CN109636667A - A kind of low-voltage customer multiplexing electric abnormality detection method based on user's week electrical feature - Google Patents
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Abstract
A kind of low-voltage customer multiplexing electric abnormality detection method based on user's week electrical feature, is related to technical field of electric power detection.A kind of consideration electric power users week electricity consumption behavioral characteristic in multiplexing electric abnormality detection is provided, multiplexing electric abnormality detection complexity is reduced, promotes the low-voltage customer multiplexing electric abnormality detection method based on user's week electrical feature of the accuracy of multiplexing electric abnormality Data Detection.From all electricity consumption measure features of low-voltage customer, construct all electricity consumption indicatrixes of user, based on FCM clustering algorithm, obtain all electricity consumption curves of typical case of low-voltage electricity user, the low-voltage customer for marking abnormal electricity consumption using the method based on distance again, monitors multiplexing electric abnormality user finally by survey electric current, voltage, equilibrium data is actively called together.Present invention combination power information acquisition system obtains daily electricity data, and is marked according to abnormal user, calls together per hour to abnormal user and surveys electric current, voltage and equilibrium data, realizes the reliable monitoring of multiplexing electric abnormality behavior.
Description
Technical field
The invention belongs to technical field of electric power detection more particularly to a kind of electricity consumption based on low-voltage customer week electrical feature are different
Normal detection method.
Background technique
As power grid scale constantly expands, electric power information degree is continuously improved and Internet of Things, cloud computing, number
According to the control of a new generation such as excavation, the continuous development of measurement and data processing technique, big data application has been directed to electric power enterprise
Each business scope.
Currently, lack the abnormal electricity consumption behavioral study for being directed to low-voltage customer, this is because for low-voltage customer, electricity
Frequency acquisition generally once every hour, and hour electricity data quality cannot often ensure, for the authentic data of research
Only daily power consumption reduces detection reliability.
Summary of the invention
The present invention is in view of the above problems, provide a kind of consideration electric power users week electricity consumption behavior spy in multiplexing electric abnormality detection
Point reduces multiplexing electric abnormality detection complexity, promoted the accuracy of multiplexing electric abnormality Data Detection based on user's week electrical feature
Low-voltage customer multiplexing electric abnormality detection method.
The technical scheme adopted by the invention is that: the following steps are included:
1) all electricity consumption indicatrixes of low-voltage customer are established；It is used based on low pressure different types of in power information acquisition system
The daily electricity information at family, the typical all electricity consumption indicatrixes of building user；
2) all electricity consumption indicatrixes typical to low-voltage customer carry out fuzzy C-means clustering, obtain cluster centre；
3) all electricity consumption curves of low-voltage customer and cluster centre are subjected to distance metric, and multiplexing electric abnormality user is sieved
Choosing；
It is abnormal to think that data exist if threshold value is greater than constant η, marks abnormal user data；Otherwise it is assumed that being normal number
According to process terminates.
Step 1) the following steps are included:
1.1) all electricity consumption weighted averages；
1.2) all electricity consumption data normalizations.
Step 2) the following steps are included:
2.1) subordinated-degree matrix U is initialized；
2.2) c cluster centre Vi is calculated；
2.3) subordinated-degree matrix U is updated；
2.4) cost function J is calculated；
2.5) iterative step 2.2) and step 2.4) go to step 2.6) when J < threshold epsilon；
2.6) algorithm terminates, and exports c cluster centre V1, V2 ... Vc.
The present invention utilizes all electricity consumption indicatrixes of daily electricity basic data structuring user's, and poly- based on fuzzy C-mean algorithm
Class algorithm carries out clustering, obtains all electricity consumption curves of typical case of low-voltage electricity user, then use the method mark based on distance
The low-voltage customer for remembering abnormal electricity consumption monitors multiplexing electric abnormality user by actively calling survey electric current, voltage, equilibrium data together.
Compared with prior art, the present invention at least has the advantages that
1) compared with traditional multiplexing electric abnormality detection method, detection method of the invention goes out from all electricity consumption indicatrixes of user
Hair, the typical all electricity consumption indicatrixes of building user；
2) it after method of the invention is based on FCM clustering algorithm realization user's electric energy measurement data abnormality detection, can mark
Abnormal user realizes the reliable monitoring of abnormal user electricity consumption behavior.
Detailed description of the invention
Fig. 1 is the flow diagram of low-voltage customer multiplexing electric abnormality detection method of the invention.
Specific embodiment
The present invention is as shown in Figure 1, comprising the following steps:
1) all electricity consumption indicatrixes of low-voltage customer are established: being used based on low pressure different types of in power information acquisition system
The daily electricity information at family, the typical all electricity consumption indicatrixes of building user；
In, by the date of user's daily electricity, go out what day belongs to according to Cai Le formula to calculating, abscissa is 1-7, is indulged
Coordinate is daily electricity.Wherein, typical selection is chosen according to sample data.
The effect of step 1 is the sample clustered.
2) all electricity consumption indicatrixes typical to low-voltage customer carry out fuzzy C-means clustering, obtain cluster centre；
3) all electricity consumption curves of low-voltage customer and cluster centre are subjected to distance metric, and multiplexing electric abnormality user is sieved
Choosing.
Preferably, in step 1), the different type of the low-voltage customer includes that low pressure resident and low pressure are non-resident
User；
Preferably, step 1) the following steps are included:
1.1) all electricity consumption weighted averages:
The electricity consumption data for enabling user i-th week are Wi={ wi1, wi2, wi3, wi4, wi5, wi6, wi7, it extracts n weeks go through altogether
The Records of the Historian is recorded, then the weight coefficient in i-th week jth day are as follows:J=1,2 ... 7, then weighted average of the user at j days
Electricity consumption are as follows:
1.2) data set normalizes:
If data set X={ x1, x2... xn, a shared n data object, xi=(xi1, xi2... xim), share m category
Property.After then normalizingWherein max { xij, min { xijBe j-th attribute maximum value and most
Small value.
Preferably, in step 2), all electricity consumption data of different type user is clustered based on FCM algorithm, are obtained
To cluster centre collection, basis early period is established for user power utilization anomaly analysis；
Preferably, the number of cluster is c, cluster centre V1, V2... Vc；
Preferably, step 2) the following steps are included:
2.1) subordinated-degree matrix U is initialized, it is made to meet condition
2.2) c cluster centre v is calculatedi
Wherein fuzzy coefficient m value is 5；
2.3) subordinated-degree matrix U is updated,
Wherein fuzzy coefficient m value is 5；
2.4) cost function is calculated,
2.5) iterative step 2.2) and step 2.4), using the changing value J of cost function as clustering performance decision condition, when
When J < ε, approximate representation is that electricity consumption Model tying center Vn is no longer changed, and goes to step 2.6)；Wherein, ε is in Limit
Middle representative be one greater than 0 very little number, can be arbitrarily small, as long as be not equal to zero；
Preferably, depending on the specific value of ε is with actually detected precision, the number of iterations becomes more or does not restrain if value is too small,
Value is excessive, causes the omission of certain abnormal datas；More preferably ε=0.15
2.6) algorithm terminates, export c cluster centre V1, V2... Vc。
Preferably, in step 3), abnormal electricity consumption user is marked with Euclidean distance；If min (dist (x,
Vi)) then to think that data exist abnormal by > η, mark user data；Otherwise it is assumed that being normal data, process terminates.Distance metric
Min (dist (x, Vi)) be x and Vi distance minimum value, selected distance measure it is simple, practical.
Preferably, depending on the specific value of η is with actually detected precision, abnormal data becomes more if value is too small, and value is excessive
Then cause the omission of certain abnormal datas；It is highly preferred that η=1.5.
As optimal technical scheme, described detection method includes the following steps:
1) all electricity consumption indicatrixes of low-voltage customer are established: being used based on low pressure different types of in power information acquisition system
The daily electricity information at family, the typical all electricity consumption indicatrixes of building user；The different type of low-voltage customer includes low pressure resident
With the non-resident user of low pressure (including general commercial user and general industry user).The following steps are included:
1.1) all electricity consumption weighted averages；All load curve data are weighted and averaged, available each user's
Typical week load curve.
1.2) all electricity consumption data normalizations.In electricity data, user is due to type difference, and there are larger differences for electricity consumption
It is different, if in cluster process, the big attribute of the order of magnitude will affect very greatly cluster result using electricity consumption as cluster feature,
Data are normalized so generally requiring, data are limited in [0,1] range.
2) it is based on fuzzy C-means clustering, obtains cluster centre；The number of cluster is c, cluster centre V1, V2... Vc；Packet
Include following steps:
2.1) subordinated-degree matrix U is initialized；
2.2) c cluster centre V is calculatedi；
2.3) subordinated-degree matrix U is updated；
2.4) cost function J is calculated；
2.5) iterative step 2.2) and step 2.4) go to step 2.6) as J < ε；
2.6) algorithm terminates, and exports c cluster centre V1, V2... Vc。
3) all electricity consumption curves of low-voltage customer and cluster centre are subjected to distance metric, if η is apart from detection threshold value min
It is abnormal that (dist (x, Vi)) > η then thinks that data exist, and marks user data；Otherwise it is assumed that being normal data, process terminates.Its
In, η=1.5；
It should be noted that and understand, in the feelings for not departing from the spirit and scope of the present invention required by appended claims
Under condition, various modifications and improvements can be made to the present invention of foregoing detailed description.It is therefore desirable to the model of the technical solution of protection
It encloses and is not limited by given any specific exemplary teachings.
The Applicant declares that the above is only a preferred embodiment of the present invention, it is noted that for the art
For those of ordinary skill, without departing from the inventive concept of the premise, several improvement and deformations can also be made, these improvement
It also should be regarded as protection scope of the present invention with deformation.
Claims (3)
1. a kind of low-voltage customer multiplexing electric abnormality detection method based on user's week electrical feature, which is characterized in that including following step
It is rapid:
1) all electricity consumption indicatrixes of low-voltage customer are established；Based on low-voltage customer different types of in power information acquisition system
Daily electricity information, the typical all electricity consumption indicatrixes of building user；
2) all electricity consumption indicatrixes typical to low-voltage customer carry out fuzzy C-means clustering, obtain cluster centre；
3) all electricity consumption curves of low-voltage customer and cluster centre are subjected to distance metric, and multiplexing electric abnormality user is screened；
It is abnormal to think that data exist if threshold value is greater than constant η, marks abnormal user data；Otherwise it is assumed that be normal data, stream
Journey terminates.
2. a kind of low-voltage customer multiplexing electric abnormality detection method based on user's week electrical feature according to claim 1,
Be characterized in that, step 1) the following steps are included:
1.1) all electricity consumption weighted averages；
1.2) all electricity consumption data normalizations.
3. a kind of low-voltage customer multiplexing electric abnormality detection method based on user's week electrical feature according to claim 1,
Be characterized in that, step 2) the following steps are included:
2.1) subordinated-degree matrix U is initialized；
2.2) c cluster centre Vi is calculated；
2.3) subordinated-degree matrix U is updated；
2.4) cost function J is calculated；
2.5) iterative step 2.2) and step 2.4) go to step 2.6) when J < threshold epsilon；
2.6) algorithm terminates, and exports c cluster centre V1, V2 ... Vc.
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