High-voltage parallel capacitor copper busbar optimization method based on MOPSO algorithm

Abstract

A high-voltage parallel capacitor copper busbar optimization method based on an MOPSO algorithm comprises the steps that a high-voltage parallel capacitor unit thermal field simulation model considering heating of a copper busbar is established, simulation analysis is conducted under the conditions of different copper busbar widths w and copper busbar thicknesses h, and simulation data of multiple sets of capacitor unit hot spot temperatures Tmax are obtained; according to the hot spot temperature Tmax of the high-voltage parallel capacitor unit, the width w of the bus bar and the thickness h of the bus bar, establishing a multiple linear regression model for optimization of the bus bar, and substituting simulation data into the multiple linear regression model for regression analysis calculation to obtain regression coefficients b1 and b2 and a constant term b0 value; establishing a mathematical model of the copper bar consumption V, the copper bar width w and the copper bar thickness h according to the length of the copper bar, and obtaining constraint conditions of an optimization problem; and adopting a multi-target particle swarm optimization algorithm to obtain a plurality of optimal solutions of the width w and the thickness h of the copper busbar.

Description

Technical Field

[0001] The invention relates to the technical field of high-voltage shunt capacitor structure optimization design, and in particular to a high-voltage shunt capacitor busbar optimization method based on a MOPSO algorithm. Background Art

[0002] During the operation of high-voltage shunt capacitors, the temperature rises due to the generation of operating losses, and high temperature will affect the service life of the capacitor. The operating losses of high-voltage shunt capacitors include dielectric losses and metal losses. The loss of the busbar in the capacitor accounts for a large proportion of the metal loss. The greater the thickness and width of the busbar, the smaller its resistance value, and the smaller the heat generated by the copper bar. The existing busbar sizes usually adopt common specifications and are not optimized according to the hot spot temperature of the capacitor unit. At the same time, considering the economic benefits of the copper bar materials, the copper bar materials must also be taken into account when optimizing the size.

[0003] The above information disclosed in this Background section is only for enhancement of understanding of the background of the invention and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the invention

[0004] The present invention provides a method for optimizing the busbar of a high-voltage parallel capacitor based on the MOPSO algorithm, which uses the MOPSO algorithm to assist in optimization to achieve the best economic benefits and technical indicators. By optimizing the size and structure of the busbar, the power loss of the busbar is reduced, and the temperature distribution of the capacitor is made more uniform, the operating temperature of the high-voltage parallel capacitor can be effectively reduced to ensure safe and stable operation of the equipment.

[0005] A method for optimizing the busbar of a high-voltage parallel capacitor based on a MOPSO algorithm includes:

[0006] S1: Establish a thermal field simulation model of high-voltage parallel capacitor units considering the heating of the busbar, perform simulation analysis under different busbar widths w and busbar thicknesses h, and obtain the hot spot temperatures T of multiple groups of capacitor units. max The simulation data of the high-voltage parallel capacitor unit structure includes a capacitor housing, a bushing assembly, a capacitor element, and a busbar. A high-voltage parallel capacitor unit thermal field simulation model is established based on the high-voltage parallel capacitor unit structure using finite element FEM simulation software. The unit hotspot temperature is calculated by inputting the capacitor structure size and operating conditions, and multiple sets of hotspot temperature data are obtained by changing different busbar sizes.

[0007] S2: According to the hot spot temperature T of the high voltage parallel capacitor unit max The multivariate linear regression model for busbar optimization is established with busbar width w and busbar thickness h:

[0008] (1)

[0009] Among them, the busbar width w and the busbar thickness h are independent variables, and the hot spot temperature T max is the dependent variable, b1 and b2 are regression coefficients, b0 is the constant term, and the simulation data is substituted into the multivariate linear regression model for regression analysis and calculation to obtain the regression coefficients b1, b2 and the constant term b0 value;

[0010] S3: The amount of busbar V is expressed as:

[0011] (2)

[0012] Wherein, V is the amount of copper busbar, L is the length of copper busbar, w is the width of copper busbar, and h is the thickness of copper busbar;

[0013] S4: Use a multi-objective particle swarm optimization algorithm to obtain multiple optimal solutions, namely the Pareto frontier, with the input being the hotspot temperature T max and the busbar consumption V, the objective function constraint is

[0014] s . t : { m i n T m a x ( w , h ) m i n V ( w , h ) w ∈ [ w m i n , w m a x ] h ∈ [ h m i n , h m a x ] (3)

[0015] Among them, w min is the minimum width of the busbar, w max is the maximum width of the busbar, h min is the minimum busbar thickness, h max It is the maximum value of busbar thickness.

[0016] In the MOPSO algorithm-based high-voltage parallel capacitor busbar optimization method, in step S4, the multi-objective particle swarm optimization algorithm obtains multiple optimal solutions including:

[0017] Initialize the particle swarm: randomly generate a group of particles, each particle represents a potential solution, the potential solution includes the busbar width w and the busbar thickness h, the particle position and velocity initialization follows the constraint range w∈[w min , w max ] and h∈[h min , h max ],

[0018] Evaluate the fitness of particles: determine the non-dominated particles based on the Pareto dominance relationship and calculate the hotspot temperature T of the solution represented by each particle max and the busbar consumption V value, and use them as the two objectives of the fitness function, namely min T max (w,h) min V(w,h)

[0019] Update personal and global optimal solutions: Each particle updates its speed and position according to the personal optimal solution and the solution selected from the leader pool.

[0020] Update the leader pool: After each iteration, add new non-dominated solutions to the leader pool and remove solutions dominated by new solutions.

[0021] Termination condition: If the maximum number of iterations is reached, the calculation is terminated and the non-dominated solutions in the leader library are returned as the Pareto optimal solution set.

[0022] Compared with the prior art, the present invention has the following advantages: the present invention can optimize the copper busbar of the high-voltage parallel capacitor; reduce the temperature rise caused by the heating of the copper busbar while ensuring that the prescribed copper usage is not exceeded; and use the MOPSO algorithm for optimization design calculation to find the optimal solution of the design parameters with a small amount of simulation data. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] By reading the detailed description of the preferred specific embodiments below, various other advantages and benefits of the present invention will become clear to those of ordinary skill in the art. The drawings in the specification are only for the purpose of illustrating the preferred embodiments and are not considered to be limitations of the present invention. Obviously, the drawings described below are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings without creative work. Moreover, the same reference numerals are used to represent the same components throughout the drawings.

[0024] In the attached picture:

[0025] Figure 1 It is a schematic diagram of the process of the present invention;

[0026] Figure 2 A schematic diagram of a thermal field simulation model of a high-voltage parallel capacitor unit considering heating of a busbar according to the present invention;

[0027] Figure 3 This is a schematic diagram of the optimization results of the high-voltage parallel capacitor busbar based on the MOPSO algorithm of the present invention.

[0028] The present invention is further explained below in conjunction with the accompanying drawings and embodiments. DETAILED DESCRIPTION

[0029] The specific embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although the specific embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art.

[0030] It should be noted that certain words are used in the specification and claims to refer to specific components. Those skilled in the art should understand that technicians may use different nouns to refer to the same component. This specification and claims do not use the difference in nouns as a way to distinguish components, but use the difference in the functions of the components as the criterion for distinction. As mentioned throughout the specification and claims, "including" or "comprising" is an open term, so it should be interpreted as "including but not limited to". The subsequent description of the specification is a preferred embodiment of the present invention, but the description is based on the general principles of the specification and is not intended to limit the scope of the present invention. The scope of protection of the present invention shall be determined by the attached claims.

[0031] To facilitate understanding of the embodiments of the present invention, further explanation will be given below by taking specific embodiments as examples in conjunction with the accompanying drawings, and each of the accompanying drawings does not constitute a limitation on the embodiments of the present invention.

[0032] like Figures 1 to 3 As shown, the high-voltage parallel capacitor busbar optimization method based on the MOPSO algorithm includes the following steps:

[0033] S1: Establish a thermal field simulation model of high-voltage parallel capacitor units considering the heating of the busbar, perform simulation analysis under different busbar widths w and busbar thicknesses h, and obtain the hot spot temperatures T of multiple groups of capacitor units. max Simulation data of

[0034] S2: According to the hot spot temperature T of the high voltage parallel capacitor unit max The multivariate linear regression model for busbar optimization is established with busbar width w and busbar thickness h:

[0035] (1)

[0036] Among them, the busbar width w and the busbar thickness h are independent variables, and the hot spot temperature T max is the dependent variable, b1 and b2 are regression coefficients, b0 is the constant term, and the simulation data is substituted into the multivariate linear regression model for regression analysis and calculation to obtain the regression coefficients b1, b2 and the constant term b0 value;

[0037] S3: The amount of busbar V is expressed as:

[0038] (2)

[0039] Wherein, V is the amount of copper busbar, L is the length of copper busbar, w is the width of copper busbar, and h is the thickness of copper busbar;

[0040] S4: A multi-objective particle swarm optimization algorithm is used to obtain multiple optimal solutions, namely the Pareto frontier. The objective function constraint is

[0041] s . t : { m i n T m a x ( w , h ) m i n V ( w , h ) w ∈ [ w m i n , w m a x ] h ∈ [ h m i n , h m a x ] (3)

[0042] Among them, w min is the minimum width of the busbar, w max is the maximum width of the busbar, h min is the minimum busbar thickness, h max It is the maximum value of busbar thickness.

[0043] In a preferred implementation of the high-voltage parallel capacitor busbar optimization method based on the MOPSO algorithm, in step S4, the multi-objective particle swarm optimization algorithm obtains multiple optimal solutions including:

[0044] Initialize the particle swarm: Each particle represents a potential solution, which includes the busbar width w and the busbar thickness h. The particle position and velocity are initialized to follow the constraint range w∈[w min , w max ] and h∈[h min , h max ],

[0045] Evaluate the fitness of particles: determine the non-dominated particles based on the Pareto dominance relationship and calculate the hotspot temperature T of the solution represented by each particle max and the busbar usage V value, and use them as the two objectives of the fitness function.

[0046] Update personal and global optimal solutions: Each particle updates its speed and position according to the personal optimal solution and the solution selected from the leader pool.

[0047] Update the leader pool: After each iteration, add new non-dominated solutions to the leader pool and remove solutions dominated by new solutions.

[0048] Termination condition: If the maximum number of iterations is reached, the calculation is terminated and the non-dominated solutions in the leader library are returned as the Pareto optimal solution set.

[0049] In one embodiment, a method for optimizing a high-voltage parallel capacitor busbar based on a MOPSO algorithm includes the following steps:

[0050] S1: Establish a thermal field simulation model of high-voltage parallel capacitor units considering the heating of the busbar, perform simulation analysis under different busbar widths w and busbar thicknesses h, and obtain multiple groups of capacitor unit hotspot temperature data T max ;

[0051] S2: According to the hot spot temperature T of the high voltage parallel capacitor unit max Establish a multivariate linear regression model for the optimal design of busbars with copper bar width w and copper bar thickness h

[0052] (1)

[0053] Among them, w and h are independent variables, T max is the dependent variable, b1 and b2 are regression coefficients, and b0 is the constant term. The simulation data is brought into the model for regression analysis and calculation to obtain the regression coefficients b1, b2 and the constant term b0 value;

[0054] S3: The amount of copper busbar V can be expressed as

[0055] (2)

[0056] Among them, V is the amount of busbar, L is the length of the busbar, w is the width of the busbar, and h is the thickness of the busbar;

[0057] S4: The multi-objective particle swarm optimization (MOPSO) algorithm is used to obtain multiple optimal solutions, namely the Pareto frontier. The objective function constraints are

[0058] s . t : { m i n T m a x ( w , h ) m i n V ( w , h ) w ∈ [ w m i n , w m a x ] h ∈ [ h m i n , h m a x ] (3)

[0059] Among them, w min is the minimum width of the copper bar, w max is the maximum width of the copper bar, h min is the minimum copper busbar thickness, h max This is the maximum thickness of the copper busbar, which can be set according to actual needs;

[0060] S5: The MOPSO algorithm is used to optimize the design of the busbar of the high-voltage parallel capacitor. The method is as follows:

[0061] 1. Initialize the particle swarm:

[0062] Each particle represents a potential solution, where the solution contains two variables w and h. The position and velocity of the particle should be initialized to follow the constraints of w and h within the range w∈[w min , w max ] and h∈[h min , h max ].

[0063] 2. Evaluate the fitness of particles:

[0064] Determine the non-dominated particles according to the Pareto dominance relationship and calculate the T of the solution represented by each particle. max and V values, and use them as the two objectives of the fitness function.

[0065] 3. Update personal and global optimal solutions:

[0066] Each particle updates its own speed and position according to its personal best solution and the solution selected from the leader pool.

[0067] 4. Update the leader library:

[0068] After each iteration, new non-dominated solutions are added to the leader pool and solutions dominated by the new solutions are removed.

[0069] 5. Termination conditions:

[0070] If the maximum number of iterations or other termination conditions are reached, the calculation is terminated and the non-dominated solutions in the leader database are returned as the Pareto optimal solution set.

[0071] Although the embodiments of the present invention are described above in conjunction with the accompanying drawings, the present invention is not limited to the above specific embodiments and application fields, and the above specific embodiments are only illustrative and instructive, rather than restrictive. A person of ordinary skill in the art can also make many forms under the guidance of this specification and without departing from the scope of protection of the claims of the present invention, all of which belong to the protection of the present invention.

Claims

1. A method for optimizing the busbar of a high-voltage parallel capacitor based on the MOPSO algorithm, characterized in that: The steps include: S1: Establish a thermal field simulation model of high-voltage parallel capacitor units considering the heating of the busbar, perform simulation analysis under different busbar widths w and busbar thicknesses h, and obtain the hot spot temperatures T of multiple groups of capacitor units. max The simulation data of the high-voltage parallel capacitor unit structure includes a capacitor housing, a bushing assembly, a capacitor element, and a busbar. A high-voltage parallel capacitor unit thermal field simulation model is established based on the high-voltage parallel capacitor unit structure using finite element FEM simulation software. The unit hotspot temperature is calculated by inputting the capacitor structure size and operating conditions, and multiple sets of hotspot temperature data are obtained by changing different busbar sizes. S2: According to the hot spot temperature T of the high voltage parallel capacitor unit max The multivariate linear regression model for busbar optimization is established with busbar width w and busbar thickness h: （1） Among them, the busbar width w and the busbar thickness h are independent variables, and the hot spot temperature T max is the dependent variable, b1 and b2 are regression coefficients, b0 is the constant term, and the simulation data is substituted into the multivariate linear regression model for regression analysis and calculation to obtain the regression coefficients b1, b2 and the constant term b0 value; S3: The amount of busbar V is expressed as: （2） Wherein, V is the amount of copper busbar, L is the length of copper busbar, w is the width of copper busbar, and h is the thickness of copper busbar; S4: Use a multi-objective particle swarm optimization algorithm to obtain multiple optimal solutions, namely the Pareto frontier, with the input being the hotspot temperature T max and the busbar consumption V, the objective function constraint is （3） Among them, w min is the minimum width of the busbar, w max is the maximum width of the busbar, h min is the minimum busbar thickness, h max It is the maximum value of busbar thickness.

2. The method for optimizing the busbar of a high-voltage parallel capacitor based on the MOPSO algorithm according to claim 1 is characterized in that: Preferably, in step S4, the multi-objective particle swarm optimization algorithm obtains multiple optimal solutions including: Initialize the particle swarm: randomly generate a group of particles, each particle represents a potential solution, the potential solution includes the busbar width w and the busbar thickness h, the particle position and velocity initialization follows the constraint range w∈[w min , w max ] and h∈[h min , h max ], Evaluate the fitness of particles: determine the non-dominated particles based on the Pareto dominance relationship and calculate the hotspot temperature T of the solution represented by each particle max and the busbar consumption V value, and use them as the two objectives of the fitness function, namely min T max (w,h)min V(w,h) Update personal and global optimal solutions: Each particle updates its speed and position according to the personal optimal solution and the solution selected from the leader pool. Update the leader pool: After each iteration, add new non-dominated solutions to the leader pool and remove solutions dominated by new solutions. Termination condition: If the maximum number of iterations is reached, the calculation is terminated and the non-dominated solutions in the leader library are returned as the Pareto optimal solution set.

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