Inertial navigation parameter real-time estimation and updating method, system and equipment for monitoring sag and windage yaw states of transmission line conductor, and medium
By using a real-time estimation and update method for inertial navigation parameters, the problems of insufficient adaptability to dynamic environments and error accumulation in transmission line conductor monitoring were solved, achieving high-precision, real-time conductor status monitoring and risk warning.
Patent Information
- Application Number
- CN202511502373.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-10-21
- Publication Date
- 2026-02-03
AI Technical Summary
In existing transmission line conductor monitoring technologies, reliance on static calibration leads to insufficient adaptability to dynamic environments. Inertial sensor errors accumulate continuously during integration without a real-time compensation mechanism. Traditional filtering algorithms lag in response to nonlinear disturbances such as wind-induced vibrations, failing to accurately capture instantaneous attitude changes. Furthermore, they cannot maintain reliable output when GNSS signals are limited or interrupted.
Inertial sensors are used to collect conductor data, initial alignment is completed using an initialization unit, an error state model is constructed, and a filter estimator is used to fuse observation data to perform real-time error state estimation. Closed-loop feedback is used to correct the main navigation state and sensor zero bias, the sag and wind deflection parameters of the conductor are calculated, and combined with time series analysis and frequency domain transformation, the safety status monitoring results are output.
It achieves high-precision, real-time monitoring of the three-dimensional state of conductors in complex environments, accurately reflects the true deflection state of conductors, identifies dynamic characteristics, provides risk warnings, and improves the accuracy of state perception and the timeliness of response.
Smart Images
Figure CN121453091A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of three-dimensional condition monitoring technology for power conductors, specifically to a method, system, device, and medium for real-time estimation and updating of inertial navigation parameters for monitoring the sag and wind deflection of transmission line conductors. Background Technology
[0002] Inertial Navigation Systems (INS) have become an important means of monitoring the condition of transmission line conductors due to their autonomy and high-frequency output characteristics. Current technologies mainly acquire angular velocity and linear acceleration signals through inertial measurement units (INS), and combine these with trajectory estimation and attitude calculation algorithms to achieve continuous monitoring of the conductor's spatial state. In terms of data processing, Kalman filtering and its improved algorithms are widely used to suppress sensor noise, and some studies have also introduced external observation methods such as GNSS and RTK for multi-source information fusion. Furthermore, existing technologies generally employ static calibration or laboratory error modeling methods to compensate for parameters such as sensor bias and scaling factors. A few studies have attempted to combine the dynamic characteristics of transmission line conductors, such as the catenary model, to calculate state parameters such as sag and wind deflection.
[0003] However, existing technologies have the following obvious limitations: First, they rely on static calibration, which makes it difficult to adapt to the dynamic vibration environment in actual operation of the conductor; second, the error of the inertial sensor will continue to accumulate during the integration process, resulting in significant drift in attitude and position estimation, and existing methods lack an effective real-time compensation mechanism; third, traditional filtering algorithms are not adaptable to strong nonlinear disturbances such as wind-induced vibration, and it is difficult to accurately capture the instantaneous attitude change characteristics of the conductor.
[0004] Therefore, there is an urgent need for a conductor condition monitoring scheme that can overcome the above-mentioned defects in order to improve the accuracy and reliability of condition estimation in complex operating environments. Summary of the Invention
[0005] In view of the above-mentioned problems, the present invention provides a method, system, device and medium for real-time estimation and updating of inertial navigation parameters for monitoring conductor sag and wind deflection in transmission lines.
[0006] Therefore, the technical problems solved by this invention are: the core technical challenges in existing power transmission line monitoring technologies, such as insufficient adaptability to dynamic environments due to reliance on static calibration, lack of real-time compensation mechanism for the continuous accumulation of inertial sensor errors during integration, inaccurate instantaneous attitude capture due to the lag in response of traditional filtering algorithms to nonlinear disturbances such as wind-induced vibration, and inability to maintain reliable output when GNSS signals are limited or interrupted.
[0007] To address the aforementioned technical problems, this invention provides the following technical solution: a method for real-time estimation and updating of inertial navigation parameters for monitoring the sag and wind deflection of transmission line conductors, comprising: acquiring inertial observation data of the conductor through an inertial sensor and completing the initial alignment of the navigation state using an initialization unit; constructing an error state model and using a first filter estimator to fuse the inertial observation data and first auxiliary observation data to perform real-time estimation of the error state through the error state model; based on the actual estimated error state, performing closed-loop feedback correction of the main navigation state and sensor zero bias through a navigation state correction unit; using a conductor state analysis unit to calculate the sag and wind deflection parameters of the conductor according to the corrected navigation state; performing time-series analysis of the sag and wind deflection parameters through a dynamic feature extraction unit, and outputting the safety state monitoring results of the conductor through a safety state output unit.
[0008] As a preferred embodiment of the inertial navigation parameter real-time estimation and update method for monitoring conductor sag and wind deflection in transmission lines as described in this invention, the initial alignment of the navigation state using the initialization unit includes: Initial motion data of the conductor is acquired using an inertial sensor.
[0009] Based on the initial motion data, the initial spatial reference direction of the conductor is determined.
[0010] Based on the initial spatial reference direction, calculate the initial attitude matrix of the traverse and complete the initial alignment between the navigation coordinate system and the traverse body coordinate system.
[0011] As a preferred embodiment of the inertial navigation parameter real-time estimation and update method for monitoring conductor sag and wind deflection in transmission lines according to the present invention, the step of employing a first filter estimator, fusing the inertial observation data and the first auxiliary observation data, and performing real-time estimation of the error state through an error state model includes: Based on the inertial observation data and the error state model, the error state is predicted using the first filter estimator.
[0012] Acquire first auxiliary observation data containing spatial positioning information.
[0013] The error state prediction result is fused and updated with the first auxiliary observation data using the first filter estimator.
[0014] Through the fusion update process, the optimal estimation result of the error state is output in real time.
[0015] As a preferred embodiment of the inertial navigation parameter real-time estimation and updating method for monitoring conductor sag and wind deflection in transmission lines as described in this invention, the step of performing closed-loop feedback correction of the main navigation state and sensor zero bias through the navigation state correction unit includes: The optimal estimation result of the error state is input into the navigation state correction unit.
[0016] Based on the error state, the main navigation state is compensated and corrected.
[0017] Estimate and compensate for the zero bias of inertial sensor measurements.
[0018] After the correction is completed, the updated main navigation status and zero-bias information are fed back to the front end to form a closed-loop correction.
[0019] As a preferred embodiment of the inertial navigation parameter real-time estimation and update method for monitoring the sag and wind deflection of transmission line conductors according to the present invention, the step of calculating the sag and wind deflection parameters of the conductor based on the corrected navigation state includes: Based on the corrected navigation position information, the relative height difference of the guide is calculated to solve for the sag parameter.
[0020] Based on the corrected navigation attitude information, the horizontal direction angle of the guide wire is extracted to calculate the wind deflection parameter.
[0021] The calculated sag and wind deflection parameters are initially verified for rationality and filtered, and the sag and wind deflection parameters that characterize the conductor's geometry and stress state are output.
[0022] The beneficial effects of this preferred technical solution are that it establishes an effective conversion path from navigation calculation to line condition monitoring by directly mapping high-precision navigation position and attitude information to key conductor state parameters. The corrected navigation position information is used to directly calculate conductor sag, avoiding errors caused by simplification in traditional mechanical models; simultaneously, wind deflection parameters are extracted based on accurate attitude angles, accurately reflecting the true deflection state of the conductor under wind loads.
[0023] As a preferred embodiment of the inertial navigation parameter real-time estimation and update method for monitoring the sag and wind deflection of transmission line conductors according to the present invention, the step of performing time-series analysis of the sag and wind deflection parameters through a dynamic feature extraction unit includes... Time series data were constructed for the sag and wind deflection parameters.
[0024] The time series was analyzed using frequency domain transformation to extract periodic fluctuation characteristics.
[0025] Identify and quantify the dominant frequency and amplitude of the periodic fluctuations.
[0026] The output includes the dynamic characteristic parameters of the conductor, including frequency and amplitude.
[0027] The beneficial effect of this preferred technical solution is that, through time-series analysis and frequency domain transformation, it achieves a technological leap from static parameter monitoring to dynamic feature identification. This solution constructs sag and wind deflection parameters as a time series, effectively isolates environmental noise through frequency domain analysis, and accurately extracts the periodic fluctuation characteristics of the conductor under dynamic disturbances such as wind loads. By quantifying the dominant frequency and amplitude of the vibration, it is possible not only to grasp the conductor's swaying pattern in real time but also to provide crucial data support for wind-induced fatigue analysis and dynamic safety margin assessment.
[0028] As a preferred embodiment of the inertial navigation parameter real-time estimation and updating method for monitoring the sag and wind deflection of transmission line conductors according to the present invention, the method for outputting the conductor safety status monitoring results through the safety status output unit includes: The dynamic characteristic parameters of the conductor are compared with a preset safety threshold.
[0029] When the dynamic characteristic parameters exceed the safety threshold, a corresponding safety status warning signal is generated.
[0030] Based on the combined sag parameters, wind deflection parameters, and their dynamic characteristics, a complete conclusion on the safety status assessment of the conductor is formed.
[0031] The output includes real-time parameters, early warning signals, and assessment conclusions of the safety status monitoring results.
[0032] The beneficial effects of this preferred technical solution are that, by establishing a multi-parameter fusion safety assessment mechanism, a complete closed loop from data monitoring to risk early warning is achieved. Intelligent comparison of real-time dynamic characteristics with static safety thresholds enables rapid identification of risk states such as excessive sag, excessive wind deflection, and abnormal vibrations; and the automatic generation of graded early warning signals improves the timeliness of risk response.
[0033] This invention provides a real-time estimation and updating system for inertial navigation parameters for monitoring conductor sag and wind deflection in power transmission lines.
[0034] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a real-time estimation and update system for inertial navigation parameters for monitoring the sag and wind deflection of transmission line conductors, comprising: a multi-source data acquisition and initialization module, an error modeling and filtering estimation module, a closed-loop state correction and compensation module, a conductor state analysis and feature extraction module, and a safety assessment and early warning output module.
[0035] The multi-source data acquisition and initialization module acquires inertial observation data of the guide wire through an inertial sensor and completes the initial alignment of the navigation state using the initialization unit.
[0036] The error modeling and filtering estimation module constructs an error state model and uses a first filtering estimator to fuse the inertial observation data and the first auxiliary observation data, thereby performing real-time estimation of the error state through the error state model.
[0037] The closed-loop state correction and compensation module, based on the actual estimated error state, performs closed-loop feedback correction on the main navigation state and sensor zero bias through the navigation state correction unit.
[0038] The conductor state analysis and feature extraction module uses the conductor state analysis unit to calculate the conductor sag and wind deflection parameters based on the corrected navigation state.
[0039] The safety assessment and early warning output module performs time-series analysis on the sag and wind deflection parameters through the dynamic feature extraction unit, and outputs the safety status monitoring results of the conductor through the safety status output unit.
[0040] The present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, characterized in that the processor executes the computer program to implement the steps of a method for real-time estimation and updating of inertial navigation parameters for monitoring the sag and wind deflection of transmission line conductors.
[0041] The present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that, when the computer program is executed by a processor, it implements the steps of the method for real-time estimation and updating of inertial navigation parameters for monitoring the sag and wind deflection of transmission line conductors.
[0042] The beneficial effects of this invention are as follows: By establishing an attitude reference during the system initialization phase, a unified error state model including attitude, velocity, position, and sensor bias is constructed. During operation, IMU measurements and GNSS observations are used for dynamic prediction and updating, and the estimated error parameters are fed back into the main navigation solution in real time, achieving online adaptive calibration of gyroscope and accelerometer bias, scaling factor errors, etc. Based on the corrected attitude and position solutions, the three-dimensional geometric parameters of the guide wire (sag and wind deflection angle) are further calculated, thus forming a state monitoring method that combines real-time performance, accuracy, and robustness. This method overcomes the limitations of traditional methods relying on static calibration and offline compensation, achieving high-precision, real-time monitoring of the three-dimensional state of the guide wire in complex environments. Attached Figure Description
[0043] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0044] Figure 1 This is a flowchart illustrating the overall process of a real-time estimation and update method for inertial navigation parameters for monitoring conductor sag and wind deflection in transmission lines, as provided in one embodiment of the present invention.
[0045] Figure 2 This is an overall framework diagram of an inertial navigation parameter real-time estimation and update system for monitoring conductor sag and wind deflection in transmission lines, provided as an embodiment of the present invention. Detailed Implementation
[0046] To make the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.
[0047] Example 1, referring to Figure 1 This is one embodiment of the present invention, which provides a method for real-time estimation and updating of inertial navigation parameters for monitoring the sag and wind deflection of transmission line conductors, including: S1. Acquire inertial observation data of the guide wire through the inertial sensor, and complete the initial alignment of the navigation state using the initialization unit; S2. Construct an error state model and use the first filter estimator to fuse inertial observation data and first auxiliary observation data to estimate the error state in real time through the error state model.
[0048] S3. Based on the actual estimated error state, the navigation state correction unit performs closed-loop feedback correction on the main navigation state and sensor zero bias.
[0049] S4. Using the conductor state analysis unit, calculate the conductor sag and wind deflection parameters based on the corrected navigation state.
[0050] S5. Perform time-series analysis on sag and wind deflection parameters through the dynamic feature extraction unit, and output the safety status monitoring results of the conductor through the safety status output unit.
[0051] This invention achieves a leap from parameter measurement to risk early warning in traverse condition monitoring by constructing a complete technical system encompassing "error estimation - closed-loop correction - state analysis - safety assessment." Specifically, it employs real-time error state modeling and dynamic compensation mechanisms to effectively suppress the cumulative error of the inertial navigation system; by directly solving the corrected navigation parameters into engineering parameters such as sag and wind deflection, it improves the accuracy of state perception; and by combining time-series analysis and multi-threshold early warning strategies, it can accurately identify the dynamic characteristics and safety status of traverse lines.
[0052] Example 2, an embodiment of the present invention, provides a method for real-time estimation and updating of inertial navigation parameters for monitoring the sag and wind deflection of transmission line conductors, based on the previous embodiment, including: The initial alignment of the navigation state using the initialization unit in S1 includes steps A1-A3: A1. Acquire the initial motion data of the conductor using an inertial sensor.
[0053] Furthermore, the angular velocity vector output by the inertial measurement unit (IMU) is acquired. and linear acceleration vector During the short-term stationary phase, assuming that the acceleration mainly comes from the gravitational component, the direction vector of gravity can be estimated. The heading reference direction is determined by combining information from a magnetometer or solar vector. Based on this information, Euler angles are calculated and a direction cosine matrix is constructed. As an initial attitude reference.
[0054] in, , , It represents the angular velocity components (unit: rad / s) of the body coordinate system along the three axes. , , It is the linear acceleration component (unit: m / s²). It is the direction vector of gravity (unit: m / s²). It is the heading angle (Yaw). It is the pitch angle. It is the roll angle. It is the direction cosine matrix from the body coordinate system to the navigation coordinate system.
[0055] A2. Based on the initial motion data, determine the initial spatial reference direction of the conductor.
[0056] A3. Based on the initial spatial reference direction, calculate the initial attitude matrix of the traverse and complete the initial alignment between the navigation coordinate system and the traverse body coordinate system.
[0057] In this embodiment, the first filter estimator in S2, namely the Extended Kalman Filter (EKF), achieves efficient fusion of inertial observation data and first auxiliary observation data such as GNSS by constructing a nonlinear state equation and observation equation based on the characteristics of the conductor motion and performing first-order linearization on the system error model. Specifically, the state equation accurately describes the evolution of the error state (including attitude, velocity, position, and sensor bias) over time; the observation equation establishes the mathematical relationship between external observations such as GNSS and the system state. By linearizing the Jacobian matrix in real time and recursively executing prediction and update steps, this filter achieves an optimal balance between computational complexity and estimation accuracy.
[0058] In one alternative implementation, the first filter estimator can be an unscented Kalman filter (UKF). This approach employs a deterministic sampling strategy, directly approximating the probability distribution of the state through a carefully selected set of Sigma points. These points are propagated through nonlinear state and observation equations, thus avoiding the computation of complex Jacobian matrices. This method achieves higher estimation accuracy than the Unscented Kalman Filter (EKF) when dealing with highly nonlinear system models.
[0059] In another alternative implementation, the first filter estimator can also be an Error State Kalman Filter (ESKF). This scheme divides the state into a nominal state and an error state, and the filtering process is performed on the error state. Since the error state is usually a small quantity, its dynamic model is close to linear, so the standard Kalman filter formula can be used, effectively overcoming the nonlinearity problem. Finally, the actual estimated error state is injected into the nominal state.
[0060] Furthermore, a first filter estimator is used to fuse inertial observation data and first auxiliary observation data, and real-time estimation of the error state is performed through an error state model, including steps B1-B4: B1. Based on inertial observation data and error state model, the error state is predicted by the first filter estimator.
[0061] Furthermore, an inertial measurement error model is established, and the gyroscope zero bias vector is defined. and accelerometer zero bias vector And consider the scaling factor error and measuring noise Construct the error state vector .
[0062] in, , , It is the zero bias component of the gyroscope (rad / s). , , It is the zero bias component of the accelerometer (m / s²). It is the scaling factor error (dimensionless). It measures the noise vector. It is the attitude error (rad). It is the speed error (m / s). It is the position error (m). It is a unified error state vector.
[0063] B2. Obtain the first auxiliary observation data containing spatial positioning information.
[0064] B3. Using the first filter estimator, the error state prediction results are fused and updated with the first auxiliary observation data.
[0065] B3. Through the fusion update process, the optimal estimation result of the error state is output in real time.
[0066] In this embodiment, the first auxiliary observation data in B2 is the three-dimensional position and velocity information of the traverse monitoring points acquired by an RTK-GNSS receiver. Specifically, a high-precision RTK-GNSS module is installed at key traverse monitoring nodes (such as the center of the span or near the suspension point). The output latitude, longitude, and altitude coordinates are transformed to obtain a three-dimensional position vector in the geocentric-ground-fixed system or the local navigation system. Simultaneously, a three-dimensional velocity vector is calculated through carrier phase epoch difference. This data is input to an EKF filter at a frequency of 10-50Hz as an observation to directly correct position and velocity errors in the error state.
[0067] In one alternative implementation, the first auxiliary observation data can be the relative pose change of the conductor obtained through visual odometry or lidar. This scheme integrates a camera or lidar into the monitoring device, and calculates the relative rotation and translation of the carrier between adjacent time points through image feature point matching or point cloud registration between consecutive frames. This relative motion information can be used as observations for EKF (Electronic Kinematics Function), primarily for correcting attitude and velocity errors.
[0068] In another alternative implementation, the first auxiliary observation data can also be obtained through wireless ranging and communication data between adjacent monitoring nodes. This scheme deploys UWB modules on multiple monitoring nodes along the traverse line to measure the precise distances between the nodes. These distance observations constitute a ranging network, which can serve as observations for the EKF (Earthquake-Kindness-Fault) and constrain the positional errors of all nodes in the network.
[0069] Furthermore, the closed-loop feedback correction of the main navigation state and sensor zero bias by the navigation state correction unit in S3 includes steps C1-C4: C1. Input the optimal estimation result of the error state into the navigation state correction unit.
[0070] C2. Based on the error status, compensate and correct the main navigation status.
[0071] Furthermore, the time evolution of the error state is determined by the state equation. Description, external observations are expressed through observation equations Fusion. The filter updates the estimate based on the system's dynamic prediction of the error state and combined with GNSS observations.
[0072] in, It is the state transition matrix. It is a noise input matrix. It is the process noise vector. It is the velocity (m / s) measured by GNSS. It is the observation matrix. It is the observation noise vector.
[0073] C3. Estimate and compensate for the zero bias of the inertial sensor measurement.
[0074] C4. After the correction is completed, the updated main navigation status and zero-bias information are fed back to the front end to form a closed-loop correction.
[0075] In this embodiment, the navigation state correction unit in C1 performs real-time correction of the main navigation state, specifically including: feeding back the estimated error to the main navigation solution. Attitude correction is based on small-angle approximation quaternions. Update main stance The values were then normalized. The velocity and position corrections were respectively... and Zero bias compensation is and .
[0076] in, It is a small-angle corrected quaternion. It is the original host's posture quaternion. To represent quaternion multiplication, It is the velocity vector (m / s). It is a position vector (m). and These are the zero-bias correction values for the gyroscope and accelerometer, respectively.
[0077] In one alternative implementation, the navigation state correction unit can be a direct state update method based on rotation vectors and additive correction. This scheme does not employ quaternion multiplication; instead, it treats the estimated attitude error vector as a rotation vector, directly converting it into an equivalent rotation matrix, and then updates the main navigation attitude matrix through matrix multiplication. For velocity and position, the same additive correction as the main scheme is used. The correction for sensor zero bias is also additively updated.
[0078] In another alternative implementation, the navigation state correction unit can also be a simplified method based on direct reset of the error state. To achieve maximum computational efficiency, this scheme employs a more direct strategy: after completing the EKF update to obtain the error state estimate, the main navigation state is directly corrected. For attitude, a simplified quaternion update is used, followed by normalization.
[0079] In this embodiment, the conductor state analysis unit in S4 calculates the three-dimensional geometric parameters of the conductor using the corrected attitude and position. Specifically, this includes: obtaining the elevation component h based on the corrected navigation position information located at the midpoint of the conductor or a specific monitoring point. Simultaneously, it obtains the heights of two suspension points within the same span from the line design parameters or fixed-point measurements. and Wire sag Represented as, Based on the corrected navigation attitude matrix The heading angle of the carrier (guide wire) is extracted from the attitude matrix. Wind deflection angle. Through formula The dominant frequency was calculated and obtained by performing a Fourier transform on its time series. .
[0080] in, It is the wind deflection angle (rad). and These are the elements in the first row and first column of the direction cosine matrix, and the elements in the first row and second column of the direction cosine matrix, respectively. It is the dominant frequency of the wind deflection angle (Hz).
[0081] In one alternative implementation, the conductor state analysis unit can be a parameter fitting method based on a catenary model. This approach does not directly use the midpoint height, but instead utilizes the precise three-dimensional coordinates of multiple monitoring points provided by a modified navigation system. These coordinates are used as observations and fitted with the catenary equation. The catenary parameters are then derived using optimization algorithms such as the least squares method, and subsequently, a series of parameters such as conductor sag and tension are calculated.
[0082] In another alternative implementation, the conductor state analysis unit can also be based on an indirect deduction method using strain gauge and attitude fusion. This approach involves additionally installing strain sensors on the conductor to measure its axial strain. Combining the corrected conductor attitude information (used to determine the conductor direction) with known conductor mechanical properties (such as the elastic modulus EE), the conductor tension is deduced using Hooke's Law. Then, based on the mechanical relationship between tension and sag / wind deflection, the conductor's sag and wind deflection angle are indirectly calculated.
[0083] Furthermore, in S4, the calculation of the sag and wind deflection parameters of the guide wire based on the corrected navigation status includes steps D1-D3: D1. Based on the corrected navigation position information, calculate the relative height difference of the guide to solve for the sag parameter.
[0084] D2. Based on the corrected navigation attitude information, extract the horizontal direction angle of the guide wire to calculate the wind deflection parameter.
[0085] D3. Perform preliminary rationality verification and filtering on the calculated sag and wind deflection parameters, and output sag and wind deflection parameters that characterize the geometry and stress state of the conductor.
[0086] Furthermore, the time-series analysis of sag and wind deflection parameters in S5 via the dynamic feature extraction unit includes steps E1-E4: E1. Construct time series data for sag and wind deflection parameters.
[0087] Furthermore, integrated output sag Wind deflection angle and the dominant frequency of vibration Key parameters are used to form a three-dimensional status monitoring data stream for transmission line safety assessment and risk warning.
[0088] E2. Analyze time series data using frequency domain transformation methods to extract periodic fluctuation characteristics.
[0089] Furthermore, extracting periodic fluctuation features specifically includes a frequency domain transformation method using Fast Fourier Transform (FFT) as the core.
[0090] Data preprocessing: First, the time series data of sag and wind deflection angle constructed in step E1 are preprocessed, including: Remove linear or slowly changing trend terms from the data to avoid them masking the true periodic fluctuations in the frequency domain.
[0091] Apply a window function to the data segment to reduce spectral leakage caused by discontinuities at the beginning and end of the data segment.
[0092] Preprocessed time-domain data (Representing a sequence of sag or wind deflection angles) Input into an FFT algorithm, transform it to the frequency domain, and obtain its complex spectrum. .
[0093] For the spectrum Calculate the square of the modulus and divide by the sequence length to obtain the power spectral density. .
[0094] E3. Identify and quantify the dominant frequency and amplitude of periodic fluctuations.
[0095] Specifically, this includes: the calculated power spectral density On the curve, an automatic peak detection algorithm is used to identify all significant peaks. This algorithm typically uses amplitude thresholds and prominence to exclude small fluctuations caused by noise, ensuring that only frequency points with significantly concentrated energy are identified.
[0096] Among all detected peaks, the frequency corresponding to the peak with the highest power spectral density value is selected. It was determined to be the dominant frequency of conductor vibration or oscillation during that time period.
[0097] Dominant frequency Corresponding spectral components The amplitude A, after appropriate unit conversion, is quantified as the single-sided amplitude of the wind deflection angle oscillation (unit: rad or °).
[0098] Similarly, the vibration amplitude of the sag at the dominant frequency can be quantified (unit: m).
[0099] Finally, the dynamic characteristic parameters of the conductor are quantized and output as follows: Dominant frequency: Wind sway amplitude: sag vibration amplitude .
[0100] E3, outputs dynamic characteristic parameters of the conductor including frequency and amplitude.
[0101] Furthermore, the safety status monitoring results of the output wires in S6 through the safety status output unit include steps F1-F4: F1. Compare the dynamic characteristic parameters of the conductor with the preset safety threshold.
[0102] Specifically, the dominant frequency obtained from real-time analysis With the inherent dangerous resonant frequency range of the conductor Perform a comparison. If... If the price falls within this range, it is considered to have a resonance risk, triggering a corresponding warning.
[0103] The wind yaw amplitude obtained from real-time analysis and sag vibration amplitude Each value is compared with a preset multi-level amplitude threshold. These thresholds are typically divided into multiple levels, for example: Normal threshold Below this value, the state is safe.
[0104] Warning threshold If this value is exceeded, a warning level alert will be generated to prompt maintenance personnel to pay attention.
[0105] Alarm threshold If this value is exceeded, a severe alarm will be generated, indicating a possible emergency such as dancing or ice detachment.
[0106] Duration determination: The system not only determines whether a parameter exceeds the limit, but also keeps track of the time. A brief exceedance may not immediately trigger a high-level alarm, while events that last for a certain period of time (such as 10 seconds) will be recognized as valid alarms. This helps to filter out transient interference.
[0107] It also analyzes the short-term trends of dynamic characteristic parameters (especially amplitude). Even if the current value does not exceed the threshold, if it is detected that it is increasing rapidly and continuously, a trend warning may be generated in advance, achieving a more proactive risk warning.
[0108] F2. When the dynamic characteristic parameters exceed the safety threshold, a corresponding safety status warning signal is generated. F3. Combining sag parameters, wind deflection parameters, and their dynamic characteristics, a complete conclusion on the safety status assessment of the conductor is formed. F4 outputs safety status monitoring results including real-time parameters, early warning signals, and assessment conclusions.
[0109] Example 3, referring to Figure 2 This embodiment of the present invention provides a real-time estimation and update system for inertial navigation parameters for monitoring the sag and wind deflection of transmission line conductors, comprising: a multi-source data acquisition and initialization module, an error modeling and filtering estimation module, a closed-loop state correction and compensation module, a conductor state analysis and feature extraction module, and a safety assessment and early warning output module.
[0110] The multi-source data acquisition and initialization module acquires inertial observation data of the guide wire through inertial sensors and completes the initial alignment of the navigation state using the initialization unit.
[0111] The error modeling and filtering estimation module constructs an error state model and uses a first filtering estimator to fuse inertial observation data and first auxiliary observation data to perform real-time estimation of the error state through the error state model.
[0112] The closed-loop state correction and compensation module, based on the actual estimated error state, performs closed-loop feedback correction on the main navigation state and sensor zero bias through the navigation state correction unit.
[0113] The traverse state analysis and feature extraction module uses the traverse state analysis unit to calculate the sag and wind deflection parameters of the traverse based on the corrected navigation state.
[0114] The safety assessment and early warning output module performs time-series analysis of sag and wind deflection parameters through the dynamic feature extraction unit, and outputs the safety status monitoring results of the conductor through the safety status output unit.
[0115] This embodiment also provides an electronic device applicable to a method for real-time estimation and updating of inertial navigation parameters for monitoring the sag and wind deflection of transmission line conductors, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the method for real-time estimation and updating of inertial navigation parameters for monitoring the sag and wind deflection of transmission line conductors as proposed in the above embodiment.
[0116] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements a method for real-time estimation and updating of inertial navigation parameters for monitoring the sag and wind deflection of transmission line conductors, as proposed in the above embodiment.
[0117] The storage medium proposed in this embodiment and the method for real-time estimation and updating of inertial navigation parameters for monitoring the sag and wind deflection of transmission line conductors proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.
[0118] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.
[0119] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.
Claims
1. A method for real-time estimation and updating of inertial navigation parameters for monitoring conductor sag and wind deflection in transmission lines, characterized in that: include, Inertial observation data of the guide wire is acquired by inertial sensors, and the initial alignment of the navigation state is completed by the initialization unit. An error state model is constructed, and a first filter estimator is used to fuse the inertial observation data and the first auxiliary observation data to estimate the error state in real time through the error state model. Based on the actual estimated error state, the main navigation state and sensor zero bias are corrected by closed-loop feedback through the navigation state correction unit. Using the conductor state analysis unit, the sag and wind deflection parameters of the conductor are calculated based on the corrected navigation state. The sag and wind deflection parameters are analyzed in a time series by a dynamic feature extraction unit, and the safety status monitoring results of the conductor are output by a safety status output unit.
2. The method for real-time estimation and updating of inertial navigation parameters for monitoring conductor sag and wind deflection in transmission lines as described in claim 1, characterized in that: The initial alignment of the navigation state using the initialization unit includes, Initial motion data of the conductor is acquired using an inertial sensor; Based on the initial motion data, the initial spatial reference direction of the conductor is determined; Based on the initial spatial reference direction, calculate the initial attitude matrix of the traverse and complete the initial alignment between the navigation coordinate system and the traverse body coordinate system.
3. The method for real-time estimation and updating of inertial navigation parameters for monitoring conductor sag and wind deflection in transmission lines as described in claim 2, characterized in that: The step of employing a first filter estimator, fusing the inertial observation data and the first auxiliary observation data, and performing real-time estimation of the error state through an error state model includes: Based on the inertial observation data and the error state model, the error state is predicted using the first filter estimator; Acquire first auxiliary observation data containing spatial positioning information; Using the first filter estimator, the error state prediction result is fused and updated with the first auxiliary observation data; Through the fusion update process, the optimal estimation result of the error state is output in real time.
4. The method for real-time estimation and updating of inertial navigation parameters for monitoring conductor sag and wind deflection in transmission lines as described in claim 3, characterized in that: The closed-loop feedback correction of the main navigation state and sensor zero bias through the navigation state correction unit includes... The optimal estimation result of the error state is input into the navigation state correction unit; Based on the aforementioned error state, the main navigation state is compensated and corrected. Estimate and compensate for the zero bias of inertial sensor measurements; After the correction is completed, the updated main navigation status and zero-bias information are fed back to the front end to form a closed-loop correction.
5. The method for real-time estimation and updating of inertial navigation parameters for monitoring conductor sag and wind deflection in transmission lines as described in claim 4, characterized in that: The calculation of the sag and wind deflection parameters of the guide based on the corrected navigation state includes... Based on the corrected navigation position information, the relative height difference of the guide is calculated to solve the sag parameter; Based on the corrected navigation attitude information, the horizontal direction angle of the guide wire is extracted to calculate the wind drift parameter; The calculated sag and wind deflection parameters are initially verified for rationality and filtered, and the sag and wind deflection parameters that characterize the conductor's geometry and stress state are output.
6. The method for real-time estimation and updating of inertial navigation parameters for monitoring conductor sag and wind deflection in transmission lines as described in claim 4, characterized in that: The step of performing time-series analysis on the sag and wind deflection parameters through a dynamic feature extraction unit includes... Time series data were constructed for the sag and wind deflection parameters; The time series was analyzed using frequency domain transformation to extract periodic fluctuation characteristics. Identify and quantify the dominant frequency and amplitude of the periodic fluctuations; The output includes the dynamic characteristic parameters of the conductor, including frequency and amplitude.
7. The method for real-time estimation and updating of inertial navigation parameters for monitoring conductor sag and wind deflection in transmission lines as described in claim 4, characterized in that: The safety status monitoring results of the conductor output through the safety status output unit include, The dynamic characteristic parameters of the conductor are compared with a preset safety threshold. When the dynamic characteristic parameters exceed the safety threshold, a corresponding safety status warning signal is generated; By combining the sag parameters, wind deflection parameters, and their dynamic characteristics, a complete conclusion on the safety status assessment of the conductor is formed. The output includes real-time parameters, early warning signals, and assessment conclusions of the safety status monitoring results.
8. A real-time estimation and updating system for inertial navigation parameters for monitoring the sag and wind deflection of transmission line conductors, comprising the method for real-time estimation and updating of inertial navigation parameters for monitoring the sag and wind deflection of transmission line conductors as described in any one of claims 1 to 7, characterized in that, include: Multi-source data acquisition and initialization module, error modeling and filtering estimation module, closed-loop state correction and compensation module, conductor state analysis and feature extraction module, safety assessment and early warning output module; The multi-source data acquisition and initialization module acquires inertial observation data of the guide wire through an inertial sensor and completes the initial alignment of the navigation state using an initialization unit. The error modeling and filtering estimation module constructs an error state model and uses a first filtering estimator to fuse the inertial observation data and the first auxiliary observation data, thereby performing real-time estimation of the error state through the error state model. The closed-loop state correction and compensation module, based on the actual estimated error state, performs closed-loop feedback correction on the main navigation state and sensor zero bias through the navigation state correction unit. The conductor state analysis and feature extraction module uses the conductor state analysis unit to calculate the sag and wind deflection parameters of the conductor based on the corrected navigation state. The safety assessment and early warning output module performs time-series analysis on the sag and wind deflection parameters through the dynamic feature extraction unit, and outputs the safety status monitoring results of the conductor through the safety status output unit.
9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the inertial navigation parameter real-time estimation and update method for monitoring the sag and wind deflection of transmission line conductors as described in any one of claims 1 to 7.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the inertial navigation parameter real-time estimation and update method for monitoring the sag and wind deflection of transmission line conductors as described in any one of claims 1 to 7.