Conductor galloping monitoring method and related equipment

Abstract

The invention provides a conductor galloping monitoring method and related equipment. The method comprises the following steps: acquiring angular velocity and acceleration of a wire at each sampling moment; calculating a first sampling angle of the wire at the nth sampling moment based on the angular velocity and the acceleration of the wire at the nth sampling moment; based on the first sampling angle at the nth sampling moment, calculating a prediction angle at the (n + 1) th sampling moment; calculating a second sampling angle of the wire at the (n + 1)-th sampling moment based on the angular velocity and the acceleration of the wire at the (n + 1)-th sampling moment; calculating a galloping angle at the (n + 1) th sampling moment based on the second sampling angle at the (n + 1) th sampling moment and the predicted angle at the (n + 1) th sampling moment; and determining that the galloping state of the wire is abnormal, and outputting warning information. The galloping angle of the wire is calculated through the angular velocity and acceleration data of the wire, fusion and complementation of multi-source data are achieved, and the accuracy and comprehensiveness of galloping angle monitoring can be improved.

Description

Technical Field

[0001] The present application relates to the field of line monitoring, and in particular to a wire dancing monitoring method and related equipment. Background Art

[0002] Transmission lines are numerous and widespread, with complex climatic conditions, and are always facing the threat of natural disasters such as icing. The dancing of wires caused by the combined effects of wind, ice and snow will increase mechanical stress, which may cause fatigue damage, wire breakage and other mechanical failures of the wires. During the dancing process, the wires collide with each other or contact with other equipment, which may cause electrical failures such as short circuits and power outages. The dancing of wires may cause the wires to fall off, endangering the safety of ground personnel and equipment, and increasing the difficulty and cost of maintenance. The use of high-precision sensors, data acquisition systems and wireless transmission technology to monitor the dancing of wires in real time and to promptly discover and warn of potential safety hazards has become a mainstream prevention and control measure in the market, which can effectively control and reduce the impact of wire dancing and ensure the safe and reliable operation of the transmission system. In the construction of modern smart grids, the application of wire dancing monitoring devices will play an important role in improving the safety and stability of power grid operation.

[0003] At present, the wire dancing monitoring scheme usually installs angular velocity sensors and acceleration sensors at multiple monitoring points on the wire. By collecting the angular velocity data and acceleration data of each monitoring point, the wire dancing information is obtained after solving. However, since the angular velocity sensors and acceleration sensors are greatly affected by the external environment, the monitoring accuracy and sensitivity are insufficient. Summary of the invention

[0004] In order to solve the problems in the prior art, the present application provides a wire dancing monitoring method and related equipment to improve the accuracy and comprehensiveness of the dancing angle.

[0005] The present application provides a wire galloping monitoring method, comprising:

[0006] Obtain the angular velocity and acceleration of the wire at each sampling moment;

[0007] Calculating a first sampling angle of the conductor at the nth sampling moment based on the angular velocity and acceleration of the conductor at the nth sampling moment; wherein n is a positive integer;

[0008] Calculating a predicted angle of the conductor at an n+1th sampling moment based on a first sampling angle of the conductor at an nth sampling moment;

[0009] Calculating a second sampling angle of the wire at the n+1th sampling moment based on the angular velocity and acceleration of the wire at the n+1th sampling moment;

[0010] Calculating a dancing angle of the wire at the n+1th sampling moment based on the second sampling angle of the wire at the n+1th sampling moment and the predicted angle of the wire at the n+1th sampling moment;

[0011] If it is determined that the dancing state of the wire is abnormal based on the dancing angle of the wire at the (n+1)th sampling moment, a warning message is output.

[0012] In one embodiment, calculating the first sampling angle of the conductive line at the nth sampling moment based on the angular velocity and acceleration of the conductive line at the nth sampling moment includes:

[0013] Calculating the angle of the conductor based on the angular velocity of the conductor at the nth sampling moment to obtain a first angle value;

[0014] Calculating the angle of the wire based on the acceleration of the wire at the nth sampling moment to obtain a second angle value;

[0015] Complementary filtering is performed on the first angle value and the second angle value based on a preset complementary filtering algorithm to obtain a first sampling angle of the conductor at the nth sampling moment.

[0016] In one embodiment, obtaining the angular velocity and acceleration of the wire at each sampling time includes:

[0017] A three-axis coordinate system is constructed with the wire as the center; the three-axis coordinate system includes an x-axis, a y-axis and a z-axis;

[0018] Obtaining the angular velocity of the x-axis, the angular velocity of the y-axis, and the angular velocity of the z-axis of the conductor at each sampling moment;

[0019] Obtaining the x-axis acceleration, the y-axis acceleration, and the z-axis acceleration of the wire at each sampling moment;

[0020] The first angle value includes an x-axis angle value, a y-axis angle value and a z-axis angle value; the second angle value includes a pitch angle value and a roll angle value; the first sampling angle includes an x-axis first sampling angle, a y-axis first sampling angle and a z-axis first sampling angle.

[0021] In one embodiment, the calculating the angle of the conductor based on the angular velocity of the conductor at the nth sampling moment to obtain the first angle value includes:

[0022] Respectively calculating the integral of the x-axis angular velocity, the integral of the y-axis angular velocity, and the integral of the z-axis angular velocity of the wire at the nth sampling moment to obtain the x-axis angle, the y-axis angle, and the z-axis angle of the wire at the nth sampling moment;

[0023] The x-axis angle, the y-axis angle and the z-axis angle are respectively subjected to first filtering to obtain the x-axis angle value, the y-axis angle value and the z-axis angle value of the conductive line at the nth sampling moment.

[0024] In one embodiment, the calculating the angle of the wire based on the acceleration of the wire at the nth sampling moment to obtain the second angle value includes:

[0025] Determining the x-axis acceleration, the y-axis acceleration, and the z-axis acceleration of the wire at the n-th sampling moment based on the acceleration of the wire at the n-th sampling moment;

[0026] Determine the pitch angle and roll angle of the wire at the nth sampling moment based on the x-axis acceleration, the y-axis acceleration and the z-axis acceleration of the wire at the nth sampling moment;

[0027] The pitch angle and the roll angle are respectively subjected to second filtering to obtain the pitch angle value and the roll angle value.

[0028] In one embodiment, the calculating the predicted angle of the conductor at the (n+1)th sampling time based on the first sampling angle of the conductor at the nth sampling time includes:

[0029] Determining an estimated angle of the conductor at the (n+1)th sampling moment based on a first sampling angle of the conductor at the (n)th sampling moment;

[0030] Determining an angle estimation error of the conductor at the n+1th sampling moment based on the estimated angle of the conductor at the n+1th sampling moment;

[0031] Based on the estimated angle of the conductor at the (n+1)th sampling moment and the angle estimation error, the predicted angle of the conductor at the (n+1)th sampling moment is determined.

[0032] In one embodiment, the calculating the dancing angle of the wire at the n+1th sampling moment based on the second sampling angle of the wire at the n+1th sampling moment and the predicted angle of the wire at the n+1th sampling moment includes:

[0033] Calculating a Kalman gain based on an angle estimation error of the conductor at the n+1th sampling moment;

[0034] Based on the second sampling angle of the conductor at the n+1th sampling moment and the Kalman gain, updating the second sampling angle of the conductor at the n+1th sampling moment and the angle estimation error;

[0035] Based on the updated second sampling angle of the wire at the (n+1)th sampling moment and the angle estimation error, the dancing angle of the wire at the (n+1)th sampling moment is calculated.

[0036] In one embodiment, a plurality of monitoring points are provided on the conductor; and the conductor galloping monitoring method further comprises:

[0037] Obtaining the angular velocity and acceleration of the m monitoring points at the nth sampling moment; wherein m is an integer greater than or equal to 2;

[0038] Generate a forward state sequence and a reverse state sequence based on the angular velocities and accelerations of the m monitoring points at the nth sampling moment;

[0039] Determine a predicted result of the dancing posture of the wire at the nth sampling moment based on the forward state sequence and the reverse state sequence;

[0040] If the predicted result of the dancing posture of the wire at the nth sampling moment is greater than a preset value, it is determined that the dancing of the wire at the nth sampling moment is abnormal.

[0041] In one embodiment, the wire galloping monitoring method further includes:

[0042] Determine the dancing amplitude and the dancing frequency of the wire at the n+1th sampling moment based on the acceleration of the wire at the n+1th sampling moment;

[0043] The dancing trajectory of the wire is determined based on the dancing angle, dancing amplitude and dancing frequency of the wire at the n+1th sampling moment; the dancing trajectory is used to determine the dancing state of the wire.

[0044] The present application also proposes a computer storage medium, comprising a processor and a memory; the memory stores computer instructions, and when the computer instructions are executed on the processor, the processor executes the above-mentioned wire dancing monitoring method.

[0045] The present application also proposes a wire galloping monitoring system, comprising:

[0046] A parameter acquisition module, used for detecting the angular velocity and acceleration of the wire;

[0047] A main control module is used to calculate a first sampling angle of the wire at the nth sampling moment based on the angular velocity and acceleration of the wire at the nth sampling moment; and to calculate a predicted angle of the wire at the n+1th sampling moment based on the first sampling angle of the wire at the nth sampling moment; the main control module is also used to calculate a second sampling angle of the wire at the n+1th sampling moment based on the angular velocity and acceleration of the wire at the n+1th sampling moment; and to calculate a dancing angle of the wire at the n+1th sampling moment based on the second sampling angle of the wire at the n+1th sampling moment and the predicted angle of the wire at the n+1th sampling moment; the main control module is also used to determine that the dancing state of the wire is abnormal based on the dancing angle of the wire at the n+1th sampling moment, and output a warning message; wherein n is a positive integer.

[0048] In one embodiment, the parameter acquisition module includes:

[0049] The Beidou positioning module is used to obtain the position information of the wire; the position information and the dancing angle are used to determine the dancing state of the wire.

[0050] In one embodiment, the wire dancing monitoring system further includes: a photovoltaic module for converting solar energy into electrical energy to power the parameter acquisition module and the main control module.

[0051] The present application calculates the dancing angle of the conductor through the angular velocity and acceleration data of the conductor, realizes the integration and complementarity of multi-source data, and thus improves the accuracy and comprehensiveness of the dancing angle. The comprehensive analysis of multi-source data can more accurately reflect the actual movement state and external environmental conditions of the ground wire, and provide more reliable data support for dancing monitoring. The predicted data of the dancing angle is updated through the angle data obtained through actual sampling, further reducing the error of the dancing angle. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 Schematic diagram of the module structure of an embodiment of the wire galloping monitoring system of the present application.

[0053] Figure 2 This is a schematic structural diagram of an embodiment of a photovoltaic module of the present application.

[0054] Figure 3 This is a logic diagram of the application process of an embodiment of the wire galloping monitoring system of the present application.

[0055] Figure 4 This is a flow chart of an embodiment of a wire galloping monitoring method of the present application.

[0056] Figure 5 This is a flow chart of an embodiment of obtaining angular velocity and acceleration for the present application.

[0057] Figure 6 It is a schematic diagram of a three-axis coordinate system according to an embodiment of the present application.

[0058] Figure 7 This is a flow chart of an embodiment of calculating the first sampling angle of the present application.

[0059] Figure 8 This is a flow chart of an embodiment of calculating the first angle value of the present application.

[0060] Fig. 9 This is a flow logic diagram of an embodiment of complementary filtering preset in this application.

[0061] Fig.10 This is a flow chart of an embodiment of calculating the second angle value of the present application.

[0062] Fig.11 This is a flow chart of an embodiment of calculating the prediction angle of the present application.

[0063] Fig.12 This is a flow chart of an embodiment of calculating the dancing angle of the present application.

[0064] Fig.13 This is a flow chart of another embodiment of the wire galloping monitoring method of the present application.

[0065] Fig.14 This is a logic flow chart of another embodiment of the wire galloping monitoring method of the present application.

[0066] Fig.15 This is a flow chart of another embodiment of the wire galloping monitoring method of the present application.

[0067] Description of the main component symbols: wire dancing monitoring system-100; parameter acquisition module-110; main control module-120; Beidou positioning module-111; photovoltaic module-130; first control unit-121; second control unit-122.

[0068] The following specific implementation methods will further illustrate the present application in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0069] The following description will refer to the accompanying drawings to more fully describe the content of the present application. Shown in the accompanying drawings are exemplary embodiments of the present application. However, the present application can be implemented in many different forms and should not be interpreted as being limited to the exemplary embodiments set forth herein. These exemplary embodiments are provided to make the present application thorough and complete, and to fully convey the scope of the present application to those skilled in the art. Similar reference numerals represent identical or similar components.

[0070] The terms used herein are only used for the purpose of describing specific exemplary embodiments and are not intended to limit the present application. As used herein, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to also include the plural forms. In addition, when used herein, "including" and / or "comprising" and / or "having", integers, steps, operations, components and / or components, but do not exclude the existence or addition of one or more other features, regions, integers, steps, operations, components and / or groups thereof.

[0071] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those of ordinary skill in the art to which this application belongs. In addition, unless explicitly defined herein, terms such as those defined in general dictionaries should be interpreted as having a meaning consistent with their meaning in the relevant technology and the content of this application, and will not be interpreted as an idealized or overly formal meaning.

[0072] The following will describe exemplary embodiments in conjunction with the accompanying drawings. It should be noted that the components depicted in the reference drawings are not necessarily shown to scale; and the same or similar components will be given the same or similar reference numerals or similar technical terms.

[0073] Reference Figure 1 The present application proposes a wire dancing monitoring system 100, comprising a parameter acquisition module 110 and a main control module 120. The parameter acquisition module 110 is used to detect the angular velocity and acceleration of the wire. The main control module 120 is used to calculate the first sampling angle of the wire at the nth sampling moment based on the angular velocity and acceleration of the wire at the nth sampling moment; and, based on the first sampling angle of the wire at the nth sampling moment, calculate the predicted angle of the wire at the n+1th sampling moment; the main control module 120 is also used to calculate the second sampling angle of the wire at the n+1th sampling moment based on the angular velocity and acceleration of the wire at the n+1th sampling moment; based on the second sampling angle of the wire at the n+1th sampling moment and the predicted angle of the wire at the n+1th sampling moment, calculate the dancing angle of the wire at the n+1th sampling moment; wherein n is a positive integer.

[0074] In this embodiment, the parameter acquisition module 110 may include a gyroscope and an accelerometer. By installing a gyroscope and an accelerometer at each monitoring point of the wire, the angular velocity and acceleration of the wire at different positions can be detected. The main control module 120 can calculate the dancing angle of the wire at each monitoring point based on the angular velocity and acceleration of each monitoring point.

[0075] The gyroscope can detect the angular velocity of the wire. By integrating the angular velocity, the angle (roll angle, pitch angle, yaw angle) of the wire can be obtained, and the attitude of the wire can be obtained. However, the angular velocity detected by the gyroscope has measurement errors, noise and drift, and after integration, cumulative errors will be formed. The accelerometer can obtain the acceleration of the wire. By calculating the acceleration detected by the accelerometer, the real-time attitude angle of the wire can be obtained. Therefore, this embodiment calculates the acceleration of the accelerometer to obtain the real-time attitude angle of the wire, and corrects the cumulative error generated by the angle of the wire obtained by the gyroscope, which can improve the accuracy of the dancing angle of the wire.

[0076] The main control module 120 can also calculate the predicted angle at the next sampling moment according to the first sampling angle at the nth sampling moment, and calculate the second sampling angle at the n+1th sampling moment according to the angular velocity and acceleration detected at the n+1th sampling moment. In this way, the predicted angle is updated according to the second sampling angle at the n+1th sampling moment, so that the difference between the final calculated dancing angle and the actual dancing angle is smaller.

[0077] In one embodiment, the main control module 120 is further configured to output a warning message when determining that the dancing state of the wire is abnormal based on the dancing angle of the wire at the (n+1)th sampling moment.

[0078] For example, if the dancing angle at the n+1th sampling moment is greater than or equal to the preset angle, the main control module 120 can output a warning message to the tower to prompt the monitoring personnel to perform maintenance in time. If the dancing angle at the n+1th sampling moment is less than the preset angle, continue monitoring.

[0079] In one embodiment, the main control module 120 can be arranged on the wire. Alternatively, the main control module 120 may include a first control unit 121 arranged on the wire and a second control unit 122 arranged on the tower. The first control unit 121 can be used to collect angular velocity and acceleration on the wire, and send the angular velocity and acceleration to the second control unit 122 after preliminary filtering. The second control unit 122 analyzes the angular velocity and acceleration to obtain data such as dancing angle, dancing amplitude, and dancing frequency, and displays and stores the data in the background. If there is an abnormality in the analyzed data, for example, the dancing angle is greater than the preset angle, the second control unit 122 can also output a warning signal to prompt the monitoring personnel to perform maintenance in time. Furthermore, the second control unit 122 can also transmit the data such as dancing angle, dancing amplitude, and dancing frequency to the background main station through a dedicated network.

[0080] In one embodiment, the parameter acquisition module 110 may also include a temperature sensor. One or more temperature sensors may be provided at each monitoring point of the conductor to detect the temperature of the conductor and / or the ambient temperature. The temperature sensor may be implemented using a platinum resistance sensor. The platinum resistance sensor can cover a wide temperature measurement range from -200°C to 850°C, and has excellent anti-interference ability and adaptability, and is suitable for various environmental conditions. The linearity of the platinum resistance sensor is superior to that of other types of temperature sensors, and the system error can be reduced and the measurement accuracy can be improved through table lookup and software compensation methods. In addition, the three-wire or four-wire connection method used by the platinum resistance sensor effectively reduces the influence of the wire resistance on the measurement results, further enhancing its accuracy.

[0081] In one embodiment, the parameter acquisition module 110 may further include a humidity sensor for collecting the humidity of the wire and / or the ambient humidity.

[0082] In one embodiment, the wire dancing monitoring system 100 further includes a Beidou positioning module 111, and the Beidou positioning module 111 is used to obtain the position information of the wire; the position information and the dancing angle are used to determine the dancing information of the wire.

[0083] In one embodiment, the wire dancing monitoring system 100 further includes a data transmission module, which is used to transmit the location information and the dancing angle to the tower and / or the cloud. The data transmission module can be implemented by a LORA antenna module, a 5G antenna module, etc.

[0084] By taking advantage of the high positioning accuracy of the Beidou positioning module 111, the main control module 120 can obtain the position information of the wire in real time, and transmit it together with the dancing angle to the tower and / or the cloud for analysis, which can ensure the accuracy and real-time performance of data transmission. The tower computing center or the cloud can calculate the dancing information of the wire based on the position information and the dancing angle, such as the dancing posture, dancing trajectory, etc., to provide more accurate and reliable decision support for operation and maintenance personnel.

[0085] In one embodiment, the wire galloping monitoring system 100 further includes a photovoltaic module 130 for converting solar energy into electrical energy to power the parameter acquisition module 110 and the main control module 120. The photovoltaic module 130 can be implemented by one or more solar photovoltaic panels.

[0086] Reference Figure 2In one embodiment, the photovoltaic module 130 includes a solar photovoltaic panel, a solar energy management and charging management chip, a lithium-ion polymer battery, and a linear regulator. The solar photovoltaic panel can convert solar energy into electrical energy to charge the lithium-ion polymer battery. The electrical energy output by the lithium-ion polymer battery is converted into a suitable working voltage by the linear regulator and then output to the main control module 120 for power supply.

[0087] Figure 3 This is a logic flow chart of an embodiment of a wire dancing monitoring system 100 of the present application. After starting the wire dancing monitoring system 100, the parameter acquisition module 110 collects data such as angular velocity, acceleration, temperature, and positioning. The first control unit 121 on the wire filters, formats, compresses, and processes the collected data, and then sends it to the second control unit 122 on the tower through the LORA antenna for analysis to obtain data such as dancing angle, dancing amplitude, and dancing frequency. It is then sent to the background main station through a dedicated network to display and store data in the background. If the analyzed data is normal, monitoring continues. If there is an abnormality in the parsed data, for example, the dancing angle is greater than the preset angle, the second control unit 122 can also output a warning signal to prompt the monitoring personnel to perform maintenance in a timely manner.

[0088] The present application calculates the dancing angle of the conductor through the angular velocity and acceleration data of the conductor, realizes the integration and complementarity of multi-source data, and thus improves the accuracy and comprehensiveness of the dancing angle. The comprehensive analysis of multi-source data can more accurately reflect the actual movement state and external environmental conditions of the ground wire, and provide more reliable data support for dancing monitoring. The predicted data of the dancing angle is updated through the angle data obtained through actual sampling, further reducing the error of the dancing angle.

[0089] Reference Figure 4 , the present application also proposes a wire galloping monitoring method, comprising:

[0090] S1: Obtain the angular velocity and acceleration of the wire at each sampling moment.

[0091] In this embodiment, the angular velocity can be detected by a gyroscope, and the acceleration can be detected by an accelerometer. Multiple monitoring points can be set on the wire according to actual needs, and the angular velocity and acceleration of each monitoring point at each sampling time are obtained, and the angular velocity and acceleration of each monitoring point are calculated to obtain the dancing angle of the wire at different monitoring points. In the embodiment of this application, only one monitoring point is used as an example for explanation.

[0092] Reference Figure 5 In one embodiment, step S1 includes:

[0093] S11: constructing a three-axis coordinate system with the wire as the center; the three-axis coordinate system includes an x-axis, a y-axis and a z-axis.

[0094] By constructing a three-axis coordinate system, the posture of the wire can be intuitively described. Figure 6 The figure shows the conversion of coordinate system during attitude solution. Among them, the x-axis, y-axis and z-axis constitute the three-axis coordinate system of the conductor. The a-axis, b-axis and c-axis constitute the earth coordinate system. The angle of rotation of each axis from the earth coordinate system to the conductor coordinate system is the Euler angle, which can be (pitch angle θ, roll angle ψ, yaw angle ) is used to intuitively describe the wire posture.

[0095] The rotation matrix based on Euler angles needs to solve 9 elements, and when the pitch angle is close to ±90°, the universal lock phenomenon will occur. Therefore, the Euler angle is severely limited when used for attitude representation, which is not conducive to the detection system to perform full attitude measurement. When calculating the angle attitude of the wire, the wire coordinate system data is usually converted to the earth coordinate system data. The angle attitude of the wire can be expressed using parameters such as quaternions.

[0096] Through this rotation matrix The conversion between the traverse coordinate system and the earth coordinate system can be realized in the attitude solution:

[0097]

[0098] Quaternions can be represented as:

[0099] q(q0,q1,q2,q3)=q0+q1i+q2j+q3k,

[0100] Among them, i, j, k are unit vectors, and the normalized quaternion satisfies:

[0101] q0 2 +q1 2 +q2 2 +q3 2 =1.

[0102] The conversion relationship between the initial quaternion and the initial Euler angle is:

[0103]

[0104] After attitude solution, the rotation axis and rotation direction information generated by the coordinate system transformation relationship can be used to reversely obtain the three-axis attitude Euler angle through quaternion.

[0105] θ=-arcsin[2(q1q3-q0q2)],

[0106]

[0107] S12: Obtaining the angular velocity of the x-axis, the angular velocity of the y-axis, and the angular velocity of the z-axis of the conductor at each sampling time.

[0108] The angular velocity of the x-axis, the angular velocity of the y-axis and the angular velocity of the z-axis can be determined by gyroscope detection, so as to calculate the angular velocity of the three axes and obtain the dancing angle.

[0109] S13: Obtaining the acceleration of the x-axis, the acceleration of the y-axis, and the acceleration of the z-axis of the wire at each sampling time.

[0110] The accelerometer can detect the magnitude and direction of gravity acceleration. When an object moves, the gravity acceleration is always downward. The three components of gravity projected onto the three-axis coordinate system of the wire are the acceleration of the x-axis, the acceleration of the y-axis, and the acceleration of the z-axis.

[0111] S2: Calculate a first sampling angle of the conductor at the nth sampling moment based on the angular velocity and acceleration of the conductor at the nth sampling moment; wherein n is a positive integer.

[0112] In this embodiment, the gyroscope can detect the angular velocity of the wire, and by calculating the angular velocity, the dancing angle of the wire can be obtained, and thus the posture of the wire can be obtained. However, the angular velocity detected by the gyroscope may have measurement errors, noise, and drift, and after integration operations, cumulative errors will be formed. The dancing angle of the wire can also be obtained by obtaining the acceleration of the wire and calculating the acceleration. Therefore, the first sampling angle can be obtained by calculating the acceleration and angular velocity, and more accurate angle data can be obtained by using multi-source data fusion and complementation.

[0113] Reference Figure 7 In one embodiment, step S2 includes:

[0114] S21: Calculate the angle of the conductor based on the angular velocity of the conductor at the nth sampling moment to obtain a first angle value.

[0115] In this embodiment, the angular velocity is integrated, filtered, and other calculations are performed to obtain the required first angle value, so as to facilitate subsequent fusion calculations.

[0116] Reference Figure 8 and Fig. 9 In one embodiment, step S21 includes:

[0117] S211: respectively calculating the integral of the x-axis angular velocity, the integral of the y-axis angular velocity and the integral of the z-axis angular velocity of the conductor at the nth sampling moment to obtain the x-axis angle, the y-axis angle and the z-axis angle of the conductor at the nth sampling moment.

[0118] S212: Perform a first filtering on the x-axis angle value, the y-axis angle value, and the z-axis angle value, respectively, to obtain the x-axis angle value, the y-axis angle value, and the z-axis angle value of the wire at the nth sampling moment. The range of the first filtering can be selected according to actual needs. For example, in the detection signal of the gyroscope, there is a lot of low-frequency noise, and the first filtering can be set to a high-pass filter to filter out low-frequency noise interference and retain the required high-frequency information.

[0119] S22: Calculate the angle of the wire based on the acceleration of the wire at the nth sampling moment to obtain a second angle value.

[0120] In this embodiment, the angle of the wire is obtained by calculating the acceleration and filtering it to obtain the required second angle value, so as to facilitate subsequent fusion calculation.

[0121] Reference Fig.10 and Fig. 9 In one embodiment, step S22 includes:

[0122] S221: Calculate the x-axis acceleration, the y-axis acceleration, and the z-axis acceleration of the wire at the nth sampling moment to obtain the pitch angle value and the roll angle value of the wire at the nth sampling moment.

[0123] In this embodiment, the pitch angle value and the roll angle value can be calculated by the following formula:

[0124]

[0125] Among them, pitch is the pitch angle value, roll is the roll angle value, a x is the x-axis acceleration, a y is the acceleration on the y-axis, a z is the z-axis acceleration.

[0126] S222: Perform a second filtering on the pitch angle and the roll angle respectively to obtain the pitch angle value and the roll angle value. The range of the second filtering can be selected according to actual needs. For example, the accelerometer is easily interfered by high-frequency noise, and the second filtering can be set to a low-pass filter to filter out high-frequency noise interference and retain the required low-frequency information.

[0127] S23: performing complementary filtering processing on the first angle value and the second angle value based on a preset complementary filtering algorithm to obtain a first sampling angle of the conductor at the nth sampling moment.

[0128] For example, in order to make the reconstructed angle signal after complementary filtering equal to the actual sampled angle signal as much as possible, the high-pass filter and the low-pass filter can be set to meet the following conditions:

[0129] G1(s)+G2(s)=1,

[0130] Among them, G1(s) is the transfer function of the high-pass filter, and G2(s) is the transfer function of the low-pass filter. Its general form is generally:

[0131]

[0132] Where C(s) is the transfer function of the PI controller and s is a variable in the frequency domain. Error compensation by the PI controller can reduce signal noise and drift errors.

[0133]

[0134] Among them, k P is the proportionality coefficient, k I is the integral coefficient. The effect of PI control can be changed by adjusting the proportional coefficient and the integral coefficient.

[0135] Therefore, the transfer functions of the high-pass filter and the low-pass filter are:

[0136]

[0137] In this way, by performing high- and low-frequency complementary filtering on the first angle value and the second angle value through the above calculation, the high- and low-frequency noise in the data acquisition process can be effectively filtered out, thereby obtaining a first sampling angle close to the original posture angle. In addition, the preset complementary filtering algorithm can also be set to other conditions according to requirements, which are not limited here.

[0138] S3: Calculate the predicted angle of the conductor at the (n+1)th sampling moment based on the first sampling angle of the conductor at the nth sampling moment.

[0139] In this embodiment, after the first sampling angle of the wire at the nth sampling moment is calculated through the above steps, the angle of the wire at the next moment can be predicted based on the first sampling angle, so as to obtain a more accurate sampling angle at the next moment. For example, the first sampling angle of the wire at the nth sampling moment can be input into the prediction model to predict the angle of the wire at the next moment.

[0140] Reference Fig.11 and Fig. 9 In one embodiment, step S3 includes:

[0141] S31: Determine an estimated angle of the conductor at the (n+1)th sampling moment based on a first sampling angle of the conductor at the nth sampling moment.

[0142] In this embodiment, an extended Kalman filter may be used to predict the conductor angle at the next moment.

[0143] First, define the state vector x, (x∈R n ), the state equation is:

[0144] x k =f(x k-1 ,k-1)+m k-1 ,

[0145] Where k represents the current moment. The nonlinear function f is the transfer function, which describes the evolution of the system state over time. n ∈R n To obey N(0,Q k ) distributed Gaussian white noise, the state equation after linearization is:

[0146] x k =f(x k-1 ,u k-1 ,m k-1 ).

[0147] In this way, the angle of the wire at the (n+1)th sampling moment can be predicted.

[0148] S32: Determine an angle estimation error of the conductor at the n+1th sampling moment based on the estimated angle of the conductor at the n+1th sampling moment.

[0149] In this embodiment, the error covariance prediction equation can be used to predict the angle error of the conductor at the n+1th sampling moment. The error covariance matrix prediction is to predict the error covariance at the next moment. This step takes into account the process noise, which describes the uncertainty of the system state prediction. The error covariance prediction equation is as follows:

[0150]

[0151] Among them, F k is the Jacobian matrix of the state transfer function. The Jacobian matrix is ​​used to approximate nonlinear functions. The state transfer function and the observation function are usually nonlinear. k The Jacobian matrices of these functions are calculated at , which facilitates the linearization of these nonlinear functions in subsequent calculations, thereby approximately handling nonlinear problems at each time step.

[0152] is the process noise covariance matrix, which is used in the Kalman filter to describe the system model uncertainty, that is, the random noise in the system state transition process. This matrix is ​​an important parameter to measure the size of the internal noise of the system. It quantifies the dynamic behavior of the system and reflects the uncertainty of the state prediction.

[0153] Taking partial derivatives of the function f, we get the Jacobian matrix of the state vector:

[0154]

[0155] S33: Determine the predicted angle of the conductor at the n+1th sampling moment based on the estimated angle of the conductor at the n+1th sampling moment and the angle estimation error.

[0156] Combining the above state equation and error covariance prediction equation, substituting the angular velocity value and acceleration value, the three-axis attitude angle at the current moment can be solved and used as the predicted angle for the next moment.

[0157] S4: Calculating a second sampling angle of the conductive line at the n+1th sampling moment based on the angular velocity and acceleration of the conductive line at the n+1th sampling moment.

[0158] In this embodiment, the principle of calculating the second sampling angle of the conductor at the n+1th sampling moment is the same as the principle of calculating the first sampling angle of the conductor at the nth sampling moment in step S2, which will not be repeated here.

[0159] S5: Calculate the dancing angle of the wire at the n+1th sampling moment based on the second sampling angle of the wire at the n+1th sampling moment and the predicted angle of the wire at the n+1th sampling moment.

[0160] In this embodiment, at the n+1th sampling moment, the dancing angle of the wire at the n+1th sampling moment is obtained by calculating the predicted angle at the previous moment and the second sampling angle obtained by actual measurement. The predicted angle is updated by the second sampling angle obtained by actual measurement, so that the final dancing angle is closer to the actual state of the wire.

[0161] Reference Fig.12 In one embodiment, step S5 includes:

[0162] S51: Calculate the Kalman gain based on the angle estimation error of the wire at the n+1th sampling moment:

[0163]

[0164] in, It is the Jacobian matrix of the observation model to the state, which is used to linearize the nonlinear observation model and describes the local linear change rate of the observation model relative to the state variable. k and V k is the measurement Jacobian matrix at time k, R k represents the measurement noise covariance matrix at time k. Represents the error covariance matrix at the previous moment.

[0165] In this embodiment, the state equation and the error covariance equation may be updated by using the Kalman gain, which is a coefficient that weighs the prediction error and the measurement error and determines the proportion of the measurement value in the state update.

[0166] S52: Based on the second sampling angle of the conductor at the (n+1)th sampling moment and the Kalman gain, updating the second sampling angle of the conductor at the (n+1)th sampling moment and the angle estimation error.

[0167] In this embodiment, the Kalman gain and the second sampling angle are used to update the predicted angle. The state update equation combines the predicted state and the measurement residual (i.e., the difference between the measured value and the predicted state) to produce a more accurate state estimate:

[0168]

[0169] Define the observation vector z, (z∈R m )

[0170] The observation equation is:

[0171] z k =h(x k )+v k ,

[0172] v n ∈R n To obey N(0,R k ) distributed Gaussian white noise, after linearization processing, we get:

[0173] z k =h(x k ,v k ),

[0174] The nonlinear function h is the transfer function, which describes how to obtain observation data from the state variables of the system.

[0175] S53: Calculate the dancing angle of the wire at the n+1th sampling moment based on the updated second sampling angle of the wire at the n+1th sampling moment and the angle estimation error.

[0176] Finally, the error covariance matrix is ​​updated. This step takes into account the measurement noise, which describes the uncertainty of the measured values:

[0177]

[0178] In this way, after the fusion of complementary filtering and extended Kalman filtering, the processed quaternion is converted into Euler angle output, and the dancing angle (pitch angle θ, roll angle ψ, yaw angle θ) of the wire at the n+1th sampling moment can be obtained. ).

[0179] S6: Based on the dancing angle of the wire at the n+1th sampling moment, determine that the dancing state of the wire is abnormal, and output a warning message. For example, if the dancing angle of the wire at the n+1th sampling moment is greater than a preset angle, output a warning message to remind the monitoring personnel to perform maintenance in time.

[0180] Reference Fig.13 and Fig.14 In one embodiment, the wire galloping monitoring method further includes:

[0181] S7: Obtain the angular velocity and acceleration of the m monitoring points at the nth sampling moment; wherein m is an integer greater than or equal to 2.

[0182] In this embodiment, multiple monitoring points are usually evenly installed on the conductor. After obtaining the sampled data of multiple monitoring points, the BiLSTM (Bidirectional Long Short-Term Memory) algorithm can be used to process the sampled data. BiLSTM is a special recurrent neural network (RNN) that is widely used to process sequence data. BiLSTM can be expressed as a combination of two LSTMs. BiLSTM can consider both forward information and reverse information. In overhead transmission lines, multiple monitoring points are generally evenly installed on the same conductor or ground wire on the windward side. The use of BiLSTM can bidirectionally consider the influence between the monitoring points and more accurately represent the cable status in the transmission line.

[0183] S8: Generate a forward state sequence and a reverse state sequence based on the angular velocities and accelerations of the m monitoring points at the nth sampling moment.

[0184] First, given an input sequence E = [e1, e2, …, e T ], the internal mechanism of LSTM can be divided into forget gate, input gate, candidate memory unit, update memory unit, output gate, and hidden state. The function of forget gate is mainly to determine how much information should be forgotten from the memory unit of the previous sequence. Specifically, the formula of forget gate is as follows:

[0185] f t =σ(W f ·[h t-1 ,e t ]+b f ),

[0186] Among them, W f is the weight matrix of the forget gate, σ() is the sigmoid activation function, bf is the bias,

[0187] [h t-1 ,e t ] is the concatenation vector of the previous hidden state and the current input.

[0188] The function of the input gate is to determine how much information of the current sequence can be saved in the memory unit. The formula is as follows:

[0189] i t =σ(W i ·[h t-1 ,e t ]+b i ),

[0190] Input Gate i t Each element in gets an output between 0 and 1 through the sigmoid function. The closer the value is to 1, the more the position information needs to be updated.

[0191] The candidate state refers to the linear combination of the current sequence input and the hidden state of the previous step, and then activated by the tanh function:

[0192]

[0193] Updating the memory unit is the core step, which determines which information will be retained. The formula is as follows:

[0194]

[0195] Where * is the element-by-element product, and the output gate mainly controls how much information in the memory unit is transferred to the hidden state in each sequence. The formula is as follows:

[0196] o t =σ(W o ·[h t-1 ,e t ]+b o ).

[0197] The formula for the hidden state is as follows:

[0198] h t =o t *tanh(c t ).

[0199] The first step of the feature vectorization process is to convert the features of each sampled data at the nth sampling moment into a One-Hot vector form, where the corresponding position of the One-Hot vector will be assigned a specific value represented by the feature. Subsequently, these One-Hot vectors will be input into a hidden layer of length j for deep encoding. The encoding process follows the following formula:

[0200] e i =δ(WX i +b i ),

[0201] Where X i =[x1,x2,…,x j ] is the one-hot encoding vector corresponding to the i-th sampling moment, j is the number of features, W∈R m×n is a trainable matrix, b is a bias, δ() is a nonlinear activation function, and the present embodiment may adopt a softmax activation function.

[0202] The forward LSTM generates a forward hidden state sequence The reverse LSTM produces a reverse hidden state sequence BiLSTM can comprehensively consider the forward and backward information to obtain two hidden states. The forward hidden state sequence and the reverse hidden state sequence After splicing, it is used as the input of the next chapter. This mechanism enables BiLSTM to show higher efficiency and accuracy when dealing with tasks such as the swaying of overhead transmission lines, which require comprehensive consideration of the front and rear position information.

[0203] S9: Based on the forward state sequence and the reverse state sequence, output the dancing posture prediction result of the wire at the nth sampling moment.

[0204] After obtaining the BiLSTM hidden state output containing sequence information, it is input into the prediction layer, and the embedding containing sequence information is converted into a prediction value through a fully connected layer. The formula is as follows:

[0205]

[0206] in, is the prediction probability, ranging from 0 to 1, σ() is the sigmoid function, W r ∈

[0207] R 1×2m ,h i is the concatenated embedding calculated by BiLSTM at the i-th sampling moment. The above formula can be used to obtain the predicted probability value for the wire, and the normal or abnormal state of the wire can be determined based on the predicted probability value.

[0208] The model uses the cross entropy loss function for optimization as follows:

[0209]

[0210] r iis the true label at the i-th sampling moment, where r n =1, r j =0, if the wire is in normal condition at the jth moment, it is 0; if the wire is dancing abnormally at the ith sampling moment, it is 1. The output value represents the model's prediction value for that moment. The model uses the Adam algorithm for gradient descent with a learning rate of 0.001. Dropout is set to 0.3 to reduce the risk of overfitting. Each layer embedding will be L2Norm (L2 normalization).

[0211] S10: If the predicted result of the dancing posture of the wire at the nth sampling moment is greater than a preset value, it is determined that the dancing of the wire at the nth sampling moment is abnormal.

[0212] In this embodiment, a preset value can be set according to actual application. If the predicted probability value is greater than or equal to the preset value, it is determined that the wire galloping is abnormal. For example, the preset value is set to 0.6. If it is greater than or equal to 0.6, it is determined that the wire galloping is abnormal.

[0213] Reference Fig.15 In one embodiment, the wire galloping monitoring method further includes:

[0214] S20: Determine the dancing amplitude and the dancing frequency of the wire at the n+1th sampling moment based on the acceleration of the wire at the n+1th sampling moment.

[0215] In this embodiment, the acceleration of the wire at the n+1th sampling moment can be double integrated to obtain the displacement of the wire. The displacement of the wire at multiple sampling moments within a preset time is analyzed to extract the dancing amplitude and zero crossing times of the wire, and the dancing frequency of the wire can be obtained according to the zero crossing times and the preset time.

[0216] S30: Determine the dancing trajectory of the wire based on the dancing angle, dancing amplitude and dancing frequency of the wire at the (n+1)th sampling moment.

[0217] In this embodiment, by collecting the angular velocity and acceleration of multiple monitoring points at the n+1th sampling moment and performing the above calculation, the dancing angle, dancing amplitude and dancing frequency of the multiple monitoring points at the n+1th sampling moment can be obtained, thereby generating the dancing trajectory of the wire at the n+1th sampling moment. In this way, according to the dancing trajectory of the wire at multiple sampling moments, a continuous dancing trajectory can be generated, so that the monitoring personnel can more intuitively obtain the state of the wire.

[0218] The present application calculates the dancing angle of the conductor through the angular velocity and acceleration data of the conductor, realizes the integration and complementarity of multi-source data, and thus improves the accuracy and comprehensiveness of the dancing angle. The comprehensive analysis of multi-source data can more accurately reflect the actual movement state and external environmental conditions of the ground wire, and provide more reliable data support for dancing monitoring. The predicted data of the dancing angle is updated through the angle data obtained through actual sampling, further reducing the error of the dancing angle.

[0219] In the above, the specific implementation of the present application is described with reference to the accompanying drawings. However, those skilled in the art will appreciate that various changes and substitutions may be made to the specific implementation of the present application without departing from the spirit and scope of the present application. These changes and substitutions are all within the scope defined by the present application.

Claims

1. A method for monitoring conductor galloping, characterized in that: include: Obtain the angular velocity and acceleration of the wire at each sampling moment; Calculating a first sampling angle of the conductor at the nth sampling moment based on the angular velocity and acceleration of the conductor at the nth sampling moment; wherein n is a positive integer; Calculating a predicted angle of the conductor at an n+1th sampling moment based on a first sampling angle of the conductor at an nth sampling moment; Calculating a second sampling angle of the wire at the n+1th sampling moment based on the angular velocity and acceleration of the wire at the n+1th sampling moment; Calculating a dancing angle of the wire at the n+1th sampling moment based on the second sampling angle of the wire at the n+1th sampling moment and the predicted angle of the wire at the n+1th sampling moment; If it is determined that the dancing state of the wire is abnormal based on the dancing angle of the wire at the (n+1)th sampling moment, a warning message is output.

2. The wire dancing monitoring method according to claim 1, characterized in that: The step of calculating a first sampling angle of the conductor at the nth sampling moment based on the angular velocity and acceleration of the conductor at the nth sampling moment comprises: Calculating the angle of the conductor based on the angular velocity of the conductor at the nth sampling moment to obtain a first angle value; Calculating the angle of the wire based on the acceleration of the wire at the nth sampling moment to obtain a second angle value; Complementary filtering is performed on the first angle value and the second angle value based on a preset complementary filtering algorithm to obtain a first sampling angle of the conductor at the nth sampling moment.

3. The wire dancing monitoring method according to claim 2, characterized in that: The step of obtaining the angular velocity and acceleration of the conductor at each sampling moment includes: A three-axis coordinate system is constructed with the wire as the center; the three-axis coordinate system includes an x-axis, a y-axis and a z-axis; Obtaining the angular velocity of the x-axis, the angular velocity of the y-axis, and the angular velocity of the z-axis of the conductor at each sampling moment; Obtaining the x-axis acceleration, the y-axis acceleration, and the z-axis acceleration of the wire at each sampling moment; The first angle value includes an x-axis angle value, a y-axis angle value and a z-axis angle value; the second angle value includes a pitch angle value and a roll angle value; the first sampling angle includes an x-axis first sampling angle, a y-axis first sampling angle and a z-axis first sampling angle.

4. The wire dancing monitoring method according to claim 3, characterized in that: The step of calculating the angle of the conductor based on the angular velocity of the conductor at the nth sampling moment to obtain a first angle value includes: Respectively calculating the integral of the x-axis angular velocity, the integral of the y-axis angular velocity, and the integral of the z-axis angular velocity of the wire at the nth sampling moment to obtain the x-axis angle, the y-axis angle, and the z-axis angle of the wire at the nth sampling moment; The x-axis angle, the y-axis angle and the z-axis angle are respectively subjected to first filtering to obtain the x-axis angle value, the y-axis angle value and the z-axis angle value of the wire at the nth sampling moment.

5. The wire dancing monitoring method according to claim 3, characterized in that: The step of calculating the angle of the wire based on the acceleration of the wire at the nth sampling moment to obtain a second angle value includes: Determining the x-axis acceleration, the y-axis acceleration, and the z-axis acceleration of the wire at the n-th sampling moment based on the acceleration of the wire at the n-th sampling moment; Determine the pitch angle and roll angle of the wire at the nth sampling moment based on the x-axis acceleration, the y-axis acceleration and the z-axis acceleration of the wire at the nth sampling moment; The pitch angle and the roll angle are respectively subjected to second filtering to obtain the pitch angle value and the roll angle value.

6. The wire galloping monitoring method according to claim 1, characterized in that: The calculating, based on the first sampling angle of the conductor at the nth sampling moment, the predicted angle of the conductor at the (n+1)th sampling moment comprises: Determining an estimated angle of the conductor at the (n+1)th sampling moment based on a first sampling angle of the conductor at the (n)th sampling moment; Determining an angle estimation error of the conductor at the n+1th sampling moment based on the estimated angle of the conductor at the n+1th sampling moment; Based on the estimated angle of the conductor at the (n+1)th sampling moment and the angle estimation error, the predicted angle of the conductor at the (n+1)th sampling moment is determined.

7. The wire dancing monitoring method according to claim 6, characterized in that: The calculating the dancing angle of the wire at the n+1th sampling moment based on the second sampling angle of the wire at the n+1th sampling moment and the predicted angle of the wire at the n+1th sampling moment comprises: Calculating a Kalman gain based on an angle estimation error of the conductor at the n+1th sampling moment; Based on the second sampling angle of the conductor at the n+1th sampling moment and the Kalman gain, updating the second sampling angle of the conductor at the n+1th sampling moment and the angle estimation error; Based on the updated second sampling angle of the wire at the (n+1)th sampling moment and the angle estimation error, the dancing angle of the wire at the (n+1)th sampling moment is calculated.

8. The wire galloping monitoring method according to claim 1, characterized in that: The conductor is provided with a plurality of monitoring points; the conductor dancing monitoring method further comprises: Obtaining the angular velocity and acceleration of the m monitoring points at the nth sampling moment; wherein m is an integer greater than or equal to 2; Generate a forward state sequence and a reverse state sequence based on the angular velocities and accelerations of the m monitoring points at the nth sampling moment; Determine a predicted result of the dancing posture of the wire at the nth sampling moment based on the forward state sequence and the reverse state sequence; If the predicted result of the dancing posture of the wire at the nth sampling moment is greater than a preset value, it is determined that the dancing of the wire at the nth sampling moment is abnormal.

9. The wire galloping monitoring method according to claim 1, characterized in that: The wire galloping monitoring method further comprises: Determine the dancing amplitude and the dancing frequency of the wire at the n+1th sampling moment based on the acceleration of the wire at the n+1th sampling moment; The dancing trajectory of the wire is determined based on the dancing angle, dancing amplitude and dancing frequency of the wire at the n+1th sampling moment; the dancing trajectory is used to determine the dancing state of the wire.

10. A computer storage medium, characterized in that: The method comprises a processor and a memory; the memory stores computer instructions, and when the computer instructions are executed on the processor, the processor executes the wire galloping monitoring method according to any one of claims 1 to 8.

11. A conductor galloping monitoring system, characterized in that: include: A parameter acquisition module, used for detecting the angular velocity and acceleration of the wire; A main control module, used for calculating a first sampling angle of the wire at the nth sampling moment based on the angular velocity and acceleration of the wire at the nth sampling moment; And, based on the first sampling angle of the wire at the nth sampling moment, the predicted angle of the wire at the n+1th sampling moment is calculated; the main control module is also used to calculate the second sampling angle of the wire at the n+1th sampling moment based on the angular velocity and acceleration of the wire at the n+1th sampling moment; based on the second sampling angle of the wire at the n+1th sampling moment and the predicted angle of the wire at the n+1th sampling moment, the dancing angle of the wire at the n+1th sampling moment is calculated; the main control module is also used to output a warning message when it is determined that the dancing state of the wire is abnormal based on the dancing angle of the wire at the n+1th sampling moment; wherein n is a positive integer.

12. The wire galloping monitoring system according to claim 11, characterized in that: The parameter acquisition module includes: The Beidou positioning module is used to obtain the position information of the wire; the position information and the dancing angle are used to determine the dancing state of the wire.

13. The wire galloping monitoring system according to claim 11, characterized in that: The conductor galloping monitoring system also includes: The photovoltaic module is used to convert solar energy into electrical energy to supply power to the parameter acquisition module and the main control module.

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