Real-time monitoring and fault positioning system for vehicle-mounted mobile substation

Abstract

The invention relates to the technical field of power system automation, in particular to a real-time monitoring and fault positioning system for a vehicle-mounted mobile substation. The system comprises a signal acquisition and processing unit which monitors and acquires line parameters and environmental parameters of a transformer substation and traveling wave signals generated when a fault occurs in real time; the fault type identification unit calculates the confidence coefficient of each fault type based on the traveling wave signal so as to judge the fault type generated by the traveling wave signal, and marks the arrival time of the traveling wave head; an algorithm fusion positioning unit preliminarily positions a fault point according to the fault type and the arrival time of a traveling wave head, and then constructs a fitness function through a particle swarm optimization algorithm in combination with line topology and environmental parameters to correct a preliminary positioning error; according to the system, high-precision fault positioning and rapid isolation recovery are realized, the accuracy of fault section division in the complex power distribution network is ensured by modeling switch state and branch change, and misjudgment caused by topological change or equipment overload is effectively avoided.

Description

Technical Field

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

[0002] A mobile substation is a mobile power infrastructure that integrates power transformers, power distribution equipment, control and protection systems, and communication modules. It is usually mounted on a heavy vehicle or container platform and can be quickly deployed to a designated location within a few hours. Although mobile substations have high reliability, their operating environment is complex (such as harsh outdoor weather, frequent movement, and load fluctuations). They may still fail due to equipment aging, short circuits, insulation failures, or human misoperation. The timeliness and accuracy of fault location are the key to achieving the above goals. Any delay or misjudgment will lead to serious consequences. Therefore, the development of high-precision fault location technology that dynamically adapts to environmental and topological changes has become a core requirement for improving the practicality and safety of mobile substations. During the positioning process, the switch status (such as circuit breaker action) and branch connections of the mobile substation may change frequently. The traditional static topology model cannot be updated in real time, resulting in incorrect fault section division or failure of the isolation strategy. In addition, the traditional method only focuses on positioning accuracy and ignores line capacity limitations and recovery efficiency, which may cause equipment overload or long power outages in non-fault areas. Therefore, a vehicle-mounted mobile substation real-time monitoring and fault location system is designed. Summary of the invention

[0003] The purpose of the present invention is to provide a vehicle-mounted mobile substation real-time monitoring and fault location system to solve the problem that the traditional static topology model proposed in the above background technology cannot be updated in real time, resulting in incorrect fault section division or failure of isolation strategy, and the traditional method only focuses on positioning accuracy, ignoring line capacity limitations and recovery efficiency, which may cause equipment overload or long power outages in non-fault areas.

[0004] To achieve the above object, the present invention aims to provide a vehicle-mounted mobile substation real-time monitoring and fault location system, including.

[0005] As a further improvement of the technical solution, the fault type identification unit includes a traveling wave head detection module, a fault feature quantity calculation module and a type discrimination output module; The traveling wave head detection module is used to detect and output the arrival time of the fault wave head based on the characteristics of the traveling wave signal and the line parameters. The arrival time of the fault wave head includes the head end wave head time and tail end wave head time , also includes the first end wave time and tail end wave head time Energy amplitude index; The fault feature quantity calculation module is used to calculate according to the arrival time of the fault wavefront , , intercept the instantaneous impedance of the fault and the deviation between the instantaneous impedance and the characteristic impedance , and extract the polarity symbols of the head and tail signals at ; The type discrimination output module is used to compare and calculate the deviation and polarity symbols with the static threshold rule and the trained classifier model, and finally output the fault type and the maximum confidence value .

[0006] As a further improvement of this technical solution, the algorithm fusion positioning unit includes a traveling wave preliminary positioning module and an algorithm optimization positioning module; Among them, the traveling wave preliminary positioning module is used to identify the waveform characteristics of the fault traveling wave signal according to the fault type, calculate the preliminary fault point according to the arrival time of the traveling wave front and the line parameters, and dynamically correct the line parameters and the propagation speed of the traveling wave signal by using the environmental parameters ; The algorithm optimization positioning module is used to construct a fault positioning fitness function based on the preliminary positioning interval, 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 during the construction of the fault positioning fitness function, and output the fault position according to the fault positioning fitness function.

[0007] As a further improvement of this technical solution, in the traveling wave preliminary positioning module, the specific steps of calculating the preliminary fault point according to the arrival time of the traveling wave front and the line parameters are as follows: S311. Calculate the time difference according to the arrival time of the traveling wave front ; S312. Based on the time difference , use the double-end positioning method to calculate the distance between the fault and the head end of the wavefront and the distance between the fault and the tail end of the wavefront ; S313. The distance between the fault and the head end of the wavefront and the distance between the fault and the tail end of the wavefront , calculate the fault interval range ; S314. Output the preliminary positioning interval.

[0008] As a further improvement of this technical solution, in the traveling wave preliminary positioning module, the specific method of dynamically adjusting the line parameters and the propagation speed of the traveling wave signal by using the environmental parameters is as follows: first, correct the line parameters for temperature and humidity, and calculate the nominal wave speed according to the corrected line parameters , and then compare with the nominal wave velocity Introduce the influence of temperature and humidity to generate the corrected wave velocity .

[0009] As a further improvement of this technical solution, the algorithm optimization positioning module includes a topology correlation analysis sub-module. The topology correlation analysis sub-module is used to collect the real-time switch status and branch connection relationship of the substation, convert the substation topology into a graph structure, and construct a line topology model.

[0010] As a further improvement of this technical solution, in the topology correlation analysis sub-module, the specific steps of constructing the line topology model are as follows: S3211: Define nodes and edges according to the real-time collected data. The nodes include power supply nodes, load nodes, branch nodes, and switch nodes. Each node records the physical location, energized state, and parameters; each edge records the impedance and weight ; S3212: Construct an adjacency matrix to record the connection relationship between nodes. If there is an edge between node and node , obtain the edge weight, construct an incidence matrix, and associate the physical attributes and status information of the nodes and edges; S3213: Update the weight of the edges in the line topology model according to the switch state, historical action times, and environmental parameters to generate the updated weight .

[0011] As a further improvement of this technical solution, the specific steps for the algorithm optimization positioning module to construct a fault location fitness function based on the particle swarm optimization algorithm, the preliminary positioning interval, environmental parameters, and the line topology model are as follows: S321: Randomly generate particle positions within the preliminary positioning interval , representing the fault location; the particle velocity is initialized to a small random value, and the initial particle density is adjusted according to the environmental parameters to preferentially search the high-probability area; Among them, the fault location includes continuous variables within the preliminary positioning interval of the fault point and a binary vector. The real-time state of the switch is used as a known input parameter and is obtained in real time from the topology correlation analysis sub-module; S322: Correct the traveling wave velocity according to the real-time environmental parameters and correct the line impedance according to the real-time line parameters; S323: Dynamically adjust the line topology model in real time according to the switch state collected by the topology correlation analysis sub-module in real time, and divide the fault section and the non-fault section; S324: Construct a multi-objective fault location fitness function , the main objective of the fault location fitness function is to minimize the sum of the squared errors between the measured values and the calculated values, and the secondary objective is to consider the line capacity limit and the restoration efficiency; S325. Iteratively update the particle velocity and position ; S326. When the standard deviation is less than the threshold or the preset number of iterations is reached, output the fault point location , and at the same time, based on output the switch operation sequence.

[0012] As a further improvement of the technical solution, in S324, the influence of the topological constraint term is introduced and optimized during the construction of the multi-objective fault location fitness function, and then the optimized multi-objective fault location fitness function is generated; the topological constraint term includes the switch state and connectivity constraint and the fault path connectivity constraint.

[0013] As a further improvement of the technical solution, the fault control execution unit can also generate the operation sequence of the circuit breaker to isolate the fault area according to the line topology model constructed by the topological correlation analysis sub-module, and at the same time determine the minimum power outage range through the analysis of the line topology model, and control the standby power supply to restore the power supply of the non-fault area.

[0014] Compared with the prior art, the beneficial effects of the present invention: In the on-vehicle mobile substation real-time monitoring and fault location system, through the fusion of the traveling wave preliminary location and the particle swarm optimization algorithm, combined with the dynamic environment parameter correction, the real-time line topology modeling and the multi-objective fitness function, the high-precision fault location and the rapid isolation and restoration are realized. Through the dynamic modeling of the switch state and the branch change by the topological correlation analysis sub-module, the accuracy of the fault section division in the complex distribution network is ensured, and the misjudgment caused by the topological change or the equipment overload is effectively avoided. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is the overall flow block diagram of the present invention; Figure 2 is the flow block diagram of the algorithm optimization and location module in the present invention; The meanings of the various reference numerals in the figure are as follows: 1. Signal acquisition and processing unit; 2. Fault type identification unit; 21. Traveling wave head detection module; 22. Fault feature quantity calculation module; 23. Type discrimination output module; 3. Algorithm fusion and location unit; 31. Traveling wave preliminary location module; 32. Algorithm optimization and location module; 321. Topological correlation analysis sub-module; 4. Fault control execution unit. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] See also Figure 1-2 As shown, a vehicle-mounted mobile substation real-time monitoring and fault location system is provided, including a signal acquisition and processing unit 1, a fault type identification unit 2, an algorithm fusion location unit 3 and a fault control execution unit 4; The signal acquisition and processing unit 1 is used to deploy sensors at key nodes of the power transmission line of the substation to monitor and collect line parameters and environmental parameters of the substation in real time as well as traveling wave signals generated when a fault occurs; The fault type identification unit 2 is used to calculate the confidence of each fault type based on the traveling wave signal to determine the fault type generated by the traveling wave signal, and mark the time when the traveling wave head arrives; The fault type identification unit 2 includes a traveling wave head detection module 21, a fault feature quantity calculation module 22 and a type discrimination output module 23; The traveling wave head detection module 21 is used to detect and output the arrival time of the fault wave head based on the traveling wave signal characteristics and line parameters. The arrival time of the fault wave head includes the head end wave head time and tail end wave head time , also includes the first end wave time and tail end wave head time Energy amplitude index; Accurately capture the wave head arrival time and energy characteristics of the traveling wave signal when a fault occurs, providing a time reference and key parameters for subsequent fault analysis; The fault characteristic quantity calculation module 22 is used to calculate the fault characteristic quantity according to the arrival time of the fault wave head. , , intercepting the instantaneous impedance of the fault And instantaneous impedance and characteristic impedance The deviation is obtained by extracting the head-end and tail-end signals. The polarity sign at the fault location is extracted to provide a quantitative basis for fault type identification. The type discrimination output module 23 is used to compare and calculate the deviation and polarity symbol with the static threshold rule and the trained classifier model, and finally output the fault type and the maximum confidence value. ; The static threshold rule is the impedance deviation threshold rule, which is based on the line characteristic impedance. and the measured instantaneous impedance Deviation Set a threshold interval for preliminary judgment of the fault type. If , it is determined as a severe short circuit (such as a three-phase short circuit); if , it is determined as a grounding fault (such as a single-phase grounding); if , it is determined that the line is normal.

[0018] The classifier model is based on a random forest classifier. Input: impedance deviation , combination of polarity symbols, energy amplitude index, environmental parameters (temperature, humidity), line historical fault records (such as historical short circuit frequency); Output: fault type label (such as "single-phase grounding", "phase-to-phase short circuit") and maximum confidence value ; The algorithm fusion positioning unit 3 is used to initially locate the fault point according to the fault type and the arrival time of the traveling wave head, and then construct a fitness function through the particle swarm optimization algorithm combined with the line topology and environmental parameters, so as to correct the initial positioning error, and optimize considering the influence of line branches, switch state changes and environmental parameters during the construction of the fitness function; The algorithm fusion positioning unit 3 includes a traveling wave initial positioning module 31 and an algorithm optimization positioning module 32; Among them, the traveling wave initial positioning module 31 is used to identify the waveform characteristics of the fault traveling wave signal according to the fault type, calculate the initial fault point according to the arrival time of the traveling wave head and the line parameters, and dynamically correct the line parameters and the traveling wave signal propagation speed using environmental parameters ; Identifying the waveform characteristics of the fault traveling wave signal according to the fault type is based on the mapping rule between the fault type and the waveform characteristics in the historical knowledge base, and each fault type corresponds to a traveling wave waveform characteristic.

[0019] In the traveling wave initial positioning module 31, the specific steps for calculating the initial fault point according to the arrival time of the traveling wave head and the line parameters are as follows: S311. Calculate the time difference according to the arrival time of the traveling wave head ; ; Provide a key time reference for the subsequent double-end positioning method to avoid large deviations caused by wave speed errors in the single-end positioning method. Provide a key time reference for the subsequent double-end positioning method to avoid large deviations caused by wave speed errors in the single-end positioning method.

[0020] S312. Based on the time difference , use the double-end positioning method to calculate the distance between the fault and the head end of the wave and the distance between the fault and the tail end of the wave ; According to Judge the fault direction: Head-end direction: Tail-end direction: Wherein, is the total length of the line; S313. The distance between the fault and the head end of the wavefront and the distance between the fault and the tail end of the wave are used to calculate the fault interval range ; Wherein, is the time error; is the wave velocity error; is the error interval; By introducing the time error and the wave velocity error, the possible fluctuation interval of the fault point is clarified, avoiding the over-large or over-small positioning interval caused by measurement noise or wave velocity estimation deviation. The calculation of combines the dual effects of the time error and the wave velocity error on the positioning result, making the preliminary positioning interval closer to the actual fault location and providing a more accurate search range for the subsequent optimization algorithm.

[0021] S314. Output the preliminary positioning interval. Provide an efficient starting point for the subsequent particle swarm optimization algorithm (such as the algorithm optimization positioning module 32), reduce the computational amount of the global search, and still provide a credible fault area even when there are errors in wave velocity or time measurement, laying a foundation for subsequent high-precision correction.

[0022] In the traveling wave preliminary positioning module 31, the specific method of dynamically adjusting the line parameters and the traveling wave signal propagation speed by using the environmental parameters is as follows: first, perform temperature correction and humidity correction on the line parameters, and calculate the nominal wave velocity according to the corrected line parameters , and then introduce the influence of temperature and humidity on the nominal wave velocity to generate the corrected wave velocity .

[0023] The algorithm optimization positioning module 32 is used to construct a fault positioning fitness function based on the preliminary positioning interval, environmental parameters and line topology model by using the particle swarm optimization algorithm. The line topology model can consider the influence of line branch and switch state changes during the construction of the fault positioning fitness function, and output the fault location according to the fault positioning fitness function; The algorithm optimization positioning module 32 includes a topology correlation analysis sub-module 321. The topology correlation analysis sub-module 321 is used to collect the real-time switch status 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 sub-module 321, the specific steps for constructing the line topology model are as follows: 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 location, energized status, and parameters; each edge records the impedance and weight ; S3212. Construct an adjacency matrix to record the connection relationships between nodes. If there is an edge between node and node , obtain the edge weight, construct an incidence matrix, and associate the physical attributes and status information of the node and the edge; The adjacency matrix can quickly determine whether nodes are connected through matrix operations (for example, the adjacency matrix indicates that node and are directly connected), supporting fast fault path search or connectivity analysis. The incidence matrix binds the weight and impedance of the edge to the node status (such as whether the switch is closed) to ensure the consistency of the topology model with the real-time data. By jointly using the adjacency matrix and the incidence matrix, the minimum spanning tree, critical path, etc. can be quickly calculated to assist in formulating fault isolation and power supply restoration strategies.

[0024] S3213. Update the weight of the edge in the line topology model according to the switch status, historical action times, and environmental parameters to generate the updated weight ; In the formula, is the nominal weight of the edge under static impedance; is the switch status function; is the switch status of node and node ; is the historical action function; is the historical action times of node and node ; is the environmental function; is the temperature parameter; is the humidity parameter; When a change in the switch status is detected, trigger the following operations: Update the and trigger local recalculation of the topology structure through the correlation matrix; record the historical action times of the switches and duration for long-term weight correction; This avoids frequent reconstruction of the entire network topology, only updates the affected areas, improves real-time performance and computational efficiency, and dynamically adjusts the weights through the historical action times to reflect the device reliability and assist preventive maintenance.

[0025] In the algorithm optimization positioning module 32, the specific steps of constructing a fault location fitness function using the particle swarm optimization algorithm based on the preliminary positioning interval, environmental parameters, and line topology model are as follows: S321. Randomly generate particle positions within the preliminary positioning interval , representing the physical coordinates of the fault point; the particle velocity is initialized to a small random value, and the initial particle density is adjusted according to the environmental parameters to preferentially search high-probability regions; Among them, the fault location includes continuous variables within the preliminary positioning interval of the fault point and binary vectors, and the real-time state of the switches is used as known input parameters and obtained in real time from the topology correlation analysis sub-module 321; By combining the preliminary positioning interval to narrow the search range, avoid the high computational cost of global search, and improve the algorithm convergence speed; adjust the particle density according to environmental parameters (such as the area where the wire expands due to high temperature) to increase the sampling density in high-risk regions and reduce the positioning deviation caused by environmental changes; at the same time, encode the fault point location and switch status to ensure that the particle position not only reflects the physical fault point but also considers the impact of the switch status on the topology, improving the comprehensiveness of the positioning result.

[0026] S322. Correct the traveling wave velocity according to the real-time environmental parameters and correct the line impedance according to the real-time line parameters; update the environmental parameters in each iteration to ensure that the fitness function is calculated based on the latest conditions, especially suitable for the mobility and complex environment of in-vehicle substations; S323. According to the switch status collected in real time by the topology correlation analysis sub-module 321 , dynamically adjust the line topology model in real time to divide the fault section and non-fault section; ensure that the topology model is consistent with the real switch status (such as setting the edge weight of the disconnected switch to infinity) to avoid positioning errors caused by topological contradictions; quickly determine the fault section through the topology model to provide a basis for subsequent recovery efficiency optimization.

[0027] S324. Construct a multi-objective fault location fitness function , the main objective of the fault location fitness function is to minimize the sum of the squared errors between the measured values and the calculated values, and the secondary objective is to consider the line capacity limit and the restoration efficiency; In the formula, 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; is the current calculated based on the current particle position; is the total number of voltage measurement points; is the weight coefficient of the voltage; is the measured voltage value; is the voltage calculated based on the current particle position; In the formula, is the multi-objective fault location fitness function; is the penalty term for the line capacity limit; is the weight of the penalty term; is the restoration efficiency; is the weight of the restoration efficiency; During the construction of the multi-objective fault location fitness function, the influence of the topological constraint term is introduced for optimization, and then the optimized multi-objective fault location fitness function is generated; the topological constraint term includes the switch state and connectivity constraint and the fault path connectivity constraint.

[0028] The topological constraint term is used to quantify the connectivity relationship between the fault location and the topological model (such as whether the fault point is on the effective path of the current topology), and prevent positioning errors caused by the fault point falling in the invalid area (such as the disconnected line).

[0029] In the formula, is the optimized multi-objective fault location fitness function; is the weight for controlling the topological constraint; is the penalty term of the topological constraint; Specifically, when the line current exceeds the rated capacity, a squared penalty term is applied: The restoration efficiency is inversely proportional to the minimum power outage range after fault isolation: Among them, represents the calculated current value of the th line, represents the th line's rated maximum current, Represents the total number of lines in the system. Represents the set of loads that can be restored to power after fault isolation. Represents the power demand of the load at the th node. power demand of the load at the th node.

[0030] S325. Iteratively update the particle velocity and position ; S326. When the standard deviation of is less than the threshold or the preset number of iterations is reached, output the fault location , and at the same time, based on output the switch operation sequence; the output switch operation sequence (based on ) can be directly used to isolate the fault section, reduce the power outage time in the non-fault area, and quickly locate and control the output to meet the mobility and emergency fault handling requirements of in-vehicle substations.

[0031] The fault control execution unit 4 is used to convert the fault location and fault type into executable control instructions and notify the staff through audible and visual alarms and screen pop-ups. The fault control execution unit 4 can also generate the operation sequence of the circuit breaker to isolate the fault area according to the line topology model constructed by the topology association analysis sub-module 321, and at the same time determine the minimum power outage range through the analysis of the line topology model and control the standby power supply to restore power supply to the non-fault area.

[0032] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art of this industry should understand that the present invention is not limited by the above embodiments. The above embodiments and the descriptions in the specification are only preferred examples of the present invention and are not used to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed.

Claims

1. A real-time monitoring and fault location system for in-vehicle mobile substations, characterized in that: Including: A signal acquisition and processing unit (1), which is used to deploy sensors at key nodes of the transmission lines in the substation to monitor and collect the line parameters, environmental parameters of the substation in real time, and the traveling wave signals generated during the occurrence of faults; A fault type identification unit (2), which is used to calculate the confidence of each fault type based on the traveling wave signal to judge the fault type generated by the traveling wave signal, and mark the arrival time of the traveling wave head; An algorithm fusion and positioning unit (3), which is used to initially locate the fault point according to the fault type and the arrival time of the traveling wave head, and then construct a fitness function through the particle swarm optimization algorithm combined with the line topology and environmental parameters, so as to correct the initial positioning error, and optimize by considering the influence of line branches, switch state changes and environmental parameters during the construction of the fitness function; A fault control execution unit (4), which is used to convert the fault location and fault type into executable control instructions, and notify the staff through audible and visual alarms and screen pop-ups.

2. The on-vehicle mobile substation real-time monitoring and fault location system according to claim 1, wherein: The fault type identification unit (2) includes a traveling wave head detection module (21), a fault feature quantity calculation module (22) and a type discrimination output module (23); The traveling wave head detection module (21) is used to detect and output the arrival time of the fault wave head based on the traveling wave signal characteristics and line parameters. The arrival time of the fault wave head includes the head end wave head time and tail end wave head time , also includes the first end wave time and tail end wave head time Energy amplitude index; The fault feature quantity calculation module (22) is used to intercept the instantaneous impedance of the fault according to the arrival time of the fault wavefront , , and the deviation between the instantaneous impedance and the characteristic impedance and extract the polarity symbols of the head and tail signals at ; ​ The type discrimination output module (23) is used to compare and calculate the deviation and polarity symbol with the static threshold rule and the trained classifier model, and finally output the fault type and the maximum confidence value .

3. The on-vehicle mobile substation real-time monitoring and fault location system according to claim 2, wherein: The algorithm fusion and positioning unit (3) includes a traveling wave initial positioning module (31) and an algorithm optimization and positioning module (32); Among them, the traveling wave preliminary positioning module (31) 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 head and the line parameters, and dynamically correct the line parameters and the traveling wave signal propagation speed by using the environmental parameters ; The algorithm optimization and positioning module (32) is used to construct a fault location fitness function based on the initial positioning interval, environmental parameters and 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 during the construction of the fault location fitness function, and output the fault location according to the fault location fitness function.

4. The on-vehicle mobile substation real-time monitoring and fault location system according to claim 3, characterized in that: In the traveling wave initial positioning module (31), the specific steps to calculate the initial fault point according to the arrival time of the traveling wave head and the line parameters are as follows: S311. Calculate the time difference based on the arrival time of the traveling wave front ; 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 wavefront and the distance between the fault and the tail end of the wavefront ; S313. Distance between the fault and the head end of the wavefront and the distance between the fault and the tail end of the wavefront to calculate the fault interval range ; S314. Output the initial positioning interval.

5. The on-vehicle mobile substation real-time monitoring and fault location system according to claim 4, 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 using environmental parameters is as follows: First, perform temperature correction and humidity correction on the line parameters, and calculate the nominal wave speed according to the corrected line parameters , and then introduce the influence of temperature and humidity to generate the corrected wave speed .

6. The on-vehicle mobile substation real-time monitoring and fault location system according to claim 5, wherein: The algorithm optimization and positioning module (32) includes a topology correlation analysis sub-module (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.

7. The on-vehicle mobile substation real-time monitoring and fault location system according to claim 6, characterized in that: In the topology correlation analysis sub-module (321), the specific steps to construct the line topology model are as follows: S3211. Define nodes and edges according to the real-time collected data. The nodes include power source nodes, load nodes, branch nodes, and switch nodes. Each node records the physical location, energized state, and parameters. Each edge records the impedance and weight ; S3212. Construct an adjacency matrix to record the connection relationships between nodes. If there is an edge between node and node , obtain the edge weight, construct an incidence matrix, and associate the physical attributes and status information of the nodes and edges; S3213. Update the weights of the edges in the line topology model according to the switch status, historical action times, and environmental parameters to generate the updated weights .

8. The real-time monitoring and fault location system for in-vehicle mobile substations according to claim 7, wherein: In the algorithm optimization and positioning module (32), the specific steps to construct a fault location fitness function based on the initial positioning interval, environmental parameters and line topology model by using the particle swarm optimization algorithm are as follows: S321. Randomly generate particle positions within the preliminary positioning interval , representing the fault position; the particle velocity is initialized to a small random value, and the initial particle density is adjusted according to the environmental parameters to preferentially search the high-probability area; Among them, the fault location includes continuous variables within the preliminary location interval of the fault point and a binary vector, the real-time state of the switch As known input parameters, they are obtained in real time from the topological association analysis sub-module (321); S322. Correct the traveling wave velocity according to the real-time environmental parameters and correct the line impedance according to the real-time line parameters; S323. According to the switch states collected in real time by the topology association analysis sub-module (321), dynamically adjust the line topology model in real time, and divide the fault section and the non-fault section; S324. Construct a multi-objective fault location fitness function , where the main objective of this fault location fitness function is to minimize the sum of the squared errors between the measured values and the calculated values, and the secondary objectives are to consider the line capacity limit and the restoration efficiency; S325. Iteratively update the particle velocity and position ; S326. When has a standard deviation less than the threshold or reaches the preset number of iterations, output the location of the fault point , and at the same time, based on output the switch operation sequence.

9. The real-time monitoring and fault location system for in-vehicle mobile substations according to claim 8, characterized in that: In S324, the influence of the topology constraint term is introduced for optimization during the construction of the multi-objective fault location fitness function, and then the optimized multi-objective fault location fitness function is generated; the topology constraint term includes switch state and connectivity constraints and fault path connectivity constraints.

10. The on-vehicle mobile substation real-time monitoring and fault location system according to claim 9, characterized in that: 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 association analysis sub-module (321). At the same time, it analyzes and determines the minimum power outage range through the line topology model, and controls the standby power supply to restore power supply to the non-fault area.

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