CN105825315A - Electric energy quality early warning method - Google Patents
Electric energy quality early warning method Download PDFInfo
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- CN105825315A CN105825315A CN201510012840.4A CN201510012840A CN105825315A CN 105825315 A CN105825315 A CN 105825315A CN 201510012840 A CN201510012840 A CN 201510012840A CN 105825315 A CN105825315 A CN 105825315A
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Abstract
The present invention relates to an electric energy quality early warning method which is realized through a mean value-standard deviation control chart. The control chart comprises a mean value control chart and a standard deviation control chart. The method comprises the steps of determining the early warning indicator of electric energy quality, judging whether the early warning indicator excesses a standard, preparing the sample group of the historical data of electric energy quality, determining the control limit of a mean value-standard deviation control chart, judging the stability of the data through the sample group through the mean value-standard deviation control chart, judging whether the real-time monitoring data of the electric energy quality is abnormal or not, judging an early warning level according to the abnormal type of the monitoring data, and giving early warning information. According to the invention, the electric energy quality early warning method is used to establish an electric energy quality early warning system, the safety problems or potential problems in a grid are found timely, early warning information is given, the timely adoption of the control measures by an operator is facilitated, the problems are processed as soon so possible or the potential problems are solved in the bud, the grid safety operation and equipment maintenance are served, and the method has a practical meaning for ensuring the reliable operation of a grid.
Description
Technical field:
The present invention relates to quality of power supply method for early warning, be more particularly to a kind of quality of power supply method for early warning based on mean-standard deviation control figure.
Background technology:
Development along with power electronics new technique, generation of electricity by new energy, quickly growth and the quality of power supply interference source user of distributed power generation access in electrical network in a large number, the factor producing electrical energy power quality disturbance in power transmission network is continuously increased, electrical energy power quality disturbance also presents new feature, brings huge challenge to operation of power networks.Wind-powered electricity generation, photovoltaic etc. generating base be scattered in " three Norths " and southeastern coast, extensive generation of electricity by new energy need long distance delivery to load center, the stochastic volatility of its power and intermittence, make the power quality problem such as system frequency and voltage highlight.The disturbance load such as high-speed railway, smelting using high power is directly accessed 220kV or 330kV electrical network, constantly expands the coverage of the electrical network quality of power supply, brings even more serious harmonic wave and negative phase-sequence problem to electrical network.And the scale of modern industry production is big, automaticity is high, the dependence to power supply reliability is higher, and any electric power accident is all likely to result in serious economic loss.Therefore, in order to improve electrical network quality of power supply level, reduce the electrical energy power quality disturbance impact on electrical network, find power quality problem as early as possible, provide quality of power supply early warning accurately most important to the safe operation of electrical network.
At present, a lot of Utilities Electric Co.s all establish electric energy quality monitoring system, it is achieved that the basic functions such as data monitoring and generation form.But utilize Monitoring Data to the extraction of electrical energy power quality disturbance information, analyze, the research of the aspect such as identification the most deep, power quality problem is alerted mainly by judging the method whether indices exceedes threshold value, judgment mode is single, and alarm can only be provided after there is power quality problem, and the abnormality detection of achievement data and the research of early warning mechanism have been short of.In order to electric energy quality monitoring data are carried out degree of depth excavation, find in time to exceed standard and abnormal data, the potential problems in electrical network are provided early warning, need research quality of power supply method for early warning badly, set up early warning mechanism.
Summary of the invention:
It is an object of the invention to provide a kind of quality of power supply method for early warning, technical scheme provides the method judging electric energy quality monitoring data exception, and establishes Steady State Power Quality early warning mechanism.
For achieving the above object, the present invention is by the following technical solutions: a kind of quality of power supply method for early warning, described method controls figure by mean-standard deviation and realizes；Described control figure includes mean chart and Standard Deviation Charts；Said method comprising the steps of:
(1) warning index of the quality of power supply is determined；
(2) judge whether described warning index exceeds standard；
(3) the sample group of the historical data of the preparation quality of power supply；
(4) the control limit of mark mean-standard deviation control figure is determined；
(5) stability of data described in figure judgment sample group is controlled by mean-standard deviation；
(6) judge that the Real-time Monitoring Data of the quality of power supply is the most abnormal；
(7) judge warning level according to the Exception Type of described Monitoring Data and provide early warning information.
By electric energy quality monitoring data, facility information and electrical network parameter, a kind of quality of power supply method for early warning that the present invention provides, determines that the power quality index of needs is as the warning index in described step (1).
A kind of quality of power supply method for early warning that the present invention provides, when some indexs early warning occur simultaneously, is then chosen by the most serious warning level；
Described electric energy quality monitoring data include frequency departure, voltage deviation, voltage pulsation, flickering and three-phase imbalance.
Described electric energy quality monitoring data include frequency, voltage, electric current, voltage deviation, voltage pulsation, harmonic wave, m-Acetyl chlorophosphonazo, flickering and three-phase imbalance.
Described facility information includes main transformer parameter；
Described electrical network parameter includes electric network composition, substation information, position, monitoring point and points of common connection minimum capacity of short circuit.
Another a kind of quality of power supply method for early warning that the present invention provides, compares described warning index and GB limit value, determines whether the warning index of described step (2) exceeds standard；If exceeding international limit value, send level Four early warning；If less than international limit value, then the Monitoring Data of this warning index being carried out abnormality detection and trend analysis.
Another a kind of quality of power supply method for early warning that the present invention provides, the sample group of described step (3) is by procedure below preparation:
It is as the criterion with the quality of power supply historical data not exceeded standard；Being that a sample group chooses n point with the data in the t time according to demand, each point is spaced apart 3 seconds～10 minutes；Described sample group is at least 30 groups.
Another preferred a kind of quality of power supply method for early warning that the present invention provides, the control limit of the mean-standard deviation control figure in described step (4) includes that mean chart controls limit and Standard Deviation Charts controls limit；
By determining that mean chart controls limit and Standard Deviation Charts controls limit respectively, judge that described sample group data are the most stable according to sentencing steady criterion.
Another preferred a kind of quality of power supply method for early warning that the present invention provides, described Standard Deviation Charts controls limit and includes upper control limit UCL, centrage CL and lower control limit LCL；Described upper control limit UCL, centrage CL and lower control limit LCL are determined by following formula respectively:
Wherein, if total m group sample group, often group data are counted as n, the standard deviation of all sample groupsAs n > 25 time,
As n=40, B3≈ 0.6581, B4≈1.3419。
Another preferred a kind of quality of power supply method for early warning that the present invention provides, it is characterized in that: draw described Standard Deviation Charts and judgment sample point is the most stable, if steady, carry out average figure control, if unstable, after then removing the point of instability, return described step (3):
Judge that stable criterion is as follows:
1) continuous 25 points, it is 0 that control line is out-of-bounds counted；
2) continuous 35 points, control line is out-of-bounds counted less than or equal to 1；
3) continuous 100 points, control line is out-of-bounds counted less than or equal to 2；
When judging to stablize, from the beginning of judging stability criterion 1, if criterion 1 can not judge stable, then carry out criterion 2, if criterion 2 still can not judge stable, then carry out criterion 3, if criterion 3 still can not judge stable, need to reject unstable sample point, return described step (3).
Another preferred a kind of quality of power supply method for early warning that the present invention provides, described mean chart controls limit and includes upper control limit UCL, centrage CL and lower control limit LCL；Described mean chart is controlled upper control limit UCL, centrage CL and the lower control limit LCL of limit and is determined by following formula respectively:
Wherein, if total m group sample group, often group data are counted as n, the average of all sample groupsAs n > 25 time,
Another preferred a kind of quality of power supply method for early warning that the present invention provides, draw described mean chart and judgment sample point is the most stable, if stable, carry out described step (6), if shakiness, after removing the point of instability, return described step (3)；
Judge that stable criterion is as follows:
1) continuous 25 points, it is 0 that control line is out-of-bounds counted；
2) continuous 35 points, control line is out-of-bounds counted less than or equal to 1；
3) continuous 100 points, control line is out-of-bounds counted less than or equal to 2；
When judging to stablize, from the beginning of judging stability criterion 1, if criterion 1 can not judge stable, then carry out criterion 2, if criterion 2 still can not judge stable, then carry out criterion 3, if criterion 3 still can not judge stable, need to reject unstable sample point, return described step (3).
The data come in real-time Transmission are plotted on described mean-standard deviation control figure and judge the Real-time Monitoring Data whether exception of the quality of power supply of described step (6) by another preferred a kind of quality of power supply method for early warning that the present invention provides；Judge that abnormal criterion is as follows:
1: one point of criterion falls beyond control line A district；
The average of this power quality index Monitoring Data is beyond control line, but is not above GB limit value, although illustrating that this index does not exceeds standard, but there are potential risks, at any time it may happen that excessive problem, it should provide early warning information, take precautions against in advance；
Criterion 2: continuous 9 points fall in centrage the same side；
The average of this power quality index Monitoring Data, at centrage homonymy, illustrates that this index distribution center there occurs skew the most up or down, needs to arouse attention, it should provide early warning information, analyzes skew reason according to pointer type and offset direction；
Criterion 3: continuous 6 increasing or decreasings；
This criterion illustrates that this index creates and becomes trend that is big or that diminish, there is super upper control line or the possibility of lower control line, it should provide early warning information, and according to pointer type and Change in Mean trend analysis reason；
Criterion 4: continuous 14 consecutive points are alternatively up and down；
There is periodic electrical energy power quality disturbance in the explanation of this criterion or the layering of this achievement data is inadequate, it should provides early warning information；
Criterion 5: have fall beyond the B district of centrage the same side at 2 in continuous 3；Described B district is that average is between μ+σ to μ+2 σ or between μ-2 σ to μ-σ；
This criterion illustrates that the average of this index there occurs change, it should provide early warning information；
Criterion 6: have fall beyond the C district of centrage the same side at 4 in continuous 5；Described C district is that average is between μ+2 σ to μ+3 σ or between μ-3 σ to μ-2 σ；
With criterion 5, illustrate that the average of this index there occurs change, it should provide early warning information；
Criterion 7: continuous 15 centrages in C district are upper and lower；
This criterion illustrates that the standard deviation of this index diminishes, and data distribution concentrates near standard value, does not meets the feature of normal distribution, it should provide early warning；
Criterion 8: continuous 8 in centrage both sides, but none is in C district；
This criterion explanation data hierarchy is inadequate, data exception, it should provide early warning.
Another preferred a kind of quality of power supply method for early warning that the present invention provides, the early warning information rank in described step (7) is divided into level Four, and rank the highest then existing problems or potential risk is the biggest, more should arouse attention；
Wherein, one-level early warning is:
When average figure meets abnormal criterion 4 or 7；Or
A point in standard deviation figure falls on when controlling to limit above；Or
Point when average figure meets abnormal criterion 4 or 7 and in standard deviation figure falls on when controlling to limit above；
Two grades of early warning are:
Average figure meet abnormal criterion 2,3,5,6 and 8 one of them time；Or
Average figure meet abnormal criterion 2,3,5,6 and 8 one of them time and standard deviation figure in a point fall on control limit above time；
Three grades of early warning are:
When average figure meets abnormal criterion 1；
Level Four early warning is:
When a point in average figure falls beyond standard limit.
With immediate prior art ratio, the present invention provides technical scheme to have following excellent effect
1, the method for the present invention has carried out statistical analysis to the Monitoring Data of stable-state index of power quality, it is achieved that excavate the degree of depth of power quality data and abnormality detection；
2, the method for the present invention can find power quality problem as early as possible, find potential quality of power supply hidden danger in advance；
3, the methods analyst Indexes Abnormality type of the present invention provide early warning information, in order to operations staff makes preventive measure in time；
4, the method for the present invention reduces the electrical energy power quality disturbance impact on power transmission network, thus improves power grid security, economic operation level；
5, the method for the present invention is to ensureing that electrical network reliability service has realistic meaning.
Accompanying drawing explanation
Fig. 1 is the method flow diagram of the present invention；
Fig. 2 is the standard deviation S control figure of the present invention；
Fig. 3 is the average of the present inventionControl figure；
Fig. 4 is the present invention'sControl figure judges abnormal criterion figure.
Detailed description of the invention
Below in conjunction with embodiment, the invention will be described in further detail.
Embodiment 1:
As Figure 1-4, the quality of power supply method for early warning that the invention of this example provides uses mean-standard deviation to control figureThe method being used in combination is to judge warning level.For selected monitoring point, its each quality of power supply warning index is analyzed, to judge that its running status is the most abnormal, the need of early warning and warning level.The flow process of this method for early warning is as shown in Figure 1.
(1) step 1: determine warning index.
In conjunction with electric energy quality monitoring data, facility information and electrical network parameter, from table 1, choose the power quality index of concern as early warning Con trolling index.When multiple indexs early warning occur simultaneously, then warning level is chosen by the most serious.
Form 1 quality of power supply stable state warning index
(2) step 2: judge whether to exceed standard.
This warning index and GB limit value are compared, exceedes limit value and then send level Four early warning, do not exceed standard, then the Monitoring Data of this index is carried out abnormality detection and trend analysis.
(3) step 3: preliminary date.
Prepare the quality of power supply historical data for warning index abnormality detection.As a example by voltage deviation, taking the voltage deviation data not exceeded standard monitored recently, data break is 3 minutes, is a sample group with 2 hours interior data, and often 40 points of group, take 30 groups of data.For different warning indexs, possible different (referring specifically to tables 1) are counted at the minimum data interval chosen with sample group, data granularity is as the criterion with actual monitoring granularity, sample group is counted and is represented the sampling number of each sample group selected when drawing control figure, for ensureing the accuracy calculated, sample data group at least takes 30 groups of up-to-date data monitored.
It should be noted that the sample data chosen must be stable, if there being several groups of data unstable, then need these data to reject, more again supplement stable data sample point.
(4) step 4: calculate S figure and control limit
Calculating average and the standard deviation of often group sample group data point, if total m group sample group, often group data are counted as n, the then average of i-th group of sample pointWith standard deviation SiComputing formula as follows:
Calculate the average of all sample groupsAnd standard deviationFormula is as follows:
Calculating the upper control limit (UCL) of standard deviation figure (S figure), centrage (CL), lower control limit (LCL), formula is as follows:
Wherein, coefficient formulas is as follows:
As n > 25 time,
Therefore, as n=40, B3≈ 0.6581, B4≈1.3419。
(5) step 5: draw S and control figure, it is judged that sample group data are the most stable
Draw Standard Deviation Charts, preliminary date is got ready in S figure, as shown in Figure 2.Judge that these sample points are the most stable, if steady, carry out step 6, if unstable, then, after removing the point of instability, return step 3.
Judge that stable criterion is as follows:
1) continuous 25 points, it is 0 that control line is out-of-bounds counted；
2) continuous 35 points, control line is out-of-bounds counted less than or equal to 1；
3) continuous 100 points, control line is out-of-bounds counted less than or equal to 2.
When sentencing steady, from the beginning of sentencing steady criterion 1, if can not sentence steady, then should carry out criterion 2, if still can not sentence steady, then carry out criterion 3, if still can not sentence steady, need to reject unstable sample point, return step 3.
(6) step 6: calculateFigure controls limit
Figure centrage, upper control limit and lower control limit computing formula are as follows:
Wherein, as n > 25 time,
(7) step 7: drawControl figure, it is judged that sample group data are the most stable
Drawing mean chart, as it is shown on figure 3, wherein, μ and σ is meansigma methods and the standard deviation of all sample group data in preliminary date.Preliminary date is existedFigure is got ready, it is judged that whether it meets is sentenced steady criterion, if stable, carries out step 8, if shakiness, returns step 3 after removing the point of instability.
(8) step 8: judge that Real-time Monitoring Data is the most abnormal
The control completing stable is usedAfter controlling figure, the statistical value of the data that real-time Transmission is come is drawn inControl on figure.As a example by voltage deviation, 3 minutes real time datas were calculated an average value mu and standard deviation sigma by every 2 hours, is drawn in respectivelyOn figure and S figure, judge that Monitoring Data is the most abnormal according to identifying indices.The criterion judging exception of figure is following (see accompanying drawing 4):
1: one point of criterion falls beyond A district.
The average of this power quality index Monitoring Data is beyond control line, but is not above GB limit value, although illustrating that this index does not exceeds standard, but there are potential risks, at any time it may happen that excessive problem, it should provide early warning information, take precautions against in advance.
Criterion 2: continuous 9 points fall in centrage the same side.
The average of this power quality index Monitoring Data, at centrage homonymy, illustrates that this index distribution center there occurs skew the most up or down, needs to arouse attention, it should provide early warning information, analyzes skew reason according to pointer type and offset direction.
Criterion 3: continuous 6 increasing or decreasings.
This criterion is that the trend for process average is designed, and illustrates that this index creates and becomes trend that is big or that diminish, there is super upper control line or the possibility of lower control line, it should provide early warning information, and according to pointer type and Change in Mean trend analysis reason.
Criterion 4: continuous 14 consecutive points are alternatively up and down.
Illustrate to there is periodic electrical energy power quality disturbance or the layering of this achievement data is inadequate, it should provide early warning information.
Criterion 5: have fall beyond the B district of centrage the same side at 2 in continuous 3.
Illustrate that the average of this index there occurs change, it should provide early warning information.
Criterion 6: have fall beyond the C district of centrage the same side at 4 in continuous 5.
With criterion 5, illustrate that the average of this index there occurs change, it should provide early warning information.
Criterion 7: continuous 15 upper and lower in C zone centerline.
Illustrate that the standard deviation of this index diminishes, data distribution concentrates near standard value, does not meets the feature of normal distribution, it may be possible to data hierarchy is inadequate or data are false causes, it should provide early warning.
Criterion 8: continuous 8 in centrage both sides, but none is in C district.
Illustrate that data hierarchy is inadequate, data exception, it should provide early warning.
(9) step 9: early warning
Exceeding standard according to Monitoring Data and the result of anomaly analysis, warning level can be divided into level Four, rank the highest then existing problems or potential risk is the biggest, more should arouse attention, can provide early warning information according to the Exception Type of Monitoring Indexes data.The judgement of warning level is as shown in table 2.
The judgement of form 2 warning level
In sum, this quality of power supply method for early warning has carried out degree of depth excavation to electric energy quality monitoring data, can analyze whether power quality index exceeds standard or abnormal in time, and data exception type, and the variation tendency and Exception Type according to statistics such as Monitoring Data average, standard deviations divides warning level, provide early warning information and the preliminary analysis of causes.
Finally should be noted that: above example is only in order to illustrate that technical scheme is not intended to limit; although those of ordinary skill in the field with reference to above-described embodiment it is understood that still the detailed description of the invention of the present invention can be modified or equivalent; these are without departing from any amendment of spirit and scope of the invention or equivalent, within the claims of the present invention all awaited the reply in application.
Claims (12)
1. a quality of power supply method for early warning, it is characterised in that: described method controls figure by average value standard deviation and realizes；Described control figure includes mean chart and Standard Deviation Charts；Said method comprising the steps of:
(1) warning index of the quality of power supply is determined；
(2) judge whether described warning index exceeds standard；
(3) the sample group of the historical data of the preparation quality of power supply；
(4) the control limit of mark average value standard deviation control figure is determined；
(5) stability of data described in figure judgment sample group is controlled by average value standard deviation；
(6) judge that the Real-time Monitoring Data of the quality of power supply is the most abnormal；
(7) judge warning level according to the Exception Type of described Monitoring Data and provide early warning information.
2. a kind of quality of power supply method for early warning as claimed in claim 1, it is characterised in that: determine that the power quality index of needs is as the warning index in described step (1) by electric energy quality monitoring data, facility information and electrical network parameter.
3. a kind of quality of power supply method for early warning as claimed in claim 2, it is characterised in that: when some indexs early warning occur simultaneously, then choose by the most serious warning level；
Described electric energy quality monitoring data include frequency, voltage, electric current, voltage deviation, voltage pulsation, harmonic wave, m-Acetyl chlorophosphonazo, flickering and three-phase imbalance.
Described facility information includes main transformer parameter；
Described electrical network parameter includes electric network composition, substation information, position, monitoring point and points of common connection minimum capacity of short circuit.
4. a kind of quality of power supply method for early warning as described in claim 1-3 any one, it is characterised in that: described warning index and GB limit value are compared, determines whether the warning index of described step (2) exceeds standard；If exceeding international limit value, send level Four early warning；If less than international limit value, then the Monitoring Data of this warning index being carried out abnormality detection and trend analysis.
5. a kind of quality of power supply method for early warning as claimed in claim 4, it is characterised in that: the sample group of described step (3) is by procedure below preparation:
It is as the criterion with the quality of power supply historical data not exceeded standard；Being that a sample group chooses n point with the data in the t time according to demand, each point is spaced apart 3 seconds～10 minutes；Described sample group is at least 30 groups.
6. a kind of quality of power supply method for early warning as claimed in claim 1, it is characterised in that: the control limit of the average value standard deviation control figure in described step (4) includes that mean chart controls limit and Standard Deviation Charts controls limit；
By determining that mean chart controls limit and Standard Deviation Charts controls limit respectively, judge that described sample group data are the most stable according to sentencing steady criterion.
7. a kind of quality of power supply method for early warning as claimed in claim 6, it is characterised in that: described Standard Deviation Charts controls limit and includes upper control limit UCL, centrage CL and lower control limit LCL；Described upper control limit UCL, centrage CL and lower control limit LCL are determined by following formula respectively:
CLS=S
Wherein, if total m group sample group, often group data are counted as n, the standard deviation of all sample groupsAs n > 25 time,
As n=40, B3≈ 0.6581, B4≈1.3419。
8. a kind of quality of power supply method for early warning as claimed in claim 7, it is characterized in that: draw described Standard Deviation Charts and judgment sample point is the most stable, if steady, carry out average figure control, if unstable, after then removing the point of instability, return described step (3):
Judge that stable criterion is as follows:
1) continuous 25 points, it is 0 that control line is out-of-bounds counted；
2) continuous 35 points, control line is out-of-bounds counted less than or equal to 1；
3) continuous 100 points, control line is out-of-bounds counted less than or equal to 2；
When judging to stablize, from the beginning of judging stability criterion 1, if criterion 1 can not judge stable, then carry out criterion 2, if criterion 2 still can not judge stable, then carry out criterion 3, if criterion 3 still can not judge stable, need to reject unstable sample point, return described step (3).
9. a kind of quality of power supply method for early warning as described in claim 6-8 any one, it is characterised in that: described mean chart controls limit and includes upper control limit UCL, centrage CL and lower control limit LCL；Described mean chart is controlled upper control limit UCL, centrage CL and the lower control limit LCL of limit and is determined by following formula respectively:
Wherein, if total m group sample group, often group data are counted as n, the average of all sample groupsAs n > 25 time,
10. a kind of quality of power supply method for early warning as claimed in claim 1, it is characterized in that: draw described mean chart and judgment sample point is the most stable, if stable, carry out described step (6), if shakiness, after removing the point of instability, return described step (3)；
Judge that stable criterion is as follows:
1) continuous 25 points, it is 0 that control line is out-of-bounds counted；
2) continuous 35 points, control line is out-of-bounds counted less than or equal to 1；
3) continuous 100 points, control line is out-of-bounds counted less than or equal to 2；
When judging to stablize, from the beginning of judging stability criterion 1, if criterion 1 can not judge stable, then carry out criterion 2, if criterion 2 still can not judge stable, then carry out criterion 3, if criterion 3 still can not judge stable, need to reject unstable sample point, return described step (3).
11. quality of power supply method for early warning as claimed in claim 1 a kind of, it is characterised in that: the data come in real-time Transmission are plotted on described average value standard deviation control figure and judge the Real-time Monitoring Data whether exception of the quality of power supply of described step (6)；Judge that abnormal criterion is as follows:
1: one point of criterion falls beyond control line A district；
The average of this power quality index Monitoring Data is beyond control line, but is not above GB limit value, although illustrating that this index does not exceeds standard, but there are potential risks, at any time it may happen that excessive problem, it should provide early warning information, take precautions against in advance；
Criterion 2: continuous 9 points fall in centrage the same side；
The average of this power quality index Monitoring Data, at centrage homonymy, illustrates that this index distribution center there occurs skew the most up or down, needs to arouse attention, it should provide early warning information, analyzes skew reason according to pointer type and offset direction；
Criterion 3: continuous 6 increasing or decreasings；
This criterion illustrates that this index creates and becomes trend that is big or that diminish, there is super upper control line or the possibility of lower control line, it should provide early warning information, and according to pointer type and Change in Mean trend analysis reason；
Criterion 4: continuous 14 consecutive points are alternatively up and down；
There is periodic electrical energy power quality disturbance in the explanation of this criterion or the layering of this achievement data is inadequate, it should provides early warning information；
Criterion 5: have fall beyond the B district of centrage the same side at 2 in continuous 3；Described B district is that average is between μ+σ to μ+2 σ or between μ-2 σ to μ-σ；
This criterion illustrates that the average of this index there occurs change, it should provide early warning information；
Criterion 6: have fall beyond the C district of centrage the same side at 4 in continuous 5；Described C district is that average is between μ+2 σ to μ+3 σ or between μ-3 σ to μ-2 σ；
With criterion 5, illustrate that the average of this index there occurs change, it should provide early warning information；
Criterion 7: continuous 15 centrages in C district are upper and lower；
This criterion illustrates that the standard deviation of this index diminishes, and data distribution concentrates near standard value, does not meets the feature of normal distribution, it should provide early warning；
Criterion 8: continuous 8 in centrage both sides, but none is in C district；
This criterion explanation data hierarchy is inadequate, data exception, it should provide early warning.
12. a kind of quality of power supply method for early warning as claimed in claim 11, it is characterised in that: the early warning information rank in described step (7) is divided into level Four, and rank the highest then existing problems or potential risk is the biggest, more should arouse attention；
Wherein, one-level early warning is:
When average figure meets abnormal criterion 4 or 7；Or
A point in standard deviation figure falls on when controlling to limit above；Or
Point when average figure meets abnormal criterion 4 or 7 and in standard deviation figure falls on when controlling to limit above；
Two grades of early warning are:
Average figure meet abnormal criterion 2,3,5,6 and 8 one of them time；Or
Average figure meet abnormal criterion 2,3,5,6 and 8 one of them time and standard deviation figure in a point fall on control limit above time；
Three grades of early warning are:
When average figure meets abnormal criterion 1；
Level Four early warning is:
When a point in average figure falls beyond standard limit.
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| CN108053095A (en) * | 2017-11-22 | 2018-05-18 | 全球能源互联网研究院有限公司 | A kind of electrical energy power quality disturbance affair character extracting method and system |
| CN108549754A (en) * | 2018-03-29 | 2018-09-18 | 中交隧道局电气化工程有限公司 | Tramcar system energy consumption analysis method based on control figure |
| CN109613324A (en) * | 2018-11-07 | 2019-04-12 | 全球能源互联网研究院有限公司 | A kind of detection method and device of Harmonics amplification |
| CN110906171A (en) * | 2019-10-29 | 2020-03-24 | 中国石油化工股份有限公司 | Pipeline heat preservation condition monitoring and hierarchical control method based on dynamic envelope curve method |
| CN111932102A (en) * | 2020-08-03 | 2020-11-13 | 南京航空航天大学 | Civil aircraft system risk early warning method based on dynamic control chart |
| CN112083279A (en) * | 2020-08-26 | 2020-12-15 | 深圳供电局有限公司 | Electric power automatic distributed information acquisition system and method |
| CN113627699A (en) * | 2020-05-07 | 2021-11-09 | 中国移动通信集团山西有限公司 | Information early warning method, device, equipment and storage medium |
| CN113807211A (en) * | 2021-08-31 | 2021-12-17 | 武汉理工大学 | Equipment operation state early warning method, computer equipment and storage medium |
| CN115684717A (en) * | 2022-08-26 | 2023-02-03 | 国网浙江省电力有限公司海盐县供电公司 | High-voltage line harmonic detection device and method based on electric field coupling |
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| CN108053095A (en) * | 2017-11-22 | 2018-05-18 | 全球能源互联网研究院有限公司 | A kind of electrical energy power quality disturbance affair character extracting method and system |
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| CN108549754A (en) * | 2018-03-29 | 2018-09-18 | 中交隧道局电气化工程有限公司 | Tramcar system energy consumption analysis method based on control figure |
| CN109613324A (en) * | 2018-11-07 | 2019-04-12 | 全球能源互联网研究院有限公司 | A kind of detection method and device of Harmonics amplification |
| CN109613324B (en) * | 2018-11-07 | 2021-10-08 | 全球能源互联网研究院有限公司 | Harmonic amplification detection method and device |
| CN110906171B (en) * | 2019-10-29 | 2021-08-10 | 中国石油化工股份有限公司 | Pipeline heat preservation condition monitoring and hierarchical control method based on dynamic envelope curve method |
| CN110906171A (en) * | 2019-10-29 | 2020-03-24 | 中国石油化工股份有限公司 | Pipeline heat preservation condition monitoring and hierarchical control method based on dynamic envelope curve method |
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| CN111932102A (en) * | 2020-08-03 | 2020-11-13 | 南京航空航天大学 | Civil aircraft system risk early warning method based on dynamic control chart |
| CN111932102B (en) * | 2020-08-03 | 2023-10-31 | 南京航空航天大学 | Civil aircraft system risk early warning method based on dynamic control diagram |
| CN112083279A (en) * | 2020-08-26 | 2020-12-15 | 深圳供电局有限公司 | Electric power automatic distributed information acquisition system and method |
| CN113807211A (en) * | 2021-08-31 | 2021-12-17 | 武汉理工大学 | Equipment operation state early warning method, computer equipment and storage medium |
| CN115684717A (en) * | 2022-08-26 | 2023-02-03 | 国网浙江省电力有限公司海盐县供电公司 | High-voltage line harmonic detection device and method based on electric field coupling |
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