Seabed observation network power supply system fault positioning method considering continuous node data abnormity

Abstract

The invention provides a submarine observation network power supply system fault positioning method considering continuous node data anomaly. The method comprises the steps of constructing a node admittance matrix through a node space relationship; acquiring the voltage of each node and the shore-based station current, calculating a leakage current vector matrix, calculating a leakage current error threshold, and judging whether the leakage current exceeds the leakage current error threshold; when the leakage current exceeds the threshold value, judging whether a short-circuit condition is met or not, and directly carrying out fault positioning if the short-circuit condition is met; and if not, correcting the leakage vector matrix by adopting a negative infinite norm iteration method, and returning to the step S3 by adopting the corrected leakage vector matrix to carry out fault location again. According to the submarine observation network power supply system fault positioning method considering the continuous node data anomaly, the problem that an existing method fails in a continuous multi-node data anomaly environment is solved, and the reliability and the safety of a submarine observation network direct current power supply system are effectively improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of power supply system fault, in particular to a method for locating power supply system fault of an ocean observatory network considering abnormal data of continuous nodes. BACKGROUND

[0002] The ocean observatory network has the ability to monitor the marine environment and underwater activities in real time all day long, and is widely used in the fields of marine resource exploration, disaster warning and national defense reconnaissance. Compared with the land power grid, the power supply system of the ocean observatory network is in a complex and changeable marine environment for a long time, and is affected by factors such as ship anchor dragging and biological erosion, resulting in power cable failure. Once the power supply system of the ocean observatory network fails, its maintenance cost is high and the cycle is long. When the MARS ocean observatory network in the United States was first operated, a short-circuit fault occurred, and it was not until half a year later that it was restored and successfully operated. Accurate fault location has become a key problem to effectively improve the reliability and economy of the ocean observatory network.

[0003] When the short-circuit fault of the submarine cable causes a sudden drop in the voltage of the nodes nearby, the power supply of the measuring device is insufficient, resulting in sampling distortion, nonlinear error and data packet loss, forming abnormal data of continuous nodes with spatial correlation. Its similarity with the fault signal is easy to produce false fault points, which seriously affects the accuracy and precision of fault location. The existing impedance method realizes fault location through distributed parameter modeling, but the error increases significantly when the transition resistance is high, and it cannot analyze the false impedance characteristics formed by data anomalies; the traveling wave method relies on time-frequency joint analysis technology, which can process non-stationary signals, but the signal attenuation in the ring network makes the resolution decrease significantly, while the multi-terminal time difference method increases the hardware cost by 30% due to the need for full-network synchronous measurement, and data anomalies will drown out the ineffective traveling wave signals; intelligent algorithms are excellent in resisting transition resistance, but the training data hypothesis is significantly different from the actual fault data anomaly situation. These methods have not established a cooperative model of abnormal data and line fault, resulting in failure in the dynamic process of voltage drop-sensor failure-data anomaly.

[0004] For data anomaly problem, some literatures use power system physical model to construct detection framework, identify false data through residual analysis or parameter anomaly, and the proposed false data detection algorithm can effectively identify data anomaly. Combined with graph theory segmentation and sparse matrix optimization, high-dimensional state estimation problem is decomposed into low-dimensional subspace, which significantly reduces the computational complexity and realizes the rapid detection of false data. The dynamic detection method based on Kullback-Leibler distance is used to identify anomalies by quantifying the difference between measurement data and historical normal distribution, which can accurately detect hidden attacks disguised as historical data. In order to extend the false data detection algorithm to the multi-DC microgrid cluster interaction scene, a distributed detection framework is constructed based on the dynamic voltage-current coupling model, and false data injection attacks are identified through local residual analysis and global information interaction. The above methods focus on single-dimensional data anomaly detection under steady state, but the node data of the submarine observation network is affected by the dynamic coupling of line faults and measurement anomalies, resulting in the failure of single-dimensional detection due to mechanism mismatch, and the detection model is difficult to distinguish between pseudo-faults formed by line faults and abnormal data, resulting in the coexistence of false positives and false negatives.

[0005] For fault location problem under data anomaly, Dobakhshari A S et al. proposed a fault location method based on wide-area PMU voltage data, which effectively improves the positioning accuracy in high-resistance fault scenarios; by combining measurement error covariance modeling and line parameter uncertainty joint estimation, Liao Y proposed a weighted least squares-based transmission line fault location method; in order to improve the robustness of submarine observation network fault location, W. Huang et al. proposed a multi-node fault location framework based on leakage current vector feature enhancement and dynamic threshold collaborative optimization, which enhances the tolerance to low-quality data. The above methods are based on multi-source data fusion and ideal single-point data anomaly assumption, which to some extent improves the positioning robustness in specific scenarios, but does not reveal the spatial correlation and conflict principle of multi-point data anomaly, resulting in failure in complex multi-dimensional coupled data anomaly scenarios.

[0006] In order to improve the fault location ability of the system under the condition of multi-point data anomaly, the compensation mechanism of incomplete data and dynamic parameter adaptive estimation are fused to identify and eliminate abnormal data before fault location. On the basis of the traditional abnormal data identification method, a variable error model is constructed, and the fault location fault tolerance is improved by using the weighted total least squares method; the data of the damaged sensor is corrected by using the minimum absolute value state estimator, and the complex threat of abnormal data and fault coupling of the power supply system is coped with. The above method is based on the ideal isolated and dispersed condition of abnormal data, and the spatial correlation characteristics of unmodeled abnormal data and line fault data are far away from the fault, which leads to the limited applicability of the positioning mechanism in the ocean observation network. Therefore, in view of the complex fault of the ocean observation network, developing new fault location technology, especially the effective identification and processing of multi-point abnormal data, has become the key to improve the robustness of fault location. SUMMARY

[0007] The purpose of the present application is to provide a kind of ocean observation network power supply system fault location method considering continuous node data anomaly, can realize the fault location under the condition of complex data anomaly.

[0008] In order to achieve the above purpose, the present application provides a kind of ocean observation network power supply system fault location method considering continuous node data anomaly, comprising the following steps: S1, the ocean observation network DC single pole power supply system is regarded as a node space, and the node admittance matrix Y is constructed by the relationship of node space; S2, the voltage of each node and the current of shore base station are obtained, and the leakage current vector matrix is calculated according to the voltage of each node, the current of shore base station and the node admittance matrix Y; S3, according to the leakage current vector matrix, the node admittance matrix Y and the sensor measurement error of the node, the leakage current error threshold is calculated, and the leakage current of each node in the leakage current vector matrix is compared with the leakage current error threshold, to judge whether the leakage current exceeds the leakage current error threshold; When there is no leakage current exceeding the threshold, it is normal, and fault location is not needed; S4, when the leakage current exceeds the threshold, the nodes corresponding to the multiple leakage currents exceeding the threshold form a fault area, and the situation of the fault area is analyzed to determine whether it meets the short circuit condition. If it is a short circuit condition, fault location is directly performed; S5, if it is not a short circuit condition, the leakage current vector matrix is modified by using the method of negative infinite norm iteration, and the modified leakage current vector matrix is returned to step S3 to re-perform fault location.

[0009] Preferably, the calculation formula of the leakage current vector matrix is: (1); Wherein, is the leakage current vector composed of the leakage current of each node,Y Here is the nodal admittance matrix. U The voltage matrix for each node, Current flowing out of the shore base station The current flowing into the shore base station.

[0010] Preferably, the formula for calculating the leakage current error threshold is: (2); in, For nodes Voltage sensor measurement error This is the leakage current error threshold. m The maximum number of adjacent nodes of a node. For nodes and their neighboring nodes The maximum line admittance in the main cable. This represents the maximum measurement error of the voltage sensor.

[0011] Preferably, the fault area is analyzed to determine whether it meets the criteria for a short circuit, specifically: Short circuit condition: node p With nodes q When a short circuit fault occurs in the main cable, the fault point is taken as a new node. The node leakage current is calculated as shown in Equation (3). The two adjacent fault nodes have leakage current exceeding the threshold. (3); Represents a node p Leakage current, Represents a node q Leakage current, Represents a node p , q Inter-line conductivity, Represents a node p Line conductivity at the fault point Represents a node q Line conductivity at the fault point Represents a node j Voltage, Represents a node k Voltage, This indicates the voltage at the fault location.

[0012] Preferably, the leakage current vector matrix is ​​corrected using the negative infinity norm iteration method, specifically as follows: Based on the leakage current vector matrix, a correction matrix is ​​constructed by selecting the node spatial location and fault segment within the fault area. The leakage current vector matrix is ​​then corrected using the correction matrix to obtain a new leakage current vector matrix. By integrating the new leakage current vector data generated by each iteration with historical data, a leakage current iterative cumulative matrix is constructed, the negative infinity norm of the matrix is calculated, each calculation will gradually converge to the fault area, and it is judged whether the fault area converges to the node, when it converges to the node, the iteration is completed, when it does not converge to the node, a correction matrix is re-constructed, and the leakage current vector matrix is corrected.

[0013] Preferably, the correction matrix is: (4) ; Wherein, represents k + i -1 order unit matrix, represents k - i -1 order unit matrix, is the correction matrix of the first iteration, which is divided into left and right parts by the fault position , each of which is composed of a plurality of small correction matrices , represents the main elements of the correction matrix.

[0014] Preferably, the new leakage current vector matrix formula is : (5).

[0015] Preferably, the leakage current iterative cumulative matrix is : (6) ; In the formula, T represents the transpose of the matrix.

[0016] Preferably, the standard for judging whether the fault area converges to the node is: (7) ; In the formula, represents the leakage current after the correct correction of the node abnormal data, represents the negative infinity norm of the matrix.

[0017] Preferably, the fault distance is used for fault location, and the calculation method of the fault distance is: (8) ; represents the distance from the node p to the fault point, represents the unit length resistance of the submarine cable, is the line impedance from the node p to the fault point, representing a node p to a node q line impedance, representing a node q leakage current, representing a node p leakage current.

[0018] Therefore, the application adopts the above-mentioned method for fault positioning of a submarine observation network power supply system considering continuous node data anomalies, adopts spatially related leakage current characteristics, which is different from the traditional time-related anomaly data identification method, and can effectively distinguish between "pseudo fault points" and fault points by the error term caused by the successive transfer of fault branch leakage current. Simulation results show that the proposed method can identify abnormal nodes with 100% accuracy in a variety of complex data anomaly scenarios, and the average error of fault positioning is 0.9% for short-circuit faults with transition resistance of 10Ω-1000Ω, and the positioning error is less than 1.6% in the high transition resistance (1000Ω) scenario, which has good anti-transition resistance ability. Finally, compared with existing methods, the problem of existing methods failing in continuous multi-node data anomaly environment is solved. This paper effectively improves the reliability and safety of the submarine observation network DC power supply system. BRIEF DESCRIPTION OF DRAWINGS

[0019] Figure 1 is a double-ended ring-shaped submarine observation network; Figure 1 (a) is the normal operation of the system; Figure 1 (b) is a short-circuit fault of the system; Figure 1 (c) is a data anomaly of the system node; Figure 2 is a partial node and cable topology graph under data anomaly; Figure 3 is a schematic diagram of the leakage current accumulation characteristics after selecting different section data correction; Figure 4 is a flow chart of fault positioning with multiple node data anomalies; Figure 5 is a Neptune submarine observation network model; Figure 6 is the leakage current before and after data correction between nodes 11 and 12; Figure 7 is the leakage current before and after data correction between nodes 28 and 29; Figure 8 is the leakage current before and after data correction between nodes 5 and 6. DETAILED DESCRIPTION

[0020] The technical solutions of the application are further described below through the drawings and examples.

[0021] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.

[0022] Example 1 I. Leakage Current in the Power Supply System of the Submarine Observation Network 1.1 DC Monopole Power Supply System for Submarine Observation Network A multi-terminal ring-shaped seabed observation network consists of shore base stations, branch units, junction boxes, electrodes, and submarine cables, such as... Figure 1 As shown, the constant voltage DC negative pole transmission method has strong network expansion capability, as well as strong power supply capacity and efficiency, making it suitable for use in large-scale multi-terminal ring-shaped seabed observation networks. It can minimize transmission losses while ensuring stable system operation.

[0023] like Figure 1 The seabed observation network shown has two shore base stations, powered by -10kV high-voltage DC. This allows the other shore base station to provide power in case of a failure on one side, significantly improving overall power supply reliability. The seabed observation network uses a fiber optic composite cable to achieve power supply and data transmission. Branch units enable flexible expansion of the network topology, power distribution, signal routing, and fault isolation. The junction boxes mainly power scientific measuring instruments and transmit measurement data to the shore base stations via optical fiber. They are divided into main junction boxes and secondary junction boxes, with internal DC-DC converters that convert 10kV to 375V, and 375V to 12V, 24V, and 48V. Based on a sacrificial anode cathodic protection mechanism, and taking into account both engineering feasibility and maintenance economy, anodes are usually equipped at the shore base stations, forming a seawater power transmission circuit with the cathodes of the junction boxes.

[0024] 1.2 Leakage Current Vector By considering the voltage of the branch unit, seawater, and shore base station, the current of all lines can be calculated, and the leakage current can be calculated based on KCL. The leakage current calculation formula is shown in equation (1). The seawater is equivalent to a node. n The leakage current is the sum of the node current vectors.

[0025] (1); in, This is the leakage current vector composed of the leakage currents of each node. Here is the nodal admittance matrix. The voltage matrix for each node, , For shore-based base stations, the outflow and inflow currents.

[0026] 1) Normal situation like Figure 1 As shown in (a), when the power supply system is operating normally, theoretically, the node... The leakage current is zero, but in reality, due to measurement errors, the leakage current is not zero under normal operation. The leakage current error is mainly determined by the measurement voltage error and the cable node admittance.

[0027] To ensure that the leakage current of all nodes is contained within a leakage current error threshold, the maximum leakage current error of the nodes with the most adjacent nodes in the power supply system must be calculated.

[0028] (2); in, For nodes Voltage sensor measurement error This is the leakage current error threshold. m The maximum number of adjacent nodes of a node. For nodes and their neighboring nodes The maximum line admittance in the main cable. This represents the maximum measurement error of the voltage sensor.

[0029] 2) Short circuit situation like Figure 1 As shown in (b), node p With nodes q When a short circuit fault occurs in the main cable, the fault point is taken as a new node. The node leakage current is calculated as shown in Equation (3). The two adjacent fault nodes have leakage current exceeding the threshold. (3); Represents a node p Leakage current, Represents a node q Leakage current, Represents a node p , q Inter-line conductivity, Represents a node p Line conductivity at the fault point Represents a node q Line conductivity at the fault point Represents a node j Voltage, Represents a node k Voltage, This indicates the voltage at the fault location.

[0030] 3) Abnormal node measurement data like Figure 1 As shown in (c), the branch node d Anomalies in measurement data occurred at the node. d The adjacent nodes are all affected by abnormal data, and their leakage current equation is shown in equation (4).

[0031] (4); wherein, denotes the leakage current of the node a , denotes the leakage current of the node b , denotes the leakage current of the node c , denotes the leakage current of the node d , denotes the leakage current of the node a , d the line conductance between the nodes , b , d the line conductance between the nodes , c , d the line conductance between the nodes , i , d the line conductance between the nodes is a branch node d ,

[0032] II. Fault and data anomaly identification method for seafloor observatory network 2.1 Leakage current characteristics under continuous node data anomaly Under the cable fault, the voltage of the adjacent node decreases rapidly, and the node electrical quantity measuring device is not normally operated due to low voltage, which is easy to produce continuous node data anomaly. The threshold-crossing leakage current in this case can be divided into data anomaly component and fault component, which are generated by abnormal data and line short-circuit fault data respectively. The branch number of the abnormal node is , the short-circuit fault occurs between node and node , and the leakage current is shown in formula (5).

[0033] (5); wherein is the line impedance between the two nodes, is the line impedance from node to the fault point, is the short-circuit current, is the voltage deviation of the abnormal data of node i from the real data, is the voltage deviation of the abnormal data of node j from the real data, is the voltage deviation of the abnormal data of node k from the real data.

[0034] As Figure 2 shown in Fig. 6, the leakage current at short-circuit fault is shown in equation (6). With the change of the spatial correlation between the fault location and the abnormal node, the leakage current constitutes differently, where (a), (b), (c) are three cases, the leakage current is affected by both abnormal data and fault data, (d) represents the fault and the abnormal node is separated by two or more nodes, and the leakage current is only affected by abnormal data or fault data. The above cases may exist similar to the data under short-circuit fault.

[0035] (6); where, Z represents the line impedance between nodes k 、 i , Z represents the line impedance from node k to the fault point.

[0036] As Figure 2 shown in Fig. 6, if there is no short-circuit fault, and only the data of node j is abnormal, it will produce j +1 over-threshold leakage current. If there is a short-circuit fault and the data of node j is abnormal, due to the different fault locations, it will produce j , j +1, j +2, j +3 over-threshold leakage currents. The node data abnormality is complex, and the over-threshold leakage current node number under different system states of short-circuit fault, data abnormality and short-circuit plus abnormality is shown in Table 1.

[0037] Table 1 Over-threshold leakage current under different system states

[0038] where, j is the branch number of the data abnormal node.

[0039] As can be seen from Table 1, the over-threshold leakage current node number may be the same under multiple conditions, and when the over-threshold leakage current node number is 2, it can be determined whether the system exists data abnormality by judging whether equation (3) is satisfied, but it cannot determine the specific state of the seabed observation network, and further cannot identify and remove the "pseudo fault point".

[0040] 2.2 Fault branch leakage current transfer characteristics Multiple over-limit leakage currents generated by consecutive node anomalies and fault components will form a fault region (the smallest region containing all nodes with over-limit leakage currents). Anomalies need to be identified and corrected to complete subsequent fault location. The correction principle is based on Kirchhoff's Current Law (KCL). Using normal node voltage data and line impedance, the fault region's node voltage data is corrected. The difference between the corrected node voltage and the original node voltage should be greater than the voltage sensor measurement error; otherwise, the node is considered a normal node that does not require correction. When using adjacent fault nodes (such as...) Figure 3 node j Data for another faulty neighboring node (e.g.) Figure 3 node j -1) During voltage correction, the leakage current fault component will change dynamically as the correction process progresses.

[0041] like Figure 3 As shown, the fault area M , N Only one exists at a time, with seawater equivalently representing a node. n The fault is located at node j , j Section 1, between -1, has formed a fault area due to the influence of data anomaly nodes. M Now traverse the fault area a Select a node in the segment between two adjacent nodes. j - m , j - m The section between +1 m For the faulty section, abnormal data is corrected using data from normal nodes, and the resulting over-limit leakage current is obtained. , As shown in equations (7) and (8).

[0042] (7); (8); (9); From equations (7), (8), and (9), we can obtain that in m =1, the sum of the absolute values ​​of the over-limit leakage current: ; When, the sum of their absolute values ​​is divided by In addition, there is a leakage current error term caused by the transfer, so the sum of the absolute values ​​of the two leakage currents in the actual fault section 1 is the smallest.

[0043] (10); The following conclusion can be drawn through the above derivation: the sum of the absolute values of the out-of-limit leakage currents of the actual fault section is the smallest after traversing the possible fault section and correcting the voltage data. Based on the conclusion, a method can be designed to realize fault location under continuous abnormal data.

[0044] III. Power supply system fault location method considering continuous node data abnormality 3.1 Fault location based on negative infinity norm iteration To realize the convergence of the fault area, a correction matrix needs to be constructed based on the leakage current vector matrix, according to the spatial position of the nodes in the fault area and the selection of the fault section, and a plurality of new leakage current vector matrices are generated. As shown in formula (10), a short circuit fault occurs between nodes Figure 3 , , , j +1, j +2, j ..., j +k, k + j + k + m Data abnormal nodes in the fault area b are selected and corrected, and the corrected leakage current vector matrix formula is shown in formula (11), and the correction matrix construction is shown in formula (12).

[0045] (11); (12); Wherein, denotes the voltage matrix after the x th data correction, denotes k + i -1 order unit matrix, denotes k - i -1 order unit matrix, is the correction matrix of the th iteration, which is divided into left and right parts by the fault position , each of which is composed of a plurality of small correction matrices , and the main elements of the matrix are shown in formula (13).

[0046] (13); In the formula, , , , are simplified symbols of simplified formulas and have no actual meaning, v( z ), u ( z ) represents the main element in the correction matrix, represents the node j + z 、 j + z +1 between the line impedance.

[0047] By integrating the new leakage current vector data generated by each iteration with historical data, a leakage current iterative cumulative matrix is constructed, the negative infinity norm of the matrix is calculated, each calculation will gradually converge the fault area, and finally the fault area is converged to two nodes.

[0048] (14); (15); In the formula, represents the transpose of the matrix.

[0049] After the negative infinity norm iteration, the abnormal data is corrected, and the leakage current abnormal component of the "pseudo fault point" is eliminated. The leakage current exceeding the limit only has the fault component, and the fault impedance can be obtained by formula (3). Since the submarine observation network cable is of uniform material, its unit length impedance is mainly affected by temperature, as shown in formula (16), and its fault distance is shown in formula (17), and the transition resistance is shown in formula (18).

[0050] (16); (17); (18); Where is the resistivity of the conductor material, is the effective cross-sectional area of the conductor, is the resistance temperature coefficient, are the actual temperature and the reference temperature respectively, represents the line unit length impedance, l represents the line length, represents the node p to the line distance at the fault point.

[0051] The steady-state data in the time domain is used for fault location, and the arithmetic mean is calculated as the final positioning result.

[0052] 3.2 Fault branch leakage current transfer characteristic The flow chart of the fault location method is as follows Figure 4As shown, first, the node admittance matrix is constructed based on the spatial structure of the ocean bottom observation network and the cable parameters, the leakage current vector is calculated combined with the node voltage data transmitted by the measuring device, and the threshold is obtained by formula (2) to judge the leakage current vector. If there is an out-of-limit leakage current, data abnormal state identification is performed.

[0053] By formula (3), it is judged whether the out-of-limit leakage current meets the short circuit state - only the forward out-of-limit leakage current of the adjacent two nodes exists, it can be judged whether the data abnormal state exists. For the data abnormal state, all out-of-limit leakage currents form a fault area, and the possible fault section is traversed, and the correction matrix is formed by formula (12), and the leakage current iterative accumulation matrix is formed by formula (11) and (14) to correct the abnormal data. The matrix negative infinity norm is calculated each time, until the fault position converges to the two nodes. If there is no out-of-limit leakage current after iteration, it means that there is no short circuit fault, only node data abnormality occurs. After the negative infinity norm iteration is completed, only the fault component exists in the out-of-limit leakage current, and the accurate positioning of the fault is calculated by formula (17).

[0054] Computing power simulation verification The NEPTUNE ocean bottom observation network power supply system model is established in PSCAD / EMTDC as shown in Figure 5 The maximum measurement error of the voltage sensor in NEPTUNE is ±0.7%. The cable length between two nodes is 100-160km, which means the maximum cable admittance is 0.00476S. Considering that the voltage of NEPTUNE is 10kV, the threshold is set to ±0.9A according to formula (4). At the same time, considering that the different depths of nodes are the main reason for the temperature difference, different r0 are set for nodes connected to the sea cable at different depths. The fault positioning error is the absolute error, which is calculated by formula (19).

[0055] (19); Wherein is the positioning absolute error; is the calculated fault distance; is the actual fault distance.

[0056] Continuous abnormal data fault positioning (1) Continuous multi-node data damage A short circuit fault (Fault 1) is set on the cable between node 11 and node 12, and the damaged data of nodes 10, 11, 12, 13 and 14 is simulated, and there is a deviation of ±200-400V between the damaged data and the true data. According to formula (5), the deviation will cause a large data abnormal component in the out-of-limit leakage current, forming a continuous fault area including nodes 9-15. After negative infinity norm iteration, the leakage current before and after correction is as follows Figure 6As shown, it is determined that the fault occurs between nodes 11 and 12, and the fault distance is finally calculated.

[0057] (2) Continuous multi-node data loss A short-circuit fault (Fault 2) is set on the cable between node 28 and node 29, and it is simulated that nodes 25, 26, 27, 28, 29, and 39 have lost data. Since the lost data is usually set to 0 in simulation, a larger data abnormal component is generated in the threshold-crossing leakage current, greatly reducing the proportion of the fault component, and possibly forming multiple threshold-crossing leakage currents that are significantly larger than in the fault case. A fault area containing nodes 24-30, 39, 40, and 42 is formed, and after negative infinity norm iteration, the leakage currents before and after correction are as shown in Figure 7 As shown, it is determined that the fault occurs between nodes 28 and 29, and the fault distance is finally calculated.

[0058] (3) Data corruption and data loss occur simultaneously A short-circuit fault (Fault 3) is set on the cable between node 5 and node 6, and it is simulated that nodes 4, 6, and 8 have corrupted data, and nodes 3, 5, 7, and 9 have lost data. Data abnormalities occur in 7 consecutive nodes, the abnormal data in the 7 nodes affect each other, and the fault also occurs in the 7 nodes. The threshold-crossing leakage current situation is relatively complex, forming a fault area containing nodes 2-10 and 31. However, after negative infinity norm iteration, the leakage currents before and after correction are as shown in Figure 8 As shown, it is determined that the fault occurs between nodes 28 and 29, and the fault distance is finally calculated, accurately locating the fault position.

[0059] The fault positioning effect of the above three data abnormality cases is shown in Table 2, and the positioning effect under the condition of no data abnormality is also set. It can be seen that in the case of multiple consecutive node data abnormalities, the fault positioning error is larger than that without data abnormality, but it can still be kept below 0.7%, achieving accurate fault positioning in a harsh data environment.

[0060] Table 2 Fault positioning accuracy under continuous node data abnormality

[0061] Analysis of positioning accuracy considering transition resistance Nodes 25, 27, and 29 have data corruption, and nodes 26, 28, and 39 have data loss. Short-circuit faults are set between nodes 28 and 29, and nodes 28 and 39, respectively. Transition resistances of 10Ω, 50Ω, 100Ω, 500Ω, and 1kΩ are set, respectively. The positioning accuracy is shown in Table 3. The proposed method can correct abnormal data under different fault scenarios, and can control the average fault positioning error to 1.6%.

[0062] Table 3 fault location accuracy with transition resistance

[0063] Comparison with prior art To prove the accuracy of the method proposed in this paper, Newton interpolation method and spatial interpolation method are taken as examples to compare the fault location performance of the three methods, and the results are shown in Table 4, wherein the short circuit fault between nodes 20 and 21, node 21 has data damage, and the transition resistance is 1000Ω.

[0064] Table 4 comparison of positioning accuracy of different fault location methods

[0065] The results show that after correcting the data, the Newton interpolation method has larger fault location error, the spatial interpolation method has higher positioning accuracy in fault 6 scenario, but still cannot realize fault location under continuous node data anomaly, and is accompanied by uncertainty, because under continuous node data anomaly, the former does not identify abnormal data and directly uses abnormal data for calculation, and the latter can identify abnormal node data under the condition that adjacent node data are normal, but cannot effectively identify continuous node data anomaly, resulting in using abnormal data for interpolation, and abnormal data itself has randomness, which cannot guarantee the accuracy of the data after interpolation, and the positioning effect is poor. The method proposed in this paper first identifies normal data through leakage current, and then uses normal data for negative infinity norm iterative interpolation to realize accurate identification and correction of abnormal data.

[0066] Therefore, the submarine observation network power supply system fault location method considering continuous node data anomaly is adopted, the problem that the existing method is invalid in the continuous multi-node data anomaly environment is solved, and the reliability and safety of the submarine observation network DC power supply system are effectively improved.

[0067] Finally, it should be noted that: the above examples are only used to illustrate the technical solutions of the present application and not to limit it, although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that: it can still modify or equivalently replace the technical solutions of the present application, and these modifications or equivalent replacements cannot make the modified technical solutions deviate from the spirit and scope of the technical solutions of the present application.

Claims

1. A method for fault location in the power supply system of a submarine observation network considering anomalies in continuous node data, characterized in that, Includes the following steps: S1. Treat the DC monopole power supply system of the seabed observation network as a node space, and construct the node admittance matrix Y through the node space relationship; S2. Obtain the voltage and shore base station current of each node, and calculate the leakage current vector matrix based on the voltage of each node, shore base station current and node admittance matrix Y. S3. Calculate the leakage current error threshold based on the leakage current vector matrix, the node admittance matrix Y, and the sensor measurement error of the node. Compare the leakage current of each node in the leakage current vector matrix with the leakage current error threshold to determine whether the leakage current exceeds the leakage current error threshold. If there is no leakage current exceeding the threshold, it is considered normal and no fault location is required. S4. When the leakage current exceeds the threshold, the nodes corresponding to multiple leakage currents exceeding the threshold are formed into a fault area. The fault area is analyzed to determine whether it meets the short circuit condition. If it is a short circuit condition, the fault is located directly. S5. If it is not a short circuit, the leakage current vector matrix is ​​corrected using the negative infinity norm iteration method. The corrected leakage current vector matrix is ​​then used to return to step S3 to re-locate the fault.

2. The method for fault location of a submarine observation network power supply system considering continuous node data anomalies as described in claim 1, characterized in that, The formula for calculating the leakage current vector matrix is: (1)； in, This is the leakage current vector composed of the leakage currents of each node. Y Here is the nodal admittance matrix. U The voltage matrix for each node, Current flowing out of the shore base station The current flowing into the shore base station.

3. The method for fault location of a submarine observation network power supply system considering continuous node data anomalies as described in claim 1, characterized in that, The formula for calculating the leakage current error threshold is: (2)； in, For nodes Voltage sensor measurement error This is the leakage current error threshold. m The maximum number of adjacent nodes of a node. For nodes and their neighboring nodes The maximum line admittance in the main cable. This represents the maximum measurement error of the voltage sensor.

4. The method for fault location of a submarine observation network power supply system considering continuous node data anomalies as described in claim 1, characterized in that, The fault area is analyzed to determine whether it meets the criteria for a short circuit. Specifically: Short circuit condition: node p With nodes q When a short circuit fault occurs in the main cable, the fault point is taken as a new node. The node leakage current is calculated as shown in Equation (3). The two adjacent fault nodes have leakage current exceeding the threshold. (3)； Represents a node p Leakage current, Represents a node q Leakage current, Represents a node p , q Inter-line conductivity, Represents a node p Line conductivity at the fault point Represents a node q Line conductivity at the fault point Represents a node j Voltage, Represents a node k Voltage, This indicates the voltage at the fault location.

5. The method for fault location of a submarine observation network power supply system considering continuous node data anomalies as described in claim 1, characterized in that, The leakage current vector matrix is ​​corrected using the negative infinity norm iteration method as follows: Based on the leakage current vector matrix, a correction matrix is ​​constructed by selecting the node spatial location and fault segment within the fault area. The leakage current vector matrix is ​​then corrected using the correction matrix to obtain a new leakage current vector matrix. By integrating the new leakage current vector data generated in each iteration with historical data, a leakage current iterative accumulation matrix is ​​constructed, and the negative infinity norm of the matrix is ​​calculated. Each calculation gradually converges the fault region. It is determined whether the fault region has converged to the nodes. When it has converged to the nodes, the iteration is completed. When it has not converged to the nodes, the correction matrix is ​​reconstructed and the leakage current vector matrix is ​​corrected.

6. A method for fault location of a submarine observation network power supply system considering continuous node data anomalies, as described in claim 5, is characterized in that... The correction matrix is: (4)； in, express k + i -1 order identity matrix express k - i -1 order identity matrix For the first The correction matrix for the next iteration is divided into left and right parts based on the fault location. Each of them consists of multiple small correction matrices. composition, This represents the principal element of the correction matrix.

7. A method for fault location of a submarine observation network power supply system considering continuous node data anomalies, as described in claim 6, is characterized in that... New leakage current vector matrix formula : (5)。 8. A method for fault location of a submarine observation network power supply system considering anomalies in continuous node data, as described in claim 5, is characterized in that... Leakage current iterative accumulation matrix : (6)； In the formula, T This represents the transpose of a matrix.

9. A method for fault location of a submarine observation network power supply system considering continuous node data anomalies, as described in claim 5, is characterized in that... The criterion for determining whether the fault region has converged to the nodes is: (7)； In the formula, This represents the leakage current after the abnormal node data has been correctly corrected. It represents the negative infinity norm of a matrix.

10. A method for fault location of a submarine observation network power supply system considering anomalies in continuous node data, as described in claim 1, characterized in that, Fault location is performed using fault distance, and the calculation method for fault distance is as follows: (8)； Represents a node p Distance to the fault point This represents the resistance per unit length of the submarine cable. For nodes p Line impedance at the fault point Represents a node p To the node q The line impedance, Represents a node q Leakage current, Represents a node p Leakage current.

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