A method for optimizing a probe arrangement for GIS electrical contact structure condition communication

Abstract

The application relates to a probe arrangement optimization method for GIS electric contact structure state communication, which comprises the following steps: step 1, a mathematical model for acoustic carrier receiving probe arrangement optimization is established; step 2, the arrangement condition of the probe is encoded by using a variable-length chromosome, and scheme coding representing the probe arrangement scheme is randomly generated according to the variable-length chromosome coding; step 3, the scheme coding is divided into a predetermined number of populations, new scheme coding is generated by genetic iteration according to the populations, the fitness of each scheme coding in the population is evaluated according to the mathematical model, and the scheme coding is screened by using an elite reservation strategy; and step 4, step 3 is repeatedly performed until a set iteration number is reached, and finally, the probe arrangement scheme represented by the scheme coding with the highest fitness is the optimized probe arrangement scheme. Compared with the prior art, the application provides an efficient solution for the complex receiving probe arrangement optimization problem.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of power system state monitoring, in particular to a probe arrangement optimization method for GIS electrical contact structure state communication. BACKGROUND

[0002] As a pivotal component in modern power systems, GIS (GAS INSULATED SWITCHGEAR) bears the heavy responsibility of power distribution, control and protection, and is widely used in urban power grids, industrial substations and other key fields, playing an important role in ensuring stable and reliable power supply for users in different regions. However, GIS, which has been running in high-voltage and high-current conditions for a long time, is prone to small fault hidden dangers such as increased contact resistance and loose connection at electrical contact points due to the continuous effects of electrical corrosion, mechanical stress and thermal cycling. If not discovered and addressed in a timely manner, it may cause local overheating and even insulation breakdown, leading to short circuit faults and causing large-scale power outages, causing immeasurable losses to daily production and life.

[0003] The online monitoring technology based on acoustic carrier provides an important means for evaluating the state of GIS electrical contact structure. This technology captures acoustic emission signals generated by faults inside the GIS device by reasonably arranging ultrasonic probes outside the device to judge the health status of the electrical contact structure. The current probe arrangement method either arranges a large number of redundant probes to achieve comprehensive perception of the state of the electrical contact structure, which not only increases the cost of investment but also significantly increases the complexity of the system, making it difficult to operate and maintain, or there are many monitoring blind spots due to insufficient number of probes, which cannot timely discover potential faults.

[0004] Chinese patent application CN118518752A discloses a GIS insulator defect detection method and system based on a frequency-sweeping ultrasonic wave method. By injecting a frequency-sweeping ultrasonic wave signal into the GIS insulator, the propagation law of the ultrasonic wave on the clean / defective insulator is tested, and the difference in the frequency domain signal of the ultrasonic wave on the clean / defective insulator is used to realize the detection of the defect of the pot-type insulator, which makes up for the shortcomings of the existing detection methods in terms of sensitivity and effectiveness, improves the power supply reliability of GIS equipment, and embodies the advantages of ultrasonic wave detection of defects. However, the arrangement of the ultrasonic wave signal transmitting and receiving probes in this patent is only roughly limited in direction. In actual application, instead of arranging only one pair of probes as in the patent embodiment, a large number of redundant probes will be arranged to achieve comprehensive perception of the state of the electrical contact structure, thus increasing the cost. The cost saved by the frequency-sweeping ultrasonic wave detection method is offset by the redundant probes.

[0005] Therefore, there is an urgent need to propose an acoustic wave receiving probe optimization arrangement method for GIS electrical contact structure state communication. SUMMARY

[0006] The present application aims to overcome the defects of the prior art and provides a probe arrangement optimization method for GIS electrical contact structure state communication, which provides an efficient solution for complex receiving probe arrangement optimization problems.

[0007] The object of the present application can be achieved by the following technical solutions:

[0008] A probe arrangement optimization method for GIS electrical contact structure state communication, the method comprising:

[0009] Step 1: According to the arrangement condition of the probe, a mathematical model of the acoustic carrier receiving probe arrangement optimization is established;

[0010] Step 2: The arrangement condition of the probe is coded with a variable-length chromosome, and a scheme code representing the probe arrangement scheme is randomly generated according to the variable-length chromosome coding;

[0011] Step 3: The scheme code is divided into a predetermined number of populations, and new scheme codes are generated by genetic iteration according to the population, and the fitness of each scheme code in the population is evaluated according to the mathematical model, and the scheme code is screened by using the elite reservation strategy;

[0012] Step 4: Step 3 is repeatedly performed until a set number of iterations is reached, and the probe arrangement scheme represented by the final scheme code with the highest fitness is the optimized probe arrangement scheme.

[0013] Further, the arrangement condition of the probe includes the number of probes, the circumferential position, the axial position and the placement angle.

[0014] Further, the expression of the mathematical model includes:

[0015] ;

[0016] ;

[0017] ;

[0018] ;

[0019] ;

[0020] wherein X represents the received probe arrangement scheme, N is the number of probes, X i is the spatial position and arrangement angle of the Nth probe, is the spatial position column coordinate of the ith probe, is the arrangement angle of the ith probe, a normalized standard deviation of the maximum ultrasound signal received by N probes, a standard deviation of the maximum ultrasound signal received by N probes, an average of the maximum ultrasound signal received by N probes, a maximum ultrasound signal received by the jth probe, an average separation of different temperature demodulation frequency intervals, K is the total number of frequency bands, a center frequency of the ith frequency interval, a bandwidth of the ith frequency interval, a target function of probe optimization arrangement, a, b, c and d are all weight coefficients, and S is a maintenance convenience evaluation index of probe arrangement position.

[0021] Further, in step 3, the process of evaluating the fitness of each scheme code in the population includes: substituting the scheme code into the expression of the mathematical model for calculation, taking the reciprocal of the target function value of the probe optimization arrangement obtained after calculation, and the obtained value is the fitness of the scheme code.

[0022] Further, the constraint conditions of the mathematical model include: the total monitoring range of the probes must cover the region to be monitored; the distance between any two probes is greater than or equal to the set minimum distance; the installation angle of the probe is [0, 27°], and the monitoring range of the probe is directly below the probe [-27°+α, 27°+α].

[0023] Further, in step 3, the process of evaluating the fitness of each scheme code in the population also includes: verifying whether the scheme code satisfies the constraint conditions of the mathematical model, and if the scheme code does not satisfy any constraint, the fitness of this scheme code in the population is set to 0.

[0024] Further, in step 2, the variable-length chromosome code contains a position code and a state code, the position code represents the circumferential position, axial position and placement angle of the probe, and the state code represents the number of probes.

[0025] Further, in step 3, the process of genetic iteration includes: in the same population, crossing and mutating the scheme code to generate a new scheme code.

[0026] The crossover process includes: selecting at least two probe placement scheme codes from the population with fitness higher than the current average fitness using a roulette wheel selection method as parent codes; determining whether to crossover the parent codes according to a preset probability; if so, randomly selecting a position in the coding sequence of the parent codes as a crossover point, and swapping the codes before or after the crossover point of each parent code to obtain the child code; otherwise, directly copying the parent code as the child code.

[0027] The mutation process includes: after generating the offspring code, determining whether to mutate the offspring code according to a preset mutation probability; if so, randomly selecting a position in the encoding sequence of the offspring code, inverting the binary value of that position, obtaining the mutated offspring code, and outputting it as a new scheme code; otherwise, directly outputting the offspring code as a new scheme code.

[0028] Furthermore, if the number of probes in each parent code is different, then in the crossover, only the probes that are common to each parent code and the corresponding code parts of the probes are crossovered; after mutation, if the mutation position of the child code exceeds the specified range, then the mutation position that exceeds the specified range is adjusted to a valid value through modulo operation or boundary value adjustment.

[0029] Furthermore, in step 3, the process of the elite retention strategy filtering the scheme codes includes:

[0030] In each iteration, the scheme with the highest and lowest fitness and the population with the highest and lowest average fitness are selected.

[0031] Within the same population, the code of the scheme with the highest fitness in the previous iteration is used to replace the code of the scheme with the lowest fitness in the current iteration.

[0032] In different populations, the scheme code with the highest fitness in the population with the highest average fitness in this iteration is used to replace the scheme code with the lowest fitness in the population with the lowest average fitness in this iteration.

[0033] Compared with the prior art, the beneficial effects of the present invention include:

[0034] 1. This invention establishes a mathematical model for optimizing the arrangement of acoustic carrier receiving probes and encodes the probe arrangement conditions using variable-length chromosomes. This transforms the probe arrangement optimization problem into an optimization problem of the objective function under encoding, providing an efficient solution to the complex problem of receiver probe arrangement optimization. Based on the actual size of the GIS and the required monitoring accuracy, the arrangement scheme of the acoustic receiving probes to be placed on the shell can be obtained through simple parameter tuning. This ensures the effectiveness of monitoring and eliminates redundant probe arrangement, achieving cost savings and improved efficiency. Furthermore, when optimizing the probe arrangement, this invention randomly divides the scheme encoding into multiple populations and performs genetic iteration operations simultaneously, improving the global search capability and optimization effect, and avoiding premature convergence and local optima in traditional genetic algorithms.

[0035] 2. The objective function of the acoustic carrier receiving probe layout optimization mathematical model of this invention simultaneously considers signal uniformity, maintenance convenience and frequency separation. It achieves a balance of multiple indicators through weight allocation, avoiding performance bias caused by single optimization. Constraints such as coverage, spacing and angle are also verified in the fitness evaluation to ensure that all feasible solutions meet the actual engineering requirements.

[0036] 3. In the genetic iteration, this invention maintains a high degree of selection randomness through random crossover and mutation operations, and comprehensively considers the possibility of various probe deployment schemes. In the screening process, an elite retention strategy is adopted, in which the best individual of each generation is retained to the next generation to prevent the loss of high-quality solutions in the iteration and accelerate the convergence process. At the same time, the best individuals will also migrate between populations, which promotes global collaborative optimization and avoids local stagnation.

[0037] 4. This invention ensures comprehensive monitoring with a minimum number of probes by using mathematical modeling and optimizing the objective function, thereby reducing redundant equipment, lowering hardware costs, and reducing system complexity;

[0038] 5. This invention does not limit the number of probes and supports dynamic adjustment of the number of probes to adapt to the needs of GIS equipment of different sizes and structures. The method has high versatility. Attached Figure Description

[0039] Figure 1 This is a flowchart of the method of the present invention;

[0040] Figure 2 This is a schematic diagram of an optimized arrangement model for acoustic carrier communication reception in the contact structure state according to an embodiment of the present invention;

[0041] Figure 3 This is the sound pressure distribution on the GIS shell under the condition that the excitation direction is 0° in one embodiment of the present invention;

[0042] Figure 4This is a schematic diagram illustrating the implementation process of the elite retention strategy in one embodiment of the present invention;

[0043] Figure 5 This is a schematic diagram of crossover and mutation operations in one embodiment of the present invention. Detailed Implementation

[0044] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0045] Example 1

[0046] This embodiment aims to disclose a probe layout optimization method for GIS electrical contact structure status communication, the method as follows: Figure 1 As shown, the specific steps are as follows:

[0047] Step S1: Based on the probe arrangement conditions, establish a mathematical model for optimizing the arrangement of the acoustic carrier receiving probe.

[0048] The probe placement requirements include the number of probes, their circumferential position, axial position, and placement angle. The probe setup is as follows: Figure 2 As shown in the figure, the labels in the attached figures represent: 1- GIS all-metal enclosed shell, 2- high-voltage conductor, 3- electrical contact structure status communication receiving probe, 4- temperature sensing device, 5- acoustic wave, 6- electrical contact structure. The electrical contact structure status communication receiving probe 3 is installed outside the GIS all-metal enclosed shell to receive acoustic emission signals generated by internal faults in the equipment to determine the health status of the electrical contact structure. When the excitation direction is 0°, the sound pressure distribution on the GIS shell is as follows: Figure 3 As shown.

[0049] The mathematical model for optimal probe placement includes the following expressions:

[0050] ;

[0051] ;

[0052] ;

[0053] ;

[0054] ;

[0055] Where X represents the receiving probe arrangement scheme, and N is the number of probes, X iGiven the spatial position and arrangement angle of the Nth probe, ( Let be the cylindrical coordinates of the spatial position of the i-th probe. Let be the arrangement angle of the i-th probe. Let N be the normalized standard deviation of the maximum ultrasonic signal received by N probes. Let be the standard deviation of the maximum value of the ultrasound signal received by N probes. This represents the average of the maximum values ​​of the ultrasound signals received by N probes. The maximum value of the ultrasonic signal received by the j-th probe. This represents the average separation across different temperature demodulation frequency ranges, where K is the total number of frequency bands. Let i be the center frequency of the i-th frequency interval. Let be the bandwidth of the i-th frequency interval. The objective function for optimizing probe placement is defined by a, b, c, and d, where a, b, c, and d are weighting coefficients, and S is an evaluation index for the ease of maintenance of the probe placement location.

[0056] In this embodiment, the number of probes N is selected to be 3-6.

[0057] This reflects the uniformity of the signal strength received by each probe; The larger the value of S, the easier it is to maintain.

[0058] The constraints of the mathematical model for optimal probe placement include:

[0059] The total monitoring range of the probes must cover the area to be monitored; the distance between any two probes must be greater than or equal to the set minimum distance; the probe installation angle must be 0 to... 27°, and the probe monitoring range is directly below the probe [-27°+α, 27°+α].

[0060] like Figure 3 As shown, the sound pressure level of the probe is uniform and relatively high within a range of ±27°, indicating good reception efficiency within this angle range. Therefore, the detection range covered by the probe with an installation angle of α is directly below the probe.

[0061] To avoid mutual interference between receiving probes and to ensure the monitoring independence of each receiving probe, a certain minimum distance must be maintained between any two receiving probes.

[0062] Step S2: Variable-length chromosome coding is performed on the probe placement conditions, and a scheme code representing the probe placement scheme is randomly generated based on the variable-length chromosome coding.

[0063] In step S2, the variable-length chromosome encoding includes a position code and a status code. The position code represents the circumferential position, axial position, and placement angle of the probe, while the status code represents the number of probes.

[0064] For this embodiment, variable-length chromosome encoding is performed. Since r is relatively fixed, only the chromosome length needs to be encoded. h Encode the data at circumferential positions, every... Considering one arrangement for the curvature, there are a total of One scheme; in the axial position, every Considering a layout based on distance, there are a total of One option; Every Considering one arrangement for the curvature, there are a total of There are several options. If there are... ,Right now Then adopt Encode using binary bits. Pick , Pick , Take 100mm.

[0065] The specific processes of steps S3 and S4 are as follows: Figure 4 As shown.

[0066] Step S3: Divide the scheme codes into a predetermined number of populations, perform genetic iteration according to the populations to generate new scheme codes, evaluate the fitness of each scheme code in the populations according to the mathematical model, and use an elite retention strategy to screen the scheme codes.

[0067] In step S3, the genetic iteration process includes encoding the scheme within the same population, as follows: Figure 5 The crossovers and mutations shown generate new scheme codes;

[0068] The crossover process includes: selecting at least two probe placement schemes with fitness higher than the current average fitness from the population using a roulette wheel selection method as parent codes; determining whether to crossover the parent codes based on a preset probability; if so, randomly selecting a position in the parent code sequence as the crossover point, and swapping the codes before or after the crossover point to obtain the child codes; otherwise, directly copying the parent codes as the child codes.

[0069] The mutation process includes: after generating the offspring code, determining whether to mutate the offspring code according to the preset mutation probability. If so, randomly select a position in the encoding sequence of the offspring code, invert the binary value of that position, obtain the mutated offspring code, and output it as the new scheme code. Otherwise, directly output the offspring code as the new scheme code.

[0070] In this embodiment, the initial number of code groups randomly generated to represent the probe arrangement scheme is 30, the population size is set to 3, and in the crossover and mutation operations, the crossover probability is set to 0.85, the mutation probability is set to 0.2, and the number of iterations is 50 rounds.

[0071] Cross example:

[0072] Parent 1 encoding: [Location code 1][Status code 01 (N=4)]

[0073] Parent 2 encoding: [Position code 2][Status code 10 (N=5)]

[0074] The intersection point is selected in the middle of the position code, and offspring are generated:

[0075] Child 1: [First half of position code 1 + Second half of position code 2] [Status code 01]

[0076] Child 2: [First half of position code 2 + Second half of position code 1] [Status code 10]

[0077] Example of mutation:

[0078] Original code: [Circumferential: 001][Axial: 110][Angle: 01][Status code: 10 (N=5)]

[0079] The variant bit is the first bit of the angle code, which, when inverted, becomes:

[0080] [Circumferential: 001][Axial: 110][Angle: **11**][Status Code: 10 (N=5)]

[0081] If the number of probes in each parent code is different, then during the crossover, only the probes that are common to each parent code and the corresponding code parts of the probes are crossovered.

[0082] After mutation, if the mutation position of the offspring code exceeds the specified range, the mutation position exceeding the specified range will be adjusted to a valid value through modulo operation or boundary value adjustment.

[0083] In step S3, the process of evaluating the fitness of each scheme code in the population includes: verifying whether the scheme code satisfies the constraints of the mathematical model for optimal probe placement; if the scheme code does not satisfy any constraint, then the fitness of this scheme code in the population is set to 0.

[0084] The scheme code is substituted into the expression of the probe optimization layout mathematical model for calculation. The reciprocal of the objective function value of the probe optimization layout obtained after calculation is the fitness of the scheme code.

[0085] like Figure 4As shown, the process of the elite retention strategy in filtering scheme codes includes:

[0086] In each iteration, the scheme with the highest and lowest fitness and the population with the highest and lowest average fitness are selected.

[0087] Within the same population, individuals are replaced by replacing the code of the scheme with the highest fitness in the previous iteration with the code of the scheme with the lowest fitness in this iteration.

[0088] In different populations, inter-population communication is carried out, and the scheme code with the highest fitness in the population with the highest average fitness in this round of iteration is used to replace the scheme code with the lowest fitness in the population with the lowest average fitness in this round of iteration.

[0089] The population with the highest average fitness is Figure 4 The optimal population is the population with the lowest average fitness. Figure 4 The worst population in the group.

[0090] Step S4: Repeat step S3 until the set number of iterations is reached. The probe arrangement scheme represented by the scheme code with the highest fitness is the optimized probe arrangement scheme.

[0091] In large substations, there are a large number of GIS (Gas Insulators and Controllers). By using the optimized arrangement method of acoustic wave receiving probes in this embodiment, the electrical contact structure status inside the GIS can be monitored efficiently, and contact problems caused by long-term operation, temperature changes and other factors can be detected in time. Early warning of possible faults can be given to ensure the stable operation of the substation.

[0092] In urban power supply networks, GIS equipment is widely used in distribution rooms and switching stations at various voltage levels. Utilizing the optimized layout method described in this embodiment, the status communication monitoring of GIS electrical contact structures in the urban power grid can be performed efficiently. Once an abnormality is detected in the electrical contact of GIS equipment in a certain area, the power department can respond quickly, dispatching maintenance personnel to handle the issue, preventing the fault from escalating and affecting the normal power supply to urban residents and businesses.

[0093] In industrial plant power supply, especially in large industrial areas such as steel mills and chemical plants, the stability of the power supply is extremely critical. Installing acoustic wave receiving probes on the GIS equipment within the plant using an optimized layout allows for rapid and effective real-time monitoring of the electrical contact structure. When potential electrical contact problems arise, production processes can be adjusted promptly or equipment maintenance can be scheduled to prevent production interruptions due to power outages and minimize economic losses for the company.

[0094] Example 2

[0095] Based on Embodiment 1, this embodiment provides an electronic device, including: one or more processors and a memory, wherein the memory stores one or more programs, and the one or more programs include instructions for executing the probe arrangement optimization method for GIS electrical contact structure status communication as described above.

[0096] At the hardware level, the electronic device includes a processor, internal bus, network interface, memory, and non-volatile memory, and may also include other hardware required for business operations. The processor reads the corresponding computer program from the non-volatile memory into memory and then runs it to implement the aforementioned probe placement optimization method for GIS electrical contact structure status communication. Of course, in addition to software implementation, this invention does not exclude other implementation methods, such as logic devices or a combination of hardware and software, etc. That is to say, the execution subject of the following processing flow is not limited to individual logic units, but can also be hardware or logic devices.

[0097] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0098] Computer-readable media include both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0099] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for optimizing probe placement for GIS electrical contact structure status communication, characterized in that, The method includes: Step 1: Based on the probe placement conditions, establish a mathematical model for optimizing the placement of the acoustic carrier receiving probe; Step 2: Variable-length chromosome coding is performed on the probe placement conditions, and a scheme code representing the probe placement scheme is randomly generated based on the variable-length chromosome coding. Step 3: Divide the scheme codes into a predetermined number of populations, perform genetic iteration according to the populations to generate new scheme codes, evaluate the fitness of each scheme code in the populations according to the mathematical model, and use an elite retention strategy to screen the scheme codes. Step 4: Repeat step 3 until the set number of iterations is reached. The probe arrangement scheme represented by the scheme code with the highest fitness is the optimized probe arrangement scheme. The mathematical model includes the following expressions: ； ； ； ； ； Where X represents the receiving probe arrangement scheme, and N is the number of probes, X i Given the spatial position and arrangement angle of the Nth probe, ( Let be the cylindrical coordinates of the spatial position of the i-th probe. Let be the arrangement angle of the i-th probe. Let be the normalized standard deviation of the maximum value of the ultrasound signal received by N probes. Let be the standard deviation of the maximum value of the ultrasound signal received by N probes. This represents the average of the maximum values ​​of the ultrasound signals received by N probes. The maximum value of the ultrasonic signal received by the j-th probe. This represents the average separation across different temperature demodulation frequency ranges, where K is the total number of frequency bands. Let i be the center frequency of the i-th frequency interval. Let be the bandwidth of the i-th frequency interval. The objective function for optimizing probe placement is defined by a, b, c, and d, where a, b, c, and d are weighting coefficients, and S is the evaluation index for the ease of maintenance of the probe placement location. In step 3, the process of evaluating the fitness of each scheme code in the population includes: substituting the scheme code into the expression of the mathematical model for calculation, taking the reciprocal of the objective function value of the optimized probe layout obtained after calculation, and the resulting value is the fitness of the scheme code.

2. The probe arrangement optimization method for GIS electrical contact structure status communication according to claim 1, characterized in that, The arrangement conditions of the probes include the number of probes, circumferential position, axial position, and placement angle.

3. The probe arrangement optimization method for GIS electrical contact structure status communication according to claim 1, characterized in that, The constraints of the mathematical model include: the total monitoring range of the probes must cover the area to be monitored; the distance between any two probes must be greater than or equal to the set minimum distance; the probe installation angle must be [0, [27°], and the probe monitoring range is directly below the probe [-27°+α, 27°+α].

4. The probe arrangement optimization method for GIS electrical contact structure status communication according to claim 3, characterized in that, In step 3, the process of evaluating the fitness of each scheme code in the population further includes: verifying whether the scheme code satisfies the constraints of the mathematical model; if the scheme code does not satisfy any constraint, then the fitness of this scheme code in the population is set to 0.

5. The probe arrangement optimization method for GIS electrical contact structure status communication according to claim 1, characterized in that, In step 2, the variable-length chromosome encoding includes a position code and a status code. The position code represents the circumferential position, axial position, and placement angle of the probe, and the status code represents the number of probes.

6. The probe arrangement optimization method for GIS electrical contact structure status communication according to claim 1, characterized in that, In step 3, the genetic iteration process includes crossover and mutation of the scheme code in the same population to generate a new scheme code; The crossover process includes: selecting at least two probe placement scheme codes from the population with fitness higher than the current average fitness using a roulette wheel selection method as parent codes; determining whether to crossover the parent codes according to a preset probability; if so, randomly selecting a position in the coding sequence of the parent codes as a crossover point, and swapping the codes before or after the crossover point of each parent code to obtain the child code; otherwise, directly copying the parent code as the child code. The mutation process includes: after generating the offspring code, determining whether to mutate the offspring code according to a preset mutation probability; if so, randomly selecting a position in the encoding sequence of the offspring code, inverting the binary value of that position, obtaining the mutated offspring code, and outputting it as a new scheme code; otherwise, directly outputting the offspring code as a new scheme code.

7. The probe arrangement optimization method for GIS electrical contact structure status communication according to claim 6, characterized in that, If the number of probes in each parent code is different, then in the crossover, only the probes that are common to each parent code and the corresponding code parts of the probes are crossovered; after mutation, if the mutation position of the child code exceeds the specified range, the mutation position that exceeds the specified range is adjusted to a valid value through modulo operation or boundary value adjustment.

8. The probe arrangement optimization method for GIS electrical contact structure status communication according to claim 1, characterized in that, In step 3, the process of the elite retention strategy filtering the scheme codes includes: In each iteration, the scheme with the highest and lowest fitness and the population with the highest and lowest average fitness are selected. Within the same population, the code of the scheme with the highest fitness in the previous iteration is used to replace the code of the scheme with the lowest fitness in the current iteration. In different populations, the scheme code with the highest fitness in the population with the highest average fitness in this iteration is used to replace the scheme code with the lowest fitness in the population with the lowest average fitness in this iteration.

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