Power transmission line icing thickness monitoring system based on distributed sensor

Abstract

The invention relates to the technical field of intelligent sensors, in particular to a power transmission line icing thickness monitoring system based on a distributed sensor, and the system comprises a data collection unit which is used for collecting the wire temperature and environment data of a target monitoring point on a power transmission line; the quantification unit is used for calculating an environment change factor and an icing trend; the correction unit is used for determining an expansion factor and multiplying the expansion factor by a process noise covariance matrix in the Kalman filtering algorithm to obtain a corrected Kalman filtering algorithm; and the output unit is used for inputting the conductor temperature, the environmental data and the observation thickness of the target monitoring point into the corrected Kalman filtering algorithm for recursive estimation, and outputting the final icing thickness of the target monitoring point. According to the technical scheme, the accuracy of monitoring the icing thickness of the power transmission line can be improved.

Description

Technical Field

[0001] This application relates to the field of equipment control technology, and in particular to a transmission line icing thickness monitoring system based on distributed sensors. Background Technology

[0002] In power transmission networks, transmission lines serve as the carriers of electrical energy, and their safe and stable operation is crucial to the power system. However, under severe weather conditions such as cold and dampness, transmission lines are highly susceptible to icing. Icing not only significantly increases the load on the lines but can also lead to serious accidents such as line breaks and tower tilting, directly threatening the safety of the power grid.

[0003] As a crucial link in ensuring the safe operation of the power grid, how to achieve real-time monitoring of the ice thickness of transmission lines in complex and ever-changing environments, and provide accurate basis for de-icing decisions, has become a core issue that urgently needs to be addressed in the field of smart grid technology. Summary of the Invention

[0004] To address the technical challenge of real-time monitoring of icing thickness on power transmission lines in complex and variable environments, this application provides a power transmission line icing thickness monitoring system based on distributed sensors, which can improve the accuracy of icing thickness monitoring.

[0005] In a first aspect, this application provides a transmission line icing thickness monitoring system based on distributed sensors. The monitoring system includes: a data acquisition unit for acquiring conductor temperature and environmental data at a target monitoring point on the transmission line using distributed optical fiber sensors, the environmental data including wind speed, ambient humidity, and ambient temperature; a quantization unit for calculating an environmental change factor based on the dispersion of wind speed and the fluctuation range of ambient temperature, and calculating an icing trend based on the differences between ambient humidity, conductor temperature, and icing formation conditions; a correction unit for determining an expansion factor and multiplying the expansion factor by the process noise covariance matrix in a Kalman filter algorithm to obtain a corrected Kalman filter algorithm, wherein the expansion factor is positively correlated with both the environmental change factor and the icing trend; and an output unit for recursively estimating the conductor temperature, environmental data, and observed thickness at the target monitoring point in the corrected Kalman filter algorithm, and outputting the final icing thickness at the target monitoring point.

[0006] By real-time correction of the process noise covariance matrix of the Kalman filter algorithm, the filter parameters are adaptively adjusted to the dynamic environment, effectively eliminating the influence of complex meteorological conditions on icing thickness monitoring and significantly improving the accuracy of icing thickness monitoring of transmission lines.

[0007] Preferably, the step of collecting conductor temperature and environmental data of the target monitoring point on the transmission line based on the distributed optical fiber sensor includes: collecting conductor temperature and environmental data of the target monitoring point at any time based on the distributed optical fiber sensor set at the target monitoring point.

[0008] Preferably, the calculation of the environmental change factor based on the dispersion of wind speed and the fluctuation range of ambient temperature includes: constructing a preset time window with the current time as the endpoint; calculating the standard deviation of wind speed data within the preset time window as the dispersion of wind speed; calculating the range of ambient temperature within the preset time window as the fluctuation range of ambient temperature; and using the product of the standard deviation and the temperature fluctuation term as the environmental change factor, wherein the temperature fluctuation term is positively correlated with the ratio of the range to the temperature sensitivity coefficient.

[0009] The instability of the current meteorological environment was accurately quantified, providing a data foundation for the subsequent calculation of the expansion factor.

[0010] Preferably, the calculation of the icing trend based on the difference between ambient humidity and conductor temperature and icing formation conditions includes: the icing formation conditions include a humidity threshold and a temperature threshold; determining a humidity term based on the absolute value of the difference between the ambient humidity and the humidity threshold; within a preset time window, calculating the difference between the temperature threshold and the conductor temperature at any time at the target monitoring point; in response to the difference being less than 0, setting the temperature value at that time to 0; otherwise, setting the temperature value at that time to the difference; and summing the temperature values ​​at all times as the temperature accumulation term for the target monitoring point; the icing trend is the product of the humidity term and the temperature accumulation term.

[0011] It accurately quantifies the activity level of ice growth on the surface of transmission lines, and can detect the nonlinear variation trend of ice thickness in advance.

[0012] Preferably, the temperature accumulation term further includes: obtaining the temperature accumulation term of at least one neighboring monitoring point of the target monitoring point, calculating the sum of the temperature accumulation terms of the target monitoring point and the neighboring monitoring points, wherein the icing trend is the product of the humidity term and the sum of the temperature accumulation terms.

[0013] Preferably, the method for obtaining at least one neighboring monitoring point of the target monitoring point is as follows: on the left and right sides of the target monitoring point, obtain a preset number of monitoring points that are closest to it as neighboring monitoring points.

[0014] Preferably, determining the expansion factor includes: calculating the product of the environmental change factor and the icing trend; normalizing the product using a hyperbolic tangent function; and adding the normalization result to the basic weights to obtain the expansion factor.

[0015] Preferably, the step of multiplying the inflation factor with the process noise covariance matrix in the Kalman filter algorithm to obtain the modified Kalman filter algorithm includes: multiplying the inflation factor at the current time by a preset process noise covariance matrix to obtain the modified process noise covariance matrix; and updating the prediction error covariance matrix using the modified process noise covariance matrix to obtain the modified Kalman filter algorithm.

[0016] Preferably, the monitoring system further includes an observation unit for acquiring the observed thickness. The observation unit measures the strain change of the target monitoring point through a strain sensor and converts the strain change into the observed thickness of the target monitoring point through a preset mechanical model.

[0017] Preferably, after outputting the final icing thickness of the target monitoring point, the monitoring system further includes: summarizing the final icing thickness of all monitoring points on the transmission line to construct a final icing thickness distribution network for the entire transmission line; identifying severe icing monitoring points where the icing thickness exceeds the safety threshold, and generating de-icing operation instructions.

[0018] It enables comprehensive monitoring of icing risks along transmission lines, allowing maintenance personnel to take timely de-icing measures for specific high-risk sections, effectively preventing safety accidents caused by icing overload.

[0019] The technical solution of this application has the following beneficial technical effects: By collecting conductor temperature and environmental data of transmission lines, an environmental change factor characterizing environmental instability and an icing trend characterizing the activity of icing growth are constructed. This leads to the determination of an expansion factor to real-time correct the process noise covariance matrix in the Kalman filter algorithm. The corrected Kalman filter algorithm can dynamically adjust the confidence weights between the predicted and observed icing thickness values. When facing complex meteorological conditions such as gusts, turbulence, sudden temperature drops, or freezing rain outbreaks, the corrected Kalman filter algorithm can effectively eliminate the tracking lag phenomenon of the traditional Kalman filter algorithm, significantly improving the accuracy of icing thickness monitoring of transmission lines. Attached Figure Description

[0020] Figure 1 This is a structural block diagram of a power transmission line icing thickness monitoring system based on distributed sensors, according to an embodiment of this application.

[0021] Figure 2 This is a comparison chart of the observed thickness and final icing thickness of the target monitoring point over time, according to an embodiment of this application.

[0022] Figure 3 This is a spatial distribution diagram of the observed thickness at each monitoring point on the transmission line according to an embodiment of this application.

[0023] Figure 4This is a spatial distribution map of the final icing thickness at each monitoring point on the power transmission line according to an embodiment of this application. Detailed Implementation

[0024] This application provides a power transmission line icing thickness monitoring system based on distributed sensors, which can be applied to power transmission networks in cold mountainous areas, across rivers and streams, and other areas with complex meteorological environments where manual inspection is difficult. Figure 1 This is a structural block diagram of a power transmission line icing thickness monitoring system based on distributed sensors, according to an embodiment of this application. Figure 1 As shown, the transmission line icing thickness monitoring system based on distributed sensors includes a data acquisition unit, a quantization unit, a correction unit, and an output unit, which are described in detail below.

[0025] The data acquisition unit is used to collect conductor temperature and environmental data of target monitoring points on the transmission line based on distributed optical fiber sensors. The environmental data includes wind speed, ambient humidity and ambient temperature.

[0026] In one embodiment, distributed fiber optic sensors installed at target monitoring points collect conductor temperature and environmental data at any given time for each target monitoring point. The distributed fiber optic sensors include fiber optic temperature measurement units at each monitoring point and sensing fibers laid along the transmission line. The fiber optic temperature measurement units at the target monitoring points are used to collect conductor temperature data. The environmental data is collected using a micro-meteorological monitoring subsystem deployed along the transmission line. The micro-meteorological monitoring subsystem includes wind speed sensors, humidity sensors, and ambient temperature sensors distributed along the line. Each sensor transmits conductor temperature and environmental data to a data processing host via sensing fibers.

[0027] The transmission line is discretized according to a set spatial resolution, which can be set to 50 meters, to determine multiple monitoring points. The target monitoring point is any one of these multiple monitoring points. To ensure that the conductor temperature and environmental data are aligned in time and space, linear interpolation is used to fill in the missing values ​​in the conductor temperature and environmental data, and the environmental data is mapped to the location of the target monitoring point. This yields the comprehensive state vector of the target monitoring point at any given time. The time interval for collecting the comprehensive state vector is set to 1 second.

[0028] For example, at the current moment The comprehensive state vector of the target monitoring point is as follows: conductor temperature -2 degrees Celsius, ambient temperature -5 degrees Celsius, wind speed 8 meters per second, and relative humidity 92%.

[0029] The quantization unit is used to calculate the environmental change factor based on the dispersion of wind speed and the fluctuation range of ambient temperature, and to calculate the icing trend based on the difference between ambient humidity and conductor temperature and icing formation conditions.

[0030] In one embodiment, calculating the environmental change factor based on the dispersion of wind speed and the fluctuation range of ambient temperature includes: constructing a preset time window with the current time as the endpoint; calculating the standard deviation of wind speed data within the preset time window as the dispersion of wind speed; calculating the range of ambient temperature within the preset time window as the fluctuation range of ambient temperature; and using the product of the standard deviation and the temperature fluctuation term as the environmental change factor, wherein the temperature fluctuation term is positively correlated with the ratio of the range to the temperature sensitivity coefficient.

[0031] Understandably, in windy weather with rapidly changing wind speeds and cold weather with a sharp drop in temperature, the drastic changes in wind speed and temperature can cause the steady-state assumption of the thermal balance model to fail, which in turn can lead to a large error in the predicted value of ice thickness in the Kalman filter algorithm. Therefore, the environmental change factor can quantify the degree of disturbance of environmental fluctuations on the predicted value of ice thickness in the Kalman filter algorithm.

[0032] Specifically, environmental change factors satisfy the following relationship: ; In the formula, Indicates the current time Environmental change factors; This represents the standard deviation of wind speed data within a preset time window, used to reflect the degree of dispersion of wind speed. This represents the range of ambient temperature within a preset time window, that is, the difference between the maximum and minimum values, used to reflect the fluctuation range of ambient temperature. This represents the temperature sensitivity coefficient, used to adjust the sensitivity while making temperature fluctuations dimensionless; an example value of 0.5 degrees Celsius is used. The preset time window length can be set to 30 sampling points.

[0033] In one embodiment, the icing formation conditions include a humidity threshold and a temperature threshold. For example, the humidity threshold is set to 85% and the temperature threshold is set to -1 degree Celsius. When the conductor temperature of the target monitoring point is lower than the temperature threshold and the ambient humidity is higher than the humidity threshold, the target monitoring point meets the icing formation conditions and has an icing tendency.

[0034] Specifically, the calculation of the icing trend based on the difference between ambient humidity and conductor temperature and icing formation conditions includes: determining a humidity term based on the absolute value of the difference between the ambient humidity and the humidity threshold; within a preset time window, calculating the difference between the temperature threshold and the conductor temperature at any time at the target monitoring point; in response to the difference being less than 0, setting the temperature value at that time to 0; otherwise, setting the temperature value at that time to the difference; and summing the temperature values ​​at all times as the temperature accumulation term for the target monitoring point; the icing trend is the product of the humidity term and the temperature accumulation term.

[0035] In this embodiment, the icing trend satisfies the following relationship: ; In the formula, Indicates the current time Ice accumulation trend at target monitoring points; Indicates the current time The ambient humidity; Indicates the humidity threshold; Indicates the temperature threshold; Indicates the time of the target monitoring point within the preset time window. The temperature of the conductor; This indicates that the difference is taken when the temperature threshold is higher than the conductor temperature, otherwise it is 0; This represents the number of moments within a preset time window.

[0036] In another embodiment, the temperature accumulation term further includes: obtaining the temperature accumulation term of at least one neighboring monitoring point of the target monitoring point, calculating the sum of the temperature accumulation terms of the target monitoring point and the neighboring monitoring points, wherein the icing trend is the product of the humidity term and the sum of the temperature accumulation terms.

[0037] The method for obtaining at least one neighboring monitoring point of the target monitoring point is as follows: A predetermined number of monitoring points closest to the target monitoring point are obtained on both the left and right sides of the target monitoring point, and these are designated as neighboring monitoring points. For example, the predetermined number is 5, meaning that the 5 nearest monitoring points on each of the left and right sides of the target monitoring point are designated as neighboring monitoring points.

[0038] In this embodiment, the icing trend satisfies the following relationship: ; In the formula, Indicates the current time Ice accumulation trend at target monitoring points; Indicates the current time The ambient humidity; Indicates the humidity threshold; Indicates the temperature threshold; This indicates the time of the target monitoring point within the preset time window. The temperature of the conductor; This indicates that the difference is taken when the temperature threshold is higher than the conductor temperature, otherwise it is 0; The number of moments within a preset time window; This represents the total number of target monitoring points and neighboring monitoring points. It can be either the target monitoring point or a neighboring monitoring point.

[0039] Thus, by constructing environmental change factors and icing trends, we can accurately quantify the instability of the environment and the activity of icing growth, providing a quantitative basis for the subsequent dynamic adjustment of the Kalman filter algorithm's confidence in the predicted values, thereby ensuring the robustness of icing thickness monitoring results under complex meteorological conditions.

[0040] The correction unit is used to determine the expansion factor and multiply the expansion factor by the process noise covariance matrix in the Kalman filter algorithm to obtain the corrected Kalman filter algorithm. The expansion factor is positively correlated with both the environmental change factor and the icing trend.

[0041] In one embodiment, determining the expansion factor includes: calculating the product of the environmental change factor and the icing trend; normalizing the product using a hyperbolic tangent function; and adding the normalization result to the basic weights to obtain the expansion factor.

[0042] Specifically, in order to map the severity of the environment and icing conditions to the adjustment range of the algorithm parameters, the expansion factor satisfies the following relationship: ; In the formula, Indicates the expansion factor; This represents the base weight, with an example value of 1, which represents the baseline value when the environment is stable. Indicates environmental change factors; Indicates a tendency for icing; Represents the hyperbolic tangent function, used to... and The product of these values ​​maps to the interval between 0 and 1. When the environment is extremely unstable or icing is very active, the hyperbolic tangent function output approaches 1, causing the inflation factor to increase.

[0043] In one embodiment, multiplying the inflation factor with the process noise covariance matrix in the Kalman filter algorithm to obtain the modified Kalman filter algorithm includes: multiplying the inflation factor at the current time by a preset process noise covariance matrix to obtain the modified process noise covariance matrix; and updating the prediction error covariance matrix using the modified process noise covariance matrix to obtain the modified Kalman filter algorithm.

[0044] Understandably, the Kalman filter algorithm includes two stages: prediction update and measurement update. Prediction Update Phase: Based on the state and thermal equilibrium physical model of the previous time step, the ice thickness at the current time step is predicted, yielding the predicted value of the ice thickness at the current time step; during this process, the process noise covariance matrix... This represents the Kalman filter algorithm's estimation of the uncertainty in the predicted value. In traditional Kalman filter algorithms, the process noise covariance matrix is ​​usually set to a fixed value.

[0045] Measurement update phase: Introduce the current observation data, i.e. the observed value of ice thickness at the current moment, correct the predicted value, and obtain the final estimated value.

[0046] However, icing of transmission lines is a dynamic process. During icing monitoring, environmental disturbances and icing trends affect the reliability of the predicted icing thickness. That is, the uncertainty of the predicted value changes continuously at each moment. If a fixed process noise covariance matrix is ​​still used, over-reliance on the predicted icing thickness will lead to a significant lag in tracking the actual thickness. Therefore, the process noise covariance matrix is ​​corrected in real time based on an expansion factor determined by environmental change factors and icing trends. The correction process satisfies the following relationship: ; ; In the formula, This represents the corrected process noise covariance matrix; Indicates the inflation factor at the current moment; This represents the initially preset fixed process noise covariance matrix; Represents the prediction error covariance matrix; This represents the covariance component predicted based on the previous time step.

[0047] In subsequent measurement update phases, based on the increased prediction error covariance matrix... The Kalman gain is automatically adjusted to give higher weight to observations while reducing the weight to predictions.

[0048] In this way, by dynamically correcting the process noise covariance matrix in real time, the algorithm can automatically reduce the confidence weight of the predicted value and increase the confidence weight of the observed value when the environment changes abruptly or the icing changes, thereby eliminating the tracking lag phenomenon of the traditional Kalman filter algorithm and significantly improving the accuracy of monitoring the icing thickness of transmission lines under complex meteorological conditions.

[0049] The output unit is used to input the conductor temperature, environmental data, and observed thickness of the target monitoring point into the corrected Kalman filter algorithm for recursive estimation, and output the final icing thickness of the target monitoring point.

[0050] In one embodiment, the monitoring system further includes an observation unit for acquiring the observed thickness. The observation unit measures the strain change of the target monitoring point through a strain sensor and converts the strain change into the observed thickness of the target monitoring point through a preset mechanical model.

[0051] For example, strain sensors are installed at the insulator string connections of a transmission tower. When the ice on the conductor thickens, causing an increase in gravity, the change in tension will induce a change in strain, from which the observed thickness can be calculated. After obtaining the observed thickness, the conductor temperature, environmental data, and observed thickness at the target monitoring point are input into a corrected Kalman filter algorithm for recursive estimation, thus outputting the final ice thickness at the target monitoring point at any given time. Please refer to [link to relevant documentation]. Figure 2 This is a comparison chart of the observed thickness and final icing thickness of the target monitoring point over time, according to an embodiment of this application.

[0052] In one embodiment, after outputting the final icing thickness of the target monitoring point, the monitoring system further includes: summarizing the final icing thickness of all monitoring points on the transmission line to construct a final icing thickness distribution network for the entire transmission line; identifying severe icing monitoring points where the icing thickness exceeds a safety threshold, and generating a de-icing operation instruction. The safety threshold is exemplarily set to 20 mm.

[0053] Please see Figure 3 and Figure 4 , Figure 3 This is a spatial distribution diagram of the observed thickness at each monitoring point on the transmission line according to an embodiment of this application; Figure 4 This is a spatial distribution map of the final icing thickness at each monitoring point on the transmission line according to an embodiment of this application; Figure 4 Within the boxed area, the final ice thickness can be seen, eliminating measurement errors in the observed thickness and achieving high-precision estimation of the ice thickness.

[0054] In this way, high-precision real-time estimation of ice thickness at target monitoring points is achieved, which in turn enables monitoring of ice thickness along the entire transmission line. It can automatically identify dangerous sections and generate work instructions, realizing intelligent monitoring from data collection to operation and maintenance decision-making.

[0055] It should be noted that the scope of protection of this patent application shall be determined by the appended claims.

Claims

1. A transmission line icing thickness monitoring system based on distributed sensors, the system comprising: The data acquisition unit is used to collect conductor temperature and environmental data at target monitoring points on the power transmission line; Quantization unit, used to calculate environmental change factors and icing trends; The correction unit is used to determine the inflation factor and multiply the inflation factor by the process noise covariance matrix in the Kalman filter algorithm to obtain the corrected Kalman filter algorithm. The output unit is used to recursively estimate the final icing thickness of the target monitoring point by inputting the conductor temperature, environmental data, and observed thickness into a corrected Kalman filter algorithm. This solution improves the accuracy of icing thickness monitoring for transmission lines.

2. The transmission line icing thickness monitoring system based on distributed sensors according to claim 1, characterized in that, The method of collecting conductor temperature and environmental data of target monitoring points on the transmission line based on distributed optical fiber sensors includes: collecting conductor temperature and environmental data of the target monitoring points at any time based on distributed optical fiber sensors set up at the target monitoring points.

3. The transmission line icing thickness monitoring system based on distributed sensors according to claim 1, characterized in that, The calculation of environmental change factors based on the dispersion of wind speed and the fluctuation range of ambient temperature includes: Construct a preset time window with the current time as the endpoint; Calculate the standard deviation of the wind speed data within the preset time window as the degree of dispersion of the wind speed; Calculate the range of ambient temperature within the preset time window as the fluctuation range of ambient temperature; The product of the standard deviation and the temperature fluctuation term is used as the environmental change factor, and the temperature fluctuation term is positively correlated with the ratio of the range to the temperature sensitivity coefficient.

4. The transmission line icing thickness monitoring system based on distributed sensors according to claim 1, characterized in that, The calculation of icing trends based on the differences between ambient humidity and conductor temperature and icing formation conditions includes: The conditions for icing formation include a humidity threshold and a temperature threshold; The humidity term is determined based on the absolute value of the difference between the ambient humidity and the humidity threshold. Within a preset time window, the difference between the temperature threshold and the conductor temperature at any time of the target monitoring point is calculated. In response to the difference being less than 0, the temperature value at that time is set to 0; otherwise, the temperature value at that time is set to the difference. The sum of the temperature values ​​at all times is used as the temperature accumulation term of the target monitoring point. The icing trend is the product of the humidity term and the temperature accumulation term.

5. The transmission line icing thickness monitoring system based on distributed sensors according to claim 4, characterized in that, The temperature accumulation term also includes: Obtain the temperature accumulation term of at least one neighboring monitoring point of the target monitoring point, calculate the sum of the temperature accumulation terms of the target monitoring point and the neighboring monitoring points, and the icing trend is the product of the humidity term and the sum of the temperature accumulation terms.

6. The transmission line icing thickness monitoring system based on distributed sensors according to claim 5, characterized in that, The method for obtaining at least one neighboring monitoring point of the target monitoring point is as follows: on the left and right sides of the target monitoring point, obtain the nearest preset number of monitoring points as neighboring monitoring points.

7. The transmission line icing thickness monitoring system based on distributed sensors according to claim 1, characterized in that, The determination of the expansion factor includes: Calculate the product of the environmental change factor and the icing trend; The product is normalized using the hyperbolic tangent function, and the normalized result is added to the basic weights to obtain the inflation factor.

8. The transmission line icing thickness monitoring system based on distributed sensors according to claim 1, characterized in that, The step of multiplying the expansion factor with the process noise covariance matrix in the Kalman filter algorithm to obtain the modified Kalman filter algorithm includes: Multiply the current inflation factor by the preset process noise covariance matrix to obtain the corrected process noise covariance matrix; The modified Kalman filter algorithm is obtained by updating the prediction error covariance matrix using the modified process noise covariance matrix.

9. The transmission line icing thickness monitoring system based on distributed sensors according to claim 1, characterized in that, The monitoring system also includes an observation unit for acquiring the observed thickness. The observation unit measures the strain change of the target monitoring point through a strain sensor and converts the strain change into the observed thickness of the target monitoring point through a preset mechanical model.

10. The transmission line icing thickness monitoring system based on distributed sensors according to any one of claims 1 to 9, characterized in that, After outputting the final icing thickness of the target monitoring point, the monitoring system further includes: summarizing the final icing thickness of all monitoring points on the transmission line to construct a final icing thickness distribution network for the entire transmission line; identifying severe icing monitoring points where the icing thickness exceeds the safety threshold, and generating de-icing operation instructions.

Images