Big data analysis and processing method for distributed continuity monitoring of high-voltage cables

Abstract

The present invention discloses a method for analyzing and processing big data of distributed continuity monitoring of high-voltage cables, and relates to the technical field of high-voltage cable operation monitoring and fault early warning. The main scheme is: calculating the temperature change rate through operation status data, and then calculating the local discharge pulse average value, current harmonic component, and voltage fluctuation amplitude in combination with the temperature change rate; then calculating the predicted temperature influence index according to the temperature change rate, temperature data and operation status data, and then calculating the predicted local discharge influence index, predicted current influence index and predicted voltage influence index in combination with the predicted temperature influence index, calculating the operation basic index according to the basic parameter data, and finally calculating the cable operation risk index according to the operation basic index and the predicted voltage influence index, setting a risk index threshold set, judging whether to trigger an early warning according to the comparison result, and generating an adjustment instruction and a risk assessment report to ensure the safe operation of the high-voltage cable.

Description

Technical Field

[0001] The present invention relates to the technical field of high-voltage cable operation monitoring and fault early warning, and in particular to a method for analyzing and processing big data of distributed continuity monitoring of high-voltage cables. Background Art

[0002] High-voltage cables play a vital role in power transmission, and their safe and stable operation is of great significance to ensuring power supply reliability. In actual operation, high-voltage cables are affected by a variety of complex factors, such as temperature changes, partial discharge, current and voltage fluctuations, etc. These factors are intertwined and may lead to cable performance degradation and insulation damage, and even cause failures, affecting the normal operation of the power system.

[0003] In the existing technology, the operating status of high-voltage cables is usually monitored to a certain extent. Temperature sensors, partial discharge detection devices, etc. are installed to obtain some operating data of the cables, and the cable status is evaluated based on some simple threshold judgments or single parameter analysis. Some technologies use statistical analysis methods to analyze the average and extreme values ​​of temperature, current and other data in historical data, and issue an alarm when the monitored value exceeds the set normal range.

[0004] Existing technologies have obvious defects. On the one hand, most of them only analyze one or several parameters in isolation, and fail to fully consider the interrelationships and influences among multiple factors. Partial discharge is related to temperature changes, and focusing solely on the partial discharge threshold may ignore the potential risks caused by this correlation. On the other hand, there is a lack of a comprehensive assessment mechanism for cable operation risks, and it is impossible to provide timely and effective cable fault risk warnings, making it difficult to meet the high requirements of modern complex power systems for the safety and reliability of high-voltage cable operations. Summary of the invention

[0005] 1. Technical issues to be resolved

[0006] In view of the shortcomings of the prior art, the present invention provides a big data analysis and processing method for distributed continuity monitoring of high-voltage cables. By comprehensively collecting real-time temperature, local discharge, current, voltage data, operating status data and basic parameter data, the method calculates the temperature change rate to the local discharge pulse average value, current harmonic components and voltage fluctuation amplitude, avoids isolated analysis of parameters, explores the intrinsic connection between factors, and solves the problem of insufficient consideration of multi-factor correlation; the cable operation risk index is calculated by the operation basic index and the predicted voltage impact index to form a comprehensive evaluation index, which solves the problem of inaccurate comprehensive evaluation of operation risks, sets a risk index threshold set, realizes dynamic risk warning based on the comparison result with the threshold, and generates adjustment instructions and risk assessment reports, effectively improving the prediction and warning capabilities and protection level of cable faults.

[0007] (II) Technical solution

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: a method for analyzing and processing big data of distributed continuity monitoring of high-voltage cables, comprising:

[0009] Collect real-time temperature data, partial discharge data, current data, voltage data and operation status data of high-voltage cables, and obtain basic parameter data of high-voltage cables;

[0010] Calculate the temperature change rate based on the operating status data ; According to the operating status data and temperature change rate , calculate the average value of partial discharge pulse ; Based on the average value of partial discharge pulse And operating status data, calculate current harmonic components ;According to the current harmonic components And the voltage fluctuation amplitude H is calculated based on the operating status data;

[0011] According to the temperature change rate , temperature data and operating status data, calculate and predict the temperature impact index ; Based on the predicted temperature impact index , Partial discharge pulse average value And partial discharge data, calculate the predicted partial discharge impact index PD; according to the predicted partial discharge impact index PD, current harmonic components According to the predicted current impact index EOI, voltage fluctuation amplitude H, voltage data and operation status data, the predicted voltage impact index FII is calculated; according to the basic parameter data, the basic operation index GII is calculated; according to the basic operation index GII and the predicted voltage impact index FII, the cable operation risk index is calculated. ;

[0012] Set the cable operation risk index threshold set JV, according to the cable operation risk index The comparison result with the cable operation risk index threshold set JV is used to determine whether an operation failure risk warning is triggered, and generate corresponding adjustment instructions and risk assessment reports.

[0013] In the preferred embodiment of the above-mentioned high-voltage cable distributed continuity monitoring big data analysis and processing method, the temperature change rate is calculated The specific steps are:

[0014] Running status data includes the current time point , the previous time point , Current temperature value And the temperature value at the previous moment ;

[0015] According to the current time point , the previous time point , the temperature value at the previous moment And the temperature value at the previous moment , calculate the temperature change rate The specific formula is as follows:

[0016]

[0017] in, The value of is not equal to 0.

[0018] In the preferred embodiment of the above-mentioned high-voltage cable distributed continuity monitoring big data analysis and processing method: calculating the average value of partial discharge pulses The specific steps are:

[0019] The operating status data also includes the partial discharge pulse amplitude in the previous unit time period ;

[0020] According to the partial discharge pulse amplitude And the temperature change rate , calculate the average value of partial discharge pulse The specific formula is as follows:

[0021]

[0022] in, is the amplitude of the ith partial discharge pulse, i is in the range of [1, N], N represents the total number of partial discharge pulses detected in the previous unit time period, and k and m represent the temperature change rate The fitting coefficients of k are [0.1, 10], m is [0.5, 2], and a represents the adjustment coefficient of the temperature change rate, which is [0.01, 0.5].

[0023] In the preferred embodiment of the above-mentioned high-voltage cable distributed continuity monitoring big data analysis and processing method: calculating the current harmonic components The specific steps are:

[0024] The operating status data also includes the maximum current value in the previous unit time period And the average current value ;

[0025] According to the average value of partial discharge pulse , Maximum current value And the average current value , calculate the current harmonic components , and the specific formula is as follows:

[0026]

[0027] Among them, x1 represents the square of the average value of partial discharge pulses weight coefficient, with a value range of 0.1 ≤ x1 ≤ 0.3, x2 represents weight coefficient, with a value range of 0.3 ≤ x2 ≤ 0.4, x3 represents weight coefficient, with a value range of 0.2 ≤ x3 ≤ 0.5, and a value range of 0.1 < x4 ≤ 0.3, and x1 + x2 + x3 = 1.

[0028] In the preferred solution of the above-mentioned big data analysis and processing method for distributed continuous monitoring of high-voltage cables: The specific steps for calculating the voltage fluctuation amplitude H are as follows:

[0029] The operating state data also includes the average voltage in the previous unit time period and the rated voltage ;

[0030] According to the average voltage , the rated voltage and the current harmonic component , calculate the voltage fluctuation amplitude H, and the specific formula is as follows:

[0031]

[0032] Among them, q1 represents - weight coefficient, with a value range of 0.1 ≤ q1 ≤ 0.4, q2 represents the current harmonic component weight coefficient one, with a value range of 0.2 ≤ q2 ≤ 0.5, q3 represents the current harmonic component weight coefficient two, with a value range of 0.3 ≤ q2 ≤ 0.4, and q1 + q2 + q3 = 1.

[0033] In the preferred solution of the above-mentioned big data analysis and processing method for distributed continuous monitoring of high-voltage cables: The specific steps for calculating the predicted temperature influence index are as follows:

[0034] The real-time temperature data includes the cable temperature in the current unit time period , the average temperature value in the current unit time period and the temperature standard deviation in the current unit time period ;

[0035] The operating state data also includes the average temperature value in the previous unit time period and the temperature standard deviation in the previous unit time period ;

[0036] According to the temperature change rate , real-time temperature data, average temperature value in the previous unit time period And the standard deviation of temperature in the previous unit time period , calculate and predict the temperature impact index The specific formula is as follows:

[0037]

[0038] Where s1 represents the temperature change rate The weight coefficient is 0.1≤s1≤0.4, s2 represents The weight coefficient is 0.2≤s2≤0.5, s3 represents The weight coefficient is 0.3≤s3≤0.4, and s1+s2+s3=1; represents the cable temperature value at the tth moment, t represents the sequence number of different moments, and m is the total number of moments.

[0039] In the preferred embodiment of the above-mentioned high-voltage cable distributed continuity monitoring big data analysis and processing method: the specific steps of calculating and predicting the partial discharge impact index PD are:

[0040] Setting the partial discharge pulse threshold ;

[0041] Partial discharge data includes the partial discharge pulse value in the current unit time period And the rated partial discharge pulse value ;

[0042] According to the predicted temperature impact index , Partial discharge pulse average value , Partial discharge pulse threshold And partial discharge data, calculate and predict the partial discharge impact index PD, the specific formula based on which is as follows:

[0043]

[0044] Among them, f1 represents the predicted temperature impact index The weight coefficient is 0.2≤f1≤0.5, and f2 represents the average value of the partial discharge pulse The weight coefficient is 0.3≤f2≤0.4, and f3 represents the partial discharge pulse value. The weight coefficient is 0.1≤f3≤0.4, and f1+f2+f3=1.

[0045] In the preferred embodiment of the above-mentioned high-voltage cable distributed continuity monitoring big data analysis and processing method: the specific steps of calculating the predicted current influence index EOI are:

[0046] Current data includes the current value in the current unit time period And rated current ;

[0047] According to the predicted partial discharge impact index PD, current harmonic components And current data, calculate the predicted current impact index EOI, the specific formula is as follows:

[0048]

[0049] Among them, the real-time current The value of >0.

[0050] In the preferred embodiment of the above-mentioned high-voltage cable distributed continuity monitoring big data analysis and processing method: the specific steps of predicting the voltage influence index FII are:

[0051] Voltage data includes the voltage value in the current unit time period ;

[0052] The operating status data also includes the voltage standard deviation ;

[0053] According to the predicted current influence index EOI, voltage fluctuation amplitude H, voltage value And the voltage standard deviation , calculate the predicted voltage impact index FII, the specific formula is as follows:

[0054]

[0055] The voltage standard deviation is The value of >0.

[0056] In the preferred embodiment of the above-mentioned high-voltage cable distributed continuity monitoring big data analysis and processing method: the specific steps of calculating the cable operation comprehensive risk index and predicting the fault risk are:

[0057] The basic parameter data include cable length L and cable cross-sectional area S;

[0058] Set the maximum allowed cable length And the maximum cross-sectional area allowed by the cable ;

[0059] According to the basic parameter data, the maximum allowable length of the cable , Maximum allowable cross-sectional area of ​​cable , Current value , Rated current , voltage value And rated voltage , calculate the operation basic index GII, the specific formula is as follows:

[0060]

[0061] Calculate the cable operation risk index based on the operation basic index GII and the predicted voltage impact index FII The specific formula is as follows:

[0062]

[0063] The cable operation risk index threshold set JV includes a first-level threshold Jv0, a second-level threshold Jv1, and a third-level threshold Jv2, wherein Jv0<Jv1<Jv2;

[0064] When the cable runs risk index When ≤Jv0, the operation failure risk warning will not be triggered;

[0065] When Jv0<Cable operation risk index When ≤Jv1, the first-level operation fault risk warning is triggered, and the primary adjustment instructions and risk assessment report are generated;

[0066] When Jv1<Cable operation risk index When <Jv2, the second-level operation fault risk warning is triggered, and the intermediate adjustment instructions and risk assessment report are generated;

[0067] When Jv2<Cable operation risk index When the fault occurs, the third-level operation fault risk warning is triggered, and high-level adjustment instructions and risk assessment reports are generated.

[0068] (III) Beneficial effects

[0069] The present invention provides a method for analyzing and processing big data of distributed continuity monitoring of high-voltage cables, which has the following beneficial effects:

[0070] (1) Real-time collection of temperature, partial discharge, current, voltage, operating status and basic parameter data provides a rich data basis for a comprehensive understanding of the operating status of high-voltage cables. This makes subsequent analysis no longer limited to a single type of data and can more accurately reflect the actual status of the cable under complex working conditions. It overcomes the problem of one-sided data in traditional methods and helps to improve the accuracy of diagnosis of potential cable problems.

[0071] (2) The temperature change rate is obtained by analyzing the historical operating status data, which can reflect the trend of cable temperature change; the local discharge pulse average value is calculated by the temperature change rate and operating status data, making the calculation of the local discharge pulse average value more accurate, avoiding the limitations of isolated analysis of local discharge data, and helping to more accurately grasp the relationship between local discharge and the overall health status of the cable; the local discharge pulse average value is combined with the operating status data to calculate the current harmonic component, revealing the inherent connection between local discharge and current harmonics, helping to discover current anomalies that may be caused by local discharge problems in advance, and providing a more comprehensive basis for predicting cable faults; the voltage fluctuation amplitude is calculated by considering the current harmonic component and operating status data. This calculation helps to timely discover abnormal voltage fluctuations, improve the accuracy of cable voltage stability assessment, and ensure that the cable operates under stable voltage.

[0072] (3) The predicted temperature impact index is able to more comprehensively reflect the impact of temperature factors on cable operation by combining the temperature change rate, real-time temperature and operating status data. This not only takes into account the current temperature conditions, but also combines the temperature change trend and the information of the previous time period, providing a more representative temperature-related indicator for the subsequent comprehensive assessment of cable operation risks. The predicted temperature impact index, the average value of partial discharge pulses and the real-time partial discharge data are integrated to comprehensively consider the interaction and respective states of the two key factors of temperature and partial discharge, so that the predicted partial discharge impact index can more accurately measure the potential harm of partial discharge to cable operation. The predicted partial discharge impact index, current harmonic components and real-time current data can be combined to comprehensively evaluate the impact of partial discharge and current harmonics on current, so that the predicted current impact index can more accurately reflect the actual operating risk of current. The predicted current impact index, voltage fluctuation amplitude, real-time voltage data and historical operating status data are combined to enable the predicted voltage impact index to more comprehensively reflect the operating risk of voltage, taking into account the impact of current on voltage and the fluctuation of voltage itself, which helps to more accurately predict the impact of voltage anomalies on cables and ensure the safe operation of cables at appropriate voltages.

[0073] (4) The basic operation index is calculated based on the basic parameter data, and the basic operation conditions of the cable are evaluated from the perspective of its basic properties. This provides a benchmark for subsequent evaluation combined with other operation influencing factors, making the entire cable operation evaluation system more complete and able to more accurately judge the advantages and disadvantages of the cable in terms of design and physical properties.

[0074] (5) The cable operation risk index is calculated by combining the basic operation index and the predicted voltage impact index, which integrates the basic conditions of the cable and the voltage-related operation risks. This method can comprehensively evaluate the operation risk status of the cable, avoiding the one-sidedness of focusing on a single factor or part of the factors, and provides a quantitative basis for accurately judging the overall operation health of the cable.

[0075] (6) Set a set of cable operation risk index thresholds, and determine whether to trigger an operation failure risk warning based on the comparison results with the risk index. At the same time, generate adjustment instructions and risk assessment reports. This dynamic early warning mechanism, adjustment instructions, and report generation can timely detect cable operation risks and take corresponding measures. Compared with traditional methods without dynamic early warning or simple threshold alarms, it can more effectively prevent failures from occurring and guide operation and maintenance personnel to perform reasonable maintenance and adjustments on cables. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 It is a flow chart of the big data analysis and processing method for distributed continuity monitoring of high-voltage cables of the present invention. DETAILED DESCRIPTION

[0077] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0078] See also Figure 1 The present invention provides a method for analyzing and processing big data of distributed continuity monitoring of high-voltage cables, comprising:

[0079] Collect real-time temperature data, partial discharge data, current data, voltage data and operation status data of high-voltage cables, and obtain basic parameter data of high-voltage cables.

[0080] Specifically, by installing temperature sensors, partial discharge detection sensors, current transformers, voltage transformers and other equipment on high-voltage cables, real-time temperature data, partial discharge data, current data and voltage data can be obtained during the operation of the high-voltage cables. The operating status data refers to the historical operation information of the cable. The basic parameter data of the high-voltage cable is obtained from the installation records and equipment data, providing comprehensive basic information for subsequent analysis and evaluation.

[0081] Calculate the temperature change rate based on the operating status data ; According to the operating status data and temperature change rate , calculate the average value of partial discharge pulse ; Based on the average value of partial discharge pulse And operating status data, calculate current harmonic components ;According to the current harmonic components And the voltage fluctuation amplitude H is calculated based on the operating status data.

[0082] Calculate the rate of temperature change The specific steps are:

[0083] Running status data includes the current time point , the previous time point , Current temperature value And the temperature value at the previous moment .

[0084] According to the current time point , the previous time point , temperature value And temperature value , calculate the temperature change rate The specific formula is as follows:

[0085]

[0086] in, The value of is not equal to 0.

[0087] It should be noted that the denominator represents the difference between the two different time points. and The denominator represents the change in cable temperature between these two time points. By dividing the time interval by the temperature change, the result is the temperature change rate of the cable during this period of time. .

[0088] This formula calculates the temperature change rate based on the temperature values ​​before and after and the corresponding time points, intuitively reflecting the change of cable temperature over time. This helps operation and maintenance personnel to promptly understand the dynamic trend of cable temperature, rather than just focusing on the temperature value at a certain moment. In the operation monitoring of high-voltage cables, temperature change is an important indicator reflecting the operating status of the cable. This formula provides a standardized calculation method that can accurately quantify temperature changes and provide reliable data support for subsequent judgment of whether the cable has potential problems such as overheating and poor heat dissipation. The data required for its calculation are all from the operating status data and are easy to obtain. The formula is simple and clear, which is easy to apply in the actual monitoring system, and helps to improve the efficiency and accuracy of cable operation monitoring.

[0089] Calculation of the average value of partial discharge pulses The specific steps are:

[0090] The operating status data also includes the partial discharge pulse amplitude in the previous unit time period .

[0091] According to the partial discharge pulse amplitude And the temperature change rate , calculate the average value of partial discharge pulse The specific formula is as follows:

[0092]

[0093] in, is the amplitude of the ith partial discharge pulse, i is in the range of [1, N], N represents the total number of partial discharge pulses detected in the previous unit time period, and k and m represent the temperature change rate The fitting coefficients of k are [0.1, 10], m is [0.5, 2], and a represents the adjustment coefficient of the temperature change rate, which is [0.01, 0.5].

[0094] It should be noted that the fitting coefficients k, m and the adjustment coefficient a can be obtained by numerical simulation. The specific steps are as follows: using professional numerical simulation software such as ANSYS or COMSOL, modeling is performed according to the actual geometric structure of the high-voltage cable, such as accurately constructing the shape and size of each part such as the cable core, insulation layer, and sheath to ensure that the model can accurately reflect the physical form of the cable; inputting the accurate material property parameters of each component of the cable into the model, including thermal parameters such as thermal conductivity, specific heat capacity, density, and setting boundary conditions that are consistent with the actual situation, such as using appropriate convection heat transfer coefficients and radiation heat transfer coefficients for the heat exchange boundary between the cable surface and the external environment, setting initial temperature conditions, etc., and setting parameters related to the current load to simulate the process of heat generation by current passing through the cable; setting different simulation conditions, such as changing the current size, ambient temperature, etc., running the numerical simulation software, and obtaining the temperature distribution inside and on the surface of the cable under various conditions and the temperature change results over time, comparing the simulation results with the actual cable temperature measurement data, and analyzing the deviation between the two. By continuously adjusting the fitting coefficients k, m and adjustment coefficient a in the numerical model, simulation and comparison are repeated until the simulation results match the actual data. At this time, the corresponding parameter values ​​in the model are the required values ​​of k, m, a and other coefficients.

[0095] It should be noted that the first part of the formula It represents a component related to the temperature change rate. The effect of the temperature change rate on the average value of the partial discharge pulse is adjusted by fitting coefficients k and m.

[0096] The second part of the formula Indicates the amplitude of all partial discharge pulses Calculate the average value, and then adjust the temperature change rate by adjusting the coefficient a , for the influence on the amplitude of each partial discharge pulse, the value range of k is [0.1, 10], the value range of m is [0.5, 2], and the value range of a is [0.01, 0.5]. The value ranges of these coefficients are determined according to historical operation experience and are used to adjust the influence degree of the temperature change rate on the average value of partial discharge pulses.

[0097] This formula comprehensively considers the partial discharge pulse amplitude and the temperature change rate. Temperature change has an important impact on the cable insulation performance. By incorporating the temperature change rate into the calculation, it can more accurately reflect the partial discharge situation. The coefficients in the formula have certain value ranges, and these coefficients can adjust the influence degree of the temperature change rate on the average value of partial discharge pulses according to the actual situation. This flexibility helps to meet the monitoring requirements of high-voltage cables under different types and operating environments. By accurately calculating the average value of partial discharge pulses, the change in the cable insulation performance can be detected in a timely manner, which is crucial for ensuring the safe operation of high-voltage cables. It can detect potential fault hazards in the early stage, take maintenance measures in advance, avoid cable failures caused by partial discharge, and improve the reliability of the power system.

[0098] Calculate the current harmonic components The specific steps are as follows:

[0099] The operating state data also includes the maximum current value in the previous unit time period and the average current value .

[0100] According to the average value of partial discharge pulses , the maximum current value and the average current value , calculate the current harmonic components , and the specific formula is as follows:

[0101]

[0102] Among them, x1 represents the square of the average value of partial discharge pulses The weight coefficient, with a value of 0.1 ≤ x1 ≤ 0.3, x2 represents The weight coefficient, with a value of 0.3 ≤ x2 ≤ 0.4, x3 represents The weight coefficient, with a value of 0.2 ≤ x3 ≤ 0.5, and a value of 0.1 < x4 ≤ 0.3, and x1 + x2 + x3 = 1.

[0103] This part represents the contribution of the square of the average value of partial discharge pulses to the current harmonic components; This part represents the contribution of the ratio of the maximum current value to the average current value to the current harmonic components; This part represents the contribution of the logarithmic function with base 10 to the harmonic components of the current. In the distributed continuity monitoring of high-voltage cables, calculating the harmonic components of the current helps to evaluate the current quality of the cable. Current harmonics can affect the operation of the cable, such as causing cable heating and insulation aging. By calculating the harmonic components of the current by considering the average value of the partial discharge pulse and the current value, the current characteristics of the cable can be monitored more accurately and potential fault risks can be discovered in time.

[0104] The formula comprehensively considers the average value of partial discharge pulse, maximum current value and average current value. Partial discharge will affect the electrical performance of the cable. The maximum current value and average current value can reflect the fluctuation of current. Combining these factors can more comprehensively evaluate the cable status. The coefficients in the formula have a specific value range, and the influence of various factors on the harmonic components of current can be adjusted according to actual conditions. This design makes the calculation more flexible and can adapt to cable monitoring under different operating conditions. By accurately calculating the harmonic components of current, it is helpful to promptly discover abnormalities in cable operation. Current harmonics may cause problems such as cable heating and increased losses. If they are detected in advance, corresponding measures can be taken to avoid faults and ensure the safe and stable operation of high-voltage cables.

[0105] The specific steps for calculating the voltage fluctuation amplitude H are:

[0106] The operating status data also includes the average voltage in the previous unit time period And rated voltage .

[0107] According to the average voltage , Rated voltage And the current harmonic components , calculate the voltage fluctuation amplitude H, the specific formula is as follows:

[0108]

[0109] Among them, q1 represents - The weight coefficient is 0.1≤q1≤0.4, q2 represents the current harmonic component The weight coefficient is 1, and the value is 0.2≤q2≤0.5, q3 represents the current harmonic component The weight coefficient is 2, which takes a value of 0.3≤q2≤0.4, and q1+q2+q3=1.

[0110] Each item in the formula takes into account the effects of different factors: - ) item represents the impact of the deviation of the historical average voltage relative to the rated voltage on the voltage fluctuation; Item and The items all take into account the influence of current harmonic components on voltage fluctuations. As the current harmonic components increase, the voltage fluctuations will change in a more complex way.

[0111] The formula comprehensively considers the average voltage, rated voltage and current harmonic components. The difference between the average voltage and the rated voltage reflects the voltage deviation, and the current harmonic components have an impact on voltage stability. Combining these factors can comprehensively evaluate the voltage fluctuation. The coefficients q1, q2, and q3 in the formula have specific value ranges, and their sum is 1. These coefficients can adjust the degree of influence of each factor on the voltage fluctuation amplitude according to actual conditions, making the calculation more flexible and accurate, and can adapt to cable monitoring under different operating conditions. By accurately calculating the voltage fluctuation amplitude, it is helpful to promptly detect voltage anomalies in cable operation and ensure the safe and stable operation of high-voltage cables.

[0112] According to the temperature change rate , temperature data and operating status data, calculate and predict the temperature impact index ; Based on the predicted temperature impact index , Partial discharge pulse average value And partial discharge data, calculate the predicted partial discharge impact index PD; according to the predicted partial discharge impact index PD, current harmonic components According to the predicted current impact index EOI, voltage fluctuation amplitude H, voltage data and operation status data, the predicted voltage impact index FII is calculated; according to the basic parameter data, the basic operation index GII is calculated; according to the basic operation index GII and the predicted voltage impact index FII, the cable operation risk index is calculated. .

[0113] Calculate the predicted temperature impact index The specific steps are:

[0114] Real-time temperature data includes the cable temperature within the current unit time period , the average temperature value in the current unit time period And the temperature standard deviation of the current unit time period ;

[0115] The operating status data also includes the average temperature value in the previous unit time period And the standard deviation of temperature in the previous unit time period ;

[0116] According to the temperature change rate , real-time temperature data, average temperature value in the previous unit time period And the standard deviation of temperature in the previous unit time period , calculate and predict the temperature impact index The specific formula is as follows:

[0117]

[0118] Where s1 represents the temperature change rate The weight coefficient is 0.1≤s1≤0.4, s2 represents The weight coefficient is 0.2≤s2≤0.5, s3 represents The weight coefficient is 0.3≤s3≤0.4, and s1+s2+s3=1; represents the cable temperature value at the tth moment, t represents the sequence number of different moments, and m is the total number of moments.

[0119] It should be noted that the average temperature The average temperature value in the previous unit time period is calculated by summing up all the temperature measurement values ​​in the previous unit time period and then dividing it by the total number of measurement times. ; Temperature standard deviation Calculate the difference between each temperature measurement and the average temperature value, square these differences, add all the differences, divide by the number of measurements, and take the square root to get the temperature standard deviation in the previous unit time period. The average temperature and standard deviation of the temperature in the current unit time period are also calculated.

[0120] The part represents the contribution of the temperature change rate to the predicted temperature impact index; The part indicates the contribution of the difference between the actual cable temperature and the current average temperature relative to the temperature standard deviation to the predicted temperature impact index. The degree of deviation between the actual cable temperature and the average temperature reflects the non-uniformity of the cable temperature, which is helpful for evaluating local overheating of the cable. The part represents the contribution of the exponential function with natural constants as the base to the predicted temperature influence index. The numerator in the exponential function is the difference between the actual cable temperature and the average temperature of the previous time period, and the denominator is the temperature standard deviation, which reflects the fluctuation of the cable temperature relative to the historical average temperature.

[0121] This formula comprehensively considers relevant factors such as the temperature change rate, the difference between the actual cable temperature and the average temperature, and the temperature standard deviation, and comprehensively reflects the cable temperature conditions. The weight coefficients s1, s2, and s3 have specific value ranges and the sum of the weight coefficients is 1. The degree of influence of each factor on the predicted temperature impact index can be flexibly adjusted according to actual conditions to make the results more accurate. By accurately calculating the index, the operating risks of the cable due to temperature can be predicted in advance, which facilitates the operation and maintenance personnel to take timely measures.

[0122] The specific steps for calculating and predicting the partial discharge impact index PD are:

[0123] Setting the partial discharge pulse threshold .

[0124] Partial discharge data includes the partial discharge pulse value in the current unit time period And the rated partial discharge pulse value .

[0125] According to the predicted temperature impact index , Partial discharge pulse average value , Partial discharge pulse threshold And partial discharge data, calculate and predict the partial discharge impact index PD, the specific formula based on which is as follows:

[0126]

[0127] Among them, f1 represents the predicted temperature impact index The weight coefficient is 0.2≤f1≤0.5, and f2 represents the average value of the partial discharge pulse The weight coefficient is 0.3≤f2≤0.4, and f3 represents the partial discharge pulse value. The weight coefficient is 0.1≤f3≤0.4, and f1+f2+f3=1.

[0128] This part indicates the contribution of the predicted temperature impact index to the predicted partial discharge impact index. Temperature has an impact on partial discharge, and this part can reflect the role of temperature factors. This part indicates the contribution of the ratio of the average partial discharge pulse value to the rated partial discharge pulse value to the predicted partial discharge impact index. The average partial discharge pulse value reflects the overall level of partial discharge. The relative size of partial discharge can be evaluated by comparing it with the rated value. This part indicates the contribution of the ratio of the current partial discharge pulse value to the partial discharge pulse threshold to the predicted partial discharge impact index. The comparison between the current partial discharge pulse value and the threshold can reflect whether the partial discharge is close to or exceeds the safety limit.

[0129] This formula comprehensively considers the predicted temperature impact index, the relationship between the local discharge pulse average value and the rated value, and the relationship between the current local discharge pulse value and the threshold value, and comprehensively evaluates the local discharge situation. The coefficients in the formula have a specific value range and the sum of the weight coefficients is 1. The influence of each factor on the predicted local discharge impact index can be flexibly adjusted according to actual conditions to make the calculation result more accurate. By predicting the local discharge impact index PD, the risk of cable failure due to local discharge can be discovered in advance, which is convenient for operation and maintenance personnel to take timely measures to ensure the safe operation of the cable.

[0130] The specific steps for calculating the predicted current impact index EOI are:

[0131] Current data includes the current value in the current unit time period And rated current .

[0132] According to the predicted partial discharge impact index PD, current harmonic components And current data, calculate the predicted current impact index EOI, the specific formula is as follows:

[0133]

[0134] Among them, the real-time current The value of >0.

[0135] It should be noted that the rated current is set according to the cable design and manufacturing standards. , molecular part PD+ + This part combines the predicted partial discharge impact index, current harmonic components and the current actual current value. The denominator part PD+ + + ,In addition to the three items in the numerator, the rated current is also added.,In the distributed continuity monitoring of high-voltage cables,,calculating the predicted current impact index helps to evaluate the current-related risks of,the cables. The current has an important impact on the operating status of the cables.,By considering the predicted partial discharge impact index,,the current harmonic components, the actual current value and the rated current value,the predicted current impact index is calculated,which can more accurately monitor the current change trend of the cables and timely discover,the potential fault risks caused by the current.

[0136] This formula comprehensively considers the predicted partial discharge impact index, current harmonic components, actual current value and rated current value. By combining these key current-related parameters, it can comprehensively reflect the actual impact of cable current and avoid the limitations of single factor evaluation. The various items in the formula are combined through reasonable calculation methods to highlight the comprehensive effect of various factors on the predicted current impact index. This calculation method helps to quantify cable current-related risks more accurately. By accurately calculating the predicted current impact index, the operating risks of the cable due to current factors can be predicted in advance, which helps operation and maintenance personnel to take timely measures to reduce the probability of failures caused by abnormal current.

[0137] The specific steps for predicting the voltage impact index FII are:

[0138] Voltage data includes the voltage value in the current unit time period .

[0139] The operating status data also includes the voltage standard deviation .

[0140] According to the predicted current influence index EOI, voltage fluctuation amplitude H, voltage value And the voltage standard deviation , calculate the predicted voltage impact index FII, the specific formula is as follows:

[0141]

[0142] Among them, the voltage standard deviation The value of >0.

[0143] The specific steps to calculate the comprehensive risk index of cable operation and predict the risk of failure are:

[0144] The basic parameter data include cable length L and cable cross-sectional area S.

[0145] Set the maximum allowed cable length And the maximum cross-sectional area allowed by the cable .

[0146] It should be noted that the parameter explanation cable length L is obtained through construction drawings, on-site measurements, etc.; the cable cross-sectional area S is obtained according to the product manual; the maximum allowable cable length And the maximum cross-sectional area allowed by the cable It is set according to the cable design manual and electrical equipment selection standards.

[0147] According to the basic parameter data, the maximum allowable length of the cable , Maximum allowable cross-sectional area of ​​cable , Current value , Rated current , voltage value And rated voltage , calculate the operation basic index GII, the specific formula is as follows:

[0148]

[0149] It should be noted that the maximum allowable length of the cable is set according to the cable design and manufacturing standards. And the maximum cross-sectional area allowed by the cable The formula comprehensively considers the ratio of the actual length of the cable to the maximum allowable length, the ratio of the actual cross-sectional area to the maximum allowable cross-sectional area, the ratio of the actual current value to the rated current value, and the ratio of the actual voltage value to the rated voltage value. By adding these ratios, the operating basic index is obtained, which is used to reflect the degree of use of the cable in terms of length, cross-sectional area, current and voltage relative to the design limit.

[0150] Calculate the cable operation risk index based on the operation basic index GII and the predicted voltage impact index FII The specific formula is as follows:

[0151]

[0152] It should be noted that the cable operation risk index is calculated based on the basic operation index and the predicted voltage impact index, which comprehensively considers the basic operation parameters of the cable and the voltage influencing factors to evaluate the operation risk of the cable.

[0153] Set the cable operation risk index threshold set JV, according to the cable operation risk index The comparison result with the cable operation risk index threshold set JV is used to determine whether an operation failure risk warning is triggered, and generate corresponding adjustment instructions and risk assessment reports.

[0154] The cable operation risk index threshold set JV includes a primary threshold Jv0, a secondary threshold Jv1 and a tertiary threshold Jv2, wherein Jv0<Jv1<Jv2.

[0155] It should be noted that the threshold value Jv0 is usually determined based on the risk index range under normal operation of the cable; when the cable temperature rises and the current fluctuates but has not yet reached the level that affects the operation, the lower limit of the corresponding risk index range is determined to be Jv1; when the cable temperature continues to rise and the current fluctuates significantly, the lower limit of the corresponding risk index range is set to Jv2; when Jv2 < cable operation risk index When the cable fails, the third-level operation fault risk warning is triggered. This threshold is set according to the risk index in the critical state before the cable fails.

[0156] When the cable runs risk index When ≤Jv0, the operation failure risk warning is not triggered.

[0157] When Jv0<Cable operation risk index When ≤Jv1, the first-level operation fault risk warning is triggered, and the primary adjustment instructions and risk assessment report are generated.

[0158] When Jv1<Cable operation risk index When <Jv2, the second-level operation fault risk warning is triggered, and the intermediate adjustment instructions and risk assessment report are generated.

[0159] When Jv2<Cable operation risk index When the third-level operation fault risk warning is triggered, high-level adjustment instructions and risk assessment reports are generated.

[0160] When the cable runs risk index When ≤Jv0, the operation failure risk warning is not triggered, which means that within this range, the operation risk of the cable is considered to be low and is in a safe operating state.

[0161] When Jv0<Cable operation risk index When ≤Jv1, the first-level operation fault risk warning is triggered, and the primary adjustment instructions and risk assessment report are generated. This indicates that the operation risk of the cable begins to increase and certain adjustment measures need to be taken to maintain the normal operation of the cable; the primary adjustment instructions include slight adjustments to the cable load, such as appropriately reducing the current load to alleviate the operating pressure of the cable.

[0162] When Jv1<Cable operation risk index When <Jv2, the second-level operation fault risk warning is triggered, and the intermediate adjustment instructions and risk assessment report are generated, which means that the operation risk of the cable is further increased and a higher level of adjustment measures are required; the intermediate adjustment instructions involve the improvement of the cable operation environment, such as strengthening heat dissipation measures, or making more in-depth adjustments to the cable operation parameters, such as adjusting the voltage level.

[0163] When Jv2<Cable operation risk index When the cable is in a high-risk operation state, strict adjustment measures need to be taken immediately to prevent the occurrence of failures. Advanced adjustment instructions may include immediately stopping part or all of the cable operation, conducting comprehensive inspections and maintenance, or even replacing some cable components to ensure the safe operation of the cable.

[0164] By comprehensively considering basic parameters such as cable length, cross-sectional area, current and voltage to calculate the basic operation index and operation risk index, the cable operation status can be comprehensively and accurately evaluated, avoiding the one-sidedness of single-factor evaluation, setting multi-level thresholds to divide risk levels, triggering corresponding warnings at different risk stages and generating corresponding adjustment instructions and risk assessment reports. This helps operation and maintenance personnel to take targeted measures, such as simple load adjustments at primary risks and comprehensive inspections at high risks. It can effectively prevent cable failures, ensure the safe operation of cables, and reduce problems such as power supply interruptions caused by cable failures.

[0165] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product. A person of ordinary skill in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented in electronic hardware or in combination with computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution.

[0166] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0167] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A method for analyzing and processing big data of distributed continuity monitoring of high-voltage cables, characterized in that: include: Collect real-time temperature data, partial discharge data, current data, voltage data and operation status data of high-voltage cables, and obtain basic parameter data of high-voltage cables; Calculate the temperature change rate based on real-time temperature data ; According to the operating status data and temperature change rate , calculate the average value of partial discharge pulse ; Based on the average value of partial discharge pulse And operating status data, calculate current harmonic components ;According to the current harmonic components And the voltage fluctuation amplitude H is calculated based on the operating status data; According to the temperature change rate , temperature data and operating status data, calculate and predict the temperature impact index ; Based on the predicted temperature impact index , Partial discharge pulse average value And partial discharge data, calculate the predicted partial discharge impact index PD; according to the predicted partial discharge impact index PD, current harmonic components and current data, calculate the predicted current impact index EOI; calculate the predicted voltage impact index FII based on the predicted current impact index EOI, voltage fluctuation amplitude H, voltage data and operating status data; calculate the basic operating index GII based on the basic parameter data; Calculate the cable operation risk index based on the operation basic index GII and the predicted voltage impact index FII ; Set the cable operation risk index threshold set JV, according to the cable operation risk index The comparison result with the cable operation risk index threshold set JV determines whether the operation failure risk warning is triggered, and generates corresponding adjustment instructions and risk assessment reports; Calculation of current harmonic components The specific steps are: The operating status data also includes the maximum current value in the previous unit time period And the average current value ; According to the average value of partial discharge pulse , Maximum current value And the average current value , calculate the current harmonic components The specific formula is as follows: Among them, x1 represents the square of the average value of partial discharge pulses The weight coefficient of, and the value range is 0.1 ≤ x1 ≤ 0.

3. x2 represents The weight coefficient of, and the value range is 0.3 ≤ x2 ≤ 0.

4. x3 represents The weight coefficient of, and the value range is 0.2 ≤ x3 ≤ 0.

5. The value range is 0.1 < x4 ≤ 0.3, and x1 + x2 + x3 = 1; Calculate the predicted temperature impact index The specific steps are: Real-time temperature data includes the cable temperature within the current unit time period , the average temperature value in the current unit time period And the temperature standard deviation of the current unit time period ; The operating status data also includes the average temperature value in the previous unit time period And the standard deviation of temperature in the previous unit time period ; According to the temperature change rate , real-time temperature data, average temperature value in the previous unit time period And the standard deviation of temperature in the previous unit time period , calculate and predict the temperature impact index The specific formula is as follows: Where s1 represents the temperature change rate The weight coefficient is 0.1≤s1≤0.4, s2 represents The weight coefficient is 0.2≤s2≤0.5, s3 represents The weight coefficient is 0.3≤s3≤0.4, and s1+s2+s3=1; Represents the cable temperature value at the tth moment, t is the sequence number of different moments, and m is the total number of moments.

2. The method for analyzing and processing big data of high-voltage cable distributed continuity monitoring according to claim 1 is characterized in that: Calculate the rate of temperature change The specific steps are: Real-time temperature data including current temperature value , the temperature value at the previous moment , the current time point and the previous time point ; According to the current temperature value , the temperature value at the previous moment , the current time point and the previous time point , calculate the temperature change rate The specific formula is as follows: in, The value of is not equal to 0.

3. The method for analyzing and processing big data of high-voltage cable distributed continuity monitoring according to claim 2 is characterized in that: Calculation of the average value of partial discharge pulses The specific steps are: The operating status data includes the partial discharge pulse amplitude in the previous unit time period; According to the partial discharge pulse amplitude and the temperature change rate , calculate the average value of partial discharge pulse The specific formula is as follows: in, is the amplitude of the i-th partial discharge pulse, i is [1, N], N represents the total number of partial discharge pulses detected in the previous unit time period, k and m both represent the temperature change rate The fitting coefficients of k are [0.1, 10], m is [0.5, 2], and a represents the adjustment coefficient of the temperature change rate, which is [0.01, 0.5].

4. The method for analyzing and processing big data of high-voltage cable distributed continuity monitoring according to claim 1 is characterized in that: The specific steps for calculating the voltage fluctuation amplitude H are: The operating status data also includes the average voltage in the previous unit time period And rated voltage ; According to the average voltage , Rated voltage And the current harmonic components , calculate the voltage fluctuation amplitude H, the specific formula is as follows: Among them, q1 represents - The weight coefficient is 0.1≤q1≤0.4, where q2 represents the square of the current harmonic component. The weight coefficient is 0.2≤q2≤0.5, and q3 represents the square root of the current harmonic component. The weight coefficient is 0.3≤q3≤0.4, and q1+q2+q3=1.

5. The method for analyzing and processing big data of high-voltage cable distributed continuity monitoring according to claim 1 is characterized in that: The specific steps for calculating and predicting the partial discharge impact index PD are: Setting the partial discharge pulse threshold ; Partial discharge data includes the partial discharge pulse value in the current unit time period And the rated partial discharge pulse value ; According to the predicted temperature impact index , Partial discharge pulse average value , Partial discharge pulse threshold And partial discharge data, calculate and predict the partial discharge impact index PD, the specific formula based on which is as follows: Among them, f1 represents the predicted temperature impact index The weight coefficient is 0.2≤f1≤0.5, and f2 represents the average value of the partial discharge pulse The weight coefficient is 0.3≤f2≤0.4, and f3 represents the partial discharge pulse value. The weight coefficient is 0.1≤f3≤0.4, and f1+f2+f3=1.

6. The method for analyzing and processing big data of high-voltage cable distributed continuity monitoring according to claim 5 is characterized in that: The specific steps for calculating the predicted current impact index EOI are: Current data includes the current value in the current unit time period And rated current ; According to the predicted partial discharge impact index PD, current harmonic components And current data, calculate the predicted current impact index EOI, the specific formula is as follows: Among them, the real-time current The value of >

0.

7. The method for analyzing and processing big data of high-voltage cable distributed continuity monitoring according to claim 1 is characterized in that: The specific steps for predicting the voltage impact index FII are: Voltage data includes the voltage value in the current unit time period ; The operating status data also includes the voltage standard deviation ; According to the predicted current influence index EOI, voltage fluctuation amplitude H, voltage value And the voltage standard deviation , calculate the predicted voltage impact index FII, the specific formula is as follows: Among them, the voltage standard deviation The value of >

0.

8. The method for analyzing and processing big data of high-voltage cable distributed continuity monitoring according to claim 7 is characterized in that: The specific steps to calculate the comprehensive risk index of cable operation and predict the risk of failure are: The basic parameter data include cable length L and cable cross-sectional area S; Set the maximum allowed cable length And the maximum cross-sectional area allowed by the cable ; According to the basic parameter data, the maximum allowable length of the cable , Maximum allowable cross-sectional area of ​​cable , Current value , Rated current , voltage value And rated voltage , calculate the operation basic index GII, the specific formula is as follows: Calculate the cable operation risk index based on the operation basic index GII and the predicted voltage impact index FII The specific formula is as follows: The cable operation risk index threshold set JV includes a first-level threshold Jv0, a second-level threshold Jv1, and a third-level threshold Jv2, wherein Jv0<Jv1<Jv2; When the cable runs risk index When ≤Jv0, the operation failure risk warning will not be triggered; When Jv0<Cable operation risk index When ≤Jv1, the first-level operation fault risk warning is triggered, and the primary adjustment instructions and risk assessment report are generated; When Jv1<Cable operation risk index When ≤Jv2, the second-level operation fault risk warning is triggered, and the intermediate adjustment instructions and risk assessment report are generated; When Jv2<Cable operation risk index When the fault occurs, the third-level operation fault risk warning is triggered, and high-level adjustment instructions and risk assessment reports are generated.

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