CN102981104A - On-line monitoring method for submarine cables - Google Patents

Abstract

The invention provides an on-line monitoring method for a submarine cable, which includes the following steps: establishing a submarine cable fault judgment model according to the pressure field information, temperature field information, electric field information and fault type of the submarine cable; Optical fiber sensor; monitoring and obtaining the pressure field information, temperature field information and electric field information of the submarine cable through the distributed optical fiber sensor; according to the pressure field information, temperature field information and electric field information, and the submarine cable fault judgment model , judging whether the submarine cable has a fault and the type of the fault. The invention can accurately monitor the state of the submarine cable on-line, obtain the state parameters of the submarine cable at any time for fault judgment, and has better identification.

Description

On-line Monitoring Method of Submarine Cable

technical field

The invention relates to the field of electric power safety monitoring technology, in particular to an on-line monitoring method of a submarine cable.

Background technique

The development of offshore energy such as offshore wind power has promoted the formation of offshore transmission networks, and high-voltage submarine power cables have increasingly broad application space, mainly including offshore wind power transmission, power supply for offshore oil and gas platforms, island networking, and coastal country networking. As the most important equipment in the offshore transmission network, the safe operation of submarine cables is very important to the power system.

The quality of cable insulation is a key factor affecting the safe and reliable operation of cables. In the past, the widely used preventive measure was to use periodic power outages for detection, which belonged to offline detection. This preventive detection of insulation is obviously unreasonable: first, the power must be cut off for detection, which often results in interruption of power supply; second, it cannot be selectively detected according to the cable insulation status, and all cables are often detected. As a result, cables with intact insulation undergo multiple testing processes, resulting in accelerated deterioration of the cable insulation performance; third, when testing, a high voltage higher than the operating voltage is often applied to the cable insulation, which will accelerate the deterioration of the cable insulation. Therefore, power cable online monitoring technology will become an inevitable development trend of cable fault diagnosis.

On-line monitoring of cable status is to monitor the cable online, and then decide whether to further inspect and repair the insulation of the cable according to the monitoring results. On-line monitoring of cable status can not only greatly reduce unnecessary detection of cables with good insulation, save the cost of the detection process, reduce the number of power outages, and reduce abnormal damage to cable insulation caused by unnecessary detection operations, but more importantly, it can Conduct continuous monitoring of the insulation to detect problems in time and reduce the occurrence of unexpected accidents. In addition, online monitoring can also establish a historical file of cable insulation performance and provide basic data for the decision-making of cable offline overhaul.

Contents of the invention

Therefore, the present invention provides an online monitoring method for submarine cables that can accurately monitor the status of submarine cables, obtain status parameters of submarine cables at any time, and perform fault judgment.

A method for on-line monitoring of submarine cables, comprising the following steps:

According to the pressure field information, temperature field information, electric field information and fault type when the submarine cable is faulty, a submarine cable fault judgment model is established;

Distributed optical fiber sensors are arranged coaxially on submarine cables;

Obtain the pressure field information, temperature field information and electric field information of the submarine cable through the monitoring of the distributed optical fiber sensor;

According to the pressure field information, temperature field information and electric field information, and the submarine cable fault judgment model, it is judged whether the submarine cable has a fault and the type of the fault occurs.

The submarine cable online monitoring method of the present invention adopts multi-feature quantity monitoring, and comprehensively multiple characteristic quantity data and submarine cable fault judgment models are used for fault discrimination, so the reliability of monitoring data is improved; and the accuracy of fault location is improved, reducing the blurred area. And the evidence interval and uncertainty probability of the distributed optical fiber sensor and the evidence interval and uncertainty probability after fusion reduce the uncertainty of the system, and at the same time make the basic reliability function after fusion better than the basic reliability function of each sensor before fusion. The credibility function has better discrimination and better recognition.

Description of drawings

Fig. 1 is the schematic flow sheet of submarine cable online monitoring method of the present invention;

Fig. 2 is a schematic diagram of a submarine cable fault judgment model of the submarine cable online monitoring method of the present invention;

Fig. 3 is a schematic diagram of a three-layer BP neural network structure for data feature-level fusion in the submarine cable online monitoring method of the present invention.

Detailed ways

Please refer to FIG. 1 , which is a schematic flowchart of the online monitoring method for submarine cables of the present invention.

The submarine cable online monitoring method comprises the following steps:

S101, according to the pressure field information, temperature field information, electric field information and fault type when the submarine cable is faulty, establish a submarine cable fault judgment model;

S102, deploying distributed optical fiber sensors coaxially on the submarine cable, and obtaining pressure field information, temperature field information, and electric field information of the submarine cable through monitoring by the distributed optical fiber sensor;

S103. According to the pressure field information, temperature field information, and electric field information, and the submarine cable fault judgment model, determine whether the submarine cable has a fault and the type of the fault.

By adopting multi-feature quantity monitoring, and combining multiple characteristic quantity data and submarine cable fault judgment model for fault discrimination, the reliability of monitoring data is improved; and the accuracy of fault location is improved, and the fuzzy area is reduced. And the sensing of the distributed optical fiber sensor is more accurate.

First, the above step S101 is the establishment of a fault judgment model.

Based on the pressure field information, temperature field information, and electric field information when the submarine cable is faulty, the fault judgment model is mainly established for the following types of faults:

The main cable insulation has aging faults. The aging fault of the submarine high-voltage cable has a certain range of influence on the normal operation of the cable, and the longer the fault, the longer the range of influence, but we can still monitor the temperature of the outer surface of the insulation layer through the distributed optical fiber sensor along the line, measured from the sensor The location where the temperature changes suddenly along the cable, so as to locate the fault location.

There is an impurity fault in the main insulation of the cable. When there are impurities in the cable insulation layer, the electric field at the location of the impurities is greatly distorted, and the degree of electric field distortion varies according to the location of the impurities. When the distance between the impurity and the conductor is different, the maximum temperature inside the cable hardly changes, but because of the presence of the impurity, the electric field strength generated around it is close to or even greater than the breakdown strength of the insulating layer, and partial discharge is easy to occur, causing Insulation breakdown. If a breakdown occurs somewhere in the insulating layer, other good insulation will withstand a greater voltage, and the electric field intensity will be further increased. If the field is strong enough to break down the insulation layer, it will increase the field strength of other good insulation, so that the vicious circle will threaten the insulation performance of the cable. At the same time, we can also see that the electric field distortion generated by the impurities closest to the conductor is the most severe, so partial discharge is more likely to occur, resulting in insulation breakdown failure. The distorted electric field along the cable can be monitored through the distributed optical fiber sensor, and the influence range of the distorted electric field caused by impurities on the normal electric field is very small, so the positioning accuracy can be improved by monitoring the change of the electric field.

The cable is squeezed by external force. When the seabed is moving and the submarine cable is squeezed or rubbed against seaside pebbles, the distributed optical fiber sensor can also be used to detect the squeezed position. The maximum pressure on the armor layer of the submarine power cable before aging does not exceed 17Mpa. Therefore, once the submarine cable is extruded by an external force, the internal pressure of the cable can be detected by the distributed optical fiber sensor, thereby judging the pressure on the outside of the cable, and calculating the extruded pressure of the cable through the data of the distributed optical fiber sensor. position, when the set limit value is exceeded, the fault can be quickly eliminated.

The cable armor layer is damaged. Submarine cables are damaged to varying degrees, and the temperature field and electric field inside the cable will change. When the armor layer of the submarine cable is damaged but not penetrated, the temperature field and electric field inside the submarine cable do not change significantly compared with normal conditions, indicating that the submarine cable can maintain normal operation for a period of time without damage to the armor layer. time. If the damage reaches the filling layer, the internal temperature field and electric field intensity of the submarine cable will hardly change, but because the filling layer is not rigid enough to withstand the deep water pressure, once it is damaged to the filling layer, the submarine cable will have a short-circuit fault within a long period of time . The most serious thing is that the damage reaches the insulation layer. The insulation layer is not only far less rigid than the armor layer, but what is more serious is that the electric field strength inside the submarine cable will rise sharply, causing the cable insulation layer to break down due to the high field strength. Using distributed fiber optic sensors, by simultaneously measuring the changes in the temperature field and pressure field, the location of the fault can be located.

Since there are three different types of characteristic quantities monitored by distributed optical fiber sensors, different fault types and fault locations, a submarine cable fault judgment model of the photoelectric signal information fusion system is built by measurement to assist in fault judgment, as shown in Figure 2.

By normalizing the two-dimensional quantities of submarine cable fault points (temperature, submarine cable position with sudden temperature change step by step), (pressure, submarine cable position with sudden pressure change step by step) and (electric field, submarine cable position with sudden electric field change step by step) One treatment. Taking the information collected by each sensor as evidence, each sensor provides a set of propositions, corresponding to the four types of faults and fault locations we are concerned about: x1...xi...x n, and establishes a corresponding reliability function. In this way, the multi-sensor information Fusion essentially becomes the process of merging different evidence bodies into a new evidence body under the same identification framework.

Specifically, when establishing the submarine cable fault judgment model, the pressure field information, temperature field information, electric field information and corresponding fault types of the submarine cable fault are used as training parameters to train the Artificial Neural Network (ANN) , generating the submarine cable fault judgment model.

The present invention provides a model of information fusion technology, which fuses the results obtained from the detected feature quantities proposed above to achieve a result of precise fault location and fault type classification.

The above step S102 is a step of acquiring the characteristic parameters of the submarine cable.

In the present invention, distributed optical fiber sensors are arranged coaxially on the submarine cable to obtain the monitored characteristic parameters. The distributed optical fiber sensor is preferably a six-core composite optical fiber, and an optical fiber signal demodulator is arranged at both ends thereof, wherein two cores are used for sensing pressure field information, two cores are used for sensing temperature field information, and two cores are used for sensing temperature field information. Measuring electric field information.

In the process of obtaining temperature, pressure and electric field strength, through the distributed optical fiber interferometer, using the principle that temperature, pressure and electric field strength will affect the pulse signal of Brillouin scattering along the coaxial fiber of the seabed cable, the obtained The pressure field information, temperature field information and electric field information of the submarine cable. Three types of physical quantities and the two-dimensional physical quantities of the cable distance along the seabed can be obtained step by step, that is:

Obtain the pressure value of the submarine cable and the location information of the pressure mutation point; the temperature value and the location information of the temperature mutation point; the electric field strength value along the line and the location of the electric field strength mutation point. As shown in Table 1 below:

Step along the line (km) 1 2 3 4 Temperature (°C) 75.6°C 76.3°C 79.6°C 88.3°C pressure 0.56Kp 1.48Kp 1.24Kp 2.09kp Electric field strength 11.5MV/m 11.5MV/m 11.8MV/m 22.6MV/m

Table 1 lists three groups of two-dimensional physical quantities obtained from the output of the distributed optical fiber interferometer, that is, the temperature, pressure and electric field intensity of the monitored submarine high-voltage cable. The so-called two-dimensional, for example, has both temperature data and the distribution of temperature measurement points on the cable. The list is for example only, the actual data processing is much more than this.

The above step S103 is a step of performing fault discrimination according to the acquired characteristic parameters.

Preferably, first judge whether the submarine cable fails:

Comparing the pressure field information, temperature field information and electric field information of the submarine cable with a preset safe value range, if it does not exceed the safe value range, it is judged that the submarine cable is not faulty;

If it exceeds the safe value range, it is judged that the submarine cable is faulty, and the pressure field information, temperature field information and electric field information are input into the submarine cable fault judgment model, and the type and location of the submarine cable fault are judged .

The safe value range is generated based on historical data. Compared with the historical data under normal conditions (that is, the safe value range), if the temperature, pressure and electric field strength of the submarine high-voltage cable are all within the normal range, then only the current data is recorded, and the No calculation is triggered.

In fault judgment, firstly, normalize the obtained pressure field information, temperature field information and electric field information, and obtain the highest temperature point of the submarine cable and the position of the temperature mutation interval where the highest temperature point is located The lower limit, the maximum pressure point of the submarine cable and the upper and lower limits of the position of the pressure mutation interval where the pressure maximum point is located, and the maximum electric field strength point of the submarine cable and the location of the electric field strength mutation interval where the electric field strength maximum point is located Position upper and lower limits;

Then, the above acquired data is used as the input of the submarine cable fault judgment model, and the type and location of the submarine cable fault are judged through the network structure, diagnostic weight and threshold in the submarine cable fault judgment model.

When judging the specific fault type, judge the cable main insulation aging fault according to the pressure field information of the submarine cable; judge the cable main insulation impurity fault according to the electric field information of the submarine cable; judge the cable according to the pressure field information of the submarine cable Extruded by force; and judging the cable armor layer damage fault according to the pressure field information and temperature field information of the submarine cable.

That is, if the submarine high-voltage cable has data exceeding the safe value range in an area, the two-dimensional quantity of the submarine cable fault point obtained after demodulating the distributed optical fiber sensor (the pressure value of the cable and the location information of the pressure mutation point ; the temperature value and the position information of the temperature mutation point; the electric field strength value along the line and the position of the electric field strength mutation point). For example, in Table 1 above, the three physical quantities at 4km. Since the three physical quantities have a certain change distribution, the calculation is complicated. In order to facilitate the processing in the later part, the values of the three characteristic quantities are first normalized:

$\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}<span\; class="notranslate">\; =</span>\frac{{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; min</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; max</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; min</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span>}$

Get a set of input quantities with credibility. The location of the fault point focuses on two quantities, that is, the location area where each physical quantity exceeds the safe value range. For example, in Table 1 above, we measured the point with the maximum temperature at 4 km, but in fact, in the area [3.9, 4.2] km, it is almost at this temperature value. Therefore, in order to enable accurate positioning, we also input the upper and lower limits of such a position interval as feature quantities. The normalized feature value is between [0,1], such a process is called pixel-level fusion.

In this way, there are 9 inputs as the next level. are the highest temperature point of the submarine cable and the upper and lower limits of the temperature mutation interval where the highest temperature point is located, the maximum pressure point of the submarine cable and the upper and lower limits of the pressure mutation interval where the pressure maximum point is located, And the maximum electric field strength point of the submarine cable and the upper and lower limits of the position of the electric field strength sudden change interval where the electric field strength maximum point is located.

After these eigenvalues are selected, we use the submarine cable fault judgment model formed by the artificial neural network (ANN) to perform feature level fusion, as shown in Figure 2, the output of the submarine cable fault judgment model is 4, They are the cable insulation aging, the impurities in the cable insulation, the local external stress of the cable exceeding the limit (anchor damage), and the location of the fault point. The value of each output is distributed between [0,1], which can be used as a probability distribution and input to the next level of fusion.

The eigenvectors extracted from the upper-level fusion that can reflect the symptoms of cable faults are used as the input of the ANN, and the trained neural network can use the diagnostic reasoning knowledge stored in the network structure, weights and thresholds for preliminary pattern classification and classification. Identify the fault location, and finally give the result of local information fusion judgment, and then submit it to the decision-making level for global decision-making.

A three-layer BP network is selected for feature-level fusion, as shown in Figure 3, by modifying the weights (ω _ij , T _li ) and threshold (θ) of the neural network, the error function E decreases along the gradient direction. The BP network uses three layers of nodes to represent input node x _j , hidden node y _i , and output node O _l .

Suppose a certain training input vector is X _k , the actual output of the network is Y _k , and there are N samples (X _k , Y _k ), k=1, 2, ..., N, and the hidden layer of the network uses the sigmoid function as the activation function , the output layer uses a linear function.

Iterative equation of weight coefficient of each neuron: ω _ij (k+1)=ω _ij (k)+μδ _kj x _ki

Output layer error: δ _kj = (y′ _kj -y _kj )f _j (net _kj )(1-f _j (net _kj ))

Hidden layer error: ${\mathrm{\&delta;}}_{\mathrm{kj}}=f_j\left({\mathrm{net}}_{\mathrm{kj}}\right)(1-f_j\left({\mathrm{net}}_{\mathrm{kj}}\right))\underset{l}{\mathrm{\&Sigma;}}\left({\mathrm{\&delta;}}_{\mathrm{kl}}{\mathrm{\&omega;}}_{\mathrm{lj}}\right)$

Because there are many local minimum points in the BP network, under some initial value conditions, the result of the algorithm will fall into the local minimum, so that the algorithm does not converge. In order to speed up the convergence and prevent oscillation, a momentum factor α is introduced to reduce the overshoot, namely:

ω _ij (k+1)＝ω _ij (k)+μδ _kj x _ki +α[ω _ij (k)-ω _ij (k-1)]

Among them, μ is the learning step size, α is the weighting factor, usually μ<1, 0<α.

Define the mean square error of the network as the average E(W) of all training samples:

$\mathrm{<span\; class="notranslate">\; E.</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; W</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\frac{\underset{\mathrm{<span\; class="notranslate">\; j</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; kj</span>}}^{<span\; class="notranslate">\; \&prime;</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; kj</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}{\mathrm{<span\; class="notranslate">\; 2</span>}}$

Finally, the comprehensive judgment of DS evidence theory is used to obtain a more accurate judgment of the fault type and fault location.

According to the output of the neural network, the recognition framework at the decision-making level is {(A ₁ ), (A ₂ ), (A ₃ ), B ₁ }, and in the training BP network samples, the output of the network is not completely equal to 0 or 1, Rather, it is a rational number between 0 and 1. Therefore, the fusion of DS evidence theory is a good solution, and the output of each neural network can be regarded as an independent evidence, making it the credibility distribution of various states under the evidence. The calculation steps are:

(a) Normalize the output of the ANN:

$\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; A</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; A</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; S</span>}}$

where y(A _i ) is the actual output of each node of the neural network, where:

$\mathrm{<span\; class="notranslate">\; S</span>}<span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; 3</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; A</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; E.</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}$

where E _n is the sample error of the network,

(b) The evidence of the neural network is fused to obtain the final basic probability assignment:

Use the D-S evidence theory to fuse the information of ANN logic binary output; judge the type of fault and the distribution area of the fault in the submarine cable.

The technical solution of the present invention also brings the following beneficial effects:

1. Adopt multi-feature quantity monitoring and discrimination, so the reliability of monitoring data is improved;

2. Provide a solution for the identification of common fault types of submarine cables;

3. The accuracy of fault location is improved and the blurred area is reduced;

4. The evidence interval and uncertainty probability of the distributed optical fiber sensor and the evidence interval and uncertainty probability after fusion, the mj(θ) after fusion is significantly reduced, which shows that information fusion reduces the uncertainty of the system, and at the same time The basic reliability function after fusion has better discrimination and better recognition than the basic reliability function of each sensor before fusion.

The above-mentioned embodiments only express several implementation modes of the present invention, and the description thereof is relatively specific and detailed, but should not be construed as limiting the patent scope of the present invention. It should be noted that those skilled in the art can make several modifications and improvements without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent for the present invention should be based on the appended claims.

Claims (7)

1. A submarine cable online monitoring method, is characterized in that, comprises the following steps: According to the pressure field information, temperature field information, electric field information and fault type when the submarine cable is faulty, a submarine cable fault judgment model is established; Distributed optical fiber sensors are arranged coaxially on the submarine cable, and the pressure field information, temperature field information and electric field information of the submarine cable are monitored and obtained through the distributed optical fiber sensor; According to the pressure field information, temperature field information and electric field information, and the submarine cable fault judgment model, it is judged whether the submarine cable has a fault and the type of the fault occurs. 2. The submarine cable online monitoring method according to claim 1, wherein, according to the pressure field information, temperature field information and electric field information, and the submarine cable fault judgment model, it is judged whether the submarine cable fails , and the steps for the type of failure include: Comparing the pressure field information, temperature field information and electric field information of the submarine cable with a preset safe value range, if it does not exceed the safe value range, it is judged that the submarine cable is not faulty; If it exceeds the safe value range, it is judged that the submarine cable is faulty, and the pressure field information, temperature field information and electric field information are input into the submarine cable fault judgment model, and the type and location of the submarine cable fault are judged . 3. submarine cable online monitoring method as claimed in claim 1, is characterized in that, according to pressure field information, temperature field information, electric field information and fault type when submarine cable fault, the step of setting up submarine cable fault judgment model comprises: The pressure field information, temperature field information, electric field information and the corresponding fault type when the submarine cable is faulty are used as training parameters to train the artificial neural network to generate the submarine cable fault judgment model. 4. submarine cable online monitoring method as claimed in claim 1, is characterized in that, the step of obtaining the pressure field information, temperature field information and electric field information of described submarine cable comprises: Obtain the pressure value of the submarine cable and the location information of the pressure mutation point; the temperature value and the location information of the temperature mutation point; the electric field strength value along the line and the location of the electric field strength mutation point. 5. The submarine cable online monitoring method according to claim 4, wherein, according to the pressure field information, temperature field information and electric field information, and the submarine cable fault judgment model, it is judged whether the submarine cable fails , and the steps for the type of failure include: Perform normalization processing on the obtained pressure field information, temperature field information and electric field information, obtain the highest temperature point of the submarine cable and the upper and lower limits of the temperature mutation interval where the highest temperature point is located, and the submarine cable The maximum pressure point and the position upper and lower limits of the pressure mutation interval where the pressure maximum point is located, and the maximum electric field strength point of the submarine cable and the position upper and lower limits of the electric field strength mutation interval where the electric field strength maximum point is located; The above-mentioned obtained data is used as the input of the submarine cable fault judgment model, and the type and location of the submarine cable fault are judged through the network structure, diagnostic weight and threshold value in the submarine cable fault judgment model. 6. submarine cable online monitoring method as claimed in claim 5, is characterized in that, the step of judging the type that described submarine cable breaks down comprises: Judging the cable main insulation aging fault according to the pressure field information of the submarine cable; According to the electric field information of the submarine cable, it is judged that the cable main insulation impurity fault; According to the pressure field information of the submarine cable, it is judged that the cable is stressed and squeezed; According to the pressure field information and temperature field information of the submarine cable, the damage fault of the cable armor layer is judged. 7. The submarine cable online monitoring method according to any one of claims 1 to 6, wherein the distributed optical fiber sensor is a six-core composite optical fiber, and its two ends are provided with optical fiber signal demodulators, wherein two cores It is used to sense pressure field information, two cores are used to sense temperature field information, and two cores are used to sense electric field information.

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