CN108961094A - Wind leaning fault method for early warning based on transmission line of electricity minimum air void online measuring - Google Patents
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Abstract
The invention discloses a kind of wind leaning fault method for early warning based on transmission line of electricity minimum air void online measuring, including carry out electric power facility and refine three-dimensional modeling, and measure the minimum air void of transmission line of electricity on threedimensional model；The meteorologic factor of analyzing influence transmission line of electricity minimum air void, establishes the nonlinear regression model (NLRM) of transmission line of electricity minimum air void Yu relevant weather factor, and resolves model parameter；The assessment of wind leaning fault warning grade is carried out in conjunction with threshold classification according to the transmission line of electricity minimum air void value under the model prediction DIFFERENT METEOROLOGICAL CONDITIONS of foundation.The present invention efficiently solves wind leaning fault orientation problem, greatly improves the safety operation level of route.
Description
Technical Field
The invention relates to a windage yaw fault early warning method based on minimum air gap online measurement of a power transmission line, in particular to a minimum air gap online measurement and windage yaw fault early warning method in windage yaw flashover of the power transmission line, and belongs to the technical field of safe operation and management of the power transmission line.
Background
The windage yaw flashover accident is a major potential safety hazard for normal operation of a power grid, and with the accelerated construction of the power grid in China, power transmission lines of various voltage classes develop rapidly, particularly, super-high voltage lines and extra-high voltage lines have long power transmission distance and complex meteorological and geographic environments along the way, once the windage yaw flashover accident occurs under extreme climatic conditions, large-area power failure is caused, the safe and stable operation of a power system is seriously influenced, and huge economic loss is caused for the power system. The wind deflection discharge is caused by a plurality of reasons, wherein the change of the electrical strength of air gaps among wires, wires-towers and wires-adjacent objects such as trees caused by the change of the wind deflection angle under severe meteorological conditions is the most fundamental reason for the wind deflection flashover fault and accident of the power transmission line.
The regular checking of the minimum air gap of the line tower, the distance between the lead and the ground and the distance between the lead and the adjacent foreign matters is a main measure for preventing and controlling the windage yaw of the power department. The existing checking method comprises the steps of 1) empirical estimation, 2) obtaining a wind deflection angle, and calculating the minimum air gap according to an existing model. Factors influencing the wind deflection angle such as wind direction, wind speed and the like are many, so the obtained value of the wind deflection angle has certain similarity, the minimum air gap estimation model is usually required to be corrected according to actual conditions, and the accurate value of the minimum air gap in actual work is difficult to obtain. Some online minimum air gap monitoring systems need to arrange data acquisition hardware in severe weather and charged environments, so that the implementation process is difficult and serious. Generally speaking, no practical method capable of accurately measuring the minimum air gap of the power transmission line exists at present.
Disclosure of Invention
The invention aims to provide a windage yaw fault early warning method based on the online measurement of the minimum air gap in the windage yaw flashover prevention of a power transmission line aiming at the defects in the prior art, and solves the problems of the measurement of the minimum air gap of the power transmission line and the early warning of the windage yaw fault in the safe operation and management process of the power transmission line.
The technical scheme of the invention provides a windage yaw fault early warning method based on the minimum air gap on-line measurement of a power transmission line, which comprises the following steps:
step 1, carrying out refined three-dimensional modeling on an electric power facility, and measuring the minimum air gap of the electric transmission line on a three-dimensional model;
step 2, analyzing meteorological factors influencing the minimum air gap of the power transmission line, establishing a nonlinear regression model of the minimum air gap of the power transmission line and the relevant meteorological factors, and resolving model parameters;
and 3, predicting the minimum air gap value of the power transmission line under different meteorological conditions according to the established model, and carrying out wind deviation fault early warning grade evaluation by combining threshold value classification.
In step 1, image data of the power transmission and transformation line tower and the surrounding environment are obtained by an oblique photography mode, aerial triangulation is performed by combining existing design data and control point data, and a three-dimensional model of the power transmission and transformation line tower and the surrounding environment is established.
In step 2, a non-linear regression model of the minimum air gap and the relevant meteorological factors is established according to a gray system theory GM (1, N) model, and least square method is adopted to fit regression model parameters.
And in step 3, predicting the size of the minimum air gap of the power transmission line under different meteorological conditions according to the established model, comparing the size with standard reference data on the basis, setting a threshold value, and evaluating the safety of the minimum air gap of the component.
Compared with the prior art, the invention has the advantages that: the method and the device have the advantages that the minimum air gap measurement between the parts of the power transmission line and the environment of the line in a non-contact mode is realized, the early warning problem of windage yaw faults is solved by establishing the correlation model of the minimum air gap and meteorological factors, the windage yaw design parameters of the power transmission line are scientifically optimized for formulating reasonable windage yaw precaution measures, and the important technical guarantee is provided for improving the safe operation level of the line.
Drawings
FIG. 1 is a flow chart of an embodiment of the present invention.
Detailed Description
The technical scheme of the invention is described in detail in the following by combining the attached drawings and the embodiment of the invention.
The invention provides an online measurement and windage yaw fault early warning system for an air gap of a power transmission line. Acquiring images of a power transmission line tower, a suspension line and a line environment in a non-contact manner through a high-resolution image sensor, realizing power transmission line model construction based on a photogrammetric method, and accurately measuring minimum air gaps among power transmission line components and the line environment; and establishing a nonlinear regression model of the minimum air gap and meteorological factors according to a grey system theory GM (1, N) model, and realizing the prediction of the minimum air gap and the early warning grade evaluation of windage yaw faults under different meteorological conditions. The method and the device can be used for positioning windage yaw faults, formulating reasonable windage yaw prevention measures, scientifically optimizing windage yaw design parameters of the power transmission line and improving the safe operation level of the line, and provide important technical guarantee.
Referring to fig. 1, the windage yaw fault early warning method based on the minimum air gap online measurement of the power transmission line provided by the embodiment of the invention includes the following specific steps:
step 1, the design data, the control point data and the multi-view images are used for realizing the refined three-dimensional modeling of electric power facilities (electric power towers, electric wires, insulators and the like and the line environment), and the minimum air gap of the electric transmission line is measured on the three-dimensional model.
Furthermore, image data of the power transmission and transformation line tower and the surrounding environment are obtained in an oblique photography mode, aerial triangulation is carried out by combining existing design data and control point data, and a three-dimensional model of the power transmission and transformation line tower and the surrounding environment is built.
In the step, the orientation parameters of the image are resolved by using multi-source control information (control points, design data and the like) as the orientation control parameters for the multi-view image data acquired in the step 1. According to the aerial triangulation result, high-density point cloud and semi-automatic fine modeling are generated by extracting image characteristic points, and a three-dimensional model of the power transmission line scene is generated. By means of the method, high-precision and refined three-dimensional model reconstruction of electric power towers, electric wires and line scenes is achieved, and basic data are provided for online measurement and dynamic monitoring of the minimum air gaps of the electric transmission lines. By means of the nearest distance search, the minimum air gaps of towers, suspension wires, insulators, line scenes (trees) and the like can be measured on the three-dimensional reconstruction model.
In the embodiment, an oblique photography mode of an electric multi-rotor unmanned aerial vehicle is adopted, and the ground is photographed in an oblique posture in the air, so that image data of a power transmission and transformation line tower and the surrounding environment are obtained; utilizing multi-source control information (control points, design data and the like) as orientation control parameters to carry out orientation parameters of the space-three resolving images; according to the aerial triangulation result, through the steps of extracting image characteristic points, generating high-density point cloud and the like, a refined three-dimensional model of electric power facilities (electric power towers, electric wires, insulators and the like) is generated, and in order to support measurement, the suggested precision can reach 0.05 m; and determining the minimum air gap between the power transmission line tower, the suspension line and the adjacent object by a space distance minimum searching mode, namely searching the minimum distance between the two lines on the model.
And 2, analyzing meteorological factors influencing the minimum air gap of the power transmission line, establishing a nonlinear regression model of the minimum air gap of the power transmission line and the relevant meteorological factors, and resolving model parameters.
Further, a nonlinear regression model of the minimum air gap and related meteorological factors is established according to a gray system theory GM (1, N) model, and least square method is adopted to fit regression model parameters.
In the embodiment, the possible nonlinear relation between the minimum air gap and the related meteorological elements is considered, the meteorological factors influencing the minimum air gap of the power transmission line are analyzed and determined, the image data of the power transmission line are collected periodically at fixed points, model data are constructed and measured, a gray system theory GM (1, N) model is selected to establish a nonlinear regression model of the minimum air gap and the related meteorological elements, and least square curve fitting regression model parameters are adopted.
According to the theory of the gray system,
is provided with Y(0)＝{y(0)(1),y(0)(2),…,y(0)(n) is a minimum air gap data sequence,
wherein, y(0)(1),y(0)(2),…,y(0)(n) the 1 st minimum air gap measurement, the 2 nd minimum air gap measurement … nth minimum air gap measurement, respectively;
let Xi (0)＝{xi (0)(1),xi (0)(2),…,xi (0)(n) (i is 1,2, …, m) is a related meteorological element data sequence,
wherein x isi (0)(1),xi (0)(2),…,xi (0)(n) respectively represents the meteorological element data i corresponding to the 1 st measurement and the meteorological element data i … corresponding to the 2 nd measurement, wherein m is the total number of relevant meteorological elements, such as the wind direction, the wind speed, the humidity and the like of the relevant meteorological elements.
Y(1)＝{y(1)(1),y(1)(2),…,y(1)(n) } and Xi (1)＝{xi (1)(1),xi (1)(2),…,xi (1)(n) are each Y(0)And Xi (0)The one-time accumulation of (a) generates a sequence,
wherein:k＝1，2,3,…,n。
i.e. the k-th y in the sequence of the accumulated generation(1)(k)、xi (1)(k) Respectively, the sum of the respective accumulations, for example: data item y(1)(2) Is Y(0)Sum of the first 2 items of the data sequence, data item y(1)(n) is Y(0)Sum of the first n terms of the data sequence. Data item xi (1)(2) Is Xi (0)Sum of the first 2 items of the data sequence, data item xi (1)(n) is Xi (0)Sum of the first n terms of the data sequence.
The GM (1, N) model of the minimum air gap and the associated meteorological element is
y(0)(k)+az(1)(k)＝b1x1 (1)(k)+b2x2 (1)(k)+…+bmxm (1)(k) (1)
Wherein: parameter z(1)(k)＝(y(1)(k)+y(1)(k-1))/2; k is 2,3, …, n is Y(1)Two mean values of two adjacent terms in (a), (b)1、 b2…bmIs GM (1, N) model parameter.
When k is 2,3, …, n, the model parameters can be obtained by the least square method(A, b1、b2…bmThe best estimate of) is
In the formula,
wherein, the matrix L is composed of the minimum air gap observed value sequence items, and the matrix B is composed of the parameter z(1)(k) And the meteorological factor primary accumulation sequence item.
After the model parameter values are determined, the approximate time response of the first-order accumulation sequence is further determined
Wherein,is the e-exponent, e is the base number,are indexes.
To pairPerforming a subtraction reduction process to obtain a prediction formula of the original sequence as
To facilitate the implementation of the reference period, specific examples of GM (1, N) models are provided as follows:
table 1 shows the data of the calculation examples, which are the minimum air gap value of the transmission line, and the 5 observation data of meteorological factors (wind direction, wind speed and relative humidity).
TABLE 1 GM (1, N) model calculation data
First step of
Minimum air gap data sequence Y according to Table 1(0)＝{y(0)(1),y(0)(2),y(0)(3),y(0)(4),y(0)(5)}＝ {265.74,309.59,347.98，389.55,456.25}
Meteorological factor Xi (0)＝{xi (0)(1),xi (0)(2),xi (0)(3),xi (0)(4),xi (0)(5)} (i＝1,2,3)
X1 (0)＝{x1 (0)(1),x1 (0)(2),x1 (0)(3),x1 (0)(4),x1 (0)(5)}＝{36.6,44.9,52.4，62.4,74.7}
X2 (0)＝{x2 (0)(1),x2 (0)(2),x2 (0)(3),x2 (0)(4),x2 (0)(5)}＝{0.91,1.11,1.32，1.42,1.66}
X3 (0)＝{x3 (0)(1),x3 (0)(2),x3 (0)(3),x3 (0)(4),x3 (0)(5)}＝{24.7,30.3,38.9，49.6,70.4}
For Y(0)Performing one-time accumulation to obtain Y(1)＝{y(1)(1),y(1)(2),y(1)(3),y(1)(4)，y(1)(5)}＝{265.74,575.33,923.31，1312.86,1769.11}
To Xi (0)Is added once to obtain
X1 (1)＝{x1 (1)(1),x1 (1)(2),x1 (1)(3),x1 (1)(4),x1 (1)(5)}＝{36.6,81.5,133.9，196.3,271.0}
X2 (1)＝{x2 (1)(1),x2 (1)(2),x2 (1)(3),x2 (1)(4),x2 (1)(5)}＝{0.91,2.02,3.34，4.76,6.42}
X3 (1)＝{x3 (1)(1),x3 (1)(2),x3 (1)(3),x3 (1)(4),x3 (1)(5)}＝{24.7,55.0,93.9，143.5,213.9}
Second step of
ByCan calculate out
Thereby can calculate
Determining approximate time dependence of a once-accumulated sequence
To pairPerforming a subtraction reduction process to obtain a prediction formula of the original sequence as
And 3, predicting the minimum air gap value of the power transmission line under different meteorological conditions according to the established model, and carrying out wind deviation fault early warning grade evaluation by combining threshold value classification.
And further, according to the established model, the size of the minimum air gap of the power transmission line under different meteorological conditions is predicted, and is compared with standard reference data on the basis, a reasonable threshold value is set, and the safety of the minimum air gap of the component is evaluated.
The model prediction value is compared with standard reference data, a threshold with proper margin is set, the wind deflection fault grade of the power transmission line is predicted through fuzzy grade classification, and safe operation of the power transmission line under the extreme climate condition can be ensured.
In the embodiment, the minimum air gap value under different meteorological conditions is predicted according to the established model, the minimum air gap value is compared with standard reference data, the set early warning level is combined, the corresponding early warning level is obtained through fuzzy classification, and the evaluation result provides decision data for the windage yaw prevention measures.
And setting the minimum air gap value allowed by the specification as L, and the minimum air gap value predicted by the model under certain meteorological conditions as d, when: (1) d is less than L, so as to give a level 1 early warning; (2) d is greater than L and less than 1.2L, so as to give 2-level early warning; (3) d is larger than 1.2L, and no early warning is given.
The method effectively realizes the accurate measurement of the minimum air gap value of the power transmission line based on the model, and changes the situation that the traditional method obtains the minimum air gap approximate value of the power transmission line through indirect calculation. In specific implementation, the automatic operation of the above processes can be realized by adopting a computer software mode.
Furthermore, the invention solves the problem of windage yaw fault early warning in the operation of the power transmission line by establishing the nonlinear model of the minimum air gap of the power transmission line and related meteorological factors, thereby improving the operation safety of the power transmission line.
While embodiments of the invention have been described above, it is not limited to the applications set forth in the description and the embodiments, which are fully applicable in various fields of endeavor to which the invention pertains, and further modifications may readily be made by those skilled in the art, it being understood that the invention is not limited to the details shown and described herein without departing from the general concept defined by the appended claims and their equivalents.
Claims (4)
1. A windage yaw fault early warning method based on minimum air gap online measurement of a power transmission line is characterized by comprising the following steps:
step 1, carrying out refined three-dimensional modeling on an electric power facility, and measuring the minimum air gap of the electric transmission line on a three-dimensional model;
step 2, analyzing meteorological factors influencing the minimum air gap of the power transmission line, establishing a nonlinear regression model of the minimum air gap of the power transmission line and relevant meteorological factors, and resolving model parameters;
and 3, predicting the minimum air gap value of the power transmission line under different meteorological conditions according to the established model, and carrying out windage yaw fault early warning grade evaluation by combining threshold classification.
2. The windage yaw fault early warning method based on the online measurement of the minimum air gap of the power transmission line according to claim 1, characterized in that: in the step 1, image data of the power transmission and transformation line tower and the surrounding environment are obtained in an oblique photography mode, aerial triangulation is carried out by combining the existing design data and control point data, and a three-dimensional model of the power transmission and transformation line tower and the surrounding environment is built.
3. The windage yaw fault early warning method based on the online measurement of the minimum air gap of the power transmission line according to claim 1, characterized in that: in step 2, a nonlinear regression model of the minimum air gap and related meteorological factors is established according to a gray system theory GM (1, N) model, and least square method is adopted to fit regression model parameters.
4. The windage yaw fault early warning method based on the online measurement of the minimum air gap of the power transmission line according to claim 1,2 or 3, characterized in that: and step 3, predicting the size of the minimum air gap of the power transmission line under different meteorological conditions according to the established model, comparing the minimum air gap with standard reference data on the basis, setting a threshold value, and evaluating the safety of the minimum air gap of the component.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112016739A (en) * | 2020-08-17 | 2020-12-01 | 国网山东省电力公司潍坊供电公司 | Fault detection method and device, electronic equipment and storage medium |
| CN112257028A (en) * | 2020-10-16 | 2021-01-22 | 广东电网有限责任公司 | Windage yaw flashover fault probability calculation method and device of power transmission line |
| CN112504208A (en) * | 2020-10-26 | 2021-03-16 | 国网河南省电力公司济源供电公司 | Power transmission line air gap analysis method |
| CN112886587A (en) * | 2021-03-29 | 2021-06-01 | 北京世纪百合科技有限公司 | Checking and representing method for air gap of tower head of power transmission line tower |
| CN117151336A (en) * | 2023-09-06 | 2023-12-01 | 连云港智源电力设计有限公司 | Device and method for evaluating limit wind resistance of power transmission line |
-
2018
- 2018-02-28 CN CN201810168386.5A patent/CN108961094A/en active Pending
Non-Patent Citations (3)
| Title |
|---|
| 李震宇等: "电力微气象风偏灾害监测预警技术及***实现", 《电力***保护与控制》 * |
| 耿中元等: "倾斜航空摄影实景三维模型技术研究及应用", 《北京测绘》 * |
| 裴慧坤等: "依托无人机倾斜摄影的电力走廊三维重建", 《测绘科学》 * |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112016739A (en) * | 2020-08-17 | 2020-12-01 | 国网山东省电力公司潍坊供电公司 | Fault detection method and device, electronic equipment and storage medium |
| CN112016739B (en) * | 2020-08-17 | 2024-02-20 | 国网山东省电力公司潍坊供电公司 | Fault detection method and device, electronic equipment and storage medium |
| CN112257028A (en) * | 2020-10-16 | 2021-01-22 | 广东电网有限责任公司 | Windage yaw flashover fault probability calculation method and device of power transmission line |
| CN112257028B (en) * | 2020-10-16 | 2022-11-29 | 广东电网有限责任公司 | Windage yaw flashover fault probability calculation method and device of power transmission line |
| CN112504208A (en) * | 2020-10-26 | 2021-03-16 | 国网河南省电力公司济源供电公司 | Power transmission line air gap analysis method |
| CN112886587A (en) * | 2021-03-29 | 2021-06-01 | 北京世纪百合科技有限公司 | Checking and representing method for air gap of tower head of power transmission line tower |
| CN117151336A (en) * | 2023-09-06 | 2023-12-01 | 连云港智源电力设计有限公司 | Device and method for evaluating limit wind resistance of power transmission line |
| CN117151336B (en) * | 2023-09-06 | 2024-04-16 | 连云港智源电力设计有限公司 | Device and method for evaluating limit wind resistance of power transmission line |
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