A power transmission line distributed fault diagnosis method and system

Abstract

This invention relates to the field of power transmission line technology, specifically to a distributed fault diagnosis method and system for power transmission lines. The method includes: first, acquiring voltage and current data and the actual arrival time of the fault traveling wave from multiple monitoring terminals, and calculating the reliability of information from each terminal; then, initializing an artificial bee colony, employing bees to search directionally according to the traveling wave propagation direction, and observing bees selecting nectar sources based on information reliability; next, calculating a stagnation index, dynamically combining the strategies of employed bees and scout bees to generate candidate locations; finally, calculating a fitness value by comprehensively considering the electrical quantity fitting degree and the traveling wave timing matching degree, and outputting the optimal candidate fault parameters. This invention improves the convergence speed through directional searching in the traveling wave propagation direction and significantly improves the accuracy and robustness of fault location by adaptively balancing local development and global exploration through the stagnation index.

Description

Technical Field

[0001] This invention relates to the field of power transmission line technology. More specifically, this invention relates to a method and system for distributed fault diagnosis of power transmission lines. Background Technology

[0002] Transmission lines are a crucial component of the power system, and their operational status directly impacts the safety and stability of the power grid. When a line fault occurs, quickly and accurately determining the fault location and type is essential for minimizing power outage time and improving power supply reliability.

[0003] Currently, the mainstream methods for fault location in transmission lines mainly include the impedance method and the traveling wave method. The impedance method calculates the distance from the fault point to the measuring end by measuring the changes in voltage and current before and after the fault. Its principle is simple and the cost is low. However, this method is susceptible to factors such as transition resistance, load fluctuations, and line asymmetry. Especially when high-resistance grounding faults occur or when applied to complex power grid topologies, the location accuracy will decrease significantly.

[0004] The traveling wave method utilizes the characteristic of electromagnetic traveling waves generated instantaneously during a fault to propagate along the line for distance measurement. Based on the advantages of traveling waves, which have a propagation speed approximately equal to the speed of light and are unaffected by transition resistance, this method theoretically possesses high distance measurement accuracy. However, traditional traveling wave methods often rely on data from single or double ends of the line. When the line structure is complex (e.g., with T-shaped branches) or the signal is severely interfered with, accurate judgments are difficult to make based solely on limited monitoring point information. Furthermore, most existing distributed traveling wave location algorithms treat all monitoring data equally, failing to effectively differentiate the data quality and importance of different monitoring terminals within the power grid. When some monitoring terminals have poor data quality or experience data loss, the reliability of the diagnostic results is severely affected.

[0005] Therefore, how to effectively integrate distributed multi-source monitoring information and design an intelligent diagnostic algorithm that can adaptively adjust the search strategy and resist the influence of data quality differences, so as to solve the problem of insufficient accuracy and robustness of fault diagnosis in the existing technology, is a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0006] The purpose of this invention is to propose a distributed fault diagnosis method and system for transmission lines to solve the problems of insufficient accuracy and robustness in fault diagnosis in the prior art.

[0007] In a first aspect, the present invention provides a distributed fault diagnosis method for transmission lines, comprising: acquiring voltage and current data and the actual arrival time of fault traveling waves collected by multiple monitoring terminals of the transmission line; initializing an artificial bee colony to randomly generate multiple nectar sources, each nectar source corresponding to a set of fault parameters, the fault parameters including at least the fault location and transition resistance; calculating fitness values ​​based on voltage and current data, iteratively searching according to the hired bee stage, observation bee stage, and scout bee stage of the artificial bee colony algorithm until a termination condition is met, and outputting the fault parameters corresponding to the globally optimal nectar source as the fault diagnosis result; the hired bee stage includes constructing the traveling wave direction of each current nectar source. New nectar sources are generated by directional vectors along the traveling wave direction vector, which is calculated based on the deviation between the actual arrival time and the theoretical arrival time of the faulty traveling wave. In the observation bee stage, new nectar sources are selected from the new nectar sources in the hired bee stage based on the selection probability, and further search for new nectar sources in their neighborhood. The selection probability is proportional to the fitness value of the nectar source and its comprehensive information reliability, which is the average information reliability of multiple monitoring terminals involved in the current nectar source. In the scout bee stage, new nectar sources are generated by performing global random initialization based on the stagnation index of the nectar source. The stagnation index is calculated based on the change in the fitness value of the current nectar source during iteration.

[0008] This invention addresses the insufficient diagnostic accuracy caused by the equal-weighted processing of data from various monitoring terminals in traditional methods by calculating the information reliability of monitoring terminals and combining data quality indicators with electrical importance indicators. A directional search mechanism based on the traveling wave propagation direction is introduced into the artificial bee colony algorithm, giving the search process clear physical meaning and avoiding blind random searches. An adaptive balance between local development and global exploration is achieved by dynamically combining the stagnation index with the strategies of hired bees and scout bees. The fitness value is calculated by comprehensively evaluating the electrical quantity fitting degree and the traveling wave timing matching degree, thus improving the accuracy and robustness of fault diagnosis.

[0009] Optionally, the hired bee stage includes: calculating the theoretical arrival time of the fault traveling wave from the fault location corresponding to the current nectar source to each monitoring terminal; calculating the deviation between the theoretical arrival time and the actual arrival time of the fault traveling wave at each monitoring terminal; constructing a traveling wave direction vector based on the deviation of all monitoring terminals; randomly perturbing the fault parameters of the current nectar source along the traveling wave direction vector to generate a new nectar source and replace the old nectar source with poor fitness value.

[0010] This targeted search strategy significantly improves the convergence speed of the algorithm, reduces invalid searches, and makes fault location more accurate and efficient.

[0011] Optionally, the calculation steps of the traveling wave direction vector include: calculating the theoretical arrival time of the fault traveling wave to each monitoring terminal based on the fault location of the current honey source and using the traveling wave transmission speed; calculating the difference between the theoretical arrival time and the actual arrival time of the fault traveling wave at each monitoring terminal to obtain the time deviation; constructing a unit direction vector from the fault location corresponding to the current honey source to each monitoring terminal; and using the time deviation as a weight to perform a weighted summation of the unit direction vectors corresponding to each monitoring terminal to obtain the traveling wave direction vector, so as to guide the search in the direction of reducing the sum of the absolute values ​​of the time deviations of all monitoring terminals.

[0012] Optionally, the observation bee stage includes: obtaining the fitness value of each nectar source and its corresponding comprehensive information credibility; calculating the selection probability of each nectar source, wherein the selection probability is obtained by normalizing the product of the fitness value and the power of the comprehensive information credibility; and the observation bee selecting a nectar source for subsequent search using a roulette wheel selection method based on the selection probability.

[0013] Optionally, the calculation steps of the stagnation index include: in the hired bee phase and the observation bee phase of each iteration, comparing the fitness value of the newly generated nectar source with the fitness value of the current nectar source; if the fitness value of the newly generated nectar source is not greater than the fitness value of the current nectar source, then incrementing the stagnation index of the current nectar source by 1; if the fitness value of the newly generated nectar source is greater than the fitness value of the current nectar source, then resetting the stagnation index of the current nectar source to 0; the stagnation index serves as the basis for judging whether the nectar source has fallen into a local optimum.

[0014] The stagnation index accurately quantifies the search potential and improvement trend of nectar sources. This index can adaptively identify nectar sources trapped in local optima and trigger global exploration, while strengthening local development of continuously improving nectar sources, effectively avoiding premature convergence of the algorithm.

[0015] Optionally, the scout bee stage includes: determining whether the stagnation index of the current nectar source exceeds a preset activity threshold; if it exceeds the activity threshold, it is determined that the nectar source has fallen into a local optimum, and the scout bee abandons the nectar source; then the scout bee randomly generates a new nectar source in the solution space to replace the abandoned nectar source, and resets the stagnation index of the new nectar source to the initial value.

[0016] Optionally, the calculation of the fitness value includes: calculating the theoretical voltage and theoretical current values ​​of each monitoring terminal based on the fault parameters; calculating the squared difference between the theoretical voltage value and the measured voltage value and the squared difference between the theoretical current value and the measured current value of each monitoring terminal; weighting and summing the squared differences of all monitoring terminals according to the information reliability of the monitoring terminals and taking the square root to obtain the total error; calculating the electrical quantity fit degree component based on the negative correlation function of the total error; calculating the squared difference between the theoretical arrival time of the fault traveling wave and the actual arrival time of the fault traveling wave of each monitoring terminal; summing the squared differences of all monitoring terminals, taking the reciprocal and normalizing to obtain the traveling wave timing matching degree component; and weighting and summing the electrical quantity fit degree component and the traveling wave timing matching degree component according to a preset weight coefficient to obtain the fitness value.

[0017] The fitness value is calculated by combining the electrical quantity fitting degree and the traveling wave timing matching degree with weights. This approach considers both the matching degree of steady-state electrical quantities and the timing characteristics of transient traveling waves. This comprehensive evaluation mechanism enables the fitness value to fully reflect the consistency between candidate fault parameters and actual faults, improving the reliability and accuracy of diagnostic results.

[0018] Optionally, the calculation process of the information credibility includes: obtaining the signal quality parameters of each monitoring terminal, wherein the signal quality parameters include at least the signal-to-noise ratio and the data sampling rate; calculating the basic credibility of the monitoring terminal based on the signal quality parameters using a preset membership function; calculating the theoretical distance between the fault location corresponding to the current honey source and each monitoring terminal; performing distance-weighted correction on the basic credibility based on the theoretical distance, wherein the closer the monitoring terminal is, the greater its weight; and using the corrected basic credibility as the information credibility of the monitoring terminal.

[0019] By comprehensively considering signal-to-noise ratio, sampling rate, and fault distance, the monitoring data is weighted differently, effectively reducing the interference of low-quality data and attenuated signals at distant points on the diagnostic results. This significantly improves the system's noise immunity and fault tolerance in complex electromagnetic environments, ensuring high accuracy and reliability in fault location.

[0020] Optionally, the termination condition is at least one of the following: the number of iterations reaches a preset maximum number of iterations; the fitness value of the global optimal honey source changes less than a preset value in a consecutive preset number of iterations.

[0021] In a second aspect, a distributed fault diagnosis system for transmission lines includes:

[0022] processor;

[0023] The memory stores computer instructions for a distributed fault diagnosis of a transmission line, which, when executed by the processor, cause the system to perform the aforementioned distributed fault diagnosis method for a transmission line.

[0024] The beneficial effects of this invention are as follows: The distributed fault diagnosis method and system for transmission lines provided by this invention constructs a complete intelligent fault diagnosis system through innovative technologies such as information reliability quantification, directional search for traveling wave propagation direction, adaptive strategy combination of stagnation index, and comprehensive evaluation of electrical quantities and traveling wave timing. This method fully utilizes multi-source heterogeneous data from distributed monitoring terminals, effectively integrates electrical quantity information and traveling wave timing information, and achieves differentiated processing of data with different reliability levels. The improved artificial bee colony algorithm has a clear adaptive search capability, significantly improving the accuracy, speed, and robustness of fault location. Attached Figure Description

[0025] Figure 1 This is a flowchart of a distributed fault diagnosis method for power transmission lines according to an embodiment of the present invention.

[0026] Figure 2 This is a structural block diagram of a distributed fault diagnosis system for power transmission lines according to an embodiment of the present invention. Detailed Implementation

[0027] The technical solutions of the embodiments of the present invention will now be clearly and completely described with reference to the accompanying drawings. Figure 1 The diagram shown is a flowchart of a distributed fault diagnosis method for transmission lines according to an embodiment of the present invention.

[0028] S1: Acquire voltage and current data and the actual arrival time of fault traveling waves collected by multiple monitoring terminals on the transmission line.

[0029] First, voltage and current data, as well as the actual arrival time of the fault traveling wave, are acquired from multiple monitoring terminals along the transmission line. These multiple monitoring terminals refer to intelligent devices with data acquisition and communication capabilities deployed at different locations along the transmission line; typically, there are no fewer than three such devices to ensure accurate fault location. Each monitoring terminal is equipped with a high-precision voltage sensor, a current sensor, and a traveling wave detection device.

[0030] When a transmission line fault occurs, each monitoring terminal collects the three-phase voltage and three-phase current values ​​in real time at the moment the fault occurs. Simultaneously, each monitoring terminal records the actual arrival time of the fault traveling wave at that terminal using a high-frequency sampling device. The traveling wave detection device uses a wavelet transform algorithm to identify the arrival time of the traveling wave front, with a sampling frequency set to no less than 1 MHz, achieving microsecond-level time measurement accuracy to ensure the accuracy of subsequent fault location calculations. The recording of the traveling wave arrival time is synchronized using a GPS time synchronization system to ensure the consistency of the time reference of each monitoring terminal.

[0031] Data collected by each monitoring terminal is aggregated to a central processing unit via fiber optic or wireless communication networks. The central processing unit is responsible for data reception, storage, and subsequent processing. Data transmission employs an encryption protocol to ensure data security. The final result is a dataset containing data from multiple monitoring terminals, with each terminal's data including three-phase voltage values, three-phase current values, and the actual arrival time of the traveling wave. This data provides a complete information foundation for subsequent fault diagnosis.

[0032] S2: Initialize the artificial bee colony and perform a targeted search using hired bees.

[0033] After acquiring monitoring data, an artificial bee colony is initialized to begin the fault diagnosis process. The artificial bee colony algorithm is a swarm intelligence optimization algorithm that simulates the foraging behavior of bees. It achieves the search for the optimal solution through the collaborative work of three types of bee colonies: hired bees, observation bees, and scout bees.

[0034] The population size of the artificial bee colony is set to 50 nectar sources in this embodiment. The colony includes 25 hired bees and 25 observation bees. Each nectar source represents a set of fault parameters, which is a potential solution in the search space. The fault parameters include fault location and transition resistance. The fault location is represented by two-dimensional plane coordinates in kilometers. The transition resistance represents the grounding resistance at the fault point or the contact resistance at the short circuit point, with a value ranging from zero to 300 ohms.

[0035] During initialization, 50 honey source locations are randomly generated within the search space. For each honey source, the x and y coordinates of its fault location are randomly generated within the transmission line coverage area, and the transition resistance is randomly generated within the range of 0 to 300 ohms. A stagnation index is initialized for each honey source, with an initial value set to 0.

[0036] The hired bee phase is the core search stage of the artificial bee colony algorithm. In this invention, the hired bees perform directional searches based on the traveling wave direction vector. First, the theoretical arrival time of the fault traveling wave from the fault location represented by the nectar source to each monitoring terminal is calculated. Then, the calculated theoretical arrival time is compared with the actual monitored arrival time of the traveling wave to obtain the time deviation of each monitoring terminal. Next, based on these deviations of all monitoring terminals, a traveling wave direction vector is constructed. Specifically, a unit direction vector is first constructed from the fault location corresponding to the current nectar source to each monitoring terminal. Then, each time deviation is used as a weight to perform a weighted summation of all unit direction vectors to obtain the traveling wave direction vector. This vector guides the search in the direction of reducing the overall time deviation. Finally, the hired bees randomly perturb the fault parameters of the current nectar source along this traveling wave direction vector, thereby generating a candidate new nectar source in the neighborhood. After a new nectar source is generated, if its fitness value is better than that of the old nectar source, the old nectar source is replaced with the new nectar source, and the stagnation index of the current nectar source is reset to 0; otherwise, the new nectar source is abandoned, and the stagnation index of the current nectar source is incremented by 1.

[0037] S3: Calculate the stagnation index and execute the observation bee and scout bee phases.

[0038] After all nectar sources in the population have been searched once by the hired bees, the algorithm enters the observation and scout bee phase. The core task of this phase is to selectively search for nectar sources based on their quality and eliminate stagnant solutions.

[0039] a. During the observation phase, firstly, to make selections, a selection probability needs to be calculated for each nectar source. This probability is determined by two core factors: the fitness value of the nectar source itself and its overall information reliability. The calculation process for the overall information reliability is as follows: obtain the signal quality parameters of each monitoring terminal related to the nectar source, such as signal-to-noise ratio and sampling rate; map these parameters to the basic reliability of each terminal through a preset membership function; then, combine the theoretical distance from the candidate fault location to each terminal, perform distance-weighted correction, and finally calculate the overall information reliability of the nectar source.

[0040] The selection probability is calculated by multiplying the fitness value of each nectar source by a power of its overall information reliability, resulting in an intermediate value proportional to the selection probability. This intermediate value is then normalized across all nectar sources in the population to obtain the final selection probability. The observation bees use a roulette wheel selection method, where the probability of each nectar source being selected is proportional to its selection probability, thus selecting one nectar source from the population. After selecting a nectar source, the observation bees randomly search within its neighborhood to generate a candidate new nectar source. Similarly, the fitness of the new nectar source is evaluated; if it is superior, the old nectar source is replaced and the stagnation index is reset to 0; otherwise, the stagnation index is incremented by 1.

[0041] b. The scout bee phase begins after the observer bees complete their selection and neighborhood search. The algorithm checks the stagnation index of each nectar source in the population to determine if it exceeds a preset activity threshold. If the stagnation index of a nectar source exceeds this threshold, it means that the nectar source has failed to produce a better solution in multiple iterations and is considered to be stuck in a local optimum or stagnant state. At this point, the scout bee abandons this stagnant nectar source and its subsequent search path. Subsequently, the scout bee randomly generates a completely new nectar source within the entire solution space, without any prior information, and replaces the abandoned stagnant nectar source with this new nectar source. Simultaneously, the stagnation index of this new nectar source is reset to 0, giving it an equal opportunity to compete.

[0042] For example, a complete iterative cycle is as follows: First, multiple nectar sources are randomly initialized and generated in the solution space of the transmission line fault parameters, and each nectar source is assigned to a hired bee for maintenance; in the hired bee stage, the hired bee searches in the neighborhood along the traveling wave direction vector to generate new nectar sources. If the new nectar source has better fitness, it replaces the old nectar source; otherwise, the old nectar source is retained and the stagnation index is accumulated; then, the observation bee stage is entered, where the observation bee calculates the selection probability by combining the fitness values ​​of all nectar sources and the overall information credibility, and selects high-quality nectar sources based on this probability and conducts further refined searches; finally, the scout bee stage is executed. For those nectar sources whose stagnation index exceeds the preset threshold due to long-term lack of updates or selection, the scout bee will perform global random initialization to generate a brand new nectar source to replace the inferior solution, thereby completing one search process.

[0043] S4: Calculate the fitness value by combining the electrical quantity fitting degree and the traveling wave timing matching degree, and output the diagnostic results.

[0044] This step includes calculating the fitness value, determining the iteration termination, and outputting the final result.

[0045] In each iteration, the fitness value of all newly generated or retained nectar sources needs to be calculated to evaluate their quality. The calculation of the fitness value includes two core components:

[0046] The electrical quantity fit component is calculated by using relevant algorithms to calculate the theoretical values ​​of fault voltage and current at each monitoring terminal based on candidate fault parameters. The theoretical values ​​are compared with the actual voltage and current values ​​collected by each monitoring terminal, the square of the difference is calculated, and the sum is weighted according to the information reliability of each monitoring terminal. The square root is then taken to obtain the total electrical error. The value of this component is negatively correlated with the total electrical error.

[0047] The traveling wave timing matching degree component is obtained by calculating the theoretical time for the traveling wave to arrive at each monitoring terminal based on the candidate fault location; squaring the difference between the theoretical time and the actual recorded arrival time of the traveling wave; summing the squares of the time differences of all monitoring terminals, taking the reciprocal, and normalizing the result to obtain this component. Finally, the electrical quantity fitting degree component and the traveling wave timing matching degree component are weighted and summed according to preset weighting coefficients to obtain the final fitness value.

[0048] In each iteration, the algorithm records and updates the globally optimal honey source and its corresponding fitness value. When the preset maximum number of iterations is reached, or the fitness value of the globally optimal honey source does not significantly improve over several consecutive iterations, the algorithm is considered to have converged and the termination condition is met. The fault parameters corresponding to the honey source with the highest fitness value are output as the final diagnostic result. The final diagnostic result includes the fault location and transition resistance. The diagnostic result is displayed to maintenance personnel through a human-machine interface, and a fault diagnosis report is generated.

[0049] According to a second aspect of the present invention, the present invention also provides a distributed fault diagnosis system for power transmission lines. Figure 2 This is a structural block diagram of a distributed fault diagnosis system for power transmission lines according to an embodiment of the present invention. Figure 2 As shown, the system includes a processor and a memory. The memory stores computer program instructions, which, when executed by the processor, implement the distributed fault diagnosis method for transmission lines according to the first aspect of the present invention. The system also includes other components well-known to those skilled in the art, such as a communication bus and a communication interface; their configuration and functions are known in the art and will not be described further here.

[0050] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be within the scope of protection of the present invention.

Claims

1. A method for distributed fault diagnosis of a power transmission line, characterized in that, The diagnostic method includes: acquiring voltage and current data and the actual arrival time of the fault traveling wave collected by multiple monitoring terminals of the transmission line; initializing an artificial bee colony to randomly generate multiple nectar sources, each nectar source corresponding to a set of fault parameters, the fault parameters including at least the fault location and transition resistance; calculating the fitness value based on the voltage and current data; iteratively searching according to the hired bee stage, observation bee stage and scout bee stage of the artificial bee colony algorithm until the termination condition is met; and outputting the fault parameters corresponding to the globally optimal nectar source as the fault diagnosis result. The hired bee phase includes constructing the traveling wave direction vector of each current nectar source and generating new nectar sources along the traveling wave direction vector. The traveling wave direction vector is calculated based on the deviation between the actual arrival time and the theoretical arrival time of the faulty traveling wave. The observation bee phase selects new nectar sources from the new nectar sources in the hired bee phase based on the selection probability and further searches for new nectar sources in their neighborhood. The selection probability is proportional to the fitness value of the nectar source and its comprehensive information reliability. The comprehensive information reliability is the average of the information reliability of multiple monitoring terminals involved in the current nectar source. The scout bee phase performs global random initialization based on the stagnation index of the nectar source to generate new nectar sources. The stagnation index is calculated based on the change in the fitness value of the current nectar source during iteration. The calculation steps of the traveling wave direction vector include: calculating the theoretical arrival time of the fault traveling wave to each monitoring terminal based on the fault location of the current honey source and using the traveling wave transmission speed; calculating the difference between the theoretical arrival time and the actual arrival time of the fault traveling wave at each monitoring terminal to obtain the time deviation; constructing a unit direction vector from the fault location corresponding to the current honey source to each monitoring terminal; and using the time deviation as a weight to perform a weighted summation of the unit direction vectors corresponding to each monitoring terminal to obtain the traveling wave direction vector, so as to guide the search in the direction of reducing the sum of the absolute values ​​of the time deviations of all monitoring terminals.

2. The method of claim 1, wherein, The hired bee phase includes: Calculate the theoretical arrival time of the fault traveling wave from the fault location corresponding to the current honey source to each monitoring terminal; Calculate the deviation between the theoretical arrival time and the actual arrival time of the fault traveling wave for each monitoring terminal; Based on the deviations of all monitoring terminals, a traveling wave direction vector is constructed; Along the traveling wave direction vector, the fault parameters of the current honey source are randomly perturbed to generate a new honey source and replace the old honey source with poor fitness value.

3. The method of claim 1, wherein, The observation bee phase includes: Obtain the fitness value of each nectar source and its corresponding comprehensive information credibility; The selection probability of each honey source is calculated, and the selection probability is obtained by normalizing the product of the fitness value and the power of the overall information confidence. The bees observe that, based on the selection probability, they use a roulette wheel selection method to choose a nectar source for subsequent searching.

4. The method of claim 1, wherein, The calculation steps for the stagnation index include: In each iteration's hired bee phase and observation bee phase, the fitness value of the newly generated nectar source is compared with the fitness value of the current nectar source; If the fitness value of the newly generated nectar source is not greater than the fitness value of the current nectar source, then the stagnation index of the current nectar source is incremented by 1. If the fitness value of the newly generated nectar source is greater than the fitness value of the current nectar source, then the stagnation index of the current nectar source will be reset to 0. The stagnation index serves as the basis for determining whether a nectar source has fallen into a local optimum.

5. The method for distributed fault diagnosis of transmission lines according to claim 4, characterized in that, The reconnaissance bee phase includes: Determine whether the current stagnation index of the nectar source exceeds the preset activity threshold; If the activity threshold is exceeded, the nectar source is determined to be trapped in a local optimum, and the scout bee abandons the nectar source. The scout bee then randomly generates a new nectar source in the solution space to replace the abandoned nectar source, and resets the stagnation index of the new nectar source to its initial value.

6. The method for distributed fault diagnosis of transmission lines according to claim 1, characterized in that, The calculation of the fitness value includes: Calculate the theoretical voltage and theoretical current values ​​of each monitoring terminal based on the fault parameters; Calculate the squared difference between the theoretical voltage value and the measured voltage value, and the squared difference between the theoretical current value and the measured current value for each monitoring terminal. Then, sum the squared differences of all monitoring terminals according to the information reliability of the monitoring terminals and take the square root to obtain the total error. Calculate the electrical quantity fit component based on the negative correlation function of the total error. Calculate the squared difference between the theoretical arrival time of the fault traveling wave and the actual arrival time of the fault traveling wave for each monitoring terminal. Sum the squared differences of all monitoring terminals, take the reciprocal, and normalize to obtain the traveling wave timing matching degree component. The fitness value is obtained by weighting and summing the electrical quantity fit degree component and the traveling wave timing matching degree component according to the preset weight coefficient.

7. The method for distributed fault diagnosis of transmission lines according to claim 1, characterized in that, The process of calculating the credibility of the information includes: Obtain the signal quality parameters of each monitoring terminal, wherein the signal quality parameters include at least the signal-to-noise ratio and the data sampling rate; Based on the signal quality parameters, the basic reliability of the monitoring terminal is calculated using a preset membership function. Calculate the theoretical distance between the fault location corresponding to the current honey source and each monitoring terminal; based on the theoretical distance, perform distance-weighted correction on the basic reliability, with the monitoring terminal that is closer to the source having a greater weight; The revised baseline credibility will be used as the information credibility of the monitoring terminal.

8. The method for distributed fault diagnosis of transmission lines according to claim 1, characterized in that, The termination condition is at least one of the following: The number of iterations has reached the preset maximum number of iterations; The fitness value of the globally optimal honey source changes less than the preset value in a series of preset iterations.

9. A distributed fault diagnosis system for transmission lines, characterized in that, include: processor; A memory, wherein a computer program is stored; When the processor is configured to execute the computer program, it implements a distributed fault diagnosis method for transmission lines as described in any one of claims 1 to 8.

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