmission of regulation.
As seen from the above analysis, digitalized electric energy metering method and traditional simulation way of energy measuring have obvious difference.Thus from signals collecting, transfer to electric energy algorithm, the source of links error all needs to carry out reappraising and analyzing for digital transformer substation electric energy metrical feature, and the impact of equipment operating environment on error in dipping can not be ignored.
At present also theory stage is in the research major part of digital electric energy metering, and there is very large deficiency in analysis on Source of Error.Generally from physics or mathematical angle, theoretical analysis is done to the error that well-known factor causes, the A/D quantization error of such as signal acquisition process and non-synchronous sampling errors, exemplary power Algorithm Error etc.Do not relate to the digitalized electric energy error in dipping that the environmental factor of quantitative analytical device operation, electromagnetic environment factor and power environment factor cause, and scientific and effective method is also lacked to the error analysis of this respect.That is, even if accurate analysis A/D quantification, non-synchronous sampling error factors, the powerful support of actual operating data cannot also be obtained.This was both unfavorable for scientific research, can not play very large effect to applying of digital electric energy metering device simultaneously.In sum, at present relatively complete analytic target both there is no to the synthetical error analysis of digital electric energy metering device, there is no scientific and effective analytical approach yet.
Summary of the invention
For above defect or the Improvement requirement of prior art, the invention provides a kind of online error analytical system of digitalized electric energy measuring apparatus, comprise current/voltage sensing unit, environmentally sensitive unit and computing machine, for carrying out online error analysis to digital electric energy metering device, described digitalized electric energy measuring apparatus comprises combined electronic transformer, merge cells and digital electric energy meter, wherein, described combined electronic transformer comprises the electronic current transducer and electronic voltage mutual induction device that fit together.
Current/voltage sensing unit comprises electromagnetic current transducer, electromagnetic potential transformer, first microprocessor, and is the first direct supply that in current/voltage sensing unit, each device is powered.According to power equipment actual motion environmental aspect, the primary current on electromagnetic current transducer and electromagnetic potential transformer energy Measurement accuracy three buses and primary voltage; First microprocessor in real time by the primary voltage of measurement and primary current to meet the frame format framing and packing of IEC61850-9-2, by Ethernet interface transmission, and have high-performance and network communications capability concurrently; First direct supply meets rig-site utilization requirement.
Environmentally sensitive unit comprises temperature sensor, humidity sensor, vibration transducer, the second microprocessor, and is the second direct supply that in environmentally sensitive unit, each device is powered.According to power equipment actual motion environmental aspect, the measurement range demand fulfillment of temperature sensor-40 DEG C ~+85 DEG C; The measurement range demand fulfillment 0 to 100% of humidity sensor; Vibration transducer needs to sense obvious vibration event, such as isolation switch folding; Second microprocessor has certain computing communication capacity and low power consumption characteristic concurrently; Second direct supply meets rig-site utilization requirement.
Computing machine obtains on the one hand the measurement data of each sensing unit, obtains the electric energy data of digital electric energy meter and the sampled data of merge cells on the other hand, the actual measurement primary current namely on three buses recording of combined electronic transformer and actual measurement primary voltage.The integrated monitoring analysis software of computer run is as Data Analysis Platform, adopt the error analysis method that association analysis and related algorithm combine, first from mass data, the influence factor of digital electric power error in dipping is excavated by association analysis, re-use related algorithm and analyze correlative factor to the influence degree of error in dipping, thus extract the error in dipping characteristic quantity of digitalized electric energy measuring apparatus.
Further, also comprise electromagnetic environment sensing unit, comprise electric-field sensor, magnetic field sensor, the 3rd microprocessor, and be the 3rd direct supply that in electromagnetic environment sensing unit, each device is powered.According to power equipment actual motion environmental aspect, electric-field sensor and magnetic field sensor need the electromagnetic interference (EMI) that 50Hz to 300MHz frequency band can be detected; 3rd microprocessor has certain computing communication capacity and low power consumption characteristic concurrently; 3rd direct supply meets rig-site utilization requirement.
Closer, also comprise power supply sensing unit, comprise current sensor, voltage sensor, the 4th microprocessor, and be the 4th direct supply that in power supply sensing unit, each device is powered.According to power equipment actual motion environmental aspect, the differential-mode current of the power supply of current sensor measurement merge cells and digital electric energy meter and common mode current; Voltage sensor measures the mains fluctuations amount of merge cells and digital electric energy meter, to the measurement of power supply mainly such as, in order to be identified by the interference of power lead coupling, surge etc.; 4th microprocessor has certain computing communication capacity and low power consumption characteristic concurrently; 4th direct supply meets rig-site utilization requirement.
Further, described computing machine carries out error analysis to the multiple measurement data obtained, and described computing machine comprises following modules:
Calculate electric energy metering error module, first described standard electric energy is calculated, described standard electric energy=(accurate primary current × accurately primary voltage) integration to the time, then calculates described electric energy metering error, described electric energy metering error=electric energy data-standard electric energy;
Extract associated volume module, obtain multiple data, described multiple data comprise described temperature, humidity, vibration frequency, Oscillation Amplitude, actual measurement primary current, actual measurement primary voltage, electric field intensity and magnetic field intensity, using the multiple association input quantities of described multiple data as association analysis, using described electric energy metering error as associated input quantity, adopt association analysis to calculate the degree of association of each data and described electric energy metering error in described multiple data, judge whether described association input quantity is associated volume respectively;
Calculate influence degree module, judge that whether the described associated volume extracted in described extraction associated volume module is the direct influence amount of described electric energy metering error, if the described associated volume extracted in described extraction associated volume module is the direct influence amount to described electric energy metering error, then the described degree of association of described associated volume and described electric energy metering error is the influence degree to described electric energy metering error, wherein, described direct influence amount comprises described actual measurement primary current and actual measurement primary voltage, if the described associated volume extracted in described extraction associated volume module is the remote effect amount to described electric energy metering error, then calculate the related coefficient between described remote effect amount direct influence amount associated with it, again according to described Calculation of correlation factor remote effect amount to the weighted influence degree of described electric energy metering error, wherein, described remote effect amount comprises described temperature, humidity, vibration frequency, Oscillation Amplitude, electric field intensity and magnetic field intensity,
Memory module, stores multiple error analysis results of drawing in described calculating influence degree module as data sample, sets up expert system.
In general, the online error analytical system of digitalized electric energy measuring apparatus provided by the invention compared with prior art, by the sensor installed at each key position of transformer station, the error effect factor related data of digital electric energy metering device and the service data of device itself can be obtained online continuously for a long time, and adopt the error analysis method that association analysis and related algorithm combine, first from mass data, excavated the influence factor of digitalized electric energy error in dipping by association analysis, re-use related algorithm and analyze the influence degree of these factors to error.Thus realization is analyzed the influence factor of digital electric energy metering directional error characteristic and mechanism, for the error characteristics research of digitalized electric energy metering provides theory and practice foundation.
Accompanying drawing explanation
Figure 1 shows that typical digitalized electric energy measuring apparatus structural representation in prior art;
Figure 2 shows that the online error analytical system composition frame chart of digitalized electric energy measuring apparatus of the present invention;
Figure 3 shows that the computing machine of the online error analytical system of digitalized electric energy measuring apparatus of the present invention carries out the process flow diagram of online error analysis.
Embodiment
In order to make object of the present invention, technical scheme and advantage clearly understand, below in conjunction with drawings and Examples, the present invention is further elaborated.Should be appreciated that specific embodiment described herein only in order to explain the present invention, be not intended to limit the present invention.In addition, if below in described each embodiment of the present invention involved technical characteristic do not form conflict each other and just can mutually combine.
The sensor of online error analytical system by installing at each key position of transformer station of digitalized electric energy measuring apparatus provided by the invention, obtain the error effect factor related data of digital electric energy metering device and the service data of device itself continuously online, comprise the electric flux etc. of three bus 5-7 vibration of the measured current of three bus 5-7 that records of electric current and voltage and combined electronic transformer and voltage, combined electronic transformer 1-3 accurately, electric field intensity, magnetic field intensity and digital electric power meter.Adopt the error analysis method that association analysis and related algorithm combine, first from mass data, excavated the influence factor of digital electric power error in dipping by association analysis, re-use related algorithm and analyze Correlative Influence Factors to the influence degree of electric energy metering error.Thus provide actual operating data for the systematic Study of digitalized electric energy metering, and better grasp the error variation of digitalized electric energy measuring apparatus, for applying of digitalized electric energy metering establishes theory and practice basis.
Figure 2 shows that the online error analytical system composition frame chart of the digitalized electric energy measuring apparatus of the embodiment of the present invention.Composition graphs 1 is described by Fig. 2, and the parts that in Fig. 2 and Fig. 1, label is identical have same or analogous function.As shown in Figure 2, the online error analytical system of digitalized electric energy measuring apparatus comprises environmentally sensitive unit 10, electromagnetic environment sensing unit 11, power supply sensing unit 12, current/voltage sensing unit 13 and computing machine 14.
Environmentally sensitive unit 10 comprises temperature sensor 101, humidity sensor 102, vibration transducer 103, first microprocessor 104, and is the first direct supply 105 that in environmentally sensitive unit 10, each device is powered.In embodiments of the present invention, temperature sensor 101 can select Digital Measurement of Temperature chip DS18B20; Humidity sensor 102 can select STTS751; Shock sensor 103 can select AD100-1A; First microprocessor 104 selects the MPS430F149 low-power scm of TI company; SD6-S05A1 unit power supply selected by first direct supply 105.
Electromagnetic environment sensing unit 11 comprises electric-field sensor 111, magnetic field sensor 112, second microprocessor 113, and is the second direct supply 114 that in electromagnetic environment sensing unit 11, each device is powered.In embodiments of the present invention, electric-field sensor 111 can select HI-6053/HI-6153 omnidirectional electric field probe; Magnetic field sensor 112 can select HMC1002 double-axis magnetic field sensor; The MPS430149 low-power scm of TI company selected by second microprocessor 113; SD6-S05A1 unit power supply selected by second direct supply 114.
Power supply sensing unit 12 comprises current sensor 121, voltage sensor 122, the 3rd microprocessor 123, and is the 3rd direct supply 124 that in power supply sensing unit 12, each device is powered.In embodiments of the present invention, current sensor 121 can select CTSR1-P closed-loop current sensors; Voltage sensor 122 can select LV25-P Hall voltage sensor measurement supply voltage; The MPS430F149 low-power consumption series monolithic of TI company selected by 3rd microprocessor 123; SD6-S05A1 unit power supply selected by first direct supply 124.
Current/voltage sensing unit 13 comprises current transformer 131, voltage transformer (VT) 132, the 4th microprocessor 133, and is the 4th direct supply 134 that in current/voltage sensing unit 13, each device is powered.In embodiments of the present invention, current transformer 131 and voltage transformer (VT) 132 require to select the electromagnetic potential transformer and electromagnetic current transducer that meet class of accuracy requirement according to accuracy of measurement, and its measured value can as standard primary voltage and primary current; 4th microprocessor 133 can select the microprocessor MPC860 of PowerPC framework; SD25-S12D1 unit power supply selected by 4th direct supply 134.
Computing machine 14 1 aspect obtains the measurement data of each sensing module 10-13, obtains the energy data of digital electric energy meter 9 and the sampled value of merge cells 8 on the other hand.Computing machine 14 comprises calculating electric energy metering error module, extracts associated volume module, calculates influence degree module and memory module, adopt the error analysis method that association analysis and related algorithm combine, first from mass data, the influence factor of digital electric power error in dipping is excavated by association analysis, re-use related algorithm and analyze correlative factor to the influence degree of error, thus extract the error character amount of digitalized electric energy measuring apparatus.Current micro computer hardware configuration is high, and its processor calculating speed is fast, and hard-disk capacity is large, generally can meet computing and memory requirement.In embodiments of the present invention, Hewlett-Packard HP PavilionP6-1480CN microcomputer selected by computing machine 14.
The data that the online error analytical system of digitalized electric energy measuring apparatus obtains comprise the related data of digitalized electric energy measuring apparatus error effect factor and the service data of digitalized electric energy measuring apparatus itself.Environmentally sensitive unit 10, electromagnetic environment sensing unit 11, power supply sensing unit 12, current/voltage sensing unit 13 form sensing and monitoring system, the continuously online related data obtaining digital electric energy metering directional error influence factor, comprising: the temperature and humidity of measured current accurately on three bus 5-7 recording of electric current and voltage and combined electronic transformer of three bus 5-7 and voltage, environment, the vibration of combined electronic transformer 1-3, electric field intensity and magnetic field intensity etc.Computing machine 14, by the service data of network interface and the direct acquisition device of RS485 itself, comprises the sampled data of merge cells 8 and the electric energy data of digital electric energy meter 9.
Illustrate the acquisition methods of each sensing unit mounting means and 6 groups of data below.
1, environmental sensory data: the pedestal place three same environmentally sensitive unit 10 being arranged on respectively combined electronic transformer 1-3, for obtaining the temperature of combined electronic transformer 1-3 running environment, humidity, vibration frequency and Oscillation Amplitude, be sent to computing machine 14 by RS485 bus.
2, electromagnetic environment sensing data: the pedestal place three same electromagnetic environment sensing units 11 being also arranged on respectively combined electronic transformer 1-3, for obtaining electric field intensity and the magnetic field intensity of combined electronic transformer 1-3 running environment, be sent to computing machine 14 by RS485 bus.
3, power environment sensing data: the power supply point of incoming cables two same power supply sensing units 12 being installed on respectively merge cells 8 and digital electric energy meter 9, for monitoring current/voltage undulate quantity and the interference of the power supply of merge cells 8 and digital electric energy meter 9, be sent to computing machine 14 by RS485 bus.
4, primary current voltage sensor data accurately: three same current/voltage sensing units 13 being installed on three bus 5-7, for obtaining the accurate primary current of three bus 5-7 and accurate primary voltage, being sent to computing machine 14 by network interface.
5, merge cells sampled data: computing machine 14 is communicated with merge cells 8 by network interface, obtains sampled data, i.e. the actual measurement primary current of three bus 5-7 of combined electronic transformer measurement and actual measurement primary voltage.
6, digital electric energy meter electric energy data: computing machine 14 is communicated with digital electric energy meter 9 by RS485, obtains the electric energy data of digital electric power meter accumulation.
Figure 3 shows that the computing machine 14 of the embodiment of the present invention carries out the process flow diagram of error analysis.Computing machine 14 direct acting factor and the degree of association thereof of the methods analyst electric energy metering error of association analysis, calculate the disturbance degree of the indirect acting factor of electric energy metering error with related algorithm.Concrete, in embodiments of the present invention, the direct influence amount belonging to electric energy metering error is the mains fluctuations amount of merge cells 8, the mains fluctuations amount of digital electric energy meter 9, the source current undulate quantity of merge cells 8, the source current undulate quantity, ECT measuring error, EVT measuring error etc. of digital electric energy meter 9; Belong to electric energy metering error remote effect amount have environment temperature, humidity, electric field intensity, magnetic field intensity, vibration etc. that isolation switch folding causes.First extracted the error effect factor of digitalized electric energy measuring apparatus by association analysis, then adopt related algorithm to analyze its influence degree.
As shown in Figure 3, calculate electric energy metering error module and as the accurate electric energy of criterion calculation, the electric energy data of digital electric energy meter is deducted the accurate electric energy calculated according to primary current voltage sensor data accurately, obtain the electric flux error of digital electric energy meter; Using primary current voltage sensor data accurately as standard, the electric current record merge cells sampled data and combined electronic transformer and voltage data and normalized current and voltage compare, and calculate the measuring error of electronic current mutual inductor (ECT) and electronic type voltage transformer (EVT).
Extract associated volume module and the environmental sensory data obtained (is specially the temperature of environment, humidity and vibration), electromagnetic environment sensing data (being specially electric field intensity and magnetic field intensity), power supply sensing data (being specially electric current and the magnitude of a voltage fluctuation of merge cells and digital electric power apparent source), primary current voltage sensor data accurately, the electric energy data of merge cells sampled data and digital electric energy meter is stored in local hard drive, and associate input quantity by calculating ECT measuring error that electric energy metering error module calculates and EVT measuring error as association analysis, to the electric flux error of the digital electric energy meter calculated in electric energy metering error module be calculated as associated input quantity, association analysis is adopted to calculate the mains fluctuations amount of merge cells 8, the mains fluctuations amount of digital electric energy meter 9, the source current undulate quantity of merge cells 8, the source current undulate quantity of digital electric energy meter 9, the degree of association of the electric flux error of ECT measuring error and EVT measuring error these 6 amount and digital electric energy meter, extract related influence amount.If the degree of association calculated is more than or equal to correlation threshold, then this association input quantity is associated volume, and namely institute's amount of analysis has impact to digital electric energy metering error; If the degree of association calculated is less than correlation threshold, then this association input quantity is not associated volume, and namely institute's amount of analysis does not affect digital electric energy metering error or affects not obvious.
The related coefficient of the direct influence amount that calculating influence degree module adopts the remote effect amount in the associated volume extracted in related algorithm calculating extraction associated volume module associated with it, then the degree of association obtained in combination extraction associated volume module obtains the weighted influence degree of this remote effect amount to electric energy metering error.
Such as, the impact of power supply disturbance on electric energy metering error of digital electric energy meter is direct, and so the degree of association of itself and electric energy metering error is its influence degree to electric energy metering error.And temperature on the impact of electric energy metering error by affecting ECT error and EVT error and indirectly producing, suppose that ECT error is γ to the influence degree of electric energy metering error t(0≤γ t≤ 1), the degree of correlation of temperature and ECT error is ρ t(0≤ρ t≤ 1), then temperature is γ to the influence degree of electric energy metering error t× ρ t.
Analysis result stores and prints display as data sample in a computer by memory module, does data encasement for setting up expert system later.
Below for the electric energy metering error impact analysis of the temperature factor in environment to digital electric power meter, the concrete operations of the submodule that detailed description extraction associated volume module and calculating influence degree module specifically comprise and execution.Environment temperature is mainly by affecting electronic mutual inductor error and affecting electric energy metering error, comprise electronic type voltage transformer and electronic current mutual inductor, therefore be the remote effect amount of digital electric power error in dipping, be described for electronic type voltage transformer (EVT) below.For electromagnetic potential transformer, temperature is very little so that can ignore on the impact of its error, therefore the primary current voltage sensor data that current/voltage sensing unit is measured can as relative real value, and the electric energy calculated by these primary current voltage sensor data can as the relative real value of electric energy metrical.
Extract associated volume module and specifically comprise following submodule:
Obtain data submodule, obtain multiple data, the data of electromagnetic potential transformer in the EVT data in merge cells sampled data and primary current voltage sensor data are compared, draws EVT error sequence x j, wherein, x j={ x j(1), x j(2) ..., x j(n) };
Compute associations degree submodule, first compares the electric energy metrical relative real value of calculating and the electric energy of digital electric energy meter, draws the electric energy metering error sequence x of digital electric energy meter 0, wherein, x 0={ x 0(1), x 0(2) ..., x 0(n) }, n counts for got error sequence, and n more can reflect x more greatly 0with x jthe degree of association; But n is larger, then need the data gathering the longer time, general n gets about 20.
With electric energy metering error sequence x 0for reference sequences, EVT error sequence x jfor relating sequence, to electric energy metering error sequence x 0with EVT error sequence x jdo correlation analysis, compute associations coefficient and the degree of association, judge that whether EVT error is relevant with electric energy metering error.
Calculate EVT error sequence x jwith electric energy metering error sequence x 0correlation coefficient ζ j(k) be:
ζ j ( k ) = min j min k | x 0 ( k ) - x j ( k ) | + ρ min j min k | x 0 ( k ) - x j ( k ) | | x 0 ( k ) - x j ( k ) | + max j max k | x 0 ( k ) - x j ( k ) |
Wherein, ρ is resolution ratio, ζ jk () is for a k is at EVT error sequence x jto electric energy metering error sequence x 0correlation coefficient, 1≤k≤n.
Calculate EVT error sequence x jto electric energy metering error sequence x 0degree of association γ jfor:
γ j = 1 n Σ k = 1 n ζ j ( k )
γ jbe worth larger, then EVT error sequence x jwith electric energy metering error sequence x 0changing trend more close, illustrate that its degree of association is larger, namely show that the degree of EVT error effect electric energy metering error is larger.
Judge correlated quantum module, judge whether the degree of association of multiple association input quantity is more than or equal to correlation threshold, is respectively, corresponding association input quantity is associated volume, otherwise corresponding association input quantity is not associated volume.Suppose that correlation threshold is set to 0.5, if calculate γ j>=0.5, then think that EVT error is relevant with electric energy metering error, then analyze EVT error further to the influence degree of electric energy metering error.If calculate γ j< 0.5, then think that EVT error does not associate with electric energy metering error.Correlation threshold can rule of thumb set, also can experimentally data setting.
Calculate influence degree module and specifically comprise following submodule:
Judge influence amount submodule, judge that whether the associated volume extracted in said extracted associated volume module is the direct influence amount of electric energy metering error, be that the degree of association of associated volume and electric energy metering error is the influence degree to electric energy metering error, otherwise processed by calculating weighted influence degree submodule.
Calculate weighted influence degree submodule, temperature is on the impact of electric energy metering error indeed through EVT effect, and the influence degree of temperature to EVT error is larger, then larger to the remote effect of electric energy metering error.If the temperature sequence obtained is t i, EVT error sequence is x j, calculate temperature sequence t iwith EVT error sequence x jrelated coefficient, only consider linear correlation degree in embodiments of the present invention, calculating correlation coefficient ρ is:
&rho; ( t i , x j ) = Cov ( t i , x j ) D ( t i ) D ( x j )
Wherein, ρ (t i, x j) be related coefficient, Cov (t i, x j) be cross covariance, D (t i) and D (x j) be autocovariance.If the related coefficient calculated is larger, then show that the influence degree of temperature to EVT error is larger, so the remote effect degree of temperature to electric energy metering error is larger.
Temperature is by affecting EVT error thus affect digital electric power error in dipping, if its degree of association of factor obtaining had a direct impact digital electric power error in dipping being respectively γ 1, γ 2γ n, wherein the degree of association of EVT error and electric energy metering error is γ; The linearly dependent coefficient of temperature and EVT is ρ, and the accounting temperature weighted influence degree η total to digital electric energy metering error is:
&eta; = &rho; &times; &gamma; &Sigma; i = 1 N &gamma; i .
Above-mentioned all direct or indirect factors affecting electric energy metering error all can adopt above-mentioned analytical approach, step and each submodule concrete operations to be analyzed its total influence degree.
Those skilled in the art will readily understand; the foregoing is only preferred embodiment of the present invention; not in order to limit the present invention, all any amendments done within the spirit and principles in the present invention, equivalent replacement and improvement etc., all should be included within protection scope of the present invention.

Claims (11)

1. the online error analytical system of a digitalized electric energy measuring apparatus, comprise current/voltage sensing unit, physical environment sensing unit, electromagnetic environment sensing unit, power supply sensing unit and computing machine, for carrying out online error analysis to the digitalized electric energy measuring apparatus of on-the-spot on-line operation, comprise error effect factor and quantitative effect level thereof, described digitalized electric energy measuring apparatus comprises combined electronic transformer, merge cells and digital electric energy meter, wherein, described combined electronic transformer comprises the electronic current mutual inductor and electronic type voltage transformer that fit together, it is characterized in that:
Described current/voltage sensing unit is used for online acquisition primary current value and primary voltage value, described current/voltage sensing unit comprises electromagnetic current transducer, electromagnetic potential transformer, first microprocessor and the first direct supply, wherein, described first direct supply is that described first microprocessor is powered, described electromagnetic current transducer, described electromagnetic potential transformer is all installed on bus, obtain the accurate primary current of described bus and accurate primary voltage respectively, by described first microprocessor, obtained described accurate primary current and accurate primary voltage are sent to described computing machine,
Described physical environment sensing unit comprises temperature sensor, humidity sensor, vibration transducer, second microprocessor and the second direct supply, wherein, described second direct supply is described second microprocessor power supply, described temperature sensor, described humidity sensor and described vibration transducer are all installed on the pedestal place of described combined electronic transformer, be respectively used to the temperature obtaining described combined electronic transformer running environment, humidity, vibration frequency and Oscillation Amplitude, by described second microprocessor by obtained described temperature, humidity, vibration frequency and Oscillation Amplitude are sent to described computing machine,
Described electromagnetic environment sensing unit comprises electric-field sensor, magnetic field sensor, the 3rd microprocessor and the 3rd direct supply, wherein, described 3rd direct supply is described 3rd microprocessor power supply, described electric-field sensor, described magnetic field sensor are all installed on the pedestal place of described combined electronic transformer, be respectively used to the electric field intensity and the magnetic field intensity that obtain described combined electronic transformer running environment, by described 3rd microprocessor, obtained described electric field intensity and magnetic field intensity be sent to described computing machine;
Described power supply sensing unit comprises current sensor, voltage sensor, 4th microprocessor and the 4th direct supply, wherein, described 4th direct supply is described 4th microprocessor power supply, wherein, according to power equipment actual motion environmental aspect, described current sensor is for measuring differential-mode current and the common mode current of the power supply of described merge cells and described digital electric energy meter, described voltage sensor is for measuring the mains fluctuations amount of described merge cells and described digital electric energy meter, to the interference that the measurement of the power supply of described digital electric energy meter is coupled for being identified by power lead, by described 4th microprocessor by the differential-mode current of the power supply of obtained described digital electric energy meter, common mode current, magnitude of a voltage fluctuation and interference are sent to described computing machine,
Described computing machine obtains by described merge cells the bus actual measurement primary current and actual measurement primary voltage that described combined electronic transformer records, and described computing machine also obtains the electric energy data of described digital electric energy meter;
The electric energy metering error of described computer calculate standard electric energy and described digital electric energy meter, and calculate described temperature, humidity, vibration frequency, Oscillation Amplitude, bus actual measurement primary current and actual measurement primary voltage respectively to the influence degree of described electric energy metering error.
2. the online error analytical system of digitalized electric energy measuring apparatus as claimed in claim 1, it is characterized in that, described computing machine comprises following module:
Calculate electric energy metering error module, first described standard electric energy is calculated, described standard electric energy=(accurate primary current × accurately primary voltage) integration to the time, then calculates described electric energy metering error, described electric energy metering error=electric energy data-standard electric energy;
Extract associated volume module, obtain multiple data, described multiple data comprise described temperature, humidity, vibration frequency, Oscillation Amplitude, actual measurement primary current, actual measurement primary voltage, electric field intensity and magnetic field intensity, using the multiple association input quantities of described multiple data as association analysis, using described electric energy metering error as associated input quantity, adopt association analysis to calculate the degree of association of each data and described electric energy metering error in described multiple data, judge whether described association input quantity is associated volume respectively;
Calculate influence degree module, judge that whether the described associated volume extracted in described extraction associated volume module is the direct influence amount of described electric energy metering error, if the described associated volume extracted in described extraction associated volume module is the direct influence amount to described electric energy metering error, then the described degree of association of described associated volume and described electric energy metering error is the influence degree to described electric energy metering error, wherein, described direct influence amount comprises described actual measurement primary current and actual measurement primary voltage, if the described associated volume extracted in described extraction associated volume module is the remote effect amount to described electric energy metering error, then calculate the related coefficient between described remote effect amount direct influence amount associated with it, again according to described Calculation of correlation factor remote effect amount to the weighted influence degree of described electric energy metering error, wherein, described remote effect amount comprises described temperature, humidity, vibration frequency, Oscillation Amplitude, electric field intensity and magnetic field intensity,
Memory module, stores multiple error analysis results of drawing in described calculating influence degree module as data sample, sets up expert system.
3. the online error analytical system of digitalized electric energy measuring apparatus as claimed in claim 2, it is characterized in that, described extraction associated volume module comprises following submodule:
Obtain data submodule, obtain multiple data, described multiple data comprise described actual measurement primary current, actual measurement primary voltage, temperature, humidity, vibration frequency, Oscillation Amplitude, electric field intensity and magnetic field intensity, and the sample sequence of described actual measurement primary current, actual measurement primary voltage is respectively sequence x j={ x j(1), x j(2) ..., x j(n) }, j=1,2; The sample sequence of described temperature, humidity, vibration frequency, Oscillation Amplitude, electric field intensity and magnetic field intensity is respectively sequence t i={ t i(1), t i(2) ..., t i(n) }, i=1,2 ... 6;
Compute associations degree submodule, first compares the described electric energy data of described standard electric energy and described digital electric energy meter, show that the electric energy metering error sequence of described digital electric energy meter is x 0={ x 0(1), x 0(2) ..., x 0(n) }, n counts for got sequence, then calculates described sequence x jwith described electric energy metering error sequence x 0correlation coefficient ζ j(k) be:
&zeta; j ( k ) = min j min k | x 0 ( k ) - x j ( k ) | + &rho; min j min k | x 0 ( k ) - x j ( k ) | | x 0 ( k ) - x j ( k ) | + max j max k | x 0 ( k ) - x j ( k ) |
Wherein, ρ is resolution ratio, ζ jk () is for a k is at described sequence x jto described electric energy metering error sequence x 0correlation coefficient, 1≤k≤n, finally, calculates described sequence x jto described electric energy metering error sequence x 0degree of association γ jfor:
&gamma; j = 1 n &Sigma; k = 1 n &zeta; j ( k )
Calculate described sequence t iwith described electric energy metering error sequence x 0correlation coefficient ζ i(k) and degree of association γ iformula with calculate described sequence x jidentical;
Judge correlated quantum module, judge whether the described degree of association of described multiple association input quantity is more than or equal to correlation threshold, is respectively, corresponding association input quantity is associated volume, otherwise corresponding association input quantity is not associated volume.
4. the online error analytical system of digitalized electric energy measuring apparatus as claimed in claim 2 or claim 3, it is characterized in that, described calculating influence degree module comprises following submodule:
Judge influence amount submodule, judge that whether the described associated volume extracted in described extraction associated volume module is the direct influence amount of described electric energy metering error, be that the described degree of association of described associated volume and described electric energy metering error is the influence degree to described electric energy metering error, otherwise processed by calculating weighted influence degree submodule;
Calculate weighted influence degree submodule, first obtain the sequence t of described remote effect amount i, the sequence x of its direct influence amount be associated j, calculate described sequence t iwith described sequence x jcorrelation coefficient ρ be:
&rho; ( t i , x j ) = Cov ( t i , x j ) D ( t i ) D ( x j )
Wherein, ρ (t i, x j) be related coefficient, Cov (t i, x j) be cross covariance, D (t i) and D (x j) be autocovariance, if the described degree of association of all described direct influence amount and described electric energy metering error is respectively γ 1γ jthe direct influence amount be wherein associated with described remote effect amount and the degree of association of described electric energy metering error are γ, the linearly dependent coefficient of the described direct influence amount that described remote effect amount is associated with it is ρ, and the weighted influence degree η calculating described remote effect amount total to described electric energy metering error is:
&eta; = &rho; &times; &gamma; &Sigma; i = 1 j &gamma; i .
5. the online error analytical system of digitalized electric energy measuring apparatus as claimed in claim 1, it is characterized in that, described current sensor, described voltage sensor are installed on the power supply point of incoming cables of described merge cells and described digital electric energy meter respectively, obtain power supply undulate quantity and the interference of described merge cells and described digital electric energy meter respectively.
6. the online error analytical system of digitalized electric energy measuring apparatus as claimed in claim 5, it is characterized in that, described computing machine comprises following module:
Calculate electric energy metering error module, first described standard electric energy is calculated, described standard electric energy=(accurate primary current × accurately primary voltage) integration to the time, then calculates described electric energy metering error, described electric energy metering error=electric energy data-standard electric energy;
Extract associated volume module, obtain multiple data, described multiple data comprise described temperature, humidity, vibration frequency, Oscillation Amplitude, actual measurement primary current, actual measurement primary voltage, electric field intensity, magnetic field intensity, power supply undulate quantity and interference, using the multiple association input quantities of described multiple data as association analysis, using described electric energy metering error as associated input quantity, adopt association analysis to calculate the degree of association of each data and described electric energy metering error in described multiple data, judge whether described association input quantity is associated volume respectively;
Calculate influence degree module, judge that whether the described associated volume extracted in described extraction associated volume module is the direct influence amount of described electric energy metering error, if the described associated volume extracted in described extraction associated volume module is the direct influence amount to described electric energy metering error, then the described degree of association of described associated volume and described electric energy metering error is the influence degree to described electric energy metering error, wherein, described direct influence amount comprises described actual measurement primary current, actual measurement primary voltage, power supply undulate quantity and interference, if the described associated volume extracted in described extraction associated volume module is the remote effect amount to described electric energy metering error, then calculate the related coefficient between described remote effect amount direct influence amount associated with it, again according to described Calculation of correlation factor remote effect amount to the weighted influence degree of described electric energy metering error, wherein, described remote effect amount comprises described temperature, humidity, vibration frequency, Oscillation Amplitude, electric field intensity and magnetic field intensity,
Memory module, stores multiple error analysis results of drawing in described calculating influence degree module as data sample, sets up expert system.
7. the online error analytical system of digitalized electric energy measuring apparatus as claimed in claim 6, it is characterized in that, described extraction associated volume module comprises following submodule:
Obtain data submodule, obtain multiple data, described multiple data comprise described actual measurement primary current, actual measurement primary voltage, power supply undulate quantity, interference, temperature, humidity, vibration frequency, Oscillation Amplitude, electric field intensity and magnetic field intensity, and the sample sequence of described actual measurement primary current, actual measurement primary voltage, power supply undulate quantity, interference is respectively sequence x j={ x j(1), x j(2) ..., x j(n) }, j=1,2 ... 4, the sample sequence of described temperature, humidity, vibration frequency, Oscillation Amplitude, electric field intensity and magnetic field intensity is respectively sequence t i={ t i(1), t i(2) ..., t i(n) }, i=1,2 ... 6;
Compute associations degree submodule, first compares the described electric energy data of described standard electric energy and described digital electric energy meter, show that the electric energy metering error sequence of described digital electric energy meter is x 0={ x 0(1), x 0(2) ..., x 0(n) }, n counts for got sequence, then calculates described sequence x jwith described electric energy metering error sequence x 0correlation coefficient ζ j(k) be:
&zeta; j ( k ) = min j min k | x 0 ( k ) - x j ( k ) | + &rho; min j min k | x 0 ( k ) - x j ( k ) | | x 0 ( k ) - x j ( k ) | + max j max k | x 0 ( k ) - x j ( k ) |
Wherein, ρ is resolution ratio, ζ jk () is for a k is at described sequence x jto described electric energy metering error sequence x 0correlation coefficient, 1≤k≤n, finally, calculates described sequence x jto described electric energy metering error sequence x 0the degree of association be:
&gamma; j = 1 n &Sigma; k = 1 n &zeta; j ( k )
Calculate described sequence t iwith described electric energy metering error sequence x 0correlation coefficient ζ i(k) and degree of association γ iformula with calculate described sequence x jidentical;
Judge correlated quantum module, judge whether the described degree of association of described multiple association input quantity is more than or equal to correlation threshold, is respectively, corresponding association input quantity is associated volume, otherwise corresponding association input quantity is not associated volume.
8. the online error analytical system of digitalized electric energy measuring apparatus as claimed in claims 6 or 7, it is characterized in that, described calculating influence degree module comprises following submodule:
Judge influence amount submodule, judge that whether the described associated volume extracted in described extraction associated volume module is the direct influence amount of described electric energy metering error, be that the described degree of association of described associated volume and described electric energy metering error is the influence degree to described electric energy metering error, otherwise processed by calculating weighted influence degree submodule;
Calculate weighted influence degree submodule, first obtain the sequence t of described remote effect amount i, the sequence x of its direct influence amount be associated j, calculate described sequence t iwith described sequence x jcorrelation coefficient ρ be:
&rho; ( t i , x j ) = Cov ( t i , x j ) D ( t i ) D ( x j )
Wherein, ρ (t i, x j) be related coefficient, Cov (t i, x j) be cross covariance, D (t i) and D (x j) be autocovariance, if the described degree of association of all described direct influence amount and described electric energy metering error is respectively γ 1γ jthe direct influence amount be wherein associated with described remote effect amount and the degree of association of described electric energy metering error are γ, the linearly dependent coefficient of the described direct influence amount that described remote effect amount is associated with it is ρ, and the weighted influence degree η calculating described remote effect amount total to described electric energy metering error is:
&eta; = &rho; &times; &gamma; &Sigma; i = 1 j &gamma; i .
9. the online error analytical system of digitalized electric energy measuring apparatus as claimed in claim 1, it is characterized in that, described computing machine comprises following module:
Calculate electric energy metering error module, first described standard electric energy is calculated, described standard electric energy=(accurate primary current × accurately primary voltage) integration to the time, then calculates described electric energy metering error, described electric energy metering error=electric energy data-standard electric energy;
Extract associated volume module, obtain multiple data, described multiple data comprise described temperature, humidity, vibration frequency, Oscillation Amplitude, actual measurement primary current and actual measurement primary voltage, using the multiple association input quantities of described multiple data as association analysis, using described electric energy metering error as associated input quantity, adopt association analysis to calculate the degree of association of each data and described electric energy metering error in described multiple data, judge whether described association input quantity is associated volume respectively;
Calculate influence degree module, judge that whether the described associated volume extracted in described extraction associated volume module is the direct influence amount of described electric energy metering error, if the described associated volume extracted in described extraction associated volume module is the direct influence amount to described electric energy metering error, then the described degree of association of described associated volume and described electric energy metering error is the influence degree to described electric energy metering error, wherein, described direct influence amount comprises described actual measurement primary current and actual measurement primary voltage, if the described associated volume extracted in described extraction associated volume module is the remote effect amount to described electric energy metering error, then calculate the related coefficient between described remote effect amount direct influence amount associated with it, again according to described Calculation of correlation factor remote effect amount to the weighted influence degree of described electric energy metering error, wherein, described remote effect amount comprises described temperature, humidity, vibration frequency and Oscillation Amplitude,
Memory module, stores multiple error analysis results of drawing in described calculating influence degree module as data sample, sets up expert system.
10. the online error analytical system of digitalized electric energy measuring apparatus as claimed in claim 9, it is characterized in that, described extraction associated volume module comprises following submodule:
Obtain data submodule, obtain multiple data, described multiple data comprise described actual measurement primary current, actual measurement primary voltage, temperature, humidity, vibration frequency and Oscillation Amplitude, and the sample sequence of described actual measurement primary current, actual measurement primary voltage is respectively sequence x j={ x j(1), x j(2) ..., x j(n) }, j=1,2; The sample sequence of described temperature, humidity, vibration frequency and Oscillation Amplitude is respectively sequence t i={ t i(1), t i(2) ..., t i(n) }, i=1,2 ... 4;
Compute associations degree submodule, first compares the described electric energy data of described standard electric energy and described digital electric energy meter, show that the electric energy metering error sequence of described digital electric energy meter is x 0={ x 0(1), x 0(2) ..., x 0(n) }, n counts for got sequence, then calculates described sequence x jwith described electric energy metering error sequence x 0correlation coefficient ζ j(k) be:
&zeta; j ( k ) = min j min k | x 0 ( k ) - x j ( k ) | + &rho; min j min k | x 0 ( k ) - x j ( k ) | | x 0 ( k ) - x j ( k ) | + max j max k | x 0 ( k ) - x j ( k ) |
Wherein, ρ is resolution ratio, ζ jk () is for a k is at described sequence x jto described electric energy metering error sequence x 0correlation coefficient, 1≤k≤n, finally, calculates described sequence x jto described electric energy metering error sequence x 0the degree of association be:
&gamma; j = 1 n &Sigma; k = 1 n &zeta; j ( k )
Calculate described sequence t iwith described electric energy metering error sequence x 0correlation coefficient ζ i(k) and degree of association γ iformula with calculate described sequence x jidentical;
Judge correlated quantum module, judge whether the described degree of association of described multiple association input quantity is more than or equal to correlation threshold, is respectively, corresponding association input quantity is associated volume, otherwise corresponding association input quantity is not associated volume.
The online error analytical system of 11. digitalized electric energy measuring apparatus as described in claim 9 or 10, it is characterized in that, described calculating influence degree module comprises following submodule:
Judge influence amount submodule, judge that whether the described associated volume extracted in described extraction associated volume module is the direct influence amount of described electric energy metering error, be that the described degree of association of described associated volume and described electric energy metering error is the influence degree to described electric energy metering error, otherwise processed by calculating weighted influence degree submodule;
Calculate weighted influence degree submodule, first obtain the sequence t of described remote effect amount i, the sequence x of its direct influence amount be associated j, calculate described sequence t iwith described sequence x jcorrelation coefficient ρ be:
&rho; ( t i , x j ) = Cov ( t i , x j ) D ( t i ) D ( x j )
Wherein, ρ (t i, x j) be related coefficient, Cov (t i, x j) be cross covariance, D (t i) and D (x j) be autocovariance, if the described degree of association of all described direct influence amount and described electric energy metering error is respectively γ 1γ jthe direct influence amount be wherein associated with described remote effect amount and the degree of association of described electric energy metering error are γ, the linearly dependent coefficient of the described direct influence amount that described remote effect amount is associated with it is ρ, and the weighted influence degree η calculating described remote effect amount total to described electric energy metering error is:
&eta; = &rho; &times; &gamma; &Sigma; i = 1 j &gamma; i .
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