CN109142979B - Method and device for detecting abnormal state of power distribution network - Google Patents

Method and device for detecting abnormal state of power distribution network Download PDF

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CN109142979B
CN109142979B CN201811085565.9A CN201811085565A CN109142979B CN 109142979 B CN109142979 B CN 109142979B CN 201811085565 A CN201811085565 A CN 201811085565A CN 109142979 B CN109142979 B CN 109142979B
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detected
power variable
probability density
electric power
period
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CN109142979A (en
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胡丽娟
盛万兴
刘科研
贾东梨
刁赢龙
董伟杰
何开元
叶学顺
白牧可
张淼
林挚
吕琛
张稳
刘杨涛
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State Grid Corp of China SGCC
China Electric Power Research Institute Co Ltd CEPRI
State Grid Beijing Electric Power Co Ltd
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State Grid Corp of China SGCC
China Electric Power Research Institute Co Ltd CEPRI
State Grid Beijing Electric Power Co Ltd
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/08—Locating faults in cables, transmission lines, or networks
    • G01R31/081—Locating faults in cables, transmission lines, or networks according to type of conductors
    • G01R31/086—Locating faults in cables, transmission lines, or networks according to type of conductors in power transmission or distribution networks, i.e. with interconnected conductors
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/08—Locating faults in cables, transmission lines, or networks
    • G01R31/088—Aspects of digital computing

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  • General Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Mathematical Physics (AREA)
  • Theoretical Computer Science (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

The application relates to a method and a device for detecting abnormal states of a power distribution network, wherein the method comprises the following steps: obtaining a maximum likelihood estimation value of a parameter of a related probability density function of the power variable to be detected; determining the degree of abnormality of the electric power variable to be detected according to the maximum likelihood estimation value of the parameter of the related probability density function of the electric power variable to be detected; the technical scheme provided by the application realizes real-time accurate monitoring of the abnormal state of the power distribution network, improves the control level of fault location, risk early warning and operation optimization of the power distribution network, standardizes the state maintenance management of the power distribution network, reduces the maintenance cost and improves the enterprise benefit.

Description

Method and device for detecting abnormal state of power distribution network
Technical Field
The application relates to the field of operation analysis and control of power distribution networks, in particular to a method and a device for detecting abnormal states of a power distribution network.
Background
Along with the development of economic construction and continuous improvement of living standard in China, the demand for electric energy is increased increasingly, and the urban treatment in China is accelerated, so that the construction of power distribution engineering in China is promoted to a certain extent, and because various problems are easy to generate in the operation process of the power distribution network, the abnormal equipment is often required to be overhauled in the operation process of the power distribution network.
The traditional operation and maintenance work of the power distribution network is carried out by taking regular maintenance of equipment as a core, and due to the lack of effective monitoring means for the running state of the equipment, the accurate positioning of faults can not be realized and the abnormal state of the faults can not be judged, so that excessive maintenance phenomenon often occurs, thereby wasting a large amount of manpower, financial resources and material resources, not only increasing the maintenance cost, but also being difficult to ensure the running reliability of the power distribution network.
Disclosure of Invention
The application provides a method and a device for detecting an abnormal state of a power distribution network, which aim to realize real-time accurate monitoring of the abnormal state of the power distribution network, improve the control level of fault location, risk early warning and operation optimization of the power distribution network, standardize the state maintenance management of the power distribution network, reduce the maintenance cost and improve the enterprise benefit.
The application aims at adopting the following technical scheme:
in a method for detecting an abnormal condition of a power distribution network, the improvement comprising:
obtaining a maximum likelihood estimation value of a parameter of a related probability density function of the power variable to be detected;
determining the degree of abnormality of the electric power variable to be detected according to the maximum likelihood estimation value of the parameter of the related probability density function of the electric power variable to be detected;
and detecting the abnormal state of the power distribution network by using the degree of abnormality of the power variable to be detected.
Preferably, the determining the degree of abnormality of the electric power variable to be detected according to the maximum likelihood estimation value of the parameter of the related probability density function of the electric power variable to be detected includes:
the degree of abnormality D (R) of the power variable to be detected is determined as follows:
wherein X is R As the power variable to be detected for the detection period in the region to be detected,x is the power variable to be detected in the non-detection period in the non-detection area G For the electric power variable to be detected in the whole region and the whole period of the power distribution network, theta R Is based on X R Maximum likelihood estimate of a parameter of the associated probability density function, +.>Is based on->Maximum likelihood estimate, θ, of a parameter of a related probability density function G Is based on X G Maximum likelihood estimate of a parameter of a related probability density function, L (θ G |X R ) The parameter being a function of the associated probability density is θ G Time X R Likelihood function value of (2), L (θ) G |X G ) Is a related summaryThe parameter of the rate density function is theta G Time X G Is the maximum likelihood function value of (2), L (θ) R |X R ) The parameter being a function of the associated probability density is θ R When X is R Maximum likelihood function value of>For distribution parameters +.>When (I)>Is used for the maximum likelihood function value of (a).
Preferably, the detecting the abnormal state of the power distribution network by using the degree of abnormality of the power variable to be detected includes:
when the abnormality degree of the electric power variable to be detected is larger than a threshold value, the area corresponding to the electric power variable to be detected is an abnormal area, and the sampling period of the electric power variable to be detected is an abnormal period;
when the abnormality degree of the electric power variable to be detected is smaller than or equal to a threshold value, the area corresponding to the electric power variable to be detected is a normal area, and the sampling period of the electric power variable to be detected is a normal period.
Preferably, after the abnormal state detection of the power distribution network by using the degree of abnormality of the power variable to be detected, the method includes:
acquiring a to-be-detected power variable of an abnormal region in an abnormal period, and determining an abnormal state type corresponding to the to-be-detected power variable of the abnormal region in the abnormal period according to a pre-established association rule base of the to-be-detected power variable and the abnormal state type;
the association rule base of the pre-established power variable to be detected and the abnormal state type is established according to the abnormal state type corresponding to the historical data of the power variable to be detected.
Further, the abnormal state type includes:
risk status, fault status, recovery status, and optimization status.
Preferably, the electric power variable to be detected includes:
voltage, current, line loss, load data, variance, skewness, and kurtosis.
In a device for detecting an abnormal condition of a power distribution network, the improvement comprising:
the acquisition unit is used for acquiring the maximum likelihood estimation value of the parameter of the related probability density function of the electric power variable to be detected;
the determining unit is used for determining the degree of abnormality of the electric power variable to be detected according to the maximum likelihood estimated value of the parameter of the related probability density function of the electric power variable to be detected;
and the detection unit is used for detecting the abnormal state of the power distribution network by utilizing the degree of abnormality of the power variable to be detected.
Preferably, the determining unit is configured to:
the degree of abnormality D (R) of the power variable to be detected is determined as follows:
wherein X is R As the power variable to be detected for the detection period in the region to be detected,x is the power variable to be detected in the non-detection period in the non-detection area G For the electric power variable to be detected in the whole region and the whole period of the power distribution network, theta R Is based on X R Maximum likelihood estimate of a parameter of the associated probability density function, +.>Is based on->Maximum likelihood estimate, θ, of a parameter of a related probability density function G Is based on X G Of the associated probability density function of (c)Maximum likelihood estimate, L (θ) G |X R ) The parameter being a function of the associated probability density is θ G Time X R Likelihood function value of (2), L (θ) G |X G ) The parameter being a function of the associated probability density is θ G Time X G Is the maximum likelihood function value of (2), L (θ) R |X R ) The parameter being a function of the associated probability density is θ R When X is R Maximum likelihood function value of>For distribution parameters +.>When (I)>Is used for the maximum likelihood function value of (a).
Preferably, the detection unit is configured to:
the first detection module is used for determining that the region corresponding to the electric power variable to be detected is an abnormal region when the degree of abnormality of the electric power variable to be detected is greater than a threshold value, and determining that the sampling period of the electric power variable to be detected is an abnormal period;
and the second detection module is used for determining that the region corresponding to the electric power variable to be detected is a normal region when the anomaly degree of the electric power variable to be detected is smaller than or equal to a threshold value, and determining that the sampling period of the electric power variable to be detected is a normal period.
Preferably, after the detecting unit, the detecting unit further includes:
acquiring a to-be-detected power variable of an abnormal region in an abnormal period, and determining an abnormal state type corresponding to the to-be-detected power variable of the abnormal region in the abnormal period according to a pre-established association rule base of the to-be-detected power variable and the abnormal state type;
the association rule base of the pre-established power variable to be detected and the abnormal state type is established according to the abnormal state type corresponding to the historical data of the power variable to be detected.
The application has the beneficial effects that:
according to the technical scheme provided by the application, the maximum likelihood estimated value of the parameter of the related probability density function of the electric power variable to be detected is obtained; determining the degree of abnormality of the electric power variable to be detected according to the maximum likelihood estimation value of the parameter of the related probability density function of the electric power variable to be detected; and detecting the abnormal state of the power distribution network by using the degree of abnormality of the power variable to be detected. Based on the technical scheme provided by the application, the real-time detection of the abnormal state of the power distribution network is realized, the positioning of the power distribution network fault and the type of the abnormal state are improved, the state maintenance management of the power distribution network can be standardized, the maintenance cost is greatly reduced, the running reliability of the power distribution network is ensured, and the current power distribution network management requirement is met.
Drawings
FIG. 1 is a flow chart of a method for detecting an abnormal state of a power distribution network according to the present application;
fig. 2 is a schematic structural diagram of a device for detecting an abnormal state of a power distribution network according to the present application.
Detailed Description
The following detailed description of specific embodiments of the application refers to the accompanying drawings.
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Example 1:
the method for detecting the abnormal state of the power distribution network provided by the application, as shown in figure 1, comprises the following steps:
101. obtaining a maximum likelihood estimation value of a parameter of a related probability density function of the power variable to be detected;
102. determining the degree of abnormality of the electric power variable to be detected according to the maximum likelihood estimation value of the parameter of the related probability density function of the electric power variable to be detected;
103. and detecting the abnormal state of the power distribution network by using the degree of abnormality of the power variable to be detected.
Wherein the information of the power variable to be detected comprises a sampling area and a sampling period.
For example: and obtaining the maximum likelihood estimation value of the parameter of the related probability density function of the power variable to be detected by adopting a maximum likelihood estimation method.
Specifically, the step 102 includes:
the degree of abnormality D (R) of the power variable to be detected is determined as follows:
wherein X is R As the power variable to be detected for the detection period in the region to be detected,x is the power variable to be detected in the non-detection period in the non-detection area G For the electric power variable to be detected in the whole region and the whole period of the power distribution network, theta R Is based on X R Maximum likelihood estimate of a parameter of the associated probability density function, +.>Is based on->Maximum likelihood estimate, θ, of a parameter of a related probability density function G Is based on X G Maximum likelihood estimate of a parameter of a related probability density function, L (θ G |X R ) The parameter being a function of the associated probability density is θ G Time X R Likelihood function value of (2), L (θ) G |X G ) The parameter being a function of the associated probability density is θ G Time X G Is the maximum likelihood function value of (2), L (θ) R |X R ) The parameter being a function of the associated probability density is θ R When X is R Maximum of (2)Likelihood function value,/->For distribution parameters +.>When (I)>Is used for the maximum likelihood function value of (a).
After determining the degree of abnormality of the to-be-detected power variable, it is necessary to perform abnormal state detection on the power distribution network by using the degree of abnormality of the to-be-detected power variable, and therefore, the step 103 includes:
when the abnormality degree of the electric power variable to be detected is larger than a threshold value, the area corresponding to the electric power variable to be detected is an abnormal area, and the sampling period of the electric power variable to be detected is an abnormal period;
when the abnormality degree of the electric power variable to be detected is smaller than or equal to a threshold value, the area corresponding to the electric power variable to be detected is a normal area, and the sampling period of the electric power variable to be detected is a normal period.
After the abnormal state detection is performed on the power distribution network, the abnormal state type corresponding to the power variable to be detected needs to be determined, so that:
after the abnormal state detection is performed on the power distribution network by utilizing the degree of abnormality of the power variable to be detected, the method comprises the following steps:
acquiring a to-be-detected power variable of an abnormal region in an abnormal period, and determining an abnormal state type corresponding to the to-be-detected power variable of the abnormal region in the abnormal period according to a pre-established association rule base of the to-be-detected power variable and the abnormal state type;
the association rule base of the pre-established power variable to be detected and the abnormal state type is established according to the abnormal state type corresponding to the historical data of the power variable to be detected.
The abnormal state type includes:
risk status, fault status, recovery status, and optimization status.
The power variable to be detected includes:
voltage, current, line loss, load data, variance, skewness, and kurtosis.
Example 2:
the preferred embodiment based on the technical scheme of the embodiment 1 provided by the application comprises the following steps:
establishing likelihood functions of parameters of a related probability density function of the power variable to be detected:
wherein f (X) R θ) is a correlation probability density function of the power variable to be detected in the detection period in the region to be detected,detecting a relative probability density function of the power variable for a non-detection period within the non-detection region, f (X) G θ) is a related probability density function of a power variable to be detected in a whole region and in a whole period of a power distribution network, and L (θ|X) R ) Parameter likelihood function value of correlation probability density function of electric power variable to be detected for detection period in the region to be detected,/>The parameter likelihood function value of the correlation probability density function for detecting the power variable for the non-detection period in the non-detection region, L (θ|X G ) Likelihood function value of parameter of correlation probability density function of electric power variable to be detected in all time periods in all regions of power distribution network, and m is electric power to be detected in detection time periods in the region to be detectedThe number of the sample data of the variable, n is the number of the sample data of the power variable detected in the non-detection period in the non-detection area, and l is the number of the sample data of the power variable to be detected in the whole period in the whole area of the power distribution network.
And respectively acquiring the maximum likelihood estimation values of the parameters of each related probability density function of the power variable to be detected by adopting a maximum likelihood estimation method.
Further determining a maximum likelihood function value L (theta) of a parameter of a correlation probability density function of the power variable to be detected in the detection period within the region to be detected R |X R ) Maximum likelihood function value of parameter of correlation probability density function of power variable to be detected in non-detection period in non-detection areaMaximum likelihood function value L (theta) of parameter of related probability density function of power variable to be detected in whole region and whole period of power distribution network G |X G )。
Predefined null hypothesis H 0 Assuming that the value of the parameter of the correlation probability density function of the electric variable to be detected in the detection period in the to-be-detected area is the same as the value of the parameter of the correlation probability density function of the electric variable to be detected in the non-detection period in the non-detection area, the assumption H is opposite 1 For both to be different, the full hypothesis H includes the null hypothesis H 0 And opposite hypothesis H 1 。
At zero assume H 0 In the method, the value of the parameter of the relevant probability density function of the power variable to be detected in the detection period in the region to be detected is the same as the value of the parameter of the relevant probability density function of the power variable to be detected in the whole region of the power distribution network in the whole period, so that the assumption H is zero 0 In the method, the maximum likelihood function value of the parameter of the correlation probability density function of the electric power variable to be detected in the detection period in the area to be detected is as follows: sup { L (θ|X) R )|H 0 }＝L(θ G |X R )；
In the full hypothesis H, the maximum likelihood function value of the parameters of the related probability density function of the power variable to be detected in the full region and the full period of the power distribution network is as follows:
calculation of the assumption H at zero 0 The log-likelihood ratio of the maximum likelihood function value of the parameter of the correlation probability density function of the power variable to be detected in the detection period in the lower detection region to the maximum likelihood function value of the parameter of the correlation probability density function of the power variable to be detected in the full period in the full region of the power distribution network under the full assumption H is the degree of abnormality of the power variable to be detected:
wherein X is R As the power variable to be detected for the detection period in the region to be detected,x is the power variable to be detected in the non-detection period in the non-detection area G For the electric power variable to be detected in the whole region and the whole period of the power distribution network, theta R Is based on X R Maximum likelihood estimate of a parameter of the associated probability density function, +.>Is based on->Maximum likelihood estimate, θ, of a parameter of a related probability density function G Is based on X G Maximum likelihood estimate of a parameter of a related probability density function, L (θ G |X R ) The parameter being a function of the associated probability density is θ G Time X R Likelihood function value of (2), L (θ) G |X G ) The parameter being a function of the associated probability density is θ G Time X G Is the maximum likelihood function value of (2), L (θ) R |X R ) The parameter being a function of the associated probability density is θ R When X is R Maximum likelihood function value of>For distribution parameters +.>When (I)>Is used for the maximum likelihood function value of (a).
When the abnormality degree of the electric power variable to be detected is larger than a threshold value, the area corresponding to the electric power variable to be detected is an abnormal area, and the sampling period of the electric power variable to be detected is an abnormal period;
when the abnormality degree of the electric power variable to be detected is smaller than or equal to a threshold value, the area corresponding to the electric power variable to be detected is a normal area, and the sampling period of the electric power variable to be detected is a normal period.
And for the abnormal region, positioning abnormal equipment and abnormal time periods of the power distribution network by using the space dimension and the adopted time periods.
Then, acquiring a to-be-detected power variable of an abnormal region in an abnormal period, and determining an abnormal state type corresponding to the to-be-detected power variable of the abnormal region in the abnormal period according to a pre-established association rule base of the to-be-detected power variable and the abnormal state type;
the association rule base of the pre-established power variable to be detected and the abnormal state type is established according to the abnormal state type corresponding to the historical data of the power variable to be detected.
The abnormal state type includes:
risk status, fault status, recovery status, and optimization status.
The power variable to be detected includes:
voltage, current, line loss, load data, voltage variance, current variance, voltage skewness, current skewness, voltage kurtosis, and current kurtosis.
Example 3:
the embodiment of the application further comprises a device for detecting the abnormal state of the power distribution network, as shown in fig. 2, wherein the device comprises:
the acquisition unit is used for acquiring the maximum likelihood estimation value of the parameter of the related probability density function of the electric power variable to be detected;
the determining unit is used for determining the degree of abnormality of the electric power variable to be detected according to the maximum likelihood estimated value of the parameter of the related probability density function of the electric power variable to be detected;
and the detection unit is used for detecting the abnormal state of the power distribution network by utilizing the degree of abnormality of the power variable to be detected.
The determining unit is used for:
the degree of abnormality D (R) of the power variable to be detected is determined as follows:
wherein X is R As the power variable to be detected for the detection period in the region to be detected,x is the power variable to be detected in the non-detection period in the non-detection area G For the electric power variable to be detected in the whole region and the whole period of the power distribution network, theta R Is based on X R Maximum likelihood estimate of a parameter of the associated probability density function, +.>Is based on->Maximum likelihood estimate, θ, of a parameter of a related probability density function G Is based on X G Maximum likelihood estimate of a parameter of a related probability density function, L (θ G |X R ) The parameter being a function of the associated probability density is θ G Time X R Likelihood function value of (2), L (θ) G |X G ) The parameter being a function of the associated probability density is θ G Time X G Is the maximum likelihood function value of (2), L (θ) R |X R ) Parameters being a function of the associated probability densityFor theta R When X is R Maximum likelihood function value of>For distribution parameters +.>When (I)>Is used for the maximum likelihood function value of (a).
The detection unit is used for:
the first detection module is used for determining that the region corresponding to the electric power variable to be detected is an abnormal region when the degree of abnormality of the electric power variable to be detected is greater than a threshold value, and determining that the sampling period of the electric power variable to be detected is an abnormal period;
and the second detection module is used for determining that the region corresponding to the electric power variable to be detected is a normal region when the anomaly degree of the electric power variable to be detected is smaller than or equal to a threshold value, and determining that the sampling period of the electric power variable to be detected is a normal period.
After the detection unit, the method further comprises:
determining an abnormal state type corresponding to the electric variable to be detected according to a pre-established association rule base of the electric variable to be detected and the abnormal state type;
the association rule base of the pre-established power variable to be detected and the abnormal state type is established according to the abnormal state type corresponding to the historical data of the power variable to be detected.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical aspects of the present application and not for limiting the same, and although the present application has been described in detail with reference to the above embodiments, it should be understood by those of ordinary skill in the art that: modifications and equivalents may be made to the specific embodiments of the application without departing from the spirit and scope of the application, which is intended to be covered by the claims.

Claims (2)

1. The method for detecting the abnormal state of the power distribution network is characterized by comprising the following steps of:
obtaining a maximum likelihood estimation value of a parameter of a related probability density function of the power variable to be detected;
determining the degree of abnormality of the electric power variable to be detected according to the maximum likelihood estimation value of the parameter of the related probability density function of the electric power variable to be detected;
establishing likelihood functions of parameters of a related probability density function of the power variable to be detected:
wherein f (X) R θ) is a correlation probability density function of the power variable to be detected in the detection period in the region to be detected,detecting a relative probability density function of the power variable for a non-detection period within the non-detection region, f (X) G θ) is a related probability density function of a power variable to be detected in a whole region and in a whole period of a power distribution network, and L (θ|X) R ) Parameter likelihood function value of correlation probability density function of electric power variable to be detected for detection period in the region to be detected,/>The parameter likelihood function value of the correlation probability density function for detecting the power variable for the non-detection period in the non-detection region, L (θ|X G ) Is thatThe method comprises the steps that (1) the likelihood function value of a parameter of a correlation probability density function of a power variable to be detected in a whole region of a power distribution network in a whole period is m, the m is the number of sample data of the power variable to be detected in a detection period in the region to be detected, the n is the number of sample data of the power variable to be detected in a non-detection period in a non-detection region, and the l is the number of sample data of the power variable to be detected in the whole region of the power distribution network in the whole period;
detecting abnormal states of the power distribution network by using the degree of abnormality of the power variable to be detected;
the abnormal state detection of the power distribution network by using the degree of abnormality of the power variable to be detected comprises the following steps:
when the abnormality degree of the electric power variable to be detected is larger than a threshold value, the area corresponding to the electric power variable to be detected is an abnormal area, and the sampling period of the electric power variable to be detected is an abnormal period;
when the anomaly degree of the electric power variable to be detected is smaller than or equal to a threshold value, the area corresponding to the electric power variable to be detected is a normal area, and the sampling period of the electric power variable to be detected is a normal period;
after the abnormal state detection is performed on the power distribution network by utilizing the degree of abnormality of the power variable to be detected, the method comprises the following steps:
acquiring a to-be-detected power variable of an abnormal region in an abnormal period, and determining an abnormal state type corresponding to the to-be-detected power variable of the abnormal region in the abnormal period according to a pre-established association rule base of the to-be-detected power variable and the abnormal state type;
the association rule base of the pre-established power variable to be detected and the abnormal state type is established according to the abnormal state type corresponding to the historical data of the power variable to be detected;
the abnormal state type includes:
risk status, fault status, recovery status, and optimization status;
the power variable to be detected includes:
voltage, current, line loss, load data, voltage variance, current variance, voltage skewness, current skewness, voltage kurtosis, and current kurtosis;
the determining the abnormality degree of the electric power variable to be detected according to the maximum likelihood estimation value of the parameter of the related probability density function of the electric power variable to be detected comprises the following steps:
the degree of abnormality D (R) of the power variable to be detected is determined as follows:
wherein X is R As the power variable to be detected for the detection period in the region to be detected,x is the power variable to be detected in the non-detection period in the non-detection area G For the electric power variable to be detected in the whole region and the whole period of the power distribution network, theta R Is based on X R Maximum likelihood estimate of a parameter of the associated probability density function, +.>Is based on->Maximum likelihood estimate, θ, of a parameter of a related probability density function G Is based on X G Maximum likelihood estimate of a parameter of a related probability density function, L (θ G |X R ) The parameter being a function of the associated probability density is θ G Time X R Likelihood function value of (2), L (θ) G |X G ) The parameter being a function of the associated probability density is θ G Time X G Is the maximum likelihood function value of (2), L (θ) R |X R ) The parameter being a function of the associated probability density is θ R When X is R Maximum likelihood function value of>For distribution parameters +.>When (I)>Is used for the maximum likelihood function value of (a).
2. A device for detecting an abnormal state of a power distribution network, the device comprising:
the acquisition unit is used for acquiring the maximum likelihood estimation value of the parameter of the related probability density function of the electric power variable to be detected;
the determining unit is used for determining the degree of abnormality of the electric power variable to be detected according to the maximum likelihood estimated value of the parameter of the related probability density function of the electric power variable to be detected;
establishing likelihood functions of parameters of a related probability density function of the power variable to be detected:
wherein f (X) R θ) is a correlation probability density function of the power variable to be detected in the detection period in the region to be detected,detecting a relative probability density function of the power variable for a non-detection period within the non-detection region, f (X) G θ) is a related probability density function of a power variable to be detected in a whole region and in a whole period of a power distribution network, and L (θ|X) R ) Correlation probability density of electric power variable to be detected for detection period in region to be detectedParameter likelihood function value of the degree function, +.>The parameter likelihood function value of the correlation probability density function for detecting the power variable for the non-detection period in the non-detection region, L (θ|X G ) The method comprises the steps that the likelihood function value of parameters of a correlation probability density function of electric power variables to be detected in all time periods in a power distribution network all-area is calculated, m is the number of sample data of the electric power variables to be detected in the detection time periods, n is the number of sample data of the electric power variables to be detected in the non-detection time periods, and l is the number of sample data of the electric power variables to be detected in all the time periods in the power distribution network all-area;
the detection unit is used for detecting abnormal states of the power distribution network by utilizing the degree of abnormality of the power variable to be detected;
the determining unit is used for:
the degree of abnormality D (R) of the power variable to be detected is determined as follows:
wherein X is R As the power variable to be detected for the detection period in the region to be detected,x is the power variable to be detected in the non-detection period in the non-detection area G For the electric power variable to be detected in the whole region and the whole period of the power distribution network, theta R Is based on X R Maximum likelihood estimate of a parameter of the associated probability density function, +.>Is based on X R Maximum likelihood estimate, θ, of a parameter of a related probability density function G Is based on X G Maximum likelihood estimate of a parameter of a related probability density function, L (θ G |X R ) The parameters as the related probability density function areθ G Time X R Likelihood function value of (2), L (θ) G |X G ) The parameter being a function of the associated probability density is θ G Time X G Is the maximum likelihood function value of (2), L (θ) R |X R ) The parameter being a function of the associated probability density is θ R When X is R Maximum likelihood function value of>For distribution parameters +.>When (I)>Is a maximum likelihood function value of (2);
the detection unit is used for:
the first detection module is used for determining that the region corresponding to the electric power variable to be detected is an abnormal region when the degree of abnormality of the electric power variable to be detected is greater than a threshold value, and determining that the sampling period of the electric power variable to be detected is an abnormal period;
the second detection module is used for determining that the region corresponding to the electric power variable to be detected is a normal region when the anomaly degree of the electric power variable to be detected is smaller than or equal to a threshold value, and determining that the sampling period of the electric power variable to be detected is a normal period;
after the detection unit, the method further comprises:
acquiring a to-be-detected power variable of an abnormal region in an abnormal period, and determining an abnormal state type corresponding to the to-be-detected power variable of the abnormal region in the abnormal period according to a pre-established association rule base of the to-be-detected power variable and the abnormal state type;
the association rule base of the pre-established power variable to be detected and the abnormal state type is established according to the abnormal state type corresponding to the historical data of the power variable to be detected.
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