CN104574201A - Electric energy quality data reduction evaluation method suitable for multiple purposes - Google Patents
Electric energy quality data reduction evaluation method suitable for multiple purposes Download PDFInfo
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- CN104574201A CN104574201A CN201410638110.0A CN201410638110A CN104574201A CN 104574201 A CN104574201 A CN 104574201A CN 201410638110 A CN201410638110 A CN 201410638110A CN 104574201 A CN104574201 A CN 104574201A
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- 238000011156 evaluation Methods 0.000 title claims abstract description 9
- 238000000034 method Methods 0.000 claims abstract description 68
- 238000005259 measurement Methods 0.000 claims abstract description 6
- 230000035939 shock Effects 0.000 claims description 2
- 238000012544 monitoring process Methods 0.000 abstract description 14
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- 238000011109 contamination Methods 0.000 description 3
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Abstract
The invention discloses an electric energy quality data reduction evaluation method suitable for multiple purposes. The electric energy quality data reduction evaluation method comprises the following steps: 1, setting a data reduction period to be an integral multiple of 3 seconds, and adjusting the data reduction period according to a load type; 2, setting a data reduction method to be a maximum value of a 3-second measurement value within the reduction period; 3, evaluating all reduction results by a large probability value of 95% within a measurement evaluation time period of one week, wherein the evaluation results can be applied to the fields of electric energy quality accident analysis, comprehensive treatment scheme design, electric energy quality high-grade application and the like. Due to the actual application of the method, the application market of mass electric energy quality monitoring data can be vitalized, and a technical support is provided for high-grade application of the electric energy quality monitoring data.
Description
Technical field
The invention belongs to power quality data assessment technology field, be specifically related to one and be applicable to the brief appraisal procedure of multiduty power quality data.
Background technology
Although people have had more deep understanding and research to pollution sources of electrical energy quality such as traditional high energy-consuming enterprises such as iron and steel, smelting, chemical industry to the power quality problem that electrical network and electricity consumption enterprise bring, but new batch (-type) power generation mode, power electronic equipment can be turned off bring again new quality of power supply subject under discussion, the such as generation of electricity by new energy such as wind-force, photovoltaic.In this context, be applicable to multiduty Power Quality Monitoring Technology and seem more important.
At present, electric energy quality monitoring data reduction method all adopts the unified approach of regulation, be difficult to the electric characteristic adapting to each type load, also just cannot obtain the true quality of power supply contamination characteristics of load, practical application cannot be obtained in quality of power supply crash analysis, comprehensive regulation field.
First method adopts IEC 61000-4-30 data reduction assessment regulation: 1) data reduction period defining is 10 minutes windows, that is, the time interval of 10 minutes, provides a net result record through specific data reduction method; 2) data reduction method adopts root-mean-square valve algorithm, that is, in 10 minutes, adopts r.m.s. algorithm by smoothing for all monitoring results, obtains net result; Algorithm is:
x: brief result, m: fundamental surveillance data recording number in the brief cycle; 3) appraisal procedure: adopt the large value appraisal procedure of 95% probability in the evaluation time section of a week.
The advantage of the method is simply, is convenient to unified; But the method ignores the electric characteristic of different load, such as steady load, impact load, intermittent load etc., prior be its basic ideas be the method only from mathematical angle lay particular emphasis on formed a kind of comparable base value magnitude, do not consider the electric characteristic of load and the subsequent applications purposes of Monitoring Data thereof, its Monitoring Data truly cannot reflect the quality of power supply contamination characteristics of load, more cannot quote these data and carry out crash analysis, power quality harnessed synthetically etc.
Second method adopts national quality of power supply standard limit data reduction method:
Table 1 gathers for national quality of power supply standard limit data reduction method, and appraisal procedure is similar to IEC61000-4-30, that is: in the evaluation time section of a week, adopt the large value appraisal procedure of 95% probability.
Table 1: national quality of power supply standard limit data reduction method
Basic point of departure and the IEC 61000-4-30 of second method are similar, although define a few class selectable data reduction cycle, do not consider the practical use of testing result yet, and the brief cycle cannot carry out freely choosing according to load electric characteristic; Simultaneously, brief method has still continued to use the thinking of IEC-61000-4-30, for root-mean-square valve or be mean value, its essence ignores the electric characteristic of different load equally, such as steady load, impact load, intermittent load etc., thus its Monitoring Data truly cannot reflect the quality of power supply contamination characteristics of load, more cannot carry out crash analysis, power quality harnessed synthetically according to Monitoring Data.
Summary of the invention
In order to overcome above-mentioned the deficiencies in the prior art, the object of this invention is to provide one and being applicable to the brief appraisal procedure of multiduty power quality data, the present invention relates to for different electrical load features, adopting can data reduction cycle of on-line selection; Consider the different characteristic of load electric energy mass defect simultaneously, lay particular emphasis on the object of Monitoring Data application, adopt in the brief cycle the brief method of getting maximal value; Overcome above-mentioned data reduction method cannot accurate assurance load electric characteristic, the shortcoming that Monitoring Data applies in the field such as crash analysis, power quality controlling cannot be realized.
To achieve these goals, the technical solution used in the present invention is: one is applicable to the brief appraisal procedure of multiduty power quality data, comprises the following steps:
The first, the setting data brief cycle is the integral multiple of 3s, adjusts its size according to load type, can consider longer if load is metastable state type, and shock wave ejector half, batch-type load adjustable are shorter;
The second, the brief method of setting data is the maximal value that in the brief cycle, 3s measures record, and maximum value process is
x: brief result, m: in the brief cycle, 3s notes down number;
3rd, in the measurement evaluation time section of a week, adopt the large value of 95% probability to assess all brief results.
Data reduction is not equal to and data reduction by the present invention, for load electric characteristic based on data reduction, for mass data, according to its following practical use, take scientific and reasonable method, retain its purposes applicable and analyze valid data that are defined condition, necessary, non-distorted, be unlikely to the conclusion and the misleading that produce mistake.
Meanwhile, the emphasis of the brief method of different pieces of information is noticed in this invention, that is: qualitative modeling is filtering enchancement factor, pays close attention to the central tendency of steady-state quantity; Root-mean-square valve method pays close attention to measured power features, namely heat-producing characteristics; Max methods pays close attention to measured safety, the Characteristics of Damage caused.On this basis, scientific and rational technical method is selected.
Key of the present invention is to adopt the selectable data reduction cycle to realize carrying out brief within the brief cycle to electric energy quality monitoring data in conjunction with the brief method of maximal value, adopt the large value of 95% probability to assess all brief results in measurement assessment cycle, assessment result is applied to the fields such as quality of power supply crash analysis, Designing of Comprehensive Treatment Scheme, the senior application of the quality of power supply.
This invention obtains practical application, can vitalize the practical application market of magnanimity electric energy quality monitoring data, for the senior application of electric energy quality monitoring data provides technical support.
Accompanying drawing explanation
Fig. 1 to be the embodiment of the present invention 1 standard deviation be 0.1 data variation schematic diagram.
Fig. 2 to be the embodiment of the present invention 2 standard deviation be 0.5 data variation schematic diagram.
Fig. 3 to be the embodiment of the present invention 3 standard deviation be 1.0 data variation schematic diagram.
Embodiment
Below in conjunction with specific embodiment, the present invention is described in further detail.
Embodiment 1
Get 3s record in 1 day 24 hours to analyze, data fit normal distribution, its standard deviation is 0.1, and namely data fluctuations is less;
The brief cycle gets 10min, and brief method adopts maximum value process of the present invention and traditional RMS method to compare:
1) maximum value process of the present invention
x: brief result, m: in the brief cycle, 3s notes down number
2) traditional RMS method
x: brief result, m: in the brief cycle, 3s notes down number
Adopt different brief methods, as shown in Figure 1, assessment result is as shown in table 2 below for original 3s data and brief data variation.
Table 1: different brief methods and results compares
| Standard deviation | Maximum | R.m.s. | C50％ | C95％ |
| 0.1 | 4.34 | 4.012 | 4.0108 | 4.16 |
Analysis the above results is visible:
1) RMS method and the large value of 50% probability close, the time of 50% can only be covered;
2) maximum value process of the present invention covers the large value of 95% probability, guarantees that the cover time is greater than 95%, information dropout is limited in minimum degree.
Embodiment 2
Get 3s record in 1 day 24 hours to analyze, data fit normal distribution.Its standard deviation is increased to 0.5, and namely data fluctuations increases.
The brief cycle gets 10min, and brief method adopts maximum value process of the present invention and traditional RMS method to compare:
1) maximum value process of the present invention
x: brief result, m: in the brief cycle, 3s notes down number
2) traditional RMS method
x: brief result, m: in the brief cycle, 3s notes down number
Adopt different brief methods, as shown in Figure 2, assessment result is as shown in table 2 below for original 3s data and brief data variation.
Table 2: different brief methods and results compares
| Standard deviation | Maximum | R.m.s. | C50％ | C95％ |
| 0.5 | 5.74 | 4.08 | 4.05 | 4.83 |
Analysis the above results is visible, and data fluctuations still has following conclusion after increasing:
1) RMS method and the large value of 50% probability close, the time of 50% can only be covered;
2) maximum value process of the present invention covers the large value of 95% probability, guarantees that the cover time is greater than 95%, information dropout is limited in minimum degree.
Embodiment 3
Get 3s record in 1 day 24 hours to analyze, data fit normal distribution.Its standard deviation is increased to 1.0, and namely data fluctuations is abnormal increases.
The brief cycle gets 10min, and brief method adopts maximum value process of the present invention and traditional RMS method to compare:
1) maximum value process of the present invention
x: brief result, m: in the brief cycle, 3s notes down number
2) traditional RMS method
x: brief result, m: in the brief cycle, 3s notes down number
Adopt different brief methods, as shown in Figure 3, assessment result is as shown in table 3 below for original 3s data and brief data variation.
Table 3: different brief methods and results compares
| Standard deviation | Maximum | R.m.s. | C50％ | C95％ |
| 1.0 | 7.5 | 4.25 | 4.12 | 5.65 |
Analysis the above results is visible, even if data exception fluctuation, still has following conclusion:
1) RMS method and the large value of 50% probability close, the time of 50% can only be covered;
2) maximum value process of the present invention covers the large value of 95% probability, guarantees that the cover time is greater than 95%, information dropout is limited in minimum degree.
Analyzing above-mentioned difference fluctuation data sample adopts the result of different brief method visible:
Closely, that is, the result adopting these methods to draw only has the time of 50% to be no more than this end value to the result of the result that a) traditional RMS (root-mean-square valve method) obtains and the large value method of 50% probability;
If b) adopt maximum value process of the present invention within the brief cycle, then, brief result represents the value that the brief time in cycle 100% is no more than, now within measurement assessment cycle, the large value of 95% probability is adopted to assess brief result, its assessment result necessarily represents the value that 100% × 95%=95% time period is no more than, thus can not lose its information needed in fields such as crash analysis, utility power quality control Capacity Selection.
Claims (1)
1. be applicable to the brief appraisal procedure of multiduty power quality data, it is characterized in that, comprise the following steps:
The first, the setting data brief cycle is the integral multiple of 3s, adjusts its size according to load type, can consider longer if load is metastable state type, and shock wave ejector half, batch-type load adjustable are shorter;
The second, the brief method of setting data is the maximal value of 3s measured value in the brief cycle, and maximum value process is
x: brief result, m: in the brief cycle, 3s notes down number;
3rd, in the measurement evaluation time section of a week, adopt the large value of 95% probability to assess all brief results.
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| CN201410638110.0A CN104574201A (en) | 2014-11-14 | 2014-11-14 | Electric energy quality data reduction evaluation method suitable for multiple purposes |
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| CN201410638110.0A CN104574201A (en) | 2014-11-14 | 2014-11-14 | Electric energy quality data reduction evaluation method suitable for multiple purposes |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105607029B (en) * | 2016-01-08 | 2018-08-03 | 江苏省电力公司电力科学研究院 | A kind of electric power metering device running quality trend analysis based on standard deviation |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101246569A (en) * | 2008-02-28 | 2008-08-20 | 江苏省电力试验研究院有限公司 | Electric network energy quality synthetic appraisement method based on analytic hierarchy process and fuzzy algorithm |
| CN103247008A (en) * | 2013-05-07 | 2013-08-14 | 国家电网公司 | Quality evaluation method of electricity statistical index data |
| CN103633648A (en) * | 2013-12-13 | 2014-03-12 | 国家电网公司 | Method for predicting medium and long term tendency of electric energy quality indexes |
| CN103700037A (en) * | 2013-12-26 | 2014-04-02 | 国电南京自动化股份有限公司 | Method for getting probability maximal value of power quality index based on proportional segmentation |
-
2014
- 2014-11-14 CN CN201410638110.0A patent/CN104574201A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101246569A (en) * | 2008-02-28 | 2008-08-20 | 江苏省电力试验研究院有限公司 | Electric network energy quality synthetic appraisement method based on analytic hierarchy process and fuzzy algorithm |
| CN103247008A (en) * | 2013-05-07 | 2013-08-14 | 国家电网公司 | Quality evaluation method of electricity statistical index data |
| CN103633648A (en) * | 2013-12-13 | 2014-03-12 | 国家电网公司 | Method for predicting medium and long term tendency of electric energy quality indexes |
| CN103700037A (en) * | 2013-12-26 | 2014-04-02 | 国电南京自动化股份有限公司 | Method for getting probability maximal value of power quality index based on proportional segmentation |
Non-Patent Citations (1)
| Title |
|---|
| 刘军成: "《电能质量分析方法》", 31 December 2011, 中国电力出版社 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105607029B (en) * | 2016-01-08 | 2018-08-03 | 江苏省电力公司电力科学研究院 | A kind of electric power metering device running quality trend analysis based on standard deviation |
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Application publication date: 20150429 |