CN113252218A - Insulator surface stress prediction method and prediction device - Google Patents
Insulator surface stress prediction method and prediction device Download PDFInfo
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Abstract
本发明公开了一种绝缘子表面应力预测方法及预测装置，其中方法包括：确定绝缘子的工况，将工况分为正常工况以及螺栓松动时的工况；采集绝缘子在正常工况下的超声波信号值，以及在螺栓不同松动工况下的超声波信号值和应力值；将正常工况下和螺栓松动工况下的超声波信号能量差值以及应力值作为BP神经网络的输入值及输出值，对BP神经网络进行训练，得到训练好的BP神经网络；对所述BP神经网络的精度进行验证；本发明实现对在线盆式绝缘子的表面应力预测。
The invention discloses a method for predicting surface stress of an insulator and a prediction device, wherein the method comprises: determining the working conditions of the insulator, dividing the working conditions into normal working conditions and working conditions when bolts are loose; collecting ultrasonic waves of the insulator under the normal working conditions Signal value, as well as ultrasonic signal value and stress value under different bolt loosening conditions; the ultrasonic signal energy difference and stress value under normal working condition and bolt loosening condition are used as the input value and output value of the BP neural network, The BP neural network is trained to obtain a trained BP neural network; the accuracy of the BP neural network is verified; the invention realizes the prediction of the surface stress of the online basin insulator.
Description
Technical Field
The invention relates to the technical field of stress prediction, in particular to a method and a device for predicting surface stress of an insulator.
Background
At present, the basin-type insulator is generally formed by gluing or mechanically clamping an insulating part and metal accessories (such as steel feet, bolts and the like) by using an adhesive. Insulators are widely used in power systems, generally belong to external insulation, and operate under atmospheric conditions. The buses of overhead transmission lines, power plants and substations and the external live conductors of various electrical equipment are supported by insulators and insulated from the ground or other conductors having potential differences.
However, when the basin-type insulator operates in a high-voltage environment, the basin-type insulator bolt can be loosened due to vibration of a breaker in a GIS during opening and closing, uneven bolt sealing in the assembling and disassembling process and other factors during the operation process, when the basin-type insulator bolt is loosened, the flange is stressed unevenly, the stress on the inner surface of the basin-type insulator is distorted, and the basin-type insulator is broken, so that a gas leakage accident occurs; in the prior art, the stress of the basin-type insulator can be theoretically detected by an ultrasonic injection method, but the technology has the problems that ultrasonic waves are difficult to penetrate deeply and internal online detection cannot be realized. The chinese patent publication No. CN110531233 discloses that the ultrasonic injection method is used to measure the surface condition of a basin-type insulator, so as to achieve the purpose of measuring the surface contamination state of the basin-type insulator, but because a gap exists between a flange and epoxy resin, the method cannot test the surface stress of the basin-type insulator.
Therefore, how to provide a method for predicting the surface stress of an insulator, which can solve the above problems, is a problem that needs to be solved by those skilled in the art.
Disclosure of Invention
In view of the above, the invention provides an insulator surface stress prediction method and a prediction device, which can realize online prediction of surface stress of a basin-type insulator and early warning of a basin-type insulator breakage accident.
In order to achieve the purpose, the invention adopts the following technical scheme:
an insulator surface stress prediction method comprises the following steps:
determining the working condition of the insulator, and dividing the working condition into a normal working condition and a working condition when the bolt is loosened;
acquiring an ultrasonic signal value of the insulator under a normal working condition, and ultrasonic signal values and stress values of the insulator under different loosening working conditions of the bolt;
taking the ultrasonic signal energy difference value and the stress value under the normal working condition and the bolt loosening working condition as the input value and the output value of the BP neural network, and training the BP neural network to obtain a trained BP neural network;
verifying the accuracy of the BP neural network
Preferably, the method further comprises the following steps: the specific process for verifying the accuracy of the BP neural network is as follows:
comparing absolute errors of the stress measured value and the stress predicted value of the insulator under different loosening working conditions;
and training and correcting the BP neural network, and obtaining the predicted BP neural network when the back propagation error of the stress measured value and the stress predicted value meets the requirement.
Preferably, the BP neural network modification process includes: and adjusting the weight and the threshold value of the BP neural network by using a back propagation error function to correct, and finishing adjustment when the back propagation error function value reaches the minimum value.
Preferably, the BP neural network comprises: the device comprises an input layer, a hidden layer and an output layer, wherein the input layer is an ultrasonic transmission signal energy difference value of an insulator loose bolt, and the output layer is a stress value of a typical stress distortion point of a basin-type insulator.
Preferably, the number of the input layers is 1-3, and the number of the output layers is 3-9.
Further, the present invention also provides an insulator surface stress prediction apparatus, including:
the stress detection unit is used for collecting the surface stress of the insulator to be detected;
the ultrasonic detection unit is used for acquiring a surface ultrasonic signal value of the insulator to be detected;
the stress detection unit and the ultrasonic detection unit are connected with the upper computer, and the upper computer is used for processing the stress detection unit and the data acquired by the ultrasonic detection unit.
Preferably, the upper computer includes:
the BP neural network building unit is used for building a BP neural network and training the BP neural network by utilizing the surface stress and the ultrasonic signal value under the normal working condition and the bolt loosening working condition;
the BP neural network evaluation unit is used for verifying the precision of the BP neural network.
According to the technical scheme, compared with the prior art, the insulator surface stress prediction method and device are provided, the basin-type insulator surface stress prediction method based on the combination of ultrasonic transmission signal energy comparison and a BP neural network overcomes the defect that ultrasonic waves are difficult to inject into a basin-type insulator, the feasibility of online prediction of the basin-type insulator surface stress is improved, and the early warning efficiency of the basin-type insulator breakage accident is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
Fig. 1 is a flow chart of a method for predicting surface stress of an insulator according to the present invention;
fig. 2 is a schematic structural block diagram of an insulator surface stress prediction apparatus provided in the present invention;
fig. 3 is a schematic structural diagram of a BP neural network according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
When the basin-type insulator flange fixing bolt is loosened, the flange is stressed unevenly, so that severe accidents such as GIS (gas insulated switchgear) gas leakage and the like can be caused, when the basin-type insulator bolt is loosened, the stress distortion of the position with smaller curvature radius at the opposite side of the loosened bolt close to the flange is most obvious,
therefore, the embodiment of the invention provides a surface stress prediction method applicable to basin-type insulators, ultrasonic transmission signals measured when basin-type insulator bolts are loosened and stress values of typical stress distortion points are used as training and testing samples of a BP neural network, a BP neural network model for stress prediction is obtained through training, and the BP neural network obtained through training can realize surface stress prediction of the basin-type insulators. The method is beneficial to improving the stress monitoring efficiency of the basin-type insulator, realizing on-line stress prediction, and achieving the purposes of eliminating hidden dangers and realizing safe production.
Example 1
Referring to the attached drawing 1, an embodiment 1 of the present invention discloses a method for predicting insulator surface stress, including:
determining the working condition of the insulator, and dividing the working condition into a normal working condition and a working condition when the bolt is loosened;
the working condition of bolt loosening of the basin-type insulator is determined, and the bolt loosening is mainly divided into three working conditions, namely one bolt loosening, two bolt loosening and three bolt loosening.
When two bolts are loosened, the two bolts are classified according to the angle between the two bolts, and six modes of 30 degrees, 60 degrees, 90 degrees, 120 degrees, 150 degrees and 180 degrees are provided according to the classification mode.
The mode classification when three bolts are loosened can be classified according to the angles among the three bolts, as shown in fig. 1, the arrow indicates a loosened bolt which is sequentially 1, 3 and 5, and the mode number is determined as "60-60, 135".
Acquiring an ultrasonic signal value of the insulator under a normal working condition, and ultrasonic signal values and stress values of the insulator under different loosening working conditions of the bolt;
specifically, under different working conditions, the bolt torques are set in a gradient mode and combined according to the number of bolts, ultrasonic transmission signal energy and stress values under the combination of various bolt torques are measured, partial group data serve as training samples of the BP neural network under the working conditions, and data of the rest groups serve as testing samples;
the stress value acquisition points are located at radial positions along the loosened bolts, and each bolt acquires the stress values of three points at corresponding positions.
Taking the ultrasonic signal energy difference value and the stress value under the normal working condition and the bolt loosening working condition as the input value and the output value of the BP neural network, and training the BP neural network to obtain a trained BP neural network;
and verifying the precision of the BP neural network.
In a specific embodiment, the method further comprises the following steps: the specific process for verifying the accuracy of the BP neural network is as follows:
comparing absolute errors of the stress measured value and the stress predicted value of the insulator under different loosening working conditions;
and training and correcting the BP neural network, and obtaining the predicted BP neural network when the back propagation error of the stress measured value and the stress predicted value meets the requirement.
Specifically, the BP neural network correction process includes: and adjusting the weight and the threshold value of the BP neural network by using a back propagation error function to correct, and finishing adjustment when the back propagation error function value E reaches the minimum value, wherein the specific expression is as follows:
wherein t isiAnd OiRespectively the expected output of the surface stress of the basin-type insulator and the calculated output of the BP neural network.
Referring to fig. 3, in a specific embodiment, the BP neural network comprises: the device comprises an input layer, a hidden layer and an output layer, wherein the input layer is an ultrasonic transmission signal energy difference value of an insulator loose bolt, and the output layer is a stress value of a typical stress distortion point of a basin-type insulator.
In a specific embodiment, the number of the input layers is 1-3, the number of the output layers is 3-9, the number of the hidden layer nodes is determined according to the number of the input and output nodes, and the value of the hidden layer nodes is usually between the number of the input layer nodes and the number of the output layer nodes and can be adjusted in the interval as required.
Specifically, the number of input layers and the number of output layers of the BP neural network are determined according to the number of loosened bolts and the number of typical stress distortion points, the number of hidden layers can be set to be 1, and the number of output layers can be selected to be 3, 6 and 9 according to the number of typical stress distortion points, namely when the number of loosened bolts is 1, the number of input layers is 1, and the number of output layers is 3; when the number of the loose bolts is 2, the number of the input layers is 2, and the number of the output layers is 6; when the number of the loose bolts is 3, the number of the input layers is 3, and the number of the output layers is 9
When one bolt and two bolts of the insulator are loosened, namely the number of input layers is respectively 1 and 2, the stress value distortion of corresponding typical stress distortion points is obvious, so that the prediction effect is more obvious.
Specifically, the number of hidden layer nodes is determined according to the number of input and output nodes, and the value is determined by the following formula, wherein n is the number of input layer neurons, m is the number of output layer neurons, and a is [1,10 ]]The expression of the number l of nodes of the hidden layer is:
specifically, parameters such as an error index, a learning rate and a relative error of the BP neural network are determined according to requirements;
the learning rate is determined according to the training speed and the loss, generally between 0.001 and 0.1, the too large learning is difficult to converge, the too small training process is greatly increased, and the preference is given0.1; the error index is selected within the range of 10-5To 10-1In between, too large may result in larger relative error of training result, too small may result in longer training time, BP neural network is not reached, and preference 10-5。
Referring to fig. 2, embodiment 1 of the present invention further provides an insulator surface stress prediction apparatus, including:
the stress detection unit 1 is used for collecting the surface stress of the insulator to be detected;
the ultrasonic detection unit 2 is used for acquiring a surface ultrasonic signal value of the insulator to be detected;
the stress detection unit 1 and the ultrasonic detection unit 2 are connected with the upper computer 3, and the upper computer 3 is used for processing data collected by the stress detection unit 1 and the ultrasonic detection unit 2.
In a specific embodiment, the upper computer 3 includes:
the BP neural network construction unit 31 is used for establishing the BP neural network, and training the BP neural network by utilizing the surface stress and the ultrasonic signal value under the normal working condition and the bolt loosening working condition;
the BP neural network evaluation unit 32 and the BP neural network evaluation unit 32 are used for verifying the accuracy of the BP neural network.
Example 2
The method provided by the embodiment 1 of the invention is applied, and the specific process is as follows:
selecting a 252kV single-phase basin-type insulator, wherein the thread specification of a fastening bolt is M16, the standard torque of the bolt is 110 N.M, and the specific steps of simulating the actual working condition are as follows:
(1) in order to simulate the actual working condition of the basin-type insulator, flanges and sealing sleeves are arranged on two sides of the insulator, and strain gauges are reasonably arranged;
(2) setting the pretightening force of all the bolts to be 110 N.M standard pretightening force by using a torque wrench;
(3) respectively injecting air with the pressure of 0.45MPa into the sealing sleeves on the two sides of the basin-type insulator by using an air compressor;
(4) the ultrasonic transmitting and receiving probes are respectively arranged on two sides of the bolt and used for transmitting and receiving ultrasonic transmission signals, and the energy difference value calculation formula of the ultrasonic transmission signals is as follows:
ΔE＝E0-E0
wherein E is0Is the ultrasonic signal transmission energy of the bolt under the standard pretightening force, E1Is the transmission energy of ultrasonic signals when the bolt is loosened fsV is the voltage amplitude for the sampling frequency.
(5) Respectively acquiring ultrasonic transmission signals of the bolt under the standard pretightening force and the loosening condition, acquiring stress values of typical stress distortion points of the basin-type insulator corresponding to the loosening condition of the bolt one by one, and calculating the difference value of the energy values of the ultrasonic transmission signals under the loosening condition of the bolt and the standard pretightening force;
(6) after the energy difference value of the ultrasonic transmission signal when the basin-type insulator bolt is loosened is obtained, the energy difference value of the ultrasonic transmission signal when the basin-type insulator bolt is loosened and the stress values of typical stress distortion points corresponding to the energy difference value of the ultrasonic transmission signal one to one are further used as training and testing samples of the BP neural network to train the BP neural network.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
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| CN119043543A (en) * | 2024-08-21 | 2024-11-29 | 南京林业大学 | Ultrasonic detection method and system for shaft force of cable clamp screw with consideration of uneven temperature and stress distribution |
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Effective date of registration: 20230902 Address after: No.6 Qingnian Road, Yingze Street, Yingze District, Taiyuan City, Shanxi Province, 030000 Applicant after: STATE GRID ELECTRIC POWER Research Institute OF SEPC Applicant after: Taiyuan University of Technology Address before: 030024 No. 79 West Main Street, Taiyuan, Shanxi, Yingze Applicant before: Taiyuan University of Technology |
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Inventor after: Song Jiancheng Inventor after: Jiao Guoxun Inventor after: Wang Wei Inventor after: Xu Yudong Inventor after: Zhang Jie Inventor after: Liu Hong Inventor after: Ge Jian Inventor after: Li Pengjiang Inventor after: Gao Jinwu Inventor before: Song Jiancheng Inventor before: Jiao Guoxun Inventor before: Ge Jian Inventor before: Li Pengjiang Inventor before: Gao Jinwu Inventor before: Zhang Jie |