A low cost repair decision method for a cable

Abstract

The present application relates to the field of power cable engineering, and discloses a low-cost repair decision method for a cable, which comprises the following steps: on-line monitoring the insulation state of the cable by a spread spectrum time domain reflection method, recording the end-of-life time of the cable, collecting the end-of-life time of a plurality of different cables; establishing a cable reliability model according to the Weibull distribution law of the end-of-life time of the plurality of different cables; establishing an average cost rate model according to a preventive repair probability, a failure probability, a preventive repair cost and a failure repair cost; introducing a chaos factor into a mathematical model of a chimpanzee optimization algorithm, and optimizing and solving a target function by a chaotic chimpanzee optimization algorithm according to the preventive repair cost and the failure repair cost to obtain a minimum average cost rate, and outputting a best maintenance period and a repair threshold of the cable. The present application can output the best maintenance period and the repair threshold of the cable under the condition of ensuring the reliability of the cable, and realize a low-cost repair decision.

Description

Technical Field

[0001] This invention relates to the field of power cable engineering, and specifically to a low-cost cable repair decision-making method. Background Technology

[0002] Cables in power distribution network tunnels operate in harsh environments, constantly exposed to rain, high temperatures, and high voltage, making them highly susceptible to insulation failures. Cases of fires caused by insulation failures are common, thus necessitating insulation repair before cable failures occur. Currently, cable insulation repair methods mainly include repair fluid filling, hot welding, heat shrink tubing, and insulating tape wrapping. Repair fluid filling effectively addresses water tree defects, but the process is relatively complex. Hot welding effectively addresses scratch defects, but this method requires the repaired material to be a homogeneous thermoplastic. Heat shrink tubing can repair large defects, but it affects heat dissipation at the defect location. Insulating tape wrapping can repair sheath damage, but its sealing performance is poor. In summary, current research focuses on repair methods, with less attention paid to repair decision-making approaches. However, repairing cables too early leads to wasted funds, while repairing them too late reduces operational reliability.

[0003] Currently, cable repair decision-making methods primarily rely on periodic maintenance and inspections to obtain cable aging indicators and compare them with critical values ​​to make repair decisions. This method fails to minimize repair costs and guarantee high reliability. Therefore, there is an urgent need to develop a scientific cable repair decision-making method to achieve low-cost repairs while ensuring reliable power supply. Summary of the Invention

[0004] To address the technical problems existing in the prior art, this invention provides a low-cost cable repair decision-making method, which can output the optimal maintenance cycle and repair threshold for the cable while ensuring cable reliability, thereby achieving low-cost repair decision output.

[0005] This invention can be achieved by adopting the following technical solutions:

[0006] A low-cost cable repair decision-making method, the method comprising:

[0007] The insulation status of cables is monitored online using the spread spectrum time-domain reflectometry method, and the end-of-life time of the cables is recorded. The end-of-life time of multiple different cables is collected.

[0008] A cable reliability model is established based on the Weibull distribution of the life end times of multiple different cables.

[0009] The probability of preventive repair and the probability of failure of the cable are calculated by using a reliability model. An average cost rate model is established based on the probability of preventive repair, the probability of failure, the cost of preventive repair, and the cost of failure repair.

[0010] By incorporating the chaos factor into the mathematical model of the chimpanzee optimization algorithm, a chaotic chimpanzee optimization algorithm is obtained.

[0011] The objective function of the average cost rate model is to minimize the average cost rate. Based on the costs of preventive repair and fault repair, the objective function is optimized using the Chaotic Gorilla Optimization Algorithm to obtain the minimum average cost rate. The optimal maintenance cycle and repair threshold for the cable are then output.

[0012] In the preferred technical solution, the online monitoring of the cable insulation status using the spread spectrum time-domain reflectometry method, recording the cable's end-of-life time, and collecting the end-of-life times of multiple cables includes:

[0013] An incident signal for spread spectrum time-domain reflection is generated by modulating an m-sequence with a 1:1 sine wave. The incident signal is then injected into a cable sample using a signal generator, and the reflected signal is acquired using an oscilloscope.

[0014] The incident signal and the reflected signal are correlated according to the calculation formula (1) to obtain the correlation calculation amplitude;

[0015]

[0016] Among them, R sx (τ) represents the correlation amplitude, and τ represents the shift value of the incident signal. i Let t be the delay time of the reflected signal relative to the incident signal, s be the signal time, and a be the incident signal time. k Here, n is the coefficient of variation of the reflected signal, and n is the noise signal.

[0017] When the relevant calculated amplitude reaches the predetermined value, the cable life is considered to have ended, and the cable life end time is recorded.

[0018] Preferably, the average cost rate model is expressed as:

[0019]

[0020]

[0021]

[0022] In the formula, A cr For the average expense ratio, A c For average cost, A T Where N is the average lifespan, P is the total number of lifespans, and N is the total number of lifespans pk P represents the probability of preventative repair.fk Let τ be the failure probability, τ be the maintenance cycle, k be the number of cycles, and u0 be the preventive repair threshold.

[0023] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0024] This invention provides a low-cost cable repair decision-making method. By using the spread spectrum time-domain reflectometry method to monitor the cable insulation status online, the end-of-life time of the cable can be obtained online. An average cost rate model is established and the optimal solution is obtained through the chaotic chimpanzee optimization algorithm. It is applicable to various types of cables and can output the optimal maintenance cycle and repair threshold of the cable while ensuring cable reliability, thereby realizing the output of low-cost repair decisions. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of the present invention 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 the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0026] Figure 1 This is a flowchart of a low-cost cable repair decision-making method according to an embodiment of the present invention;

[0027] Figure 2 This is a waveform diagram of the correlation operation between the incident signal and the reflected signal in an embodiment of the present invention;

[0028] Figure 3 This is a Weibull probability diagram of the cable life termination time in an embodiment of the present invention;

[0029] Figure 4 This is an iterative curve diagram of the chaotic chimpanzee optimization algorithm in an embodiment of the present invention. Detailed Implementation

[0030] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments, and the implementation of the present invention is not limited thereto. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0031] Example 1:

[0032] This invention utilizes spread-spectrum time-domain reflectometry to monitor the insulation status of cables online. The cable's lifespan is considered terminated when the relevant calculated amplitude reaches a specified value. The invention statistically analyzes the lifespan termination times of multiple cables. Based on the Weibull distribution of cable lifespan termination times, a cable reliability model is established. Then, the reliability model is used to calculate the probability of preventative repair and the probability of failure, and an average cost rate model is established. A chaotic factor is introduced into the mathematical model of the chimpanzee optimization algorithm, forming a chaotic chimpanzee optimization algorithm. Finally, the objective function of the average cost rate model is the lowest possible average cost rate, and the chaotic chimpanzee optimization algorithm is used to optimize and solve the problem, yielding the optimal repair decision. This invention solves the problems of high cost and low reliability in cable repair, achieving low-cost, high-reliability cable repair.

[0033] like Figure 1 As shown, the cable low-cost repair decision-making method of the present invention includes the following steps:

[0034] Step 1: Monitor the insulation status of the cable online using the spread spectrum time-domain reflectometry method, record the end-of-life time of the cable, and collect the end-of-life time of multiple cables.

[0035] Specifically, the insulation status of multiple cables is monitored online using the spread spectrum time-domain reflectometry method, including:

[0036] An incident signal for spread spectrum time-domain reflection is generated by modulating an m-sequence with a 1:1 sine wave. The incident signal is then injected into a cable sample using a signal generator, and the reflected signal is acquired using an oscilloscope.

[0037] The incident signal and the reflected signal are correlated according to the calculation formula (1) to obtain the correlation operation amplitude.

[0038]

[0039] Among them, R sx (τ) represents the correlation amplitude, and τ represents the shift value of the incident signal. i Let t be the delay time of the reflected signal relative to the incident signal, and s be the signal time. SSTDR For the incident signal, a k denoted as the reflection signal variation coefficient, and n represents the noise signal.

[0040] When the relevant calculated amplitude reaches the predetermined value, the cable life is considered to have ended, and the cable life end time is recorded.

[0041] In this embodiment, an incident signal from the spread spectrum time-domain reflectometry method is injected into the cable, and the reflected signal is acquired. The correlation waveforms of the incident and reflected signals are as follows. Figure 2As shown in Table 1, the cable lifespan ends when the relevant calculation amplitude reaches 0.026. The cable lifespan end time is recorded. The cable lifespan end times of 8 cable samples are shown in Table 1 below.

[0042] Table 1

[0043]

[0044] Step 2: Based on the Weibull distribution law of the end-of-life time of multiple different cables, establish a cable reliability model;

[0045] Specifically, Weibull probability diagrams of the lifespan termination times of different cables are plotted based on the lifespan termination times of multiple different cables. The cable lifespan termination times are fitted using the linear expression (2) of the Weibull distribution. The shape parameter and the proportional parameter in formula (2) are identified based on the least squares method.

[0046]

[0047]

[0048] Where t is the end-of-life time, η is the scaling parameter, β is the shape parameter, and F(t) is the end-of-life probability function.

[0049] Substituting the two parameters into the cable reliability model yields a cable reliability model with determined parameters, which can be expressed as:

[0050]

[0051] In the formula, R(t) is the cable reliability, t is the end of life time, η is the proportional parameter, and β is the shape parameter.

[0052] The objective function of the least squares method is as follows:

[0053]

[0054] Where Q is the sum of squared errors, Fi' is the predicted value, Fi is the actual value, and n is the number of values.

[0055] In this embodiment, a Weibull probability diagram of the cable life termination time for different cable samples is plotted based on the data in Table 1, as follows: Figure 3 As shown. Based on the least squares method, a linear fitting is performed with undetermined parameters η = 40.07 and β = 5.37, resulting in the cable life reliability model:

[0056]

[0057] In the formula, R represents cable reliability, and t represents the end of its service life.

[0058] Step 3: Calculate the probability of preventive repair and the probability of failure using the reliability model. Establish an average cost rate model based on the probability of preventive repair, the probability of failure, the cost of preventive repair, and the cost of failure repair.

[0059] The probability of preventive repair can be calculated based on the cable maintenance cycle, the number of cycles, and the preventive repair threshold, specifically using formula (7):

[0060]

[0061] In the formula, P pk The probability of preventive repair is τ, the maintenance cycle is k, the number of cycles is u0, and the preventive repair threshold is u0.

[0062] The failure probability can be calculated based on the maintenance cycle and the number of cycles, specifically calculated using formula (8):

[0063]

[0064] In the formula, P fk Let τ be the failure probability, τ be the maintenance cycle, and k be the number of cycles;

[0065] An average cost rate model is established. The average cost rate for long-term cable operation is calculated using this model, which can be expressed as follows:

[0066]

[0067]

[0068]

[0069] In the formula, A cr For the average expense ratio, A c For average cost, A T Where N is the average lifespan, P is the total number of lifespans, and N is the total number of lifespans pk P represents the probability of preventative repair. fk Let C be the probability of failure. p For preventative repair costs, C f τ represents the cost of fault repair, k represents the maintenance cycle, and u0 represents the threshold for preventive repair.

[0070] C is set based on the actual preventative repair costs to be incurred for the cable in the absence of a fault. p The value C is set based on the actual cost of repairing the cable in the event of a fault. f value.

[0071] Step 4: Introduce the chaos factor into the mathematical model of the chimpanzee optimization algorithm to obtain the chaotic chimpanzee optimization algorithm.

[0072] A mathematical model of the chaos factor is used to generate chaotic sequences. Introducing this chaos factor into the mathematical model of the chimpanzee optimization algorithm can improve the algorithm's convergence speed. The chimpanzee optimization algorithm divides the population into multiple individuals, has high search efficiency, and its technical role is to find the optimal maintenance cycle and repair threshold based on the objective function.

[0073] The chaos factor is obtained by the Logistic mapping, and its expression is shown in formula (12). The mathematical model of the chaotic chimpanzee optimization algorithm is shown in formulas (13) and (14).

[0074] m(i+1)=m(i)·μ·[1-m(i)] (12)

[0075] D(i) = |C·X p (i)-m·X C (i)| (13)

[0076] X C (i+1)=X p (i)-A·D (14)

[0077] In the formula, μ ranges from 0 to 4, m(i+1) is the chaos factor at the (i+1)th iteration, i is the current iteration number, A is the distance coefficient, D is the distance between the chimpanzee and the prey, C is the control coefficient, and X... p (i) represents the target position in the i-th iteration, X C (i) represents the chimpanzee's position at the i-th iteration, and D(i) represents the distance between the chimpanzee and the prey at the i-th iteration. C (i+1) represents the chimpanzee's position at the (i+1)th iteration.

[0078] The relationship between the chimpanzee's location and the prey's location is established using formula (13), and the distance relationship between the chimpanzee and the prey is established using formula (14).

[0079] Step 5: Using the lowest average cost rate as the objective function of the average cost rate model, optimize the objective function using the Chaotic Gorilla Optimization Algorithm based on the costs of preventive repair and fault repair to obtain the minimum average cost rate, and output the optimal cable maintenance cycle and repair threshold.

[0080] F = min(A) cr (15)

[0081] The Chaotic Chimpanzee Optimization Algorithm uses formulas (13) and (14) to hunt in accordance with the social behavior of chimpanzees in order to achieve the goal of finding the best. With formula (9) as the objective function, the Chaotic Chimpanzee Optimization Algorithm is used to find the best maintenance cycle and repair threshold so as to minimize the average cost rate.

[0082] The objective function of the cable repair decision method is solved using the Chaotic Chimpanzee Optimization Algorithm, where the preventive repair cost C... p The cost is 300, and the cost of repairing the fault is C. f When the cost is 5000, after 50 iterations of optimization, the minimum average cost rate is found to be 1.7365. At this point, the optimal maintenance cycle is 31 months, and the optimal repair threshold is 289 months. The iteration curve of the Chaotic Chimpanzee optimization algorithm is shown below. Figure 4 As shown.

[0083] In summary, this invention utilizes the spread spectrum time-domain reflectometry method to monitor the insulation status of cables online, enabling online acquisition of cable lifespan termination time. It establishes an average cost rate model and solves the optimal solution using a chaotic chimpanzee optimization algorithm. Applicable to various cable types, it can output the optimal maintenance cycle and repair threshold while ensuring cable reliability, achieving low-cost repair decisions. This solves the problems of high cost and low reliability in cable repair, realizing low-cost, high-reliability cable repair.

[0084] The above embodiments are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above embodiments. Any changes, modifications, substitutions, combinations, or simplifications made without departing from the spirit and principle of the present invention shall be considered equivalent substitutions and shall be included within the protection scope of the present invention.

Claims

1. A low-cost cable repair decision-making method, characterized in that, Including the following steps: The insulation status of cables is monitored online using the spread spectrum time-domain reflectometry method, and the end-of-life time of the cables is recorded. The end-of-life time of multiple different cables is collected. The method of online monitoring of cable insulation status using spread spectrum time-domain reflectometry, recording cable lifespan termination time, and collecting lifespan termination times of multiple cables includes: An incident signal for spread spectrum time-domain reflection is generated by modulating an m-sequence with a 1:1 sine wave. The incident signal is then injected into a cable sample using a signal generator, and the reflected signal is acquired using an oscilloscope. The incident signal and the reflected signal are correlated according to the calculation formula (1) to obtain the correlation calculation amplitude; ；(1) in, For the relevant calculation amplitude, This is the shift value of the incident signal. The time delay of the reflected signal relative to the incident signal. For signal time, For the incident signal, a k Here, n is the coefficient of variation of the reflected signal, and n is the noise signal. When the relevant calculated amplitude reaches the predetermined value, the cable's lifespan is considered to have ended, and the cable's lifespan end time is recorded. A cable reliability model is established based on the Weibull distribution of the lifespan termination times of multiple different cables, including: Based on the lifespan termination times of multiple different cables, Weibull probability diagrams of the lifespan termination times of different cables are plotted, and the cable lifespan termination times are fitted using the linear expression of the two-parameter Weibull distribution. The shape parameters and scaling parameters of the cable reliability model are identified based on the least squares method. The shape parameters and scaling parameters are then substituted into the cable reliability model to obtain a cable reliability model with determined parameters. The cable reliability model is expressed as follows: ； in, For cable reliability, t is the end of life time, η is the proportional parameter, and β is the shape parameter; The probability of preventive repair and the probability of failure of the cable are calculated by using a cable reliability model. An average cost rate model is established based on the probability of preventive repair, the probability of failure, the cost of preventive repair, and the cost of failure repair. The probability of preventative repair is calculated using the following formula: ； In the formula, P pk To preventative repair probability, The maintenance cycle is k, the number of cycles is u0, and the preventive repair threshold is u0. The failure probability is calculated using the following formula: ； In the formula, P f k This represents the probability of failure. The maintenance cycle is k, where k is the number of cycles. The average cost rate model is expressed as follows: ； ； ； In the formula, A cr For the average expense ratio, A c For average cost, A T Where N is the average lifespan, P is the total number of lifespans, and N is the total number of lifespans pk P represents the probability of preventative repair. fk This represents the probability of failure. The maintenance cycle is k, the number of cycles is u0, and the preventive repair threshold is u0. By incorporating a chaos factor into the mathematical model of the chimpanzee optimization algorithm, a chaotic chimpanzee optimization algorithm is obtained; the chaos factor is expressed as: ； Where μ ranges from 0 to 4, Let i be the chaos factor at the (i+1)th iteration, where i is the current iteration number; The mathematical model of the Chaotic Chimpanzee Optimization Algorithm is expressed as: ； ； Where μ ranges from 0 to 4, m is the chaos factor, i is the current iteration number, A is the distance coefficient, D is the distance, and C is the control coefficient. Let i be the target position in the i-th iteration. Let i be the position of the chimpanzee in the i-th iteration. Let the distance between the chimpanzee and its prey be the distance at iteration number i. This represents the chimpanzee's position at iteration number i+1. The objective function of the average cost rate model is to minimize the average cost rate. Based on the costs of preventive repair and fault repair, the objective function is optimized using the Chaotic Gorilla Optimization Algorithm to obtain the minimum average cost rate. The optimal maintenance cycle and repair threshold for the cable are then output.

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