A dancing monitoring algorithm based on spacer bars

Abstract

The present invention relates to a galloping monitoring algorithm based on spacer bars, comprising the following steps: (1) determining the number of sensor modules according to the number of split conductors; (2) setting a sensor module at the wire clamp position of the spacer bar, that is, on the split sub-conductor; the sensor module selects an inertial sensor measurement unit to respectively collect data on the three-axis acceleration, three-axis angular velocity and inclination of the transmission line; (3) pre-processing the collected signal to eliminate the DC component and trend term of the signal; (4) filtering the signal using a digital filtering method to eliminate random noise signals in the signal; (5) performing coordinate transformation to eliminate the gravitational acceleration component of the signal; (6) collecting the galloping parameters of each split sub-conductor to form a sensor matrix, and performing a multi-sensor fusion algorithm and a galloping posture solution respectively; (7) restoring the galloping trajectory through displacement solution. The present invention improves the accuracy of transmission line galloping monitoring based on inertial measurement sensors and a multi-sensor fusion algorithm.

Description

Technical Field

[0001] The present invention relates to the field of power fittings and galloping monitoring methods for overhead transmission lines, and in particular to a galloping monitoring algorithm based on spacer bars. Background Art

[0002] In harsh meteorological environments, overhead transmission lines generate low-frequency, large-amplitude self-excited vibrations stimulated by wind and ice. The galloping of transmission lines, due to its high energy, can cause safety accidents such as damage to power fittings, breakage of insulators, and damage to towers. Currently, measures such as interphase spacers, wire clamp rotary spacers, and double-swing anti-gallop devices are generally used to control transmission line galloping. Overhead transmission lines, on the other hand, often use split conductors to suppress corona discharge and reduce line reactance. Traditional spacers are installed on split conductors to fix the spacing between each split conductor to prevent the conductors from whipping each other, galloping, and sub-span oscillation. When the split conductors are affected by the meteorological environment, there is a risk of the spacer handshake breaking.

[0003] To study transmission line galloping and implement effective anti-galloping measures, it is necessary to monitor and evaluate the intensity of transmission line galloping, measuring characteristic parameters such as galloping amplitude and frequency. Furthermore, galloping monitoring terminals are generally installed separately on individual transmission conductors, and there are no monitoring solutions for split conductors. Currently, mainstream galloping monitoring technologies include monocular measurement-based galloping monitoring, acceleration sensor-based galloping monitoring, inertial measurement sensor-based galloping monitoring, and all-fiber distributed sensing-based galloping monitoring. However, these monitoring methods do not address specific methods for split conductors, resulting in high application costs and low measurement accuracy. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a dancing monitoring algorithm based on a spacer rod for improving monitoring accuracy.

[0005] To solve the above problems, the present invention provides a spacer-based dancing monitoring algorithm comprising the following steps:

[0006] (1) Determine the number of sensor modules based on the number of split wires;

[0007] (2) A sensor module is installed at the wire clamp position of the spacer bar, that is, on the split sub-conductor; the sensor module uses an inertial sensor measurement unit to collect data on the three-axis acceleration, three-axis angular velocity and inclination of the transmission line;

[0008] (3) Preprocess the collected signal to eliminate the DC component and trend term of the signal;

[0009] (4) Use digital filtering method to filter the signal and eliminate random noise signals in the signal;

[0010] (5) Perform coordinate transformation to eliminate the gravitational acceleration component of the signal;

[0011] (6) Collect the dancing parameters of each split sub-conductor to form a sensor matrix, and perform multi-sensor fusion algorithm and dancing posture solution respectively;

[0012] ①Multi-sensor fusion algorithm:

[0013] Assume that the number of sensors is m, and the fusion set of the obtained measurement parameters is {x1, x2,…, x m}; Use the probability density function to construct the joint density and maximum likelihood function containing the estimated parameter θ, and then obtain the optimal data of the fusion set

[0014]

[0015] Where: x i is a single sensor data parameter; σ i is the data fusion variance;

[0016]

[0017] ②Dancing posture calculation:

[0018] The pitch angle θ, roll angle γ, and heading angle are obtained by quaternion method. Attitude angle:

[0019]

[0020]

[0021] Where: T is the dancing posture transformation matrix Sure;

[0022] is the dancing posture transformation matrix; b is the carrier coordinate system; n is the navigation coordinate system;

[0023] The velocity is calculated as:

[0024]

[0025] in: is the velocity derivative in the carrier coordinate system, unit is m / s; is the specific force, unit is m / s 2 ;ω ie is the angular velocity of rotation, in rad / s; ω en is the carrier acceleration, unit is rad / s; g is the acceleration due to gravity, 9.8m / s 2 ;

[0026] (7) Restoring the dancing trajectory through displacement calculation:

[0027] ⅰ Based on the inertial sensor measurement unit, the transmission line galloping acceleration of the carrier coordinate system b is obtained and angular velocity Will Transform the matrix by dancing Convert to the navigation coordinate system n, and we get

[0028] ii Use the velocity equation to compensate for the acceleration of gravity and integrate the compensation result to get the dancing speed v n ;

[0029] iii v n As the input for position angular rate calculation, the position is calculated as:

[0030]

[0031] Where: L is the displacement parameter of acceleration, unit is m; λ is the displacement parameter of angular velocity, unit is m; α is the wandering azimuth, unit is degree.

[0032] In step ①, the optimal data of the fusion set is Obtained as follows:

[0033] Combine the original measurement data into an optimal fusion data and use it as the final result of the measured parameters;

[0034] The m data in the fusion set come from the same monitoring entity, and their probability density function constructs the joint density and maximum likelihood function containing the parameter to be estimated θ;

[0035] The probability density formula is:

[0036]

[0037] The maximum likelihood formula is:

[0038]

[0039] The best fusion value of the m original measurement data obtained The formula is satisfied

[0040]

[0041] Taking the natural logarithm of both sides of the formula, we get

[0042]

[0043] in Right now

[0044]

[0045] Finally, the optimal data of the fusion set is obtained

[0046] Compared with the prior art, the present invention has the following advantages:

[0047] 1. The present invention relies on spacers as carriers and arranges multiple sensor modules to synchronously collect the three-axis acceleration, three-axis angular velocity and inclination data of each sub-conductor. Through the galloping analysis algorithm, the galloping of the transmission line is monitored and analyzed, which can realize the digitization and intelligence of the spacer hardware.

[0048] 2. The present invention uses a multi-sensor data fusion algorithm to effectively monitor the galloping state of the transmission line using the galloping data collected by multiple sensor modules, analyzes and calculates the monitoring data, and ultimately achieves the accuracy of galloping monitoring.

[0049] 3. The present invention organically combines the spacer rod with the transmission line galloping monitoring terminal to solve the problems of low integration, single function, complex structure and bulky size of the monitoring terminal.

[0050] 4. The present invention improves the accuracy of transmission line galloping monitoring by installing monitoring terminals on split conductors based on inertial measurement sensors and multi-sensor fusion algorithms. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0052] Figure 1 Schematic diagram of the arrangement of multiple sensing modules based on spacer rods of the present invention.

[0053] Figure 2 This is a flow chart of the dancing algorithm of the present invention.

[0054] Figure 3 Schematic diagram of the multi-sensor data fusion algorithm of the present invention. DETAILED DESCRIPTION

[0055] The definition of the spacer rods involved in the present invention is as follows:

[0056] The spacer bar is a protective hardware that keeps multiple sub-conductors in a phase (pole) conductor in a relatively spaced position.

[0057] Damping spacer: A damping element is installed in the spacer joint to reduce the wind vibration of the split conductor and the sub-span oscillation of the spacer.

[0058] Wire clamp rotary spacer: a damping spacer in which some wire clamps can rotate relative to the conductor axis.

[0059] Phase spacer: A hardware that keeps the phase conductors at a certain geometric distance and has a specified insulation strength.

[0060] The spacers used in this invention are other types of spacers, in addition to interphase spacers. The spacer hardware body is used to ensure the spacing between the split conductors, ensuring electrical performance, reducing potential gradients, and preventing collisions caused by electromagnetic attraction. This spacer effectively secures the sensor module to the split conductors while maintaining the functionality of traditional hardware.

[0061] like Figure 2 As shown, a dancing monitoring algorithm based on a spacer bar includes the following steps:

[0062] ⑴ Determine the number of sensor modules based on the number of split wires.

[0063] (2) A sensor module is set at the wire clamp position of the spacer bar, that is, on the split sub-conductor, such as Figure 1 As shown in the figure, the sensing module uses an inertial sensor measurement unit to collect data on the three-axis acceleration, three-axis angular velocity and inclination of the transmission line to realize the collection of dancing parameters.

[0064] (3) Preprocess the collected signal to eliminate the DC component and trend term of the signal.

[0065] (4) Use digital filtering method to filter the signal and eliminate random noise signals in the signal.

[0066] (5) Perform coordinate transformation to eliminate the gravitational acceleration component of the signal.

[0067] (6) The dancing parameters of each split sub-conductor are collected to form a sensor matrix, and the multi-sensor fusion algorithm and dancing posture solution are performed respectively.

[0068] ①Multi-sensor fusion algorithm:

[0069] Assume that the number of sensors is m, and the fusion set of the obtained measurement parameters is {x1, x2,…, x m}; Use the probability density function to construct the joint density and maximum likelihood function containing the estimated parameter θ, and then obtain the optimal data of the fusion set

[0070]

[0071] Where: x i is a single sensor data parameter; σ i is the data fusion variance;

[0072]

[0073] The specific process is as follows: the original measurement data are merged into an optimal fusion data, and it is used as the final result of the measured parameters;

[0074] The m data in the fusion set come from the same monitoring entity, and their probability density function can be used to construct the joint density and maximum likelihood function containing the parameter to be estimated θ;

[0075] The probability density formula is:

[0076]

[0077] The maximum likelihood formula is:

[0078]

[0079] The best fusion value of the m original measurement data obtained The formula is satisfied

[0080]

[0081] Taking the natural logarithm of both sides of the formula, we get

[0082]

[0083] in Right now

[0084]

[0085] Finally, the optimal data of the fusion set is obtained

[0086]

[0087] ②Dancing posture calculation:

[0088] The pitch angle θ, roll angle γ, and heading angle are obtained by quaternion method. Attitude angle:

[0089] T 12 、T 22 、T 31 、T 32 、T 33 It represents the short form formed by the corresponding position q before the equal sign in the following formula;

[0090]

[0091] Where: T is the dancing posture transformation matrix Determine; the rotation of the rigid body can be equivalent to a certain angle of rotation around a fixed axis in space. The complex number representation of the quaternion Q is is the dancing posture transformation matrix; b is the carrier coordinate system; n is the navigation coordinate system;

[0092] The velocity is calculated as:

[0093]

[0094] in: is the velocity derivative in the carrier coordinate system, unit is m / s; is the specific force (non-gravitational acceleration), unit is m / s 2 ;ω ie is the angular velocity of rotation, in rad / s; ω en is the carrier acceleration, unit is rad / s; g is the acceleration due to gravity, 9.8m / s 2 ;

[0095] (7) Restoring the dancing trajectory through displacement calculation:

[0096] ⅰ Based on the inertial sensor measurement unit, the transmission line galloping acceleration of the carrier coordinate system b is obtained and angular velocity Will Transform the matrix by dancing Convert to the navigation coordinate system n, and we get

[0097] ii Use the velocity equation to compensate for the acceleration of gravity and integrate the compensation result to get the dancing speed v n ;

[0098] iii v n As the input for position angular rate calculation, the position is calculated as:

[0099]

[0100] Where: L is the displacement parameter of acceleration, unit is m; λ is the displacement parameter of angular velocity, unit is m; α is the wandering azimuth, unit is degree (°).

[0101] Figure 3 In order to collect data from multiple sensor modules into the algorithm module and perform data fusion to realize dancing calculation, the dancing module has four inputs to correspond to the four-split spacer rods.

[0102] The present invention is applied to the application scenario of split conductor dancing monitoring. It relies on the spacer rod architecture for split conductors and installs sensor modules through the split sub-conductors to realize dancing parameter collection. The dancing data analysis is completed through multi-sensor data fusion algorithm, posture solution, speed solution, displacement solution, etc.

Claims

1. A dancing monitoring algorithm based on a spacer bar, comprising the following steps: ⑴Determine the number of sensor modules according to the number of split wires; (2) A sensor module is installed at the wire clamp position of the spacer bar, that is, on the split sub-conductor; the sensor module uses an inertial sensor measurement unit to collect data on the three-axis acceleration, three-axis angular velocity and inclination of the transmission line; ⑶Preprocess the collected signal to eliminate the DC component and trend item of the signal; (4) Use digital filtering method to filter the signal and eliminate random noise signals in the signal; ⑸Perform coordinate transformation to eliminate the gravitational acceleration component of the signal; (6) Collect the dancing parameters of each split sub-conductor to form a sensor matrix, and perform multi-sensor fusion algorithm and dancing posture calculation respectively; ①Multi-sensor fusion algorithm: Assume that the number of sensors is m, and the fusion set of the obtained measurement parameters is {x1, x2,…, x m }; Use the probability density function to construct the joint density and maximum likelihood function containing the estimated parameter θ, and then obtain the optimal data of the fusion set Where: x i is a single sensor data parameter; σ i is the data fusion variance; ②Dancing posture calculation: The pitch angle ε, roll angle γ, and heading angle are obtained by quaternion method. Attitude angle: Where: T is the dancing posture transformation matrix Sure; is the dancing posture transformation matrix; b is the carrier coordinate system; n is the navigation coordinate system; The velocity is calculated as: in: is the velocity derivative in the carrier coordinate system, unit is m / s; is the specific force, unit is m / s 2 ;ω ie is the angular velocity of rotation, in rad / s; ω en is the carrier acceleration, unit is rad / s; g is the acceleration due to gravity, 9.8m / s 2 ; ⑺Restore the dancing trajectory through displacement calculation: ⅰ Based on the inertial sensor measurement unit, the transmission line galloping acceleration of the carrier coordinate system b is obtained and angular velocity Will Transform the matrix by dancing Convert to the navigation coordinate system n, and we get ⅱUse the velocity equation to compensate for the acceleration of gravity and integrate the compensation result to get the dancing velocity v n ; ⅲ v n As the input for position angular rate calculation, the position is calculated: Where: L is the displacement parameter of acceleration, unit is m; λ is the displacement parameter of angular velocity, unit is m; α is the wandering azimuth, unit is degree.

2. The spacer-based dancing monitoring algorithm according to claim 1, characterized in that: In step ①, the optimal data of the fusion set is Obtained as follows: Combine the original measurement data into an optimal fusion data and use it as the final result of the measured parameters; The m data in the fusion set come from the same monitoring entity, and their probability density function constructs the joint density and maximum likelihood function containing the parameter to be estimated θ; The probability density formula is: The maximum likelihood formula is: The best fusion value of the m original measurement data obtained The formula is satisfied Taking the natural logarithm of both sides of the formula, we get in Right now Finally, the optimal data of the fusion set is obtained

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