A real-time monitoring and fault positioning system for a vehicle-mounted mobile substation

Abstract

The present application relates to the field of power system automation, in particular to a kind of real-time monitoring and fault locating system of vehicle-mounted mobile substation.It includes signal acquisition processing unit, real-time monitoring and acquisition substation line parameters and environmental parameters and the traveling wave signal generated when fault occurs;Fault type identification unit is based on traveling wave signal to calculate the confidence of each fault type to determine the fault type of the traveling wave signal generated, and mark the time of traveling wave head arrival;Algorithm fusion positioning unit preliminary locates fault point according to fault type and the time of traveling wave head arrival, and then corrects preliminary positioning error by constructing fitness function through particle swarm optimization algorithm combined with line topology and environmental parameters;The system realizes high-precision fault location and rapid isolation recovery, through modeling switch state and branch change, ensure the accuracy of fault section division in complex distribution network, effectively avoid misjudgment caused by topology change or equipment overload.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of power system automation, in particular to a real-time monitoring and fault location system for a vehicle-mounted mobile substation. BACKGROUND

[0002] The vehicle-mounted mobile substation is a mobile power infrastructure integrating power transformers, distribution equipment, control and protection systems, and communication modules, usually mounted on heavy vehicles or container platforms, and can be quickly deployed to a designated location within a few hours. Although the vehicle-mounted mobile substation has high reliability, its operating environment is complex (such as outdoor harsh weather, frequent movement, and load fluctuation), and it may still fail due to equipment aging, short circuit, insulation failure, or human error. The timeliness and accuracy of fault location are critical to achieving the above goals, and any delay or misjudgment will result in serious consequences. Therefore, developing a high-precision, dynamically adaptive fault location technology that adapts to environmental and topological changes is a core requirement for improving the practicality and safety of vehicle-mounted mobile substations.

[0003] During the positioning process, the switch state (such as circuit breaker action) and branch connection of the mobile substation may change frequently, and the traditional static topology model cannot be updated in real time, leading to incorrect fault section division or ineffective isolation strategies. Moreover, traditional methods only focus on positioning accuracy, ignoring line capacity constraints and recovery efficiency, which may result in equipment overload or prolonged power outage in non-fault areas. Therefore, a real-time monitoring and fault location system for a vehicle-mounted mobile substation is designed. SUMMARY

[0004] The present application aims to provide a real-time monitoring and fault location system for a vehicle-mounted mobile substation to solve the problem of incorrect fault section division or ineffective isolation strategies caused by the traditional static topology model not being able to update in real time, and the problem of equipment overload or prolonged power outage in non-fault areas caused by traditional methods only focusing on positioning accuracy and ignoring line capacity constraints and recovery efficiency.

[0005] To achieve the above purpose, the present application provides a real-time monitoring and fault location system for a vehicle-mounted mobile substation, which includes.

[0006] As a further improvement of the present technical solution, the fault type identification unit includes a traveling wave front detection module, a fault feature calculation module, and a type discrimination output module.

[0007] The traveling wave front detection module is used to detect and output the arrival time of the fault wave front based on the traveling wave signal characteristics and line parameters, including the first-end wave front time and the tail-end wave front time , and the first-end wave front time and the tail-end wave front time Energy amplitude index;

[0008] The fault characteristic quantity calculation module is used to calculate the arrival time of the fault wavefront. , Intercepting the instantaneous impedance of the fault and instantaneous impedance and characteristic impedance The deviation, and extract the start and end signals at the beginning and end. The polarity sign at the location;

[0009] The type discrimination output module compares and calculates the bias and polarity sign with the static threshold rules and the trained classifier model, and finally outputs the fault type and the maximum confidence value. .

[0010] As a further improvement to this technical solution, the algorithm fusion positioning unit includes a traveling wave preliminary positioning module and an algorithm optimized positioning module;

[0011] The traveling wave preliminary location module is used to identify the waveform characteristics of the fault traveling wave signal according to the fault type, calculate the preliminary fault point based on the arrival time of the traveling wave front and line parameters, and dynamically correct the line parameters and the propagation speed of the traveling wave signal using environmental parameters. ;

[0012] The algorithm optimization and positioning module is used to construct a fault location fitness function based on the initial positioning interval, environmental parameters, and line topology model using the particle swarm optimization algorithm. The line topology model can take into account the influence of line branches and switch state changes during the construction of the fault location fitness function, and output the fault location according to the fault location fitness function.

[0013] As a further improvement to this technical solution, the specific steps for calculating the preliminary fault point based on the arrival time of the traveling wave front and the line parameters in the traveling wave preliminary positioning module are as follows:

[0014] S311. Calculate the time difference based on the arrival time of the traveling wavefront. ;

[0015] S312, Based on time difference The distance between the fault and the beginning of the wavefront was calculated using the double-ended positioning method. And the distance between the fault and the end of the wavefront. ;

[0016] S313, Distance between fault and wavefront tip And the distance between the fault and the end of the wavefront. Calculate the fault range ;

[0017] S314, Output the initial positioning range.

[0018] As a further improvement of the technical solution, in the traveling wave preliminary positioning module, the specific steps of dynamically adjusting the line parameters and the traveling wave signal propagation speed by using the environmental parameters are as follows: first, the line parameters are corrected for temperature and humidity, and the nominal wave speed is calculated according to the corrected line parameters . The influence of temperature and humidity is introduced to generate the corrected wave speed .

[0019] As a further improvement of the technical solution, the algorithm optimization positioning module includes a topological correlation analysis submodule, which is used to collect the real-time switch state and branch connection relationship of the substation, convert the substation topology into a graph structure, and construct a line topology model.

[0020] As a further improvement of the technical solution, in the topological correlation analysis submodule, the specific steps of constructing the line topology model are as follows:

[0021] S3211, define nodes and edges according to the real-time collected data, the nodes include power nodes, load nodes, branch nodes, switch nodes, each node records the physical position, the live state and the parameters; each edge records the impedance and weight .

[0022] S3212, construct an adjacency matrix to record the connection relationship between nodes, if node and node exist an edge, get the edge weight, construct the correlation matrix, and record the physical properties and state information of the nodes and edges;

[0023] S3213, update the weight of the line topology model edge according to the switch state, the historical action times and the environmental parameters, and generate the updated weight .

[0024] As a further improvement of the technical solution, the algorithm optimization positioning module uses the particle swarm optimization algorithm to construct a fault location fitness function based on the preliminary positioning interval, environmental parameters and line topology model, and the specific steps are as follows:

[0025] S321, randomly generate particle position in the preliminary positioning interval, which represents the fault location; the particle velocity is initialized as a small random value, and the initial particle density is adjusted according to the environmental parameters to preferentially search the high probability area;

[0026] Wherein, the fault position includes continuous variables and binary vectors in the preliminary positioning interval of the fault point, and the real-time state of the switch As a known input parameter, it is acquired in real time from the topology correlation analysis submodule;

[0027] S322, the traveling wave speed is corrected according to the real-time environmental parameter, and the line impedance is corrected according to the real-time line parameter;

[0028] S323, according to the switch state collected in real time by the topology correlation analysis submodule , the line topology model is dynamically adjusted in real time, and the fault section and the non-fault section are divided;

[0029] S324, a multi-objective fault location fitness function is constructed The main objective of the fault location fitness function is to minimize the sum of squares of errors between the measured value and the calculated value, and the secondary objective is to consider the line capacity limit and the recovery efficiency;

[0030] S325, iteratively update the particle velocity and position ;

[0031] S326, when The standard deviation is less than the threshold value or reaches the preset iteration number, the fault point position is output, and the switch operation sequence is output based on .

[0032] As a further improvement of the technical solution, in the S324, a topology constraint term is introduced for optimization in the process of constructing the multi-objective fault location fitness function, thereby generating an optimized multi-objective fault location fitness function; the topology constraint term includes switch state and connectivity constraint and fault path connectivity constraint.

[0033] As a further improvement of the technical solution, the fault control execution unit can also generate an operation sequence of the circuit breaker to isolate the fault area according to the line topology model constructed by the topology correlation analysis submodule, and determine the minimum power outage range through line topology model analysis to control the standby power supply to restore power supply to the non-fault area.

[0034] Compared with the prior art, the beneficial effects of the present application are:

[0035] In the vehicle-mounted mobile substation real-time monitoring and fault location system, through the fusion of traveling wave preliminary positioning and particle swarm optimization algorithm, combined with dynamic environmental parameter correction, real-time line topology modeling and multi-objective fitness function, high-precision fault location and rapid isolation and recovery are realized. Through the dynamic modeling of switch state and branch change by the topology correlation analysis submodule, the accuracy of fault section division in complex distribution network is ensured, and misjudgment caused by topology change or equipment overload is effectively avoided. BRIEF DESCRIPTION OF DRAWINGS

[0036] Fig. 1 is a whole flow chart of the application;

[0037] Fig. 2 is a flow chart of the algorithm optimization positioning module in the application;

[0038] The meanings of various labels in the figure are as follows:

[0039] 1, signal acquisition processing unit; 2, fault type identification unit; 21, traveling wave head detection module; 22, fault characteristic quantity calculation module; 23, type discrimination output module; 3, algorithm fusion positioning unit; 31, traveling wave preliminary positioning module; 32, algorithm optimization positioning module; 321, topology correlation analysis submodule; 4, fault control execution unit. DETAILED DESCRIPTION

[0040] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by a person of ordinary skill in the art without creative labor fall within the protection scope of the application.

[0041] Please refer to Figs. 1-2 as shown, a vehicle-mounted mobile substation real-time monitoring and fault positioning system is provided, comprising a signal acquisition processing unit 1, a fault type identification unit 2, an algorithm fusion positioning unit 3 and a fault control execution unit 4;

[0042] The signal acquisition processing unit 1 is used for deploying sensors at key nodes of a power transmission line of a substation, monitoring and collecting line parameters and environmental parameters of the substation and traveling wave signals generated when a fault occurs in real time;

[0043] The fault type identification unit 2 is used for calculating the confidence degree of each fault type based on the traveling wave signals to determine the fault type generated by the traveling wave signals, and marking the time when the traveling wave head arrives;

[0044] The fault type identification unit 2 comprises a traveling wave head detection module 21, a fault characteristic quantity calculation module 22 and a type discrimination output module 23;

[0045] The traveling wave head detection module 21 is used for detecting and outputting the arrival time of the fault wave head based on the traveling wave signal characteristics and the line parameters, and the arrival time of the fault wave head comprises a head wave head time and a tail wave head time , and also carries energy amplitude indicators of the head wave head time and the tail wave head time ;

[0046] Accurate capture of the wave head arrival time of the traveling wave signal at the time of fault occurrence and its energy characteristics provide a time reference and key parameters for subsequent fault analysis;

[0047] The fault characteristic quantity calculation module 22 is used to calculate the arrival time of the fault wave head , , intercept the instantaneous impedance of the fault and the deviation of the instantaneous impedance from the characteristic impedance , and extract the polarity signs of the head-end and tail-end signals at ; extract the key electrical parameters at the time of fault occurrence to provide quantitative basis for fault type discrimination;

[0048] The type discrimination output module 23 is used to compare and calculate the deviation and polarity signs with static threshold rules and trained classifier models, and finally output the fault type and maximum confidence value ;

[0049] The static threshold rule is an impedance deviation threshold rule, which sets a threshold interval according to the deviation of the line characteristic impedance and the measured instantaneous impedance , and is used to preliminarily judge the fault type. If , it is determined as a serious short circuit (such as three-phase short circuit); if , it is determined as a ground fault (such as single-phase grounding); if , it is determined as a normal line.

[0050] The classifier model is a random forest classifier based on the input: impedance deviation , polarity sign combination, energy amplitude index, environmental parameters (temperature, humidity), line historical fault record (such as historical short circuit frequency); output: fault type label (such as "single-phase grounding" "inter-phase short circuit") and maximum confidence value ;

[0051] The algorithm fusion positioning unit 3 is used to preliminarily locate the fault point according to the fault type and the time of arrival of the traveling wave wave head, and then correct the preliminary positioning error by constructing an adaptive function through a particle swarm optimization algorithm combined with the line topology and environmental parameters, and optimize the line branch, switch state change and environmental parameters in the process of constructing the adaptive function;

[0052] The algorithm fusion positioning unit 3 includes a traveling wave preliminary positioning module 31 and an algorithm optimization positioning module 32.

[0053] ​The traveling wave preliminary positioning module 31 is configured to identify the waveform feature of the fault traveling wave signal according to the fault type, and calculate the preliminary fault point according to the traveling wave front arrival time and the line parameter, and dynamically correct the line parameter and the traveling wave signal propagation speed by using the environmental parameter ;

[0054] The identification of the waveform feature of the fault traveling wave signal according to the fault type is based on the mapping rule of the fault type and the waveform feature in the historical knowledge base, and each fault type corresponds to a traveling wave waveform feature.

[0055] In the traveling wave preliminary positioning module 31, the specific steps of calculating the preliminary fault point according to the traveling wave front arrival time and the line parameter are as follows:

[0056] S311, calculating the time difference according to the traveling wave front arrival time ; ; provides a key time reference for the subsequent double-end positioning method, and avoids the large deviation caused by the wave speed error of the single-end positioning method. Provides a key time reference for the subsequent double-end positioning method, and avoids the large deviation caused by the wave speed error of the single-end positioning method.

[0057] S312, based on the time difference , the distance between the fault and the front end of the wave head is calculated by using the double-end positioning method and the distance between the fault and the tail end of the wave head ;

[0058]

[0059] According to determine the fault direction:

[0060] Front end direction:

[0061]

[0062] Tail end direction:

[0063]

[0064] In the formula, is the total length of the line;

[0065] S313, the distance between the fault and the front end of the wave head and the distance between the fault and the tail end of the wave head , the fault interval range is calculated ;

[0066]

[0067]

[0068] In the formula, is a time error; is a wave velocity error; is an error interval;

[0069] By introducing the time error and the wave velocity error, the possible fluctuation interval of the fault point is determined, and the positioning interval is avoided to be too large or too small due to measurement noise or wave velocity estimation deviation. The calculation of the positioning interval combines the double influence of the time error and the wave velocity error on the positioning result, so that the preliminary positioning interval is closer to the actual fault position, and a more accurate search range is provided for the subsequent optimization algorithm.

[0070] In S314, the preliminary positioning interval is output. An efficient starting point is provided for the subsequent particle swarm optimization algorithm (such as the algorithm optimization positioning module 32), and the calculation amount of global search is reduced. Even when there is an error in wave velocity or time measurement, a reliable fault area can still be provided, laying a foundation for subsequent high-precision correction.

[0071] In the traveling wave preliminary positioning module 31, the specific steps of dynamically adjusting the line parameters and the traveling wave signal propagation speed by using the environmental parameters are as follows: first, the line parameters are corrected for temperature and humidity, and the nominal wave velocity is calculated according to the corrected line parameters . The influence of temperature and humidity is introduced to generate the corrected wave velocity .

[0072] The algorithm optimization positioning module 32 is used to construct a fault positioning fitness function based on the preliminary positioning interval, the environmental parameters and the line topology model by using the particle swarm optimization algorithm. The line topology model can consider the influence of line branches and switch state changes in the process of constructing the fault positioning fitness function, and output the fault position according to the fault positioning fitness function;

[0073] The algorithm optimization positioning module 32 includes a topology correlation analysis submodule 321, which is used to collect the real-time switch state and branch connection relationship of the substation, convert the substation topology into a graph structure, and construct a line topology model;

[0074] In the topology correlation analysis submodule 321, the specific steps of constructing the line topology model are as follows:

[0075] S3211, define nodes and edges according to the real-time collected data, the nodes include power nodes, load nodes, branch nodes and switch nodes, each node records the physical position, the live state and the parameters; each edge records the impedance and the weight ;

[0076] S3212, construct an adjacency matrix to record the connection relationship between nodes. If node and node If edges exist, obtain the edge weights, construct an association matrix, and associate the physical attributes and state information of the nodes and edges;

[0077] Adjacency matrices quickly determine whether nodes are connected using matrix operations (e.g., adjacency matrices). Represents a node and (Directly connected), supporting rapid fault path search or connectivity analysis. The adjacency matrix binds edge weights and impedances to node states (such as whether a switch is closed), ensuring consistency between the topology model and real-time data. By using the adjacency matrix and the adjacency matrix together, minimum spanning trees, critical paths, etc., can be quickly calculated, assisting in the formulation of fault isolation and power restoration strategies.

[0078] S3213. Update the weights of the edges in the line topology model based on the switch status, historical action count, and environmental parameters, and generate the updated weights. ;

[0079]

[0080] In the formula, The nominal weight of the edge under static impedance; For switch state functions; For nodes and nodes The on / off state; For historical action functions; For nodes and nodes The number of historical actions; For environment functions; For temperature parameters; For humidity parameters;

[0081] When a change in switch state is detected, the following operation is triggered:

[0082] Update the corresponding edge in the adjacency matrix And triggering local recalculation of the topology through the correlation matrix; recording the historical number of switch actions. and duration, used for long-term weight adjustments;

[0083] This avoids frequent reconstruction of the entire network topology, updating only the affected areas, improving real-time performance and computational efficiency, and leveraging historical action counts. The weights are dynamically adjusted to reflect equipment reliability and assist in preventative maintenance.

[0084] In the algorithm optimization and localization module 32, the specific steps for constructing the fault location fitness function based on the preliminary localization interval, environmental parameters, and line topology model using the particle swarm optimization algorithm are as follows:

[0085] S321, randomly generate particle position in the preliminary positioning interval , representing the physical coordinates of the fault point; particle velocity initialized to a small random value, and the initial particle density is adjusted according to the environmental parameters, and the high probability area is preferentially searched;

[0086] wherein the fault position contains continuous variables within the preliminary positioning interval of the fault point and binary vector, real-time state of the switch as a known input parameter, obtained in real time from the topology correlation analysis submodule 321;

[0087] By combining the preliminary positioning interval to reduce the search range, the high computational cost of global search is avoided, and the algorithm convergence speed is improved; According to the environmental parameters (such as the high temperature caused by the expansion of the wire area), the particle density is adjusted, the sampling density of the high-risk area is increased, and the positioning deviation caused by environmental changes is reduced; At the same time, the fault point position and switch state , ensure that the particle position not only reflects the physical fault point, but also considers the influence of the switch state on the topology, and improves the comprehensiveness of the positioning result.

[0088] S322, according to the real-time environmental parameters, the traveling wave velocity is corrected, and the line impedance is corrected according to the real-time line parameters; The environmental parameters are updated every iteration to ensure that the fitness function is calculated based on the latest conditions, especially for the mobility and complex environment of the vehicle-mounted substation;

[0089] S323, according to the switch state collected in real time by the topology correlation analysis submodule 321, the line topology model is dynamically adjusted in real time, and the fault section and the non-fault section are divided; Ensure that the topology model is consistent with the real switch state (such as setting the edge weight of the disconnected switch to infinity), avoid positioning errors caused by topology contradictions; Through the topology model, the fault section is quickly determined, which provides a basis for subsequent recovery efficiency optimization.

[0090] S324, construct a multi-objective fault location fitness function The main objective of this fault location fitness function is to minimize the sum of squares of the error between the measured value and the calculated value, and the secondary objective is to consider the line capacity limit and recovery efficiency;

[0091]

[0092] wherein, is the main objective function; is the total number of current measurement points; is the weight coefficient of the current; is the measured current value; The current is calculated based on the current particle position; This represents the total number of voltage measurement points. This is the weighting factor for voltage; This is the measured voltage value; The voltage is calculated based on the current particle position;

[0093]

[0094] In the formula, For multi-target fault location fitness function; Penalties for line capacity limitations; The weight of the penalty term; To restore efficiency; Weights for recovery efficiency;

[0095] In the process of constructing the multi-objective fault location fitness function, the influence of topological constraint terms is introduced for optimization, thereby generating the optimized multi-objective fault location fitness function; the topological constraint terms include switch state and connectivity constraints and fault path connectivity constraints.

[0096] Topology constraints are used to quantify the connectivity between the fault location and the topology model (e.g., whether the fault point is located on a valid path in the current topology) to prevent location errors caused by the fault point falling in an invalid area (e.g., a disconnected line).

[0097]

[0098] In the formula, The optimized fitness function for multi-target fault location; To control the weights of topological constraints; This is a penalty term for topological constraints;

[0099] Specifically, when the line current exceeds the rated capacity, a squared penalty term is applied:

[0100]

[0101] Recovery efficiency is inversely proportional to the minimum power outage range after fault isolation:

[0102]

[0103] in, Indicates the first The calculated current value of the line, Indicates the first The rated maximum current of the line, This indicates the total number of lines in the system. This represents the set of loads whose power supply can be restored after fault isolation. Indicates the first power demand of the load of the node, power demand of the load of the node, power demand of the load of the node, total load of the system.

[0104] S325, iteratively updating the particle velocity and position ;

[0105] S326, when the standard deviation is less than a threshold value or reaches a preset iteration number, outputting the fault point position , and outputting a switch operation sequence based on ; the output switch operation sequence (based on ) can be directly used for isolating the fault section, reducing the outage time of the non-fault area, quickly positioning and controlling the output to meet the mobility and emergency fault handling requirements of the on-board substation.

[0106] The fault control execution unit 4 is used for converting the fault position and fault type into executable control instructions, and notifying the staff through sound-light alarm and screen pop-up window;

[0107] The fault control execution unit 4 can also generate an operation sequence of the circuit breaker to isolate the fault area according to the line topology model constructed by the topology correlation analysis submodule 321, and determine the minimum outage range through analysis of the line topology model to control the standby power supply to restore power supply to the non-fault area.

[0108] The basic principles, main features and advantages of the present application are shown and described above. It should be understood by those skilled in the art that the present application is not limited by the above examples, and the above examples and descriptions in the specification are only preferred examples of the present application and are not intended to limit the present application. Without departing from the spirit and scope of the present application, various changes and improvements can be made to the present application, and these changes and improvements all fall within the scope of the present application.

Claims

1. A real-time monitoring and fault location system for on-board mobile substations, characterized in that it comprises: The system comprises: a signal acquisition and processing unit (1) for deploying sensors at key nodes of a power transmission line of a substation, monitoring and collecting line parameters and environmental parameters of the substation and a traveling wave signal generated when a fault occurs in real time; a fault type identification unit (2) for calculating a confidence degree of each fault type based on the traveling wave signal to determine the fault type of the traveling wave signal, and marking the time when the traveling wave front arrives, the fault type identification unit (2) comprising a traveling wave front detection module (21), a fault characteristic quantity calculation module (22) and a type discrimination output module (23); Wherein, the traveling wave front detection module (21) is used for detecting and outputting the arrival time of the fault wave front based on the traveling wave signal characteristics and the line parameters, the arrival time of the fault wave front includes the head end wave front time and the tail end wave front time , also with the energy amplitude index of the head end wave front time and the tail end wave front time ; The fault feature quantity calculation module (22) is configured to calculate a time of arrival of a fault wave head , , intercept a transient impedance of the fault and a deviation of the transient impedance from a characteristic impedance , and extract a polarity sign of a head-end signal and a tail-end signal at . The type discrimination output module (23) is configured to compare the deviation and the polarity symbol with a static threshold rule and a trained classifier model, and finally output a fault type and a maximum confidence value. ; an algorithm fusion positioning unit (3) for preliminarily locating a fault point according to the fault type and the time when the traveling wave front arrives, and correcting the preliminary positioning error by constructing an adaptability function based on the particle swarm optimization algorithm, the line topology and the environmental parameters, and optimizing in the process of constructing the adaptability function by considering the influences of line branches, switch state changes and environmental parameters; a fault control execution unit (4) for converting the fault position and the fault type into executable control instructions, and notifying the staff through sound and light alarms and screen pop-up windows; the algorithm fusion positioning unit (3) comprises a traveling wave preliminary positioning module (31) and an algorithm optimization positioning module (32); The traveling wave preliminary positioning module (31) is configured to identify the waveform characteristics of the fault traveling wave signal according to the fault type, calculate a preliminary fault point according to the traveling wave front arrival time and line parameters, and dynamically correct the line parameters and the traveling wave signal propagation speed by using the environmental parameters In the traveling wave preliminary positioning module (31), the specific steps of calculating the preliminary fault point according to the traveling wave front arrival time and the line parameters are as follows: S311. Calculate the time difference based on the arrival time of the traveling wavefront. ; S312, based on the time difference , the double-end positioning method is used to calculate the distance between the fault and the head end of the wave front , and the distance between the fault and the tail end of the wave front ; S313、distance between the fault and the head of the wave front and distance between the fault and the tail of the wave front , calculate the fault interval range ; S314, outputting a preliminary positioning interval The algorithm optimization positioning module (32) is configured to construct a fault positioning adaptability function based on the preliminary positioning interval, the environmental parameters and the line topology model by using the particle swarm optimization algorithm, and output the fault position according to the fault positioning adaptability function, wherein the line topology model can consider the influences of line branches and switch state changes in the process of constructing the fault positioning adaptability function. The algorithm optimization positioning module (32) comprises a topology correlation analysis submodule (321) configured to collect real-time switch states and branch connection relationships of the substation, convert the substation topology into a graph structure, and construct a line topology model. In the topology correlation analysis submodule (321), the specific steps of constructing the line topology model are as follows: S3211、According to the real-time collected data, define nodes and edges, the nodes include power supply nodes, load nodes, branch nodes, switch nodes, each node records physical location, live state and parameters; each edge records impedance and weight ; S3212, construct the adjacency matrix, record the connection relationship between nodes, if the node and node edge exists, get the edge weight, construct the association matrix, and associate the physical properties and state information of the nodes and edges; S3213, updating the weight of the line topology model edge according to the switch state, the historical action times, and the environment parameter, and generating an updated weight ; In the algorithm optimization positioning module (32), the specific steps of constructing the fault positioning adaptability function based on the preliminary positioning interval, the environmental parameters and the line topology model by using the particle swarm optimization algorithm are as follows: S321、Randomly generate particle positions within the preliminary positioning interval , indicating the fault location; particle velocity initialized to a small random value, and the initial particle density is adjusted according to the environmental parameters, and the high probability area is preferentially searched; wherein the fault location comprises a continuous variable within a preliminary positioning interval of the fault point and a binary vector, real-time switch status as known input parameters, real-time acquired from the topology correlation analysis submodule (321) S322, correcting the traveling wave velocity according to real-time environmental parameters and correcting the line impedance according to real-time line parameters; S323、according to the topology correlation analysis submodule (321) real-time collection switch state , real-time dynamic adjustment of line topology model, division fault section and non-fault section; S324, constructing a multi-objective fault location fitness function The main objective of the fault location fitness function is to minimize the sum of square errors between the measured values and the calculated values, and the secondary objective is to consider the line capacity constraints and recovery efficiency; in the process of constructing the multi-objective fault location fitness function, the influence of the topological constraint term is introduced for optimization, and then an optimized multi-objective fault location fitness function is generated, wherein, is the optimized multi-objective fault location fitness function; is the main objective function; is the penalty term for line capacity limit; is the weight for the penalty term; is the restoration efficiency; is the weight for the restoration efficiency; is the weight for the control topology constraint; is the penalty term for the topology constraint; the topology constraint includes the switch state and connectivity constraint and the fault path connectivity constraint; S325, iteratively update the particle velocity and position ; S326、when the standard deviation of the error is less than a threshold or a preset number of iterations is reached, outputting the fault point position while based on outputting the switch operation sequence.

2. The on-board mobile substation real-time monitoring and fault location system of claim 1, wherein: In the traveling wave preliminary positioning module (31), the specific method for dynamically adjusting the line parameters and the traveling wave signal propagation speed by using the environmental parameters is: first, temperature correction and humidity correction are performed on the line parameters, and then the nominal wave speed is calculated according to the corrected line parameters . Then, the influence of temperature and humidity is introduced to generate the corrected wave speed . .

3. The on-board mobile substation real-time monitoring and fault location system of claim 1, wherein: In S324, the influence of the topology constraint term is introduced in the process of constructing the multi-objective fault positioning adaptability function for optimization, and then an optimized multi-objective fault positioning adaptability function is generated; the topology constraint term comprises switch state and connectivity constraints and fault path connectivity constraints.

4. The on-board mobile substation real-time monitoring and fault location system of claim 3, wherein: The fault control execution unit (4) can also generate an operation sequence of circuit breakers to isolate the fault area according to the line topology model constructed by the topology correlation analysis submodule (321), determine the minimum power outage range through the line topology model analysis, and control the standby power supply to restore power supply to the non-fault area.

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