Probe arrangement optimization method for GIS electric contact structure state communication

Abstract

The invention relates to a probe arrangement optimization method for GIS electric contact structure state communication. The probe arrangement optimization method comprises the following steps: step 1, establishing a mathematical model for acoustic carrier receiving probe arrangement optimization; 2, variable-length chromosome coding is carried out on the arrangement condition of the probe, and a scheme code representing a probe arrangement scheme is randomly generated according to the variable-length chromosome code; 3, dividing the scheme codes into a predetermined number of populations, performing genetic iteration according to the populations to generate new scheme codes, evaluating the fitness of each scheme code in the populations according to the mathematical model, and screening the scheme codes by adopting an elitism strategy; and 4, continuously repeating the step 3 until a set number of iterations is reached, and finally obtaining the probe arrangement scheme represented by the scheme code with the highest fitness, namely the optimized probe arrangement scheme. Compared with the prior art, an efficient solution way is provided for the complex receiving probe arrangement optimization problem.

Description

Technical Field

[0001] The present invention relates to the field of power system status monitoring, and in particular to a probe arrangement optimization method for GIS electrical contact structure status communication. Background Art

[0002] Gas-insulated metal-enclosed switchgear (GIS), a pivotal component in modern power systems, shoulders the heavy responsibility of power distribution, control, and protection. It is widely used in key areas such as urban power grids and industrial substations, playing an important role in ensuring stable and reliable power supply to users in different regions. However, GIS, which operates under high voltage and high current conditions for a long time, is prone to minor fault hazards such as increased contact resistance and loose connections due to electrical corrosion, mechanical stress, and continuous thermal cycling. If not discovered and resolved in a timely manner, these problems may cause local overheating and, in severe cases, even insulation breakdown, leading to short circuits and large-scale power outages, causing immeasurable losses to daily production and life.

[0003] Online monitoring technology based on acoustic carrier waves provides an important means for assessing the condition of GIS electrical contact structures. This technology uses ultrasonic probes strategically placed outside the GIS equipment to capture acoustic emission signals generated by internal faults, thereby determining the health of the electrical contact structure. Current probe placement methods either rely on a large number of redundant probes in pursuit of comprehensive awareness of the electrical contact structure's condition, increasing investment costs and significantly increasing system complexity, making operation and maintenance difficult. Alternatively, insufficient probes can create numerous monitoring blind spots, hindering the timely detection of potential faults.

[0004] Chinese patent application CN118518752A discloses a GIS insulator defect detection method and system based on the swept-frequency ultrasonic method. By injecting swept-frequency ultrasonic signals into the GIS insulator, the propagation law of ultrasonic waves on clean / defective insulators is tested, and the difference in ultrasonic frequency domain signals on clean / defective insulators is used to detect basin-type insulator defects, thereby compensating for the shortcomings of existing detection methods in terms of insufficient sensitivity and effectiveness, improving the power supply reliability of GIS equipment, and reflecting the advantages of ultrasonic defect detection. However, this patent only makes rough azimuth restrictions on the arrangement of ultrasonic signal transmitting and receiving probes. In actual application, only one pair of probes will be arranged as in the embodiment of this patent. When arranging the probes, a large number of redundant probes will be arranged in order to achieve comprehensive perception of the state of the electrical contact structure, thereby increasing costs. The cost that can be saved by the swept-frequency ultrasonic detection method itself is increased due to the redundant probes.

[0005] Therefore, it is urgent to propose an optimized arrangement method of acoustic wave receiving probes for GIS electrical contact structure status communication. Summary of the Invention

[0006] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and provide a probe layout optimization method for GIS electrical contact structure status communication, providing an efficient solution to the complex receiving probe layout optimization problem.

[0007] The purpose of the present invention can be achieved by the following technical solutions:

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

[0009] Step 1: Establish a mathematical model for optimizing the layout of acoustic carrier receiving probes based on the layout conditions of the probes;

[0010] Step 2: Perform variable-length chromosome encoding on the probe arrangement conditions, and randomly generate a scheme code representing the probe arrangement scheme based on the variable-length chromosome encoding;

[0011] Step 3: Divide the solution codes into a predetermined number of populations, perform genetic iteration according to the populations to generate new solution codes, evaluate the fitness of each solution code in the population according to the mathematical model, and use an elite retention strategy to screen the solution codes;

[0012] 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.

[0013] Furthermore, the arrangement conditions of the probes include the number of probes, circumferential positions, axial positions and placement angles.

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

[0015] X={N,X1,X2,……,X N};

[0016] X i ={r i ,θ i , h i , α i}(i=1,2,……,N);

[0017]

[0018]

[0019] Where X represents the received probe layout plan, N is the number of probes, and X i is the spatial position and arrangement angle of the Nth probe, (r i ,θi , h i ) is the spatial position cylindrical coordinate of the i-th probe, α i is the layout angle of the i-th probe, is the normalized standard deviation of the maximum value of the ultrasonic signal received by N probes, P s is the standard deviation of the maximum value of the ultrasonic signal received by N probes, is the average value of the maximum ultrasonic signal received by N probes, P j is the maximum value of the ultrasonic signal received by the jth probe, It represents the average separation of demodulation frequency intervals at different temperatures, K is the total number of frequency bands, is the center frequency of the ith frequency interval, is the bandwidth of the i-th frequency interval, f(X) is the objective function of the probe optimization layout, a, b, c and d are weight coefficients, and S is the maintenance convenience evaluation index of the probe layout location.

[0020] Furthermore, 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 inverse of the objective function value of the probe optimization arrangement obtained after calculation, and the obtained value is the fitness of the scheme code.

[0021] Furthermore, the constraints of the mathematical model include: the total monitoring range of the probe must cover the area to be monitored; the distance between any two probes is greater than or equal to the set minimum distance; the probe installation angle is [0, ±27°], and the probe monitoring range is [-27°+α, 27°+α] directly below the probe.

[0022] Furthermore, in step 3, the process of evaluating the fitness of each scheme code in the population also includes: verifying whether the scheme code meets the constraints of the mathematical model; if the scheme code does not meet any constraint, the fitness of the scheme code in the population is set to 0.

[0023] Furthermore, in step 2, the variable length chromosome code 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.

[0024] Furthermore, in step 3, the genetic iteration process includes performing crossover and mutation on the scheme code in the same population to generate a new scheme code;

[0025] The crossover process includes: selecting at least two probe arrangement scheme codes with fitness higher than the current average fitness from the population by a roulette wheel selection method as parent codes; judging 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 a child code; otherwise, directly copying the parent code as the child code;

[0026] The mutation process includes: after generating the offspring code, judging whether to mutate the offspring code according to a preset mutation probability; if so, randomly selecting a position in the coding sequence of the offspring code, inverting the binary value of the position, obtaining the mutated offspring code and outputting it as the new scheme code; otherwise, directly outputting the offspring code as the new scheme code.

[0027] Furthermore, if the number of probes in each parent code is different, then in the crossover, only the probes commonly owned by each parent code and the coding parts corresponding to the probes are crossed; after the mutation, if the mutation position of the child code exceeds the specified range, the mutation position exceeding the specified range is adjusted to a valid value through modulo operation or boundary value adjustment.

[0028] Furthermore, in step 3, the process of screening the scheme codes by the elite retention strategy includes:

[0029] Select the solution codes with the highest and lowest fitness among various populations in each round of iteration and the populations with the highest and lowest average fitness;

[0030] In the same population, the solution code with the highest fitness in the previous iteration is used to replace the solution code with the lowest fitness in this iteration;

[0031] Among different populations, the solution code with the highest fitness in the population with the highest average fitness in this round of iteration is used to replace the solution code with the lowest fitness in the population with the lowest average fitness in this round of iteration.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] 1. The present invention establishes a mathematical model for optimizing the placement of acoustic carrier receiving probes and performs variable-length chromosome encoding on the probe placement conditions, transforming the probe placement optimization problem into an optimization problem for the objective function under encoding. This provides an efficient solution to the complex receiving probe placement optimization problem. Based on the actual size of the GIS and the required monitoring accuracy, the arrangement scheme of the acoustic wave receiving probes required for the housing can be obtained by simply adjusting the parameters. This ensures the effectiveness of monitoring while eliminating redundant placement of probes, thereby saving costs and improving efficiency. When optimizing the probe placement, the present invention randomly divides the scheme codes into multiple groups and simultaneously performs genetic iterative operations, thereby enhancing global search capabilities, improving optimization results, and avoiding the situation in which the iterations in traditional genetic algorithms converge prematurely and fall into local optimality.

[0034] 2. The objective function of the mathematical model for optimizing the placement of acoustic carrier receiver probes in this invention simultaneously considers signal uniformity, ease of maintenance, and frequency separation. Weight distribution is used to balance multiple indicators, avoiding performance bias caused by single optimization. Constraints such as coverage range, spacing, and angle are also verified during the fitness evaluation to ensure that all feasible solutions meet actual engineering requirements.

[0035] 3. During genetic iteration, this invention maintains a high degree of selection randomness through random crossover and mutation operations, fully considering the possibility of various probe layout schemes. During screening, an elite retention strategy is adopted to retain the best individuals in each generation to the next generation, preventing the loss of high-quality solutions during iteration and accelerating the convergence process. At the same time, the best individuals are also migrated between populations, promoting global collaborative optimization and avoiding local stagnation.

[0036] 4. This invention uses mathematical modeling and optimization of the objective function to ensure comprehensive monitoring with a minimum number of probes, reduce redundant equipment, and lower hardware costs and system complexity.

[0037] 5. The present invention does not impose any restrictions on the number of probes and supports dynamic adjustment of the number of probes to meet the needs of GIS equipment of different scales and structures. The method has high versatility. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 is a flow chart of the method of the present invention;

[0039] Figure 2 Schematic diagram of an optimized arrangement model for acoustic carrier communication reception in a contact structure state according to one embodiment of the present invention;

[0040] Figure 3 The sound pressure distribution on the GIS housing in one embodiment of the present invention when the excitation direction is 0°;

[0041] Figure 4A schematic diagram of a flow chart for implementing an elite retention strategy in one embodiment of the present invention;

[0042] Figure 5 Schematic diagram of crossover and mutation operations in one embodiment of the present invention. DETAILED DESCRIPTION

[0043] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0044] Example 1

[0045] This embodiment intends to disclose a probe arrangement optimization method for GIS electrical contact structure status communication, the method is as follows Figure 1 As shown, the specific steps are as follows, including:

[0046] Step S1: establishing a mathematical model for optimizing the arrangement of acoustic carrier receiving probes according to the arrangement conditions of the probes.

[0047] The probe arrangement conditions include the number of probes, circumferential position, axial position and placement angle. Figure 2 As shown in the figure, the symbols in each figure represent: 1-GIS full metal enclosure, 2-high voltage conductor, 3-electrical contact structure status communication receiving probe, 4-temperature sensor, 5-sound wave, 6-electrical contact structure. The electrical contact structure status communication receiving probe 3 is installed outside the GIS full metal enclosure to receive the acoustic emission signal generated by internal faults in the equipment to determine the health of the electrical contact structure. When the excitation direction is 0°, the sound pressure distribution on the GIS enclosure is as follows: Figure 3 shown.

[0048] The expressions of the mathematical model for probe optimization layout include:

[0049] X={N,X1,X2,……,X N};

[0050] X i ={r i ,θ i , h i , α i}(i=1,2,……,N);

[0051]

[0052] Where X represents the received probe layout plan, N is the number of probes, and X iis the spatial position and arrangement angle of the Nth probe, (r i ,θ i , h i ) is the spatial position cylindrical coordinate of the i-th probe, α i is the layout angle of the i-th probe, is the normalized standard deviation of the maximum value of the ultrasonic signal received by N probes, P s is the standard deviation of the maximum value of the ultrasonic signal received by N probes, is the average value of the maximum ultrasonic signal received by N probes, P j is the maximum value of the ultrasonic signal received by the jth probe, It represents the average separation of demodulation frequency intervals at different temperatures, K is the total number of frequency bands, is the center frequency of the ith frequency interval, is the bandwidth of the i-th frequency interval, f(X) is the objective function of the probe optimization layout, a, b, c and d are weight coefficients, and S is the maintenance convenience evaluation index of the probe layout location.

[0053] In this embodiment, the number N of probes to be arranged is selected to be 3-6.

[0054] It reflects the uniformity of the signal strength received by each probe; S∈[0,1], the larger S is, the more convenient the maintenance is.

[0055] The constraints of the mathematical model for probe optimization layout include:

[0056] The total monitoring range of the probe 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 is 0 to ±27°, and the probe monitoring range is [-27°+α, 27°+α] directly below the probe.

[0057] like Figure 3 As shown in the figure, it can be seen that the sound pressure level of the probe is uniform and relatively high within the range of ±27°, that is, the receiving efficiency is good within this angle range. Therefore, the detection range of the probe installed at an angle of α is directly below the probe.

[0058] To avoid mutual interference between receiving probes and ensure the monitoring independence of each receiving probe, a certain minimum distance must be maintained between any two receiving probes.

[0059] Step S2: Perform variable-length chromosome encoding on the arrangement conditions of the probes, and randomly generate a scheme code representing the probe arrangement scheme based on the variable-length chromosome encoding.

[0060] In step S2, the variable-length chromosome code 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.

[0061] For this embodiment, variable length chromosome encoding is performed. Since r is relatively fixed, only θ, h, and α need to be encoded. At the circumferential position, every Consider a layout with a total of schemes; in the axial position, consider a layout every Δh distance, a total of schemes; α∈[-27°, 27°], every Consider a layout with a total of If there is Right now μ2-bit binary is used for encoding. Pick Pick Δh is taken as 100mm.

[0062] The specific process of step S3 and step S4 is as follows Figure 4 shown.

[0063] 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 population according to a mathematical model, and use an elite retention strategy to screen the scheme codes.

[0064] In step S3, the genetic iteration process includes encoding the scheme in the same population as follows: Figure 5 The crossover and mutation shown generate new scheme encodings;

[0065] The crossover process includes: selecting at least two probe arrangement scheme codes with fitness higher than the current average fitness from the population through the roulette wheel selection method as the parent code; judging whether to cross the parent code according to the preset probability, if so, randomly selecting a position in the coding sequence of the parent code as the 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.

[0066] The mutation process includes: after generating the offspring code, judging whether to mutate the offspring code according to the preset mutation probability; if so, randomly selecting a position in the coding sequence of the offspring code, inverting the binary value of the position, obtaining the mutated offspring code and outputting it as the new scheme code; otherwise, directly outputting the offspring code as the new scheme code.

[0067] In this embodiment, the number of initial code groups randomly generated to represent the probe arrangement scheme is 30, the number of populations 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.

[0068] Crossover example:

[0069] Parent 1 code: [position code 1] [status code 01 (N=4)]

[0070] Parent 2 code: [position code 2] [status code 10 (N=5)]

[0071] The intersection point is selected in the middle of the position code to generate children:

[0072] Child 1: [first half of position code 1 + second half of position code 2] [status code 01]

[0073] Child 2: [first half of position code 2 + second half of position code 1] [status code 10]

[0074] Mutation example:

[0075] Original code: [Circumferential: 001][Axial: 110][Angle: 01][Status code: 10 (N=5)]

[0076] The variation bit selects the first bit of the angle code, which becomes:

[0077] [Circumferential direction: 001][Axial direction: 110][Angle: **11**][Status code: 10 (N=5)]

[0078] If the number of probes in each parent code is different, only the probes and the code parts corresponding to the probes that are common to each parent code are crossed during the crossover.

[0079] After mutation, if the mutation position of the offspring code exceeds the specified range, the mutation position outside the specified range is adjusted to a valid value through modulo operation or boundary value adjustment.

[0080] In step S3, the process of evaluating the fitness of each solution code in the population includes: verifying whether the solution code meets the constraints of the probe optimization layout mathematical model; if the solution code does not meet any constraint, then setting the fitness of the solution code in the population to 0;

[0081] Substitute the scheme code into the expression of the mathematical model of probe optimization layout for calculation, take the inverse of the objective function value of the probe optimization layout obtained after calculation, and the obtained value is the fitness of the scheme code.

[0082] like Figure 4 As shown in Figure 2, the process of selecting scheme codes by the elite retention strategy includes:

[0083] Select the solution codes with the highest and lowest fitness among various populations in each round of iteration and the populations with the highest and lowest average fitness;

[0084] In the same population, individual replacement is performed, and the solution code with the highest fitness in the previous iteration is used to replace the solution code with the lowest fitness in this iteration;

[0085] In different populations, inter-population communication is carried out, and the solution code with the highest fitness in the population with the highest average fitness in this round of iteration is used to replace the solution code with the lowest fitness in the population with the lowest average fitness in this round of iteration.

[0086] The population with the highest average fitness is Figure 4 The optimal population in the set is the population with the lowest average fitness. Figure 4 The worst population in .

[0087] Step S4, continuously repeating step S3 until the set number of iterations is reached, and the probe arrangement scheme represented by the scheme code with the highest fitness finally obtained is the optimized probe arrangement scheme.

[0088] In large substations, there are a large number of GIS. By using the optimized arrangement method of the acoustic wave receiving probes in this embodiment, the electrical contact structure status inside the GIS can be efficiently monitored, and poor contact problems caused by long-term operation, temperature changes, and other factors can be discovered in a timely manner. Possible faults can be warned in advance, thus ensuring the stable operation of the substation.

[0089] In urban power supply networks, GIS equipment is widely used in distribution rooms and switchgear at all voltage levels. The optimized layout method described in this embodiment enables efficient communication monitoring of the status of GIS electrical contact structures within urban power grids. If an electrical contact anomaly with GIS equipment in a specific area is detected, the power department can quickly respond and dispatch maintenance personnel to address it, preventing the fault from escalating and impacting the normal power supply of city residents and businesses.

[0090] Industrial power supply systems, especially within large industrial plants such as steel mills and chemical plants, place extremely high demands on power supply stability. Installing acoustic wave receiving probes on GIS equipment within the plant in an optimized layout allows for quick and effective real-time monitoring of the electrical contact structure. When potential electrical contact problems arise, production processes can be adjusted or equipment maintenance can be scheduled promptly to prevent interruptions caused by power failures and minimize economic losses.

[0091] Example 2

[0092] Based on Example 1, this embodiment provides an electronic device, including: one or more processors and a memory, wherein one or more programs are stored in the memory, and the one or more programs include instructions for executing the probe layout optimization method for GIS electrical contact structure status communication as described above.

[0093] At the hardware level, the electronic device includes a processor, an internal bus, a network interface, memory, and non-volatile storage, and may also include other hardware required for its operations. The processor reads the corresponding computer program from the non-volatile storage into the internal 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, the present invention does not exclude other implementation methods, such as logic devices or a combination of software and hardware. In other words, the execution of the following processing flow is not limited to individual logic units; it can also be hardware or logic devices.

[0094] Memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0095] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules, 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 technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory media such as modulated data signals and carrier waves.

[0096] 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 such modifications or substitutions are intended to be within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be subject to the scope of protection of the claims.

Claims

1. A probe arrangement optimization method for GIS electrical contact structure status communication, characterized in that: The method comprises: Step 1: Establish a mathematical model for optimizing the layout of acoustic carrier receiving probes based on the layout conditions of the probes; Step 2: Perform variable-length chromosome encoding on the probe arrangement conditions, and randomly generate a scheme code representing the probe arrangement scheme based on the variable-length chromosome encoding; Step 3: Divide the solution codes into a predetermined number of populations, perform genetic iteration according to the populations to generate new solution codes, evaluate the fitness of each solution code in the population according to the mathematical model, and use an elite retention strategy to screen the solution 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.

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 positions, axial positions and placement angles.

3. The probe arrangement optimization method for GIS electrical contact structure status communication according to claim 1, characterized in that: The expression of the mathematical model includes: X={N,X1,X2,......,X N }; X i ={r i ,the i ,h i ,a i }(i=1,2,……,N); Where X represents the received probe layout plan, N is the number of probes, and X i is the spatial position and arrangement angle of the Nth probe, (r i ,θ i , h i ) is the spatial position cylindrical coordinate of the i-th probe, α i is the layout angle of the i-th probe, is the normalized standard deviation of the maximum value of the ultrasonic signal received by N probes, P s is the standard deviation of the maximum value of the ultrasonic signal received by N probes, is the average value of the maximum ultrasonic signal received by N probes, P j is the maximum value of the ultrasonic signal received by the jth probe, It represents the average separation of demodulation frequency intervals at different temperatures, K is the total number of frequency bands, is the center frequency of the ith frequency interval, is the bandwidth of the i-th frequency interval, f(X) is the objective function of the probe optimization layout, a, b, c and d are weight coefficients, and S is the maintenance convenience evaluation index of the probe layout location.

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 includes: substituting the scheme code into the expression of the mathematical model for calculation, taking the inverse of the objective function value of the probe optimization arrangement obtained after calculation, and the obtained value is the fitness of the scheme code.

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

6. The probe arrangement optimization method for GIS electrical contact structure status communication according to claim 5, characterized in that: In step 3, the process of evaluating the fitness of each scheme code in the population also includes: verifying whether the scheme code meets the constraints of the mathematical model; if the scheme code does not meet any constraint, setting the fitness of the scheme code in the population to 0.

7. 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 code includes a position code and a status code, wherein the position code represents the circumferential position, axial position and placement angle of the probe, and the status code represents the number of probes.

8. 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 performing crossover and mutation on the scheme codes in the same population to generate new scheme codes; The crossover process includes: selecting at least two probe arrangement scheme codes with fitness higher than the current average fitness from the population by a roulette wheel selection method as parent codes; judging 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 a child code; otherwise, directly copying the parent code as the child code; The mutation process includes: after generating the offspring code, judging whether to mutate the offspring code according to a preset mutation probability; if so, randomly selecting a position in the coding sequence of the offspring code, inverting the binary value of the position, obtaining the mutated offspring code and outputting it as the new scheme code; otherwise, directly outputting the offspring code as the new scheme code.

9. The probe arrangement optimization method for GIS electrical contact structure status communication according to claim 8, characterized in that: If the number of probes in each parent code is different, then in the crossover, only the probes shared by each parent code and the coding parts corresponding to the probes are crossed; after the mutation, if the mutation position of the child code exceeds the specified range, the mutation position exceeding the specified range is adjusted to a valid value through modular operation or boundary value adjustment.

10. 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 screening the scheme codes by the elite retention strategy includes: Select the solution codes with the highest and lowest fitness among various populations in each round of iteration and the populations with the highest and lowest average fitness; In the same population, the solution code with the highest fitness in the previous iteration is used to replace the solution code with the lowest fitness in this iteration; Among different populations, the solution code with the highest fitness in the population with the highest average fitness in this round of iteration is used to replace the solution code with the lowest fitness in the population with the lowest average fitness in this round of iteration.

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