CN102609776A - Method for identifying power supply index data - Google Patents
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- CN102609776A CN102609776A CN2012100238985A CN201210023898A CN102609776A CN 102609776 A CN102609776 A CN 102609776A CN 2012100238985 A CN2012100238985 A CN 2012100238985A CN 201210023898 A CN201210023898 A CN 201210023898A CN 102609776 A CN102609776 A CN 102609776A
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Abstract
The invention relates to a method for identifying power supply index data, which comprises the following steps: step 1, carrying out logic relation identification among indexes on reliability index data of a power supply enterprise; step 2, carrying out longitudinal historical data identification on the reliability index data of the power supply enterprise; and step 3, carrying out lateral dispersion data identification on the reliability index data of the power supply enterprise. The method provided by the invention has the beneficial effects of finishing preprocessing the reliability index data of the power supply enterprise, systematically identifying bad data of the reliability index, guaranteeing the accuracy of the reliability index data and being reliable and rapid.
Description
Technical field
The present invention relates to a kind of discrimination method of the achievement data of supplying power.
Background technology
For promoting power supply enterprise to improve user's power supply reliability management level, improve the power supply service ability, promotion enterprise tries to be the first and provides quality goods or brilliant, and to ensure user's power supply quality, supervision department will estimate the reliability index of power supply enterprise.Owing to power supply enterprise is responsible for reliability index typing personnel's change and artificially packs reasons such as achievement data in order to obtain better evaluation, the achievement data that power supply enterprise submits is accurate not to the utmost.Therefore, be to guarantee the accuracy of reliability index data, the reliability index data that power supply enterprise is submitted are carried out identification and are very important.The present invention is intended to provide a kind of data identification method that is used to study reliability index, makes every effort to detect the bad data in the reliability index, guarantees the accuracy of reliability index data.
Summary of the invention
The discrimination method that the purpose of this invention is to provide a kind of achievement data of supplying power overcomes the deficiency of above-mentioned aspect in the existing product.
The objective of the invention is to realize through following technical scheme:
A kind of discrimination method of the achievement data of supplying power may further comprise the steps:
Step 1: the reliability index data of power supply enterprise are carried out logical relation identification between index, if the logical relation identification is not passed through between index, then there is bad data in prompting, and whole identification process finishes; If the logical relation identification is passed through between index, then go to step 2;
The reliability index data of said power supply enterprise specifically comprise the average power off time AIHC of user, the average frequency of power cut AITC of user, total number of users, fault outage average duration MID
F, in advance arrange the average duration MID that has a power failure
S, the average frequency of power cut AITCI of customer interrupted, fault outage average user number MIC
FWith preparatory arrangement power failure average user number MIC
S
Step 1.1): the physical fault frequency of power cut that calculates power supply enterprise according to formula:
Physical fault frequency of power cut=total number of users * AFTC/MIC
F
Wherein, AFTC is user's mean failure rate frequency of power cut, AFTC and MIC
FCalculate according to formula:
Step 1.2): according to the minimum number of stoppages of formula computational scheme:
The minimum number of stoppages of the circuit=minimum probability of malfunction of the total line length * cable rate * cable line+minimum probability of malfunction of total line length * (1-cable rate) * overhead transmission line
Wherein, The cable rate belongs to the basic parameter of power supply enterprise's electrical network; The minimum probability of malfunction of cable line and the minimum probability of malfunction of overhead transmission line then from national numerous enterprises cable line and overhead transmission line minimum probability of malfunction according to normal distribution; Get probability greater than 10% numerical value, with the small probability event of forgoing;
Step 1.3): the physical fault frequency of power cut and the minimum number of stoppages of circuit are compared; If the physical fault frequency of power cut is greater than the minimum number of stoppages of circuit; Then go to step 1.4, if the physical fault frequency of power cut judges then that less than the minimum number of stoppages of circuit these data are bad data; And there is bad data in prompting, whole identification process end;
Step 1.4): calculate the average frequency of power cut AITCI of customer interrupted according to formula:
Wherein, k% accounts for total user's ratio for the customer interrupted sum, and less than 1, ASTC is the average frequency of power cut of arranging in advance of user;
Step 1.5): AITCI and AFTC+ASTC sum are compared, if AITCI >=AFTC+ASTC then goes to step 1.6, if AITCI＜AFTC+ASTC judges that then these data are bad data, and there is bad data in prompting, whole identification process end;
Step 1.6): calculate fault outage average user number MIC according to formula
F:
Step 1.7): with MIC
FN compares with the feeder line average user number, if MIC
F>=60%*N then goes to step 1.6, if MIC
F＜60%*N judges that then these data are bad data, and there is bad data in prompting, whole identification process end.
Step 2: the reliability index data of power supply enterprise are carried out vertical historical data identification; If vertically the historical data identification is not passed through; Then there is bad data in prompting, is given information by power supply enterprise the reliability index data of power supply enterprise are proved, if power supply enterprise does not give information or the data that provided can not be proved the reliability index data of power supply enterprise; Then there is bad data in prompting, and whole identification process finishes; If vertically the historical data identification through or data that power supply enterprise provided can prove the reliability index data of power supply enterprise, then go to step 3;
Step 2.1): user's mean failure rate frequency of power cut AFTC, the average frequency of power cut AITCI of customer interrupted and the average customer interrupted of fault of power supply enterprise are counted MIC
FCarry out vertical historical data identification: the rate of change of more continuous 2 years above-mentioned data; If the amplitude that reduces surpasses 60%; And power supply enterprise does not give information or the data that provided can not prove the above-mentioned data of power supply enterprise the time, judge that then above-mentioned data are bad data, and there is bad data in prompting; Whole identification process finishes, otherwise goes to step 2.2;
Step 2.2 :) total number of users of power supply enterprise is carried out vertical historical data identification: under the constant situation of geographic coverage; The rate of change of more continuous 2 years total numbers of users, if total number of users rate of growth greater than 20% or less than-10%, and power supply enterprise does not give information or the data that provided can not prove total number of users of power supply enterprise the time; Then be judged as bad data; And there is bad data in prompting, and whole identification process finishes, otherwise goes to step 3.
Step 3: the reliability index data of power supply enterprise are carried out horizontal dispersion data identification; If laterally the dispersion data identification does not pass through; Then there is bad data in prompting, is given information by power supply enterprise the reliability index data of power supply enterprise are proved, if power supply enterprise does not give information or the data that provided can not be proved the reliability index data of power supply enterprise; Then there is bad data in prompting, and whole identification process finishes; If laterally the dispersion data identification through or data that power supply enterprise provided can prove the reliability index data of power supply enterprise, then reminder-data is all qualified, detailed process is:
Calculate average frequency of power cut ASTC, the average customer interrupted of fault arranged in advance of user's mean failure rate frequency of power cut AFTC, user of power supply enterprise according to formula and count MID
FCount MID with the average customer interrupted of preparatory arrangement
SThe ratio of deviation average dispersion:
Wherein, X
iBe this business indicators,
Be whole business indicators mean values; If the ratio of above-mentioned data＜-0.6; And power supply enterprise does not give information or the data that provided can not prove the above-mentioned data of power supply enterprise the time, judge that then above-mentioned data are bad data, and there is bad data in prompting; Whole identification process finishes, otherwise reminder-data is all qualified.
Beneficial effect of the present invention is: accomplishes pre-service, systematically the identification of reliability index bad data come out, guaranteed the accuracy of reliability index data power supply enterprise's reliability index data, and reliable, rapid.
Description of drawings
According to accompanying drawing the present invention is done further explain below.
Fig. 1 is the process flow diagram of the discrimination method of the described power supply achievement data of the embodiment of the invention.
Embodiment
As shown in Figure 1, the discrimination method of the described a kind of achievement data of supplying power of the embodiment of the invention may further comprise the steps:
Step 1: the reliability index data of power supply enterprise are carried out logical relation identification between index, if the logical relation identification is not passed through between index, then there is bad data in prompting, and whole identification process finishes; If the logical relation identification is passed through between index, then go to step 2;
Wherein, the reliability index data of said power supply enterprise specifically comprise the average power off time AIHC of user, the average frequency of power cut AITC of user, total number of users, fault outage average duration MID
F, in advance arrange the average duration MID that has a power failure
S, the average frequency of power cut AITCI of customer interrupted, fault outage average user number MIC
FWith preparatory arrangement power failure average user number MIC
S
Step 1.1): the physical fault frequency of power cut that calculates power supply enterprise according to formula:
Physical fault frequency of power cut=total number of users * AFTC/MIC
F
Wherein, AFTC is user's mean failure rate frequency of power cut, and AFTC reflects rate of breakdown, and is relevant with the equipment integral general level of the health, is the stack of various kinds of equipment failure rate.AFTC and MIC
FCalculate according to formula:
Step 1.2): according to the minimum number of stoppages of formula computational scheme:
The minimum number of stoppages of the circuit=minimum probability of malfunction of the total line length * cable rate * cable line+minimum probability of malfunction of total line length * (1-cable rate) * overhead transmission line
Wherein, The cable rate belongs to the basic parameter of power supply enterprise's electrical network; The minimum probability of malfunction of cable line and the minimum probability of malfunction of overhead transmission line then from national numerous enterprises cable line and overhead transmission line minimum probability of malfunction according to normal distribution; Get probability greater than 10% numerical value, with the small probability event of forgoing.Physical fault frequency of power cut in the step 1.1 is the comprehensive of multiple element fault number of times such as circuit, switchgear, bus, user; The calculating of the minimum number of stoppages of circuit is only to count line fault; Do not consider probabilities of equipment failure such as switch, bus and user; Therefore, said physical fault frequency of power cut should be greater than the minimum number of stoppages of circuit;
Step 1.3): the physical fault frequency of power cut and the minimum number of stoppages of circuit are compared; If the physical fault frequency of power cut is greater than the minimum number of stoppages of circuit; Then go to step 1.4, if the physical fault frequency of power cut judges then that less than the minimum number of stoppages of circuit these data are bad data; And there is bad data in prompting, whole identification process end;
Step 1.4): calculate the average frequency of power cut AITCI of customer interrupted according to formula:
Wherein, k% accounts for total user's ratio for the customer interrupted sum, and less than 1, ASTC is the average frequency of power cut of arranging in advance of user;
Step 1.5): AITCI and AFTC+ASTC sum are compared, if AITCI >=AFTC+ASTC then goes to step 1.6, if AITCI＜AFTC+ASTC judges that then these data are bad data, and there is bad data in prompting, whole identification process end;
Step 1.6): calculate fault outage average user number MIC according to formula
F:
Step 1.7):, can find out MIC through equality abbreviation in the step 1.6
FN is identical with the feeder line average user number, but when following three kinds of situation occurring: 1, when line switching installing differential protection, within the specific limits with fault isolation; 2, user side is equipped with fault isolation device; 3, fault has more the sparse zone of present circuit average user, MIC
FMight but belonging to, these three kinds of situation implement behavior or small probability event among a small circle less than the N value, therefore with MIC
FWhen comparing, if MIC with feeder line average user number N
F>=60%*N then goes to step 1.6, if MIC
F＜60%*N judges that then these data are bad data, and there is bad data in prompting, whole identification process end.
Step 2: the reliability index data of power supply enterprise are carried out vertical historical data identification; If vertically the historical data identification is not passed through; Then there is bad data in prompting, is given information by power supply enterprise the reliability index data of power supply enterprise are proved, if power supply enterprise does not give information or the data that provided can not be proved the reliability index data of power supply enterprise; Then there is bad data in prompting, and whole identification process finishes; If vertically the historical data identification through or data that power supply enterprise provided can prove the reliability index data of power supply enterprise, then go to step 3;
Step 2.1): user's mean failure rate frequency of power cut AFTC, the average frequency of power cut AITCI of customer interrupted and the average customer interrupted of fault of power supply enterprise are counted MIC
FCarry out vertical historical data identification: the rate of change of more continuous 2 years above-mentioned data; If the amplitude that reduces surpasses 60%; And power supply enterprise does not give information or the data that provided can not prove the above-mentioned data of power supply enterprise the time, judge that then above-mentioned data are bad data, and there is bad data in prompting; Whole identification process finishes, otherwise goes to step 2.2;
Because electric network fault is relevant with the equipment integral general level of the health, the variation of great-leap-forward should not appear in the index of correlation of corresponding electric network fault in a short time, so the rate of change of more continuous 2 years above-mentioned data in the step 2.1.
Step 2.2 :) total number of users of power supply enterprise is carried out vertical historical data identification: under the constant situation of geographic coverage; The rate of change of more continuous 2 years total numbers of users, if total number of users rate of growth greater than 20% or less than-10%, and power supply enterprise does not give information or the data that provided can not prove total number of users of power supply enterprise the time; Then be judged as bad data; And there is bad data in prompting, and whole identification process finishes, otherwise goes to step 3;
Said total number of users is the composite target of reflection power supply enterprise power supply scale; Total number of users rate of growth is relevant with this area's load growth rate; And the load growth rate is relevant with regional GDP growth rate, and prefecture-level city GDP is generally less than 20%, therefore; Under the constant situation of geographic coverage, selected total number of users rate of growth is for being not more than 20%; In general, total number of users should show a rising trend, and perhaps more by a small margin minimizing does not have the minimizing of large-scale consumer quantity, and therefore, under the constant situation of geographic coverage, selected total number of users rate of growth is for being not less than-10%.
Step 3: the reliability index data of power supply enterprise are carried out horizontal dispersion data identification; If laterally the dispersion data identification does not pass through; Then there is bad data in prompting, is given information by power supply enterprise the reliability index data of power supply enterprise are proved, if power supply enterprise does not give information or the data that provided can not be proved the reliability index data of power supply enterprise; Then there is bad data in prompting, and whole identification process finishes; If laterally the dispersion data identification through or data that power supply enterprise provided can prove the reliability index data of power supply enterprise, then reminder-data is all qualified, detailed process is:
Calculate average frequency of power cut ASTC, the average customer interrupted of fault arranged in advance of user's mean failure rate frequency of power cut AFTC, user of power supply enterprise according to formula and count MID
FCount MID with the average customer interrupted of preparatory arrangement
SThe ratio of deviation average dispersion:
Wherein, X
iBe this business indicators,
Be whole business indicators mean values; If the ratio of above-mentioned data＜-0.6; And power supply enterprise does not give information or the data that provided can not prove the above-mentioned data of power supply enterprise the time, judge that then above-mentioned data are bad data, and there is bad data in prompting; Whole identification process finishes, otherwise reminder-data is all qualified.
The present invention is not limited to above-mentioned preferred forms; Anyone can draw other various forms of products under enlightenment of the present invention; No matter but on its shape or structure, do any variation; Every have identical with a application or akin technical scheme, all drops within protection scope of the present invention.
Claims (5)
1. the discrimination method of the achievement data of supplying power is characterized in that, may further comprise the steps:
Step 1: the reliability index data of power supply enterprise are carried out logical relation identification between index, if the logical relation identification is not passed through between index, then there is bad data in prompting, and whole identification process finishes; If the logical relation identification is passed through between index, then go to step 2;
Step 2: the reliability index data of power supply enterprise are carried out vertical historical data identification; If vertically the historical data identification is not passed through; Then there is bad data in prompting, is given information by power supply enterprise the reliability index data of power supply enterprise are proved, if power supply enterprise does not give information or the data that provided can not be proved the reliability index data of power supply enterprise; Then there is bad data in prompting, and whole identification process finishes; If vertically the historical data identification through or data that power supply enterprise provided can prove the reliability index data of power supply enterprise, then go to step 3; And
Step 3: the reliability index data of power supply enterprise are carried out horizontal dispersion data identification; If laterally the dispersion data identification does not pass through; Then there is bad data in prompting, is given information by power supply enterprise the reliability index data of power supply enterprise are proved, if power supply enterprise does not give information or the data that provided can not be proved the reliability index data of power supply enterprise; Then there is bad data in prompting, and whole identification process finishes; If laterally the dispersion data identification through or data that power supply enterprise provided can prove the reliability index data of power supply enterprise, then reminder-data is all qualified.
2. the discrimination method of power supply achievement data according to claim 1 is characterized in that: the reliability index data of said power supply enterprise specifically comprise the average power off time AIHC of user, the average frequency of power cut AITC of user, total number of users, fault outage average duration MID
F, in advance arrange the average duration MID that has a power failure
S, the average frequency of power cut AITCI of customer interrupted, fault outage average user number MIC
FWith preparatory arrangement power failure average user number MIC
S
3. the discrimination method of power supply achievement data according to claim 2 is characterized in that, said step 1 further comprises:
Step 1.1): the physical fault frequency of power cut that calculates power supply enterprise according to formula:
Physical fault frequency of power cut=total number of users * AFTC/MIC
F
Wherein, AFTC is user's mean failure rate frequency of power cut, AFTC and MIC
FCalculate according to formula:
Step 1.2): according to the minimum number of stoppages of formula computational scheme:
The minimum number of stoppages of the circuit=minimum probability of malfunction of the total line length * cable rate * cable line+minimum probability of malfunction of total line length * (1-cable rate) * overhead transmission line
Wherein, The cable rate belongs to the basic parameter of power supply enterprise's electrical network; The minimum probability of malfunction of cable line and the minimum probability of malfunction of overhead transmission line then from national numerous enterprises cable line and overhead transmission line minimum probability of malfunction according to normal distribution; Get probability greater than 10% numerical value, with the small probability event of forgoing;
Step 1.3): the physical fault frequency of power cut and the minimum number of stoppages of circuit are compared; If the physical fault frequency of power cut is greater than the minimum number of stoppages of circuit; Then go to step 1.4, if the physical fault frequency of power cut judges then that less than the minimum number of stoppages of circuit these data are bad data; And there is bad data in prompting, whole identification process end;
Step 1.4): calculate the average frequency of power cut AITCI of customer interrupted according to formula:
Wherein, k% accounts for total user's ratio for the customer interrupted sum, and less than 1, ASTC is the average frequency of power cut of arranging in advance of user;
Step 1.5): AITCI and AFTC+ASTC sum are compared, if AITCI >=AFTC+ASTC then goes to step 1.6, if AITCI＜AFTC+ASTC judges that then these data are bad data, and there is bad data in prompting, whole identification process end;
Step 1.6): calculate fault outage average user number MIC according to formula
F:
Step 1.7): with MIC
FN compares with the feeder line average user number, if MIC
F>=60%*N then goes to step 1.6, if MIC
F＜60%*N judges that then these data are bad data, and there is bad data in prompting, whole identification process end.
4. the discrimination method of power supply achievement data according to claim 3 is characterized in that, said step 2 further comprises:
Step 2.1): user's mean failure rate frequency of power cut AFTC, the average frequency of power cut AITCI of customer interrupted and the average customer interrupted of fault of power supply enterprise are counted MIC
FCarry out vertical historical data identification: the rate of change of more continuous 2 years above-mentioned data; If the amplitude that reduces surpasses 60%; And power supply enterprise does not give information or the data that provided can not prove the above-mentioned data of power supply enterprise the time, judge that then above-mentioned data are bad data, and there is bad data in prompting; Whole identification process finishes, otherwise goes to step 2.2;
Step 2.2 :) total number of users of power supply enterprise is carried out vertical historical data identification: under the constant situation of geographic coverage; The rate of change of more continuous 2 years total numbers of users, if total number of users rate of growth greater than 20% or less than-10%, and power supply enterprise does not give information or the data that provided can not prove total number of users of power supply enterprise the time; Then be judged as bad data; And there is bad data in prompting, and whole identification process finishes, otherwise goes to step 3.
5. the discrimination method of power supply achievement data according to claim 4; It is characterized in that in the said step 3: average frequency of power cut ASTC, the average customer interrupted of fault arranged in advance of user's mean failure rate frequency of power cut AFTC, user that calculates power supply enterprise according to formula counted MID
FCount MID with the average customer interrupted of preparatory arrangement
SThe ratio of deviation average dispersion:
Wherein, X
iBe this business indicators,
Be whole business indicators mean values; If the ratio of above-mentioned data＜-0.6; And power supply enterprise does not give information or the data that provided can not prove the above-mentioned data of power supply enterprise the time, judge that then above-mentioned data are bad data, and there is bad data in prompting; Whole identification process finishes, otherwise reminder-data is all qualified.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104090560B (en) * | 2014-05-06 | 2017-02-08 | 内蒙古云谷电力科技股份有限公司 | Device monitoring power supply integrated environment evaluation indexes |
| CN113553348A (en) * | 2021-06-24 | 2021-10-26 | 国网山东省电力公司济宁市任城区供电公司 | Power supply system data comprehensive management method and system |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102013085A (en) * | 2010-12-14 | 2011-04-13 | 天津市电力公司 | Evaluation method for distribution network reliability |
| CN102195341A (en) * | 2010-03-08 | 2011-09-21 | 国家电网公司 | Device for evaluating, analyzing and processing reliability of transmission system |
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2012
- 2012-02-03 CN CN2012100238985A patent/CN102609776A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102195341A (en) * | 2010-03-08 | 2011-09-21 | 国家电网公司 | Device for evaluating, analyzing and processing reliability of transmission system |
| CN102013085A (en) * | 2010-12-14 | 2011-04-13 | 天津市电力公司 | Evaluation method for distribution network reliability |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104090560B (en) * | 2014-05-06 | 2017-02-08 | 内蒙古云谷电力科技股份有限公司 | Device monitoring power supply integrated environment evaluation indexes |
| CN113553348A (en) * | 2021-06-24 | 2021-10-26 | 国网山东省电力公司济宁市任城区供电公司 | Power supply system data comprehensive management method and system |
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Application publication date: 20120725 |