A tension testing device and method for power cables

Abstract

This application relates to the field of cable tension detection technology, specifically to a tension detection device and method for power cables. The method includes: acquiring tension data and rotation speed data during cable transport; dividing the entire data acquisition time into multiple time periods; obtaining a comprehensive influence coefficient for each time period based on the fluctuation and periodic characteristics of the tension data in each time period, the correlation between the rotation speed data and the changing trends of abnormal tension characteristics in each time period, and the regularity of the fluctuation characteristics of the tension data in each time period; predicting the comprehensive influence coefficient for the next time period; and obtaining the abnormal tension state value for each time period based on the difference between the predicted value and the comprehensive influence coefficient of its nearest neighbor time period, thereby determining whether the cable tension state in each time period is abnormal. This application, by analyzing the characteristics of cable tension data under the influence of drum mechanical friction and ship swaying, can more accurately determine the state of cable tension.

Description

Technical Field

[0001] This application relates to the field of cable tension testing technology, specifically to a tension testing device and method for power cables. Background Technology

[0002] While ships are docked, they typically rely on their onboard fuel-powered auxiliary generators for electricity. However, the emissions from these generators cause significant pollution to the port city environment. To reduce pollution during docking, ships are now being powered by on-site power sources. For a long time, shore power cables have been delivered to ships via cranes, which is prone to damage and poses safety hazards. Consequently, the demand for shore power cable delivery systems in ports has increased significantly.

[0003] Cable conveying devices must ensure that the tensile force on the cable does not exceed its limit during cable winding and unwinding, maintaining stable cable tension and preventing slack or tightness. In practical applications, the operating conditions of the conveying equipment, such as mechanical friction from the drum, can damage the cable. Changes in drum speed or ship swaying can also cause some degree of tension on the cable, all of which can damage the cable during transport. To avoid damage to the cable due to friction or tension during transportation, it is necessary to monitor the cable tension in real time. Existing methods, when detecting abnormal cable tension, only judge whether the tension is abnormal based on the magnitude of the tension data, failing to fully consider the damage caused by the operating conditions of the conveying equipment and ship swaying. This can easily lead to the misclassification of abnormal cable tension caused by abnormal operation of the conveying equipment and ship swaying as normal, resulting in inaccurate cable tension detection. Summary of the Invention

[0004] To address the aforementioned technical problems, the purpose of this application is to provide a tension detection device and method for power cables, the specific technical solution of which is as follows:

[0005] In a first aspect, embodiments of this application provide a method for detecting the tension of a power cable, the method comprising the following steps:

[0006] Real-time acquisition of cable tension data and cable drum rotation speed data during cable transportation;

[0007] The entire data acquisition time is divided into multiple time periods. Based on the jitter amplitude and irregularity of all peak values ​​in the tension data of each time period, the friction anomaly coefficient of each time period is obtained. Furthermore, considering the similarity of peak distribution between each time period and the previous time period, the interference index of each time period is obtained. The calculation formula is as follows: In the formula, Let be the interference index for the i-th time period. Let be the friction anomaly coefficient for the i-th time period. DTW distance between the peak sequence corresponding to the i-th time period and the previous time period; where the peak sequence of each time period refers to the sequence composed of all the peaks of the tension data in each time period arranged in ascending order of time; and the first influence coefficient of each time period is obtained by combining the correlation between the rotational speed data change trend and the interference index change trend of each time period and its neighboring time periods.

[0008] Based on the regularity of tension data fluctuations in each time period and its neighboring time periods, a second influence coefficient is obtained for each time period. Combined with the first influence coefficient, a comprehensive influence coefficient for each time period is obtained. The comprehensive influence coefficient for the next time period is predicted based on the comprehensive influence coefficients of each time period and its neighboring time periods. Based on the difference between the predicted value and the comprehensive influence coefficients of its neighboring time periods, and the comprehensive influence coefficients of each time period, the abnormal tension state values ​​for each time period are obtained. The process involves calculating the difference between the predicted comprehensive influence coefficient of the next time period and the comprehensive influence coefficients of all its neighboring time periods. The sum of all differences and the product of the comprehensive influence coefficient of each time period are used as the abnormal value of the tension state for each time period. Then, it is determined whether the cable tension state of each time period is abnormal. The specific process is as follows: if the normalized result of the abnormal value of the tension state of any time period is greater than or equal to the preset abnormal threshold, or if there is data in the tension data of that time period that exceeds the preset tension range [u,p], then it is determined that the cable tension state of that time period is abnormal; otherwise, it is determined that the cable tension state of that time period is normal. Where u is the preset minimum tension for the cable to not loosen, and p is the preset maximum tension that the cable can withstand.

[0009] Preferably, the formula for calculating the friction anomaly coefficient for each time period is: In the formula, Let be the friction anomaly coefficient for the i-th time period. Let be the mean of the absolute differences between all tension data in the i-th time period and their preset expected value. Let f(x) be the fractal dimension corresponding to all peak values ​​in the tension data of the i-th time period.

[0010] Preferably, the process of obtaining the first influence coefficient for each time period is as follows: obtaining the statistics of rotational speed data for each time period; recording the absolute values ​​of the statistics and interference indices of each time period and its neighboring time periods in chronological order as the statistics sequence and interference sequence for each time period; and using the product of the absolute value of the correlation coefficient between the statistics sequence and the interference sequence for each time period and the interference index of the corresponding time period as the first influence coefficient for each time period.

[0011] Preferably, the formula for calculating the second influence coefficient for each time period is: In the formula, The second influence coefficient is the value for the i-th time period. Let be the standard deviation of the time difference between all adjacent extreme points in the tension data of the i-th time period and its nearest neighboring time periods. Let be the standard deviation of the amplitude difference between all adjacent extreme points in the tension data of the i-th time period and its nearest neighboring time periods.

[0012] Preferably, the comprehensive influence coefficient for each time period refers to the product of the first influence coefficient and the second influence coefficient for each time period.

[0013] Preferably, the specific process of predicting the comprehensive impact coefficient of the next period is as follows: the comprehensive impact coefficient of each period and all its neighboring periods is used as the input of a first exponential smoothing algorithm, and the predicted value of the comprehensive impact coefficient of the next period of each period is output.

[0014] Secondly, embodiments of this application also provide a tension detection device for power cables, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the steps of any of the methods described above.

[0015] This application has at least the following beneficial effects:

[0016] This application analyzes in depth the fluctuation characteristics and transient impact anomalies of cable tension caused by friction during cable transmission, as well as the coupling characteristics between tension anomalies and drum rotation speed, to construct a first influence coefficient, making subsequent assessments of tension anomalies more accurate. By analyzing the impact of ship swaying on tension data and combining it with the first influence coefficient, a comprehensive influence coefficient is constructed to comprehensively assess the degree of interference with cable tension. Through predictive analysis of the comprehensive influence coefficient of cable tension, anomaly values ​​of tension states are calculated, and real-time tension state detection is performed based on these values. Its advantage lies in its ability to detect abnormal cable tension states caused by abnormal operation of transmission equipment and ship swaying, thus helping to improve the accuracy of cable tension state detection. Attached Figure Description

[0017] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart illustrating the steps of a tension detection method for a power cable according to an embodiment of this application;

[0019] Figure 2 A flowchart illustrating the acquisition of abnormal tension values ​​at various time periods, as provided in one embodiment of this application. Detailed Implementation

[0020] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a tension detection device and method for power cables proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0022] The following description, in conjunction with the accompanying drawings, details the specific scheme of the tension testing device and testing method for power cables provided in this application.

[0023] Please see Figure 1 The diagram illustrates a flowchart of a tension detection method for a power cable according to an embodiment of this application. The method includes the following steps:

[0024] Step 1: Acquire real-time data on cable tension and cable drum rotation speed during cable transport.

[0025] This application uses a ship-to-shore power system as an example to perform real-time tension detection during cable delivery. After the ship docks, the cable is delivered from the shore to the ship via cable delivery equipment. During this process, it is necessary to ensure stable cable tension to avoid damage and thus improve cable lifespan. In the cable delivery equipment, the cable is wound on a cable reel, and a three-phase motor drives the reel to rotate for cable winding and unwinding.

[0026] In this embodiment, a tension sensor is used to collect cable tension data in real time during cable conveying. Since the cable conveying equipment is unstable during unwinding and rewinding, its speed changes may interfere with tension stability. Therefore, this embodiment utilizes the control system of the cable conveying equipment to obtain the rotational speed data of its cable reel. In this embodiment, the acquisition frequency for tension and rotational speed data is set to 100Hz.

[0027] Step 2: Divide the entire data acquisition time into multiple time periods. Based on the jitter amplitude of the tension data in each time period and the irregularity of all peaks in the tension data, obtain the friction anomaly coefficient for each time period. Combine the similarity of the peak distribution in the tension data of each time period with that of the previous time period to obtain the interference index for each time period. Combine the correlation between the rotational speed data change trend of each time period and its neighboring time period and the change trend of the interference index to obtain the first influence coefficient for each time period.

[0028] During the operation of cable conveying equipment, the conveyor motor rotates at a relatively stable speed controlled by a frequency converter, driving the cable conveyor. However, various factors can cause changes in cable tension during this process. For example, in actual operation, the cable conveying device is not stable during cable winding and unwinding, often experiencing acceleration and deceleration, which causes the tension to change with the drum speed, making the cable susceptible to damage. Furthermore, when a ship docks, varying degrees of swaying may occur, pulling on the cable and also causing tension changes. Excessive cable tension can lead to cable tautness, easily damaging the cable and causing safety accidents; insufficient tension will cause the cable to loosen, preventing the cable conveying device from functioning properly. When detecting abnormal cable tension, the abnormality is often judged solely by the numerical value of the tension data, failing to detect abnormal cable tension caused by abnormal operation of the conveying equipment or ship swaying. Based on the above issues, the following analysis of cable tension is conducted.

[0029] Cables are typically transported at a constant speed. Due to mechanical wear on the reel, each rotation can cause periodic changes in tension. Therefore, this application divides the entire tension detection time into multiple periods, starting from the start of the conveyor motor and defining the time of each rotation as a period. Taking the tension data of the i-th period as an example, the following analysis is performed: the cable may experience transient impacts of varying degrees due to sudden changes in stacking pressure or friction. Under the influence of friction from various components, the tension data exhibits a characteristic of rapid fluctuations around the expected value. Under the influence of transient impacts, the peak fluctuations of the tension data are more chaotic, and the amplitude of the tension fluctuations around the expected value is greater. Therefore, this application first uses the findpeaks detection algorithm to obtain the peak values ​​of the tension data within this period. The expected value of the tension during operation can be obtained by the control system. The obtained expected value is recorded as the preset expected value. Then, the absolute difference between each tension data point in the i-th period and the preset expected value is calculated, and the average of all absolute differences is recorded as... The result This reflects the degree of fluctuation in the difference between the actual cable tension and the expected cable tension. Then, the fractal dimension of all peak values ​​within this time period is calculated using the Higuchi algorithm, denoted as... The result This reflects the irregular characteristics of the peak value variation in tension data under the influence of transient impact.

[0030] In this embodiment, the friction anomaly coefficient for the i-th time period is denoted as... Its specific expression is: In the formula, Let be the friction anomaly coefficient for the i-th time period. Let be the mean of the absolute differences between all tension data in the i-th time period and their preset expected value. Let be the fractal dimension corresponding to all peak values ​​in the tension data of the i-th time period. The obtained... The larger the value, the more significant the fluctuation characteristics and transient impact anomalies of the actual tension data caused by the mechanical friction of the drum in the i-th time period.

[0031] Furthermore, due to the mechanical wear of the drum, each rotation may cause periodic changes in tension, especially for the peak distribution under transient impact, where the periodicity is more pronounced. Therefore, all peak values ​​of the tension data within each time period are arranged in ascending order of time to obtain the peak sequence for each time period. Then, the DTW distance between the peak sequence of the i-th time period and the peak sequence of the previous time period is denoted as... The result The smaller the value, the more significant the fluctuation periodicity of the peak value in the tension data during the i-th time period of cable transmission.

[0032] In this embodiment, the interference index of the i-th time period is denoted as... Its formula is: In the formula, Let be the interference index for the i-th time period. Let be the friction anomaly coefficient for the i-th time period. Let be the DTW distance between the peak sequence corresponding to the i-th time period and the previous time period. The obtained... The larger the value, the greater the influence of the cable tension in the i-th time period on the mechanical friction of the drum.

[0033] Furthermore, in practical applications, the rotational speed of the reel experiences varying degrees of acceleration and deceleration. Under ideal conveying conditions, the rotational speed should be relatively stable within each time period. The greater the amplitude of these acceleration and deceleration variations, the more significant the impact on cable tension changes, potentially leading to abnormal tension data that fluctuates with the rotational speed. Taking the cable transport process from shore to ship as an example, the cable is continuously being released. When the reel is accelerating, the cable release speed increases, resulting in relatively lower tension. Simultaneously, the degree of tension anomaly due to friction also decreases, and the change in anomaly degree is more significant than the change in tension. Conversely, when the reel is decelerating, the cable release speed decreases, resulting in relatively higher tension, and simultaneously, a greater degree of tension anomaly. Therefore, there is a certain coupling effect between rotational speed changes and the degree of anomaly due to friction; the degree of rotational speed change and the degree of tension anomaly exhibit a positive or negative correlation.

[0034] Therefore, taking the rotational speed data of the i-th time period as an example, in order to obtain the characteristics of its rotational speed change, this application uses the Mankendall detection algorithm to obtain the trend of its rotational speed change. The output of this algorithm is the statistical quantity of the rotational speed data of the i-th time period. The absolute value of the obtained statistical quantity reflects the degree of rotational speed change (acceleration or deceleration). The larger the absolute value of the statistical quantity, the greater the degree of rotational speed change. Then, the previous N time periods of the i-th time period are taken as the nearest neighbor time periods of the i-th time period, where the value of N is in the range of [8,10]. In this embodiment, N is taken as 8. Then, the absolute values ​​of the statistical quantity and the interference index of the i-th time period and its nearest neighbor time periods are respectively arranged in chronological order and recorded as the statistical quantity sequence and interference sequence of the i-th time period. The correlation coefficient between the statistical quantity sequence and the interference sequence of the i-th time period is calculated. The product of the absolute value of the obtained correlation coefficient and the interference index of the i-th time period is taken as the first influence coefficient of the i-th time period, denoted as . The result The larger the value, the greater the influence of the cable tension data in the i-th time period on the changes in drum rotation speed and mechanical friction.

[0035] Step 3: Based on the regularity of the tension data fluctuation characteristics of each time period and its neighboring time periods, obtain the second influence coefficient for each time period, and combine it with the first influence coefficient for each time period to obtain the comprehensive influence coefficient for each time period; predict the comprehensive influence coefficient for the next time period based on the comprehensive influence coefficient for each time period and its neighboring time periods, and obtain the abnormal values ​​of tension status for each time period based on the difference between the obtained predicted value and the comprehensive influence coefficient of its neighboring time periods, as well as the comprehensive influence coefficient for each time period, and then determine whether the cable tension status for each time period is abnormal.

[0036] Furthermore, when a ship approaches the shore, it is affected by surges, causing its attitude to sway. During the operation of the shore power system, the cable delivery equipment is placed on the shore, and the cable receiving equipment is placed on the ship. The ship's swaying inevitably causes regular fluctuations in the cable tension data, thus affecting the stability of the delivery process. Therefore, this embodiment uses a maximum-minimum algorithm to obtain the maximum and minimum values ​​of the tension data in the i-th time period. The more consistent the time interval between adjacent extreme values ​​and the more consistent the amplitude difference between adjacent extreme values, the more significant the regular fluctuation characteristics of the tension data, indicating a greater impact of the ship's swaying on the cable tension.

[0037] As a preferred implementation, a second influence coefficient is obtained for each time period based on the regularity of the tension data fluctuation characteristics of each time period and its neighboring time periods. This coefficient is used to characterize the degree to which the tension data of each time period is affected by the ship's swaying.

[0038] In this embodiment, the second influence coefficient for the i-th time period is denoted as... Its specific expression is: In the formula, The second influence coefficient is the value for the i-th time period. Let be the standard deviation of the time difference between all adjacent extreme points in the tension data of the i-th time period and its nearest neighboring time periods. Let be the standard deviation of the amplitude difference between all adjacent extreme points in the tension data of the i-th time period and its nearest neighboring time periods.

[0039] The larger the value, the more significant the regular fluctuation characteristics of the tension data in the i-th time period and its nearest neighboring time periods, indicating a greater influence of ship swaying on the tension data.

[0040] Furthermore, based on the first and second influence coefficients for each time period, a comprehensive influence coefficient for each time period is obtained to characterize the likelihood of anomalies in the tension data for each time period.

[0041] In this embodiment, the product of the first influence coefficient and the second influence coefficient for each time period is used as the comprehensive influence coefficient for that time period. The larger the comprehensive influence coefficient for each time period, the greater the possibility of anomalies in the tension data for that time period.

[0042] Furthermore, as cable conveying equipment continues to operate, its wear and tear may gradually increase, and the impact of surges on ships usually also gradually increases. Therefore, if the comprehensive influence coefficient obtained in different time periods shows an increasing trend, the possibility of cable damage risk is greater. In view of this, this application uses the comprehensive influence coefficient of the i-th time period and all its nearest neighboring time periods as input to a single exponential smoothing algorithm, outputting the predicted value of the comprehensive influence coefficient of the (i+1)-th time period; calculates the difference between the predicted value and the comprehensive influence coefficient of all nearest neighboring time periods, and multiplies the sum of all differences by the comprehensive influence coefficient of the i-th time period as the tension state anomaly value of the i-th time period. The obtained tension state anomaly value reflects the possibility of abnormal cable tension in the i-th time period. The flowchart for obtaining the tension state anomaly value for each time period is shown below. Figure 2 As shown.

[0043] A preset anomaly threshold is set to P, which is 0.8 in this embodiment. The tanh function is used to normalize the abnormal tension values ​​for the current period. If the normalized result is greater than or equal to the preset anomaly threshold, or if there are data in the current period's tension data that exceed the preset tension range [u, p], then the cable tension state for the current period is considered abnormal. Operators need to adjust the drum speed and the ship's swaying. In practical applications, the implementer can adjust according to the actual situation to avoid cable damage or safety hazards. If the normalized result is less than the preset anomaly threshold, and the tension data for the current period does not exceed the preset tension range [u, p], then the cable tension state for the current period is considered normal. Here, u is the preset minimum tension at which the cable will not loosen, and p is the preset maximum tension the cable can withstand; the implementer can choose the value according to the actual situation.

[0044] Based on the same inventive concept as the above method, this application embodiment also provides a tension detection device for power cables, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above-described tension detection methods for power cables.

[0045] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, specific embodiments of this specification have been described above. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0046] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0047] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the principles of this application should be included within the protection scope of this application.

Claims

1. A method for detecting the tension of a power cable, characterized in that, The method includes the following steps: Real-time acquisition of cable tension data and cable drum rotation speed data during cable transportation; The entire data acquisition time is divided into multiple time periods. Based on the jitter amplitude and irregularity of all peak values ​​in the tension data of each time period, the friction anomaly coefficient of each time period is obtained. Furthermore, considering the similarity of peak distribution between each time period and the previous time period, the interference index of each time period is obtained. The calculation formula is as follows: In the formula, Let be the interference index for the i-th time period. Let be the friction anomaly coefficient for the i-th time period. DTW distance between the peak sequence corresponding to the i-th time period and the previous time period; where the peak sequence of each time period refers to the sequence composed of all the peaks of the tension data in each time period arranged in ascending order of time; and the first influence coefficient of each time period is obtained by combining the correlation between the rotational speed data change trend and the interference index change trend of each time period and its neighboring time periods. Based on the regularity of tension data fluctuations in each time period and its neighboring time periods, a second influence coefficient is obtained for each time period. Combined with the first influence coefficient, a comprehensive influence coefficient for each time period is obtained. The comprehensive influence coefficient for the next time period is predicted based on the comprehensive influence coefficients of each time period and its neighboring time periods. Based on the difference between the predicted value and the comprehensive influence coefficients of its neighboring time periods, and the comprehensive influence coefficients of each time period, the abnormal tension state values ​​for each time period are obtained. The process involves calculating the difference between the predicted comprehensive influence coefficient of the next time period and the comprehensive influence coefficients of all its neighboring time periods. The sum of all differences and the product of the comprehensive influence coefficient of each time period are used as the abnormal value of the tension state for each time period. Then, it is determined whether the cable tension state of each time period is abnormal. The specific process is as follows: if the normalized result of the abnormal value of the tension state of any time period is greater than or equal to the preset abnormal threshold, or if there is data in the tension data of that time period that exceeds the preset tension range [u,p], then it is determined that the cable tension state of that time period is abnormal; otherwise, it is determined that the cable tension state of that time period is normal. Where u is the preset minimum tension for the cable to not loosen, and p is the preset maximum tension that the cable can withstand.

2. The tension detection method for power cables as described in claim 1, characterized in that, The formula for calculating the friction anomaly coefficient for each time period is as follows: In the formula, Let be the friction anomaly coefficient for the i-th time period. Let be the mean of the absolute differences between all tension data in the i-th time period and their preset expected value. Let f(x) be the fractal dimension corresponding to all peak values ​​in the tension data of the i-th time period.

3. The tension detection method for power cables as described in claim 1, characterized in that, The process of obtaining the first influence coefficient for each time period is as follows: obtain the statistics of rotational speed data for each time period; record the absolute values ​​of the statistics and interference indices of each time period and its neighboring time periods in chronological order as the statistics sequence and interference sequence for each time period; and use the product of the absolute value of the correlation coefficient between the statistics sequence and the interference sequence for each time period and the interference index of the corresponding time period as the first influence coefficient for each time period.

4. The tension detection method for power cables as described in claim 1, characterized in that, The formula for calculating the second influence coefficient for each time period is as follows: In the formula, The second influence coefficient is the value for the i-th time period. Let be the standard deviation of the time difference between all adjacent extreme points in the tension data of the i-th time period and its nearest neighboring time periods. Let be the standard deviation of the amplitude difference between all adjacent extreme points in the tension data of the i-th time period and its nearest neighboring time periods.

5. The tension detection method for power cables as described in claim 1, characterized in that, The comprehensive impact coefficient for each time period refers to the product of the first impact coefficient and the second impact coefficient for each time period.

6. The tension detection method for power cables as described in claim 1, characterized in that, The specific process for predicting the comprehensive impact coefficient of the next period is as follows: the comprehensive impact coefficient of each period and all its neighboring periods is used as the input of a first exponential smoothing algorithm, and the predicted value of the comprehensive impact coefficient of the next period of each period is output.

7. A tension detection device for power cables, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the tension detection method for a power cable as described in any one of claims 1-6.

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