CN114155515A - Power distribution station equipment defect judgment method and system - Google Patents

Power distribution station equipment defect judgment method and system Download PDF

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Publication number
CN114155515A
CN114155515A CN202111331363.XA CN202111331363A CN114155515A CN 114155515 A CN114155515 A CN 114155515A CN 202111331363 A CN202111331363 A CN 202111331363A CN 114155515 A CN114155515 A CN 114155515A
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voltage equipment
defect
equipment
image
transformer substation
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徐伟
陈金刚
杨靖玮
苗振林
张鲲鹏
陈炜强
董丽艳
杨钱江
陈耀君
杨燕华
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Jiaxing Hengguang Power Construction Co ltd Nanhu Branch
Jiaxing Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
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Jiaxing Hengguang Power Construction Co ltd Nanhu Branch
Jiaxing Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/22—Matching criteria, e.g. proximity measures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2413—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
    • G06F18/24147—Distances to closest patterns, e.g. nearest neighbour classification

Abstract

The invention discloses a method and a system for judging the defects of distribution station equipment, wherein the method for judging the defects of the distribution station equipment comprises the steps of acquiring images of high-voltage equipment of a transformer substation through an image processing module and filtering the images of the high-voltage equipment of the transformer substation; extracting features in the image of the high-voltage equipment of the transformer substation by using an identification module, and roughly classifying the categories of the high-voltage equipment according to the features; identifying the category obtained by rough classification through a defect judging module, and positioning suspected defect high-voltage equipment; calculating the time-space similarity between the suspected defect high-voltage equipment and the similar normal equipment through a data processing module, and judging the defects of the high-voltage equipment through the time-space similarity; according to the invention, through designing the filtering unit, the detection precision of the defects of the high-voltage equipment is improved, and meanwhile, the accuracy and reliability of the defect identification are further improved through the space-time similarity.

Description

Power distribution station equipment defect judgment method and system
Technical Field
The invention relates to the technical field of defect judgment of high-voltage equipment, in particular to a method and a system for judging defects of power distribution station equipment.
Background
With the national economic development, the electricity demand of residents and enterprises is rapidly increased, and the power supply of partial regions is in tension, so that a new challenge is brought to a power system. As an important component of a power grid, the normal operation of high-voltage electrical equipment of a transformer substation is closely related to the reliability of power supply, so that the key is to monitor in real time, regularly inspect and timely remove faults to ensure the safe and stable operation of the electrical equipment. At present, a remote monitoring system is installed in a plurality of transformer substations to realize the functions of monitoring field equipment, controlling the motion of a camera, recording video and the like. In addition, automatic inspection equipment such as unmanned aerial vehicles and inspection robots are also widely used. However, the large number of pictures obtained in the above process are difficult to satisfy the real-time requirement by manual viewing. And human factors can greatly influence the monitoring effect, and when the equipment is not identified by naked eyes rapidly, the conditions of misinformation, missing report and the like often occur.
The existing defect detection based on infrared images of the transformer equipment adopts a fast RCNN network for image recognition, however, the method depends on recognition effect, and misjudgment can occur once the equipment type is recognized wrongly or the positioning is deviated.
Disclosure of Invention
This section is for the purpose of summarizing some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. In this section, as well as in the abstract and the title of the invention of this application, simplifications or omissions may be made to avoid obscuring the purpose of the section, the abstract and the title, and such simplifications or omissions are not intended to limit the scope of the invention.
The present invention has been made in view of the above-mentioned conventional problems.
In order to solve the technical problems, the invention provides the following technical scheme: the method comprises the steps of acquiring a transformer substation high-voltage equipment image through an image processing module, and filtering the transformer substation high-voltage equipment image; extracting features in the image of the high-voltage equipment of the transformer substation by using an identification module, and roughly classifying the categories of the high-voltage equipment according to the features; identifying the category obtained by rough classification through a defect judging module, and positioning suspected defect high-voltage equipment; and calculating the space-time similarity between the suspected defect high-voltage equipment and the similar normal equipment through a data processing module, and judging the defects of the high-voltage equipment through the space-time similarity.
As a preferable embodiment of the method for determining defects of a distribution substation device according to the present invention, the method includes: the image processing module comprises an acquisition unit and a filtering unit; the acquisition unit is used for acquiring the image of the high-voltage equipment of the transformer substation, and then the filtering unit is used for filtering the noise of the image of the high-voltage equipment of the transformer substation.
As a preferable embodiment of the method for determining defects of a distribution substation device according to the present invention, the method includes: the filtering unit comprises a parameter setting unit for setting relevant parameters of the filtering unit based on a Gaussian low-pass filter; defining an approximation function of the filtering unit, determining the number of resonant cavities according to the approximation function, and finishing the design of the filtering unit; wherein the relevant parameters include bandwidth, rejection height at low stopband, and in-band return loss.
As a preferable embodiment of the method for determining defects of a distribution substation device according to the present invention, the method includes: the approximating function comprises a transfer function Q11And a reflection function Q21(ii) a Said transfer function Q11Comprises the following steps:
Figure BDA0003348996930000021
said reflection function Q21Comprises the following steps:
Figure BDA0003348996930000022
wherein, M is zero point number, omega is time frequency variable, epsilon is equal ripple constant of omega + -1, FN(ω)、FN(ω)、PNAnd (ω) is a characteristic polynomial of the chebyshev function.
As a preferable embodiment of the method for determining defects of a distribution substation device according to the present invention, the method includes: the identification module comprises a feature extraction unit and a rough classification unit; the feature extraction unit comprises a basic network, an additional feature layer and a prediction branch; setting a prior frame by using the basic network; extracting features in the prior frame through an additional feature layer and a prediction branch; inputting the extracted features into the rough classification unit, and roughly classifying the categories of the high-voltage equipment by the rough classification unit through an SIFT algorithm; the basic network adopts a VGG16 network, the additional feature layer adopts a preset anchor point and feature pyramid technology, and the prediction branch removes repeated recognition results through a non-maximum suppression strategy.
As a preferable embodiment of the method for determining defects of a distribution substation device according to the present invention, the method includes: the high-voltage equipment defect identification comprises the steps of setting a gray threshold value, and defining an area larger than the gray threshold value as an area where suspected defect high-voltage equipment is located, so as to locate the suspected defect high-voltage equipment.
As a preferable embodiment of the method for determining defects of a distribution substation device according to the present invention, the method includes: the spatio-temporal similarity includes a spatial similarity between,
s(Ri,Rj)＝α1sc+α2st+α3sf
Figure BDA0003348996930000023
Figure BDA0003348996930000031
Figure BDA0003348996930000032
wherein, s (R)i,Rj) Is RiAnd RjSpatial and temporal similarity between, RiAnd RjRespectively representing the area i where the suspected defect high-voltage equipment is located and the area j where the similar normal equipment is located, wherein alpha is (alpha)1,α2,α3) Is a weight vector; scAs a color similarity, ciIs the distance of adjacent pixel points in region i, cjThe distance between adjacent pixel points in the region j; stFor texture similarity, ti kThe kth texture feature vector, t, of region ij kThe kth texture feature vector of region j, k being 1, 2 … n; sfFor shape similarity, size (R)i) Indicates the number of pixels of the area i, size (R)i) Indicates the number of pixels of the area i, size (im) indicates the number of pixels of the whole picture, BijA rectangular bounding box after merging the region i and the region j.
As a preferable aspect of the system for determining defects in substation equipment according to the present invention, the system further includes: the system comprises an image module, a data processing module and a data processing module, wherein the image module is used for acquiring a transformer substation high-voltage equipment image and removing noise of the transformer substation high-voltage equipment image; the identification module is connected with the image processing module and used for extracting the features in the transformer substation high-voltage equipment image and roughly classifying the category of the transformer substation high-voltage equipment image according to the features; the defect judging module is connected with the identification module and used for judging the categories obtained by rough classification and positioning suspected defect high-voltage equipment; and the data processing module is connected with the defect judging module and is used for calculating the space-time similarity between the suspected defect high-voltage equipment and the similar normal equipment and judging the defects of the high-voltage equipment according to the space-time similarity.
As a preferable aspect of the system for determining defects in substation equipment according to the present invention, the system further includes: the image processing module comprises an acquisition unit and a filtering unit; the acquisition unit is used for acquiring images of high-voltage equipment of the transformer substation; and the filtering unit is connected with the acquisition unit and is used for removing the noise of the image of the high-voltage equipment of the transformer substation.
As a preferable aspect of the system for determining defects in substation equipment according to the present invention, the system further includes: the identification module comprises a feature extraction unit and a rough classification unit; the characteristic extraction unit is used for extracting characteristics in the image of the high-voltage equipment of the transformer substation; and the rough classification unit is connected with the feature extraction unit and is used for roughly classifying the categories of the high-voltage equipment through a SIFT algorithm.
The invention has the beneficial effects that: according to the invention, through designing the filtering unit, the detection precision of the defects of the high-voltage equipment is improved, and meanwhile, the accuracy and reliability of the defect identification are further improved through the space-time similarity.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise. Wherein:
fig. 1 is a schematic flowchart of a method for determining defects of distribution substation equipment according to a first embodiment of the present invention;
fig. 2 is a schematic structural diagram of a substation equipment defect determination system according to a second embodiment of the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, specific embodiments accompanied with figures are described in detail below, and it is apparent that the described embodiments are a part of the embodiments of the present invention, not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making creative efforts based on the embodiments of the present invention, shall fall within the protection scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those specifically described and will be readily apparent to those of ordinary skill in the art without departing from the spirit of the present invention, and therefore the present invention is not limited to the specific embodiments disclosed below.
Furthermore, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
The present invention will be described in detail with reference to the drawings, wherein the cross-sectional views illustrating the structure of the device are not enlarged partially in general scale for convenience of illustration, and the drawings are only exemplary and should not be construed as limiting the scope of the present invention. In addition, the three-dimensional dimensions of length, width and depth should be included in the actual fabrication.
Meanwhile, in the description of the present invention, it should be noted that the terms "upper, lower, inner and outer" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of describing the present invention and simplifying the description, but do not indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation and operate, and thus, cannot be construed as limiting the present invention. Furthermore, the terms first, second, or third are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The terms "mounted, connected and connected" in the present invention are to be understood broadly, unless otherwise explicitly specified or limited, for example: can be fixedly connected, detachably connected or integrally connected; they may be mechanically, electrically, or directly connected, or indirectly connected through intervening media, or may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Example 1
Referring to fig. 1, a first embodiment of the present invention provides a method for determining defects of distribution substation equipment, including:
s1: the image processing module 100 is used for acquiring the image of the high-voltage equipment of the transformer substation and filtering the image of the high-voltage equipment of the transformer substation.
The image processing module 100 includes an acquisition unit 101 and a filtering unit 102; the image of the high-voltage equipment of the transformer substation is acquired through the acquisition unit 101, and then the noise of the image of the high-voltage equipment of the transformer substation is filtered through the filtering unit 102.
The acquisition unit 101 may be a CCD infrared camera.
Further, the filter unit 102 is designed to:
(1) setting a relevant parameter of the filtering unit 102 based on the gaussian low-pass filter;
relevant parameters include bandwidth, rejection height at the low stopband, and in-band return loss.
(2) Defining an approximation function of the filtering unit 102, determining the number of resonant cavities according to the approximation function, and completing the design of the filtering unit 102;
the approximating function comprises a transfer function Q11And a reflection function Q21；
Transfer function Q11Comprises the following steps:
Figure BDA0003348996930000051
reflection function Q21Comprises the following steps:
Figure BDA0003348996930000052
wherein, M is zero point number, omega is time frequency variable, epsilon is equal ripple constant of omega + -1, FN(ω)、FN(ω)、PNAnd (ω) is a characteristic polynomial of the chebyshev function.
Preferably, the filtering unit 102 is designed, so that the noise of the image of the high-voltage equipment of the transformer substation is effectively filtered, and the time delay is reduced.
S2: the identification module 200 is used for extracting the characteristics in the image of the high-voltage equipment of the transformer substation, and the category of the high-voltage equipment is roughly classified according to the characteristics.
The recognition module 200 includes a feature extraction unit 201 and a rough classification unit 202;
(1) the feature extraction unit 201 includes a base network, an additional feature layer, and a prediction branch;
setting a prior frame by utilizing a basic network;
extracting the features in the prior frame through an additional feature layer and a prediction branch;
the basic network adopts a VGG16 network, the additional feature layer adopts a preset anchor point and feature pyramid technology, and the prediction branch removes repeated recognition results through a non-maximum suppression strategy.
(2) The extracted features are input to the rough classification unit 202, and the rough classification unit 202 performs rough classification on the category of the high-voltage equipment by using a SIFT algorithm.
S3: the classification obtained by the rough classification is identified by the defect discrimination module 300, and the suspected defect high-voltage equipment is positioned.
Setting a gray threshold, and defining an area larger than the gray threshold as an area where the suspected defect high-voltage equipment is located, so as to position the suspected defect high-voltage equipment.
S4: the time-space similarity between the suspected defect high-voltage equipment and the similar normal equipment is calculated through the data processing module 400, and the defect of the high-voltage equipment is judged through the time-space similarity.
And (3) calculating the space-time similarity:
s(Ri,Rj)＝α1sc+α2st+α3sf
Figure BDA0003348996930000061
Figure BDA0003348996930000062
Figure BDA0003348996930000063
wherein, s (R)i,Rj) Is RiAnd RjSpatial and temporal similarity between, RiAnd RjRespectively representing the area i where the suspected defect high-voltage equipment is located and the area j where the similar normal equipment is located, wherein alpha is (alpha)1,α2,α3) Is a weight vector; scAs a color similarity, ciIs the distance of adjacent pixel points in region i, cjFor the distance between adjacent pixels in the region j, c is calculated according to the Euclidean principle in the embodimentiAnd cj；stFor texture similarity, ti kThe kth texture feature vector, t, of region ij kThe kth texture feature vector of region j, k being 1, 2 … n; sfFor shape similarity, size (R)i) Indicates the number of pixels of the area i, size (R)i) Indicates the number of pixels of the area i, size (im) indicates the number of pixels of the whole picture, BijA rectangular bounding box after merging the region i and the region j.
In order to verify and explain the technical effects adopted in the method, the embodiment selects the CNN model and the YOLO model and adopts the method to perform comparison tests, and compares the test results by means of scientific demonstration to verify the real effects of the method.
In this embodiment, a CNN model, a YOLO model, and the method are used to perform defect detection and comparison on different types of high-voltage devices.
The types of the equipment comprise a transformer, switch equipment, a sleeve, a mutual inductor and a lightning arrester, 300 transformer substation equipment images are respectively identified and detected by adopting a CNN model, a YOLO model and the method, wherein 80 transformer substation equipment images comprise equipment defects and are detected on a matlab platform, and the results are shown in the following table.
Table 1: and the defect identification accuracy rate of different types of equipment is corresponded.
Figure BDA0003348996930000071
It can be seen that the accuracy rate corresponding to the method is obviously higher than that of the other two methods, and a better defect identification effect is obtained.
Example 2
Referring to fig. 2, a second embodiment of the present invention, which is different from the first embodiment, provides a substation equipment defect determination system, including,
the image module 100 is used for acquiring an image of high-voltage equipment of the transformer substation and removing noise of the image of the high-voltage equipment of the transformer substation; the image processing module 100 includes an acquisition unit 101 and a filtering unit 102; the acquisition unit 101 is used for acquiring images of high-voltage equipment of the transformer substation; and the filtering unit 102 is connected with the acquisition unit 101 and is used for removing noise of the transformer substation high-voltage equipment image.
The identification module 200 is connected with the image processing module 100, and is used for extracting features in the transformer substation high-voltage equipment image and roughly classifying the category of the transformer substation high-voltage equipment image according to the features; the recognition module 200 includes a feature extraction unit 201 and a rough classification unit 202; the feature extraction unit 201 is used for extracting features in the transformer substation high-voltage equipment image; and the rough classification unit 202 is connected with the feature extraction unit 201 and is used for roughly classifying the category of the high-voltage equipment through a SIFT algorithm.
The defect judging module 300 is connected with the identifying module 200 and used for judging the categories obtained by rough classification and positioning suspected defect high-voltage equipment;
and the data processing module 400 is connected with the defect judging module 300 and is used for calculating the space-time similarity between the suspected defect high-voltage equipment and the similar normal equipment and judging the defect of the high-voltage equipment according to the space-time similarity.
It should be recognized that embodiments of the present invention can be realized and implemented by computer hardware, a combination of hardware and software, or by computer instructions stored in a non-transitory computer readable memory. The methods may be implemented in a computer program using standard programming techniques, including a non-transitory computer-readable storage medium configured with the computer program, where the storage medium so configured causes a computer to operate in a specific and predefined manner, according to the methods and figures described in the detailed description. Each program may be implemented in a high level procedural or object oriented programming language to communicate with a computer system. However, the program(s) can be implemented in assembly or machine language, if desired. In any case, the language may be a compiled or interpreted language. Furthermore, the program can be run on a programmed application specific integrated circuit for this purpose.
Further, the operations of processes described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The processes described herein (or variations and/or combinations thereof) may be performed under the control of one or more computer systems configured with executable instructions, and may be implemented as code (e.g., executable instructions, one or more computer programs, or one or more applications) collectively executed on one or more processors, by hardware, or combinations thereof. The computer program includes a plurality of instructions executable by one or more processors.
Further, the method may be implemented in any type of computing platform operatively connected to a suitable interface, including but not limited to a personal computer, mini computer, mainframe, workstation, networked or distributed computing environment, separate or integrated computer platform, or in communication with a charged particle tool or other imaging device, and the like. Aspects of the invention may be embodied in machine-readable code stored on a non-transitory storage medium or device, whether removable or integrated into a computing platform, such as a hard disk, optically read and/or write storage medium, RAM, ROM, or the like, such that it may be read by a programmable computer, which when read by the storage medium or device, is operative to configure and operate the computer to perform the procedures described herein. Further, the machine-readable code, or portions thereof, may be transmitted over a wired or wireless network. The invention described herein includes these and other different types of non-transitory computer-readable storage media when such media include instructions or programs that implement the steps described above in conjunction with a microprocessor or other data processor. The invention also includes the computer itself when programmed according to the methods and techniques described herein. A computer program can be applied to input data to perform the functions described herein to transform the input data to generate output data that is stored to non-volatile memory. The output information may also be applied to one or more output devices, such as a display. In a preferred embodiment of the invention, the transformed data represents physical and tangible objects, including particular visual depictions of physical and tangible objects produced on a display.
As used in this application, the terms "component," "module," "system," and the like are intended to refer to a computer-related entity, either hardware, firmware, a combination of hardware and software, or software in execution. For example, a component may be, but is not limited to being: a process running on a processor, an object, an executable, a thread of execution, a program, and/or a computer. By way of example, both an application running on a computing device and the computing device can be a component. One or more components can reside within a process and/or thread of execution and a component can be localized on one computer and/or distributed between two or more computers. In addition, these components can execute from various computer readable media having various data structures thereon. The components may communicate by way of local and/or remote processes such as in accordance with a signal having one or more data packets (e.g., data from one component interacting with another component in a local system, distributed system, and/or across a network such as the internet with other systems by way of the signal).
It should be noted that the above-mentioned embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, which should be covered by the claims of the present invention.

Claims (10)

1. A method for judging defects of power distribution station equipment is characterized by comprising the following steps: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
acquiring a transformer substation high-voltage equipment image through an image processing module (100), and filtering the transformer substation high-voltage equipment image;
extracting features in the image of the high-voltage equipment of the transformer substation by using an identification module (200), and roughly classifying the categories of the high-voltage equipment according to the features;
identifying the category obtained by rough classification through a defect judging module (300), and positioning suspected defect high-voltage equipment;
the time-space similarity between the suspected defect high-voltage equipment and the similar normal equipment is calculated through the data processing module (400), and the defect of the high-voltage equipment is judged through the time-space similarity.
2. The substation device defect discrimination method according to claim 1, wherein: the image processing module (100) comprises an acquisition unit (101) and a filtering unit (102);
the acquisition unit (101) is used for acquiring the image of the high-voltage equipment of the transformer substation, and then the filtering unit (102) is used for filtering the noise of the image of the high-voltage equipment of the transformer substation.
3. The substation device defect judgment method according to claim 2, wherein: the filtering unit (102) comprises a filter unit,
setting a correlation parameter of a filtering unit (102) based on a Gaussian low-pass filter;
defining an approximation function of the filtering unit (102), determining the number of resonant cavities according to the approximation function, and finishing the design of the filtering unit (102);
wherein the relevant parameters include bandwidth, rejection height at low stopband, and in-band return loss.
4. The substation device defect judgment method according to claim 3, wherein: the approximating function comprises a transfer function Q11And a reflection function Q21；
Said transfer function Q11Comprises the following steps:
Figure FDA0003348996920000011
said reflection function Q21Comprises the following steps:
Figure FDA0003348996920000012
wherein, M is zero point number, omega is time frequency variable, epsilon is equal ripple constant of omega + -1, FN(ω)、FN(ω)、PNAnd (ω) is a characteristic polynomial of the chebyshev function.
5. The distribution station equipment defect discrimination method according to claim 1 or 2, characterized by: the recognition module (200) comprises a feature extraction unit (201) and a rough classification unit (202);
the feature extraction unit (201) comprises a base network, an additional feature layer and a prediction branch;
setting a prior frame by using the basic network;
extracting features in the prior frame through an additional feature layer and a prediction branch;
inputting the extracted features into the rough classification unit (202), wherein the rough classification unit (202) performs rough classification on the category of the high-voltage equipment by utilizing a SIFT algorithm;
the basic network adopts a VGG16 network, the additional feature layer adopts a preset anchor point and feature pyramid technology, and the prediction branch removes repeated recognition results through a non-maximum suppression strategy.
6. The substation device defect judgment method according to claim 5, wherein: the high-voltage equipment defect identification comprises the steps of,
setting a gray threshold, and defining an area larger than the gray threshold as an area where suspected defect high-voltage equipment is located, so as to position the suspected defect high-voltage equipment.
7. The substation device defect judgment method according to claim 6, wherein: the spatio-temporal similarity includes a spatial similarity between,
s(Ri,Rj)＝α1sc+α2st+α3sf
Figure FDA0003348996920000021
Figure FDA0003348996920000022
Figure FDA0003348996920000023
wherein, s (R)i,Rj) Is RiAnd RjSpatial and temporal similarity between, RiAnd RjRespectively representing the area i where the suspected defect high-voltage equipment is located and the area j where the similar normal equipment is located, wherein alpha is (alpha)1,α2,α3) Is a weight vector; scAs a color similarity, ciIs the distance of adjacent pixel points in region i, cjThe distance between adjacent pixel points in the region j; stIn order to be the degree of similarity of the texture,
Figure FDA0003348996920000024
the k-th texture feature vector of region i,
Figure FDA0003348996920000025
the kth texture feature vector of region j, k being 1, 2 … n; sfFor shape similarity, size (R)i) Indicates the number of pixels of the area i, size (R)i) Indicates the number of pixels of the area i, size (im) indicates the number of pixels of the whole picture, BijA rectangular bounding box after merging the region i and the region j.
8. A power distribution station equipment defect discrimination system is characterized in that: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
the image module (100) is used for acquiring a transformer substation high-voltage equipment image and removing noise of the transformer substation high-voltage equipment image;
the identification module (200) is connected with the image processing module (100) and is used for extracting features in the transformer substation high-voltage equipment image and roughly classifying the category of the transformer substation high-voltage equipment image according to the features;
the defect judging module (300) is connected with the identification module (200) and is used for judging the categories obtained by rough classification and positioning suspected defect high-voltage equipment;
and the data processing module (400) is connected with the defect judging module (300) and is used for calculating the space-time similarity between the suspected defect high-voltage equipment and the similar normal equipment and judging the defect of the high-voltage equipment according to the space-time similarity.
9. The substation equipment defect discrimination system of claim 8, wherein: the image processing module (100) comprises an acquisition unit (101) and a filtering unit (102);
the acquisition unit (101) is used for acquiring images of high-voltage equipment of the transformer substation;
and the filtering unit (102) is connected with the acquisition unit (101) and is used for removing noise of the transformer substation high-voltage equipment image.
10. The substation equipment defect discrimination system of claim 9, wherein: the recognition module (200) comprises a feature extraction unit (201) and a rough classification unit (202);
the characteristic extraction unit (201) is used for extracting characteristics in the transformer substation high-voltage equipment image;
and the rough classification unit (202) is connected with the feature extraction unit (201) and is used for roughly classifying the category of the high-voltage equipment through a SIFT algorithm.
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