CN102928655A - Method for detecting power abnormal data - Google Patents
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Abstract
The embodiment of the application discloses a method for detecting power abnormal data. The method comprises the following steps of: acquiring real-time power data, wherein the power data carries pre-distributed identifying markers; determining the category of the power data according to the identifying markers; detecting the power data according to a detection rule corresponding to the category; and ensuring that the power data are normal when the power data meet a preset detection condition, otherwise, the power data is abnormal. According to an abnormal data detection method provided by the embodiment of the application, the detected real-time power data are classified, different categories and different applied rules are detected to realize on-line rapid detection of massive power data, so that the detection efficiency of the real-time power data is improved, and the reliability of the real-time power data is guaranteed.
Description
Technical field
The present invention relates to the electric power data processing technology field, more particularly, relate to a kind of electricity exception data detection method.
Background technology
Along with electric network information " SG-ERP engineering " is carried out in a deep going way and the intelligent deployment enforcement of planning of electrical network " 12 ", magnanimity real-time data base data, services ability intelligent optimization has been researched and proposed higher requirement.But, in the collection storing process of existing electric power data, inevitably abnormal data can appear, therefore, how the magnanimity electric power data is detected online, guarantee that the real-time electric power the reliability of the data becomes problem demanding prompt solution.
Summary of the invention
In view of this, the application provides a kind of electricity exception data detection method, so that electric power data is detected online, guarantees the real-time electric power the reliability of the data.
For achieving the above object, the invention provides following technical scheme:
A kind of electricity exception data detection method comprises:
Obtain the real-time electric power data, described electric power data carries pre-assigned identification marking;
Determine the classification of described electric power data according to described identification marking;
According to the detection rule corresponding with described classification described electric power data is detected, when described electric power data satisfied default testing conditions, described electric power data was normal, otherwise described electric power data is unusual.
Said method, preferred, the classification of described electric power data comprises:
The voltage class, capacity reactance class, load class, main transformer class and circuit class.
Said method, preferred, the foundation detection rule corresponding with described classification detects described electric power data and comprises:
Voltage class data: judge whether described voltage class data satisfy the first default span, if so, then described voltage class data are normal, otherwise, described voltage class data exception;
Capacity reactance class data: judge whether described capacity reactance class data satisfy the default span of second under the current state, and if so, described capacity reactance class data are normal, otherwise, described capacity reactance class data exception;
Load class data: calculate the similarity distance between described load class data and the scale load value, described similarity distance and predetermined threshold value are compared, and when described similarity distance during greater than described predetermined threshold value, described load class data are normal, otherwise, described load class data exception;
Main transformer class data: described main transformer class data comprise meritorious data, idle data, current data and voltage data; Calculate the theoretical value P of described meritorious data according to following the first relational equation
*,
Circuit class data: described circuit class data comprise head end data and terminal data, described head end data comprise head end gain merit data, the idle data of head end, head end current data and head end voltage data, and described terminal data comprise terminal meritorious data, terminal idle data, end current data and terminal voltage data; Calculate the theoretical value of the meritorious data of described head end according to described the first relational expression
Theoretical value when the meritorious data of described head end
Satisfy second when pre-conditioned, described head end data are normal, otherwise, described head end data exception; Calculate the theoretical value of the meritorious data of described end according to described the first relational expression
Theoretical value when the meritorious data of described end
Satisfy the 3rd when pre-conditioned, described terminal data are normal; Otherwise, described terminal data exception.
Said method, preferred, calculate similarity distance between described load class data and the scale load value according to following formula:
Wherein, d is the similarity distance between described load class data and the scale load value, x
NkBe n days k load class data constantly,
Mean value for front τ days k data constantly.
Said method, preferred, when detecting electric power data when unusual, described electric power data is revised, comprising:
When detecting described voltage class data exception, be electric pressure with described voltage class data correction;
When detecting described capacity reactance class data exception, be 0 with described capacity reactance class data correction;
When detecting described load class data exception, with described load class data correction be
By above scheme as can be known, a kind of abnormal deviation data examination method that the application provides, real-time electric power data to be detected are classified, and to different classifications, using different rules detects it, the on-line quick detection to the magnanimity electric power data that realizes, the detection efficiency of the real-time electric power data that improved has guaranteed the real-time electric power the reliability of the data.
Description of drawings
In order to be illustrated more clearly in the embodiment of the invention or technical scheme of the prior art, the below will do to introduce simply to the accompanying drawing of required use in embodiment or the description of the Prior Art, apparently, accompanying drawing in the following describes only is some embodiments of the present invention, for those of ordinary skills, under the prerequisite of not paying creative work, can also obtain according to these accompanying drawings other accompanying drawing.
The process flow diagram of a kind of electricity exception data detection method that Fig. 1 provides for the embodiment of the present application.
Embodiment
Below in conjunction with the accompanying drawing in the embodiment of the invention, the technical scheme in the embodiment of the invention is clearly and completely described, obviously, described embodiment only is the present invention's part embodiment, rather than whole embodiment.Based on the embodiment among the present invention, those of ordinary skills belong to the scope of protection of the invention not making the every other embodiment that obtains under the creative work prerequisite.
The process flow diagram of the disclosed a kind of electricity exception data detection method of the embodiment of the present application comprises as shown in Figure 1:
Step S101: obtain the real-time electric power data, described electric power data carries pre-assigned identification marking;
In the present embodiment, in advance for electric power data has distributed identification marking, be used for electric power data is classified; In the present embodiment, electric power data is divided into voltage class data, capacity reactance class data, load class data, main transformer class data and circuit class data.For example, can identify electric power class data take following method: BusBarSection is voltage class number, and Compensator is that capacity reactance class data, SynchronousMachine position main transformer class data, LineSegment are circuit class data, Load for load class data etc.; Certainly, be not limited to above-mentioned identification method, as long as can will identify the classification of above-mentioned electric power data.
Step S102: the classification of determining described electric power data according to described identification marking;
Step S103: according to the detection rule corresponding with described classification described electric power data is detected, when described electric power data satisfied default testing conditions, described electric power data was normal, otherwise described electric power data is unusual.In the present embodiment, for each class electric power data, use the ad hoc rules corresponding with it it is detected.
Preferably, for voltage class data, judge whether described voltage class data satisfy the first default span, if so, then described voltage class data are normal, otherwise, described voltage class data exception; Concrete, the described first default span can be voltage (1 ± 20%), voltage is electric pressure, the voltage class data of supposing current collection are busbar voltage, so, and when the data value of busbar voltage drops in voltage (1 ± 20%) scope of busbar voltage, namely when voltage class data movement value electric pressure ± 20% in the time, normal during described busbar voltage, otherwise described busbar voltage is unusual.Wherein, standard voltage grade is according to the difference of equipment and difference, and for example, standard voltage grade has 500Kv, 220kV, 110kV etc., and this grade information can be known by the Common Information Model (Common InformationMode, CIM) of electric system.When detecting described voltage class data exception, be the electric pressure value with current voltage class data correction;
For capacity reactance class data, judge whether described capacity reactance class data satisfy the default span of second under the current state, and if so, described capacity reactance class data are normal, otherwise, described capacity reactance class data exception; Concrete, capacity reactance class data can be divided into idle data and current data, because in the process of image data, capacity reactance may be in running status, also may be in shut down condition, so, when capacity reactance is in running status, judge whether described idle data and current data satisfy the second default span of running status, the second default span of described running status can be | Q|〉0.5 and | I|〉20, wherein, Q represents idle data, I represents current data, that is to say, when the absolute value of idle data greater than 0.5, and current data was greater than 20 o'clock, described capacity reactance class data are normal, otherwise, described capacity reactance class data exception; When capacity reactance is in shut down condition, judge whether described idle data and current data satisfy the second default span of shut down condition, the second default span of described shut down condition can for | Q|=0 and | I|=0, wherein, Q represents idle data, I represents current data, that is to say, when idle data equal 0, and current data equals at 0 o'clock, described capacity reactance class data are normal, otherwise, described capacity reactance class data exception; Described idle data and current data can be distinguished by the code that grid equipment carries, and represent idle data such as code=30, and code=40 represents current data etc.When detecting described capacity reactance class data exception, be 0 with described capacity reactance class data correction.
For load class data, calculate the similarity distance between described load class data and the scale load value, described similarity distance and predetermined threshold value are compared, when described similarity distance during greater than described predetermined threshold value, described load class data are normal, otherwise, described load class data exception; Concrete, can calculate similarity distance between described load class data and the scale load value according to formula (1),
Wherein, d is the similarity distance between described load class data and the scale load value, x
NkBe n days k load class data constantly,
It is k scale load value constantly; Preferably, described load criterion data can be different with the value of nonworkdays (such as festivals or holidays) on weekdays, therefore, when application of formula (1) is calculated similarity distance between described load class data and the scale load value, the scale load value
Can get according to whether different values festivals or holidays.With described similarity distance with compare with predetermined threshold value, when described similarity distance during greater than described predetermined threshold value, described load class data exception, otherwise described load class data are normal; Preferably, described predetermined threshold value can be taken as 0.1, certainly, also can difference be set according to actual needs, is not specifically limited here.That is to say, as d 0.1 the time, described load class data exception at this moment, can be with this abnormal load class data correction
For main transformer class data, described main transformer class data comprise meritorious data, idle data, current data and voltage data; Calculate the theoretical value P of described meritorious data according to following the first relational equation (being formula (2))
*,
Wherein,
P is meritorious data, and Q is idle data, and U is voltage data, and I is current data; P
*Theoretical value for described diligent data P.
As described theoretical value P
*Satisfy first when pre-conditioned, described main transformer class data are normal, otherwise, described main transformer class data exception; Concrete, as described theoretical value P
*When satisfying inequality (3), described main transformer class data are normal, otherwise, described main transformer class data exception; When detecting described main transformer class data exception, testing result is write daily record, check for the technician.
|P-P
*|<0.1P (3)
For circuit class data, described circuit class data comprise head end data and terminal data, described head end data comprise head end gain merit data, the idle data of head end, head end current data and head end voltage data, and described terminal data comprise terminal meritorious data, terminal idle data, end current data and terminal voltage data; Calculate the theoretical value of the meritorious data of described head end according to described the first relational expression (being formula (2))
Theoretical value when the meritorious data of described head end
Satisfy second when pre-conditioned, described head end data are normal, otherwise, described head end data exception; Calculate the theoretical value of the meritorious data of described end according to described the first relational expression (being formula (2))
Theoretical value when the meritorious data of described end
Satisfy the 3rd when pre-conditioned, described terminal data are normal; Otherwise, described terminal data exception.
For each circuit class data, its detection method and main transformer class data are same or similar, all are to detect by formula (2) and inequality (3), repeat no more here.
A kind of abnormal deviation data examination method that the embodiment of the present application provides, real-time electric power data to be detected are classified, and to different classes of electric power data, using different rules detects it, the on-line quick detection to the magnanimity electric power data that realizes, the detection efficiency of the real-time electric power data that improved has guaranteed the real-time electric power the reliability of the data.
To the above-mentioned explanation of the disclosed embodiments, make this area professional and technical personnel can realize or use the present invention.Multiple modification to these embodiment will be apparent concerning those skilled in the art, and General Principle as defined herein can be in the situation that do not break away from the spirit or scope of the present invention, in other embodiments realization.Therefore, the present invention will can not be restricted to these embodiment shown in this article, but will meet the widest scope consistent with principle disclosed herein and features of novelty.
Claims (5)
1. an electricity exception data detection method is characterized in that, comprising:
Obtain the real-time electric power data, described electric power data carries pre-assigned identification marking;
Determine the classification of described electric power data according to described identification marking;
According to the detection rule corresponding with described classification described electric power data is detected, when described electric power data satisfied default testing conditions, described electric power data was normal, otherwise described electric power data is unusual.
2. method according to claim 1 is characterized in that, the classification of described electric power data comprises:
The voltage class, capacity reactance class, load class, main transformer class and circuit class.
3. method according to claim 2 is characterized in that, according to the detection rule corresponding with described classification described electric power data is detected to comprise:
Voltage class data: judge whether described voltage class data satisfy the first default span, if so, then described voltage class data are normal, otherwise, described voltage class data exception;
Capacity reactance class data: judge whether described capacity reactance class data satisfy the default span of second under the current state, and if so, described capacity reactance class data are normal, otherwise, described capacity reactance class data exception;
Load class data: calculate the similarity distance between described load class data and the scale load value, described similarity distance and predetermined threshold value are compared, and when described similarity distance during greater than described predetermined threshold value, described load class data are normal, otherwise, described load class data exception;
Main transformer class data: described main transformer class data comprise meritorious data, idle data, current data and voltage data; Calculate the theoretical value P of described meritorious data according to following the first relational equation
*,
Circuit class data: described circuit class data comprise head end data and terminal data, described head end data comprise head end gain merit data, the idle data of head end, head end current data and head end voltage data, and described terminal data comprise terminal meritorious data, terminal idle data, end current data and terminal voltage data; Calculate the theoretical value of the meritorious data of described head end according to described the first relational expression
Theoretical value when the meritorious data of described head end
Satisfy second when pre-conditioned, described head end data are normal, otherwise, described head end data exception; Calculate the theoretical value of the meritorious data of described end according to described the first relational expression
Theoretical value when the meritorious data of described end
Satisfy the 3rd when pre-conditioned, described terminal data are normal; Otherwise, described terminal data exception.
4. method according to claim 3 is characterized in that, calculates similarity distance between described load class data and the scale load value according to following formula:
Wherein, d is the similarity distance between described load class data and the scale load value, x
NkBe n days k load class data constantly,
Mean value for front τ days k data constantly.
5. method according to claim 4 is characterized in that, when detecting electric power data when unusual, described electric power data is revised, and comprising:
When detecting described voltage class data exception, be electric pressure with described voltage class data correction;
When detecting described capacity reactance class data exception, be 0 with described capacity reactance class data correction;
When detecting described load class data exception, with described load class data correction be
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| CN104408664A (en) * | 2014-10-30 | 2015-03-11 | 国家电网公司 | Power equipment data processing method and apparatus |
| CN104408664B (en) * | 2014-10-30 | 2018-09-18 | 国家电网公司 | Power equipment data processing method and device |
| CN105389746A (en) * | 2015-12-18 | 2016-03-09 | 广东电网有限责任公司中山供电局 | Method and system for detecting electric power data |
| CN107463469B (en) * | 2016-06-03 | 2020-08-21 | 腾讯科技（深圳）有限公司 | Equipment fault detection method and device |
| CN107463469A (en) * | 2016-06-03 | 2017-12-12 | 腾讯科技（深圳）有限公司 | Equipment fault detection method and device |
| CN108268467A (en) * | 2016-12-30 | 2018-07-10 | 广东精点数据科技股份有限公司 | A kind of abnormal deviation data examination method and device based on attribute |
| CN108268467B (en) * | 2016-12-30 | 2021-08-06 | 广东精点数据科技股份有限公司 | Attribute-based abnormal data detection method and device |
| CN108595120A (en) * | 2018-04-11 | 2018-09-28 | 广东电网有限责任公司 | A kind of scada near-realtime datas storage method and system |
| CN110826750A (en) * | 2018-08-08 | 2020-02-21 | 阿里巴巴集团控股有限公司 | Power load prediction method, device, equipment and system |
| CN110826750B (en) * | 2018-08-08 | 2023-09-26 | 阿里巴巴集团控股有限公司 | Power load prediction method, device, equipment and system |
| CN110808634A (en) * | 2019-11-06 | 2020-02-18 | 国网安徽省电力有限公司安庆供电公司 | Reactive numerical value abnormity detection system |
| CN111241158A (en) * | 2020-01-07 | 2020-06-05 | 清华大学 | Anomaly detection method and device for aircraft telemetry data |
| CN111241158B (en) * | 2020-01-07 | 2020-09-18 | 清华大学 | Anomaly detection method and device for aircraft telemetry data |
| CN111898656A (en) * | 2020-07-14 | 2020-11-06 | 许继集团有限公司 | Abnormal data identification method for measurement balance detection |
| CN111898656B (en) * | 2020-07-14 | 2023-10-24 | 许继集团有限公司 | Abnormal data identification method for measuring balance detection |
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