Parameter optimization method of electric heating device based on multi-objective hierarchical analysis and genetic algorithm

Abstract

The present invention relates to the field of electrothermal device parameter optimization, and specifically to a method for electrothermal device parameter optimization based on multi-objective hierarchical analysis and genetic algorithms. The solution includes: constructing a hierarchical evaluation model, which includes a target layer, a criterion layer, and a measure layer. The target layer is the final reflection of the indicators of each layer. The criterion layer includes factors that affect device performance, including the output line voltage of the electrothermal device, the output harmonic content of the electrothermal device, the output heating power of the electrothermal device, the efficiency of the electrothermal device, the torque ripple of the electrothermal device, and the overall power density of the electrothermal device. The measure layer includes measures taken to adjust the parameters of the electrothermal device. Then, a judgment matrix is ​​constructed to screen out the parameters of the electrothermal device structural parameters that have the greatest impact on the device's key performance indicators. Then, based on the screened parameters, a genetic algorithm is used to comprehensively optimize the device's key performance indicators to obtain the optimal parameters. The present invention is suitable for electrothermal device parameter optimization.

Description

Technical Field

[0001] The present invention relates to the field of electric heating device parameter optimization, and in particular to an electric heating device parameter optimization method based on multi-objective hierarchical analysis and genetic algorithm. Background Art

[0002] Wind thermal systems convert all wind energy into heat for reuse, effectively addressing the intermittent and fluctuating nature of wind energy and offering excellent economic benefits. However, this technical solution has low overall efficiency and cannot transmit heat over long distances, making it unsuitable for the long distances between energy production and consumption in my country.

[0003] Therefore, the current practice is to consider using electric-thermal multi-energy flexible magnetic control power generation technology that can flexibly convert wind energy into electrical energy and thermal energy at the same time. This technology realizes the simultaneous conversion of wind energy into electrical energy and thermal energy through an electric-thermal multi-energy flexible magnetic control power generation device. This technology first meets the energy demand in the power grid, and then converts excess wind energy into thermal energy for storage or utilization. This solution can effectively improve the overall efficiency of the system while solving the problem of wind energy utilization.

[0004] However, there is currently a lack of technical means to optimize the parameters of the electric heating device in the above scheme, resulting in low overall performance of the electric heating device. Summary of the Invention

[0005] The purpose of the present invention is to overcome the shortcomings of the prior art and provide an electric heating device parameter optimization method based on multi-objective hierarchical analysis and genetic algorithm, thereby optimizing the parameters of the electric heating device and improving the comprehensive performance of the electric heating device.

[0006] The present invention adopts the following technical solutions to achieve the above-mentioned purpose. The present invention provides a method for optimizing parameters of an electric heating device based on multi-objective hierarchical analysis and genetic algorithm, comprising:

[0007] S1. Construct a hierarchical evaluation model;

[0008] The hierarchical evaluation model includes a target layer, a criterion layer, and a measure layer. The target layer is the final reflection of the indicators of each layer, that is, the performance indicators of the electric heating device reach the comprehensive optimization. The criterion layer includes factors that affect the performance of the device, such as the output line voltage of the electric heating device, the output harmonic content of the electric heating device, the output heating power of the electric heating device, the efficiency of the electric heating device, the torque ripple of the electric heating device, and the overall power density of the electric heating device. The measure layer includes measures taken to adjust the parameters of the electric heating device.

[0009] S2, construct judgment matrix;

[0010] The judgment matrix determines the weight of each element relative to an element in the upper layer by comparing each element with each other. The values ​​in the judgment matrix are given by Santy's 1-9 scaling method. The 1-9 scale represents the importance of two elements compared with each other, where 1 represents that the two elements have the same importance. The larger the scale, the higher the importance. The scaling method uses the reciprocal of 1-9 to represent the importance of two elements when the order is swapped.

[0011] For the elements of any layer, a judgment matrix is ​​established as follows:

[0012]

[0013] Where a ij Represents the importance of the i-th element compared to the j-th element. The elements in A satisfy:

[0014] a ij >0

[0015]

[0016] a ii =1

[0017] S3. Calculate the weight of each indicator through the judgment matrix;

[0018] Calculate the mth power of the product of each row of the judgment matrix to obtain an m-dimensional vector. The m-dimensional vector is the weight of each indicator before normalization. m is the row and column information of the judgment matrix. The number of rows and columns of the judgment matrix is ​​equal. Its calculation formula is as follows:

[0019]

[0020] The obtained data is standardized to obtain the weight vector, and the calculation formula is as follows:

[0021]

[0022] S4. performing consistency check on the calculation results of the weight vector;

[0023] Calculate the maximum eigenvalue and CI value, where the calculation formula of the maximum eigenvalue satisfies:

[0024]

[0025] Where λ max is the maximum eigenvalue of the judgment matrix, and AW is the cumulative value of the judgment matrix multiplied by the standardized weights and then row by row;

[0026] The calculation formula of CI value is as follows:

[0027]

[0028] After obtaining the CI value, the results are checked for consistency in combination with the RI value table. The check method is to calculate the CR value. When the CR value is less than the set threshold, it is judged to have passed the consistency test. The CR value calculation formula is as follows:

[0029] RI is a statistical parameter and can be directly obtained by querying the RI value table;

[0030] S5. Calculate the total hierarchical ranking weight using the following formula:

[0031]

[0032] In the formula, w is the total weight, w bai Represents the bottom layer b method to the middle layer a i The impact weight of the target, w aiz Represents the middle layer a i The impact weight of the target on the total target z;

[0033] S6. Filter out the parameters of the electric heating device that have the greatest impact on the performance indicators of the electric heating device according to the total weight;

[0034] S7. Based on the screened parameters of the electric heating device, the performance indicators of the electric heating device are optimized according to the NSGA-II genetic algorithm.

[0035] Furthermore, step S7 specifically includes:

[0036] S71, initializing the population, that is, determining the decision variables and objective function values ​​of each individual, wherein the individual is the screened electric heating device parameters, and the objective function value is the set performance index value;

[0037] S72, sort the individuals in the population by non-dominance, and divide the individuals into different levels according to the dominance relationship. p and the dominated set S p to judge;

[0038] S73. Calculate the crowding degree of each individual to maintain the diversity of the population. The crowding degree refers to the distance between the individual and its adjacent individuals in the target space.

[0039] S74. Select the next generation population based on non-dominated sorting and crowding distribution;

[0040] S75, steps S72 to S71, until the termination condition is met.

[0041] Furthermore, step S72 specifically includes:

[0042] S701. If the dominance number is 0, then the solution p is on the first non-dominated frontier. Find the dominance number and dominated set of each solution as well as the first non-dominated frontier.

[0043] S702. For each solution p with a dominance number of 0, traverse each member q in its dominance set, and reduce the dominance number of q by 1. If any q has a dominance number of 0, put it into a separate list Q. The members of Q are the second-level non-dominated frontier.

[0044] S703. Repeat step S702 for each member in Q to find the third layer, and then repeat the process from steps S701 to S702 until all solutions are sorted.

[0045] Furthermore, step S5 further includes:

[0046] The consistency test of the total hierarchical ranking is performed. Combined with the RI value table, the results are tested for consistency. The test method is to calculate the CR value. When the CR value is less than the set threshold, it is judged that it has passed the consistency test. The CR value calculation formula is as follows:

[0047]

[0048] Furthermore, the parameters of the electric heating device include:

[0049] The inner diameter of the generating stator, the effective length of the device, the pole arc coefficient of the permanent magnet, the thickness in the magnetizing direction, the size parameters of the stator slots, the air gap length of the generating stator part, the air gap length of the heating stator part, the thickness of the heating stator, the number of holes in the heating stator, and the radius of the holes in the heating stator;

[0050] The inner diameter of the generator stator, the effective length of the device, the pole arc coefficient and thickness of the permanent magnet in the magnetizing direction of the generator part, and the size parameters of the stator slots affect the performance indicators of the generator part;

[0051] The thickness of the heating stator, the radius of the heating stator hole, the pole arc coefficient of the permanent magnet of the heating part and the thickness in the magnetizing direction affect the performance indicators of the heating part.

[0052] The beneficial effects of the present invention are:

[0053] The present invention first uses a multi-objective analytic hierarchy process to identify the impact of device structural parameters on key performance indicators. It then uses the NSGA-II genetic algorithm to comprehensively optimize these key performance indicators based on these key structural parameters, obtaining the optimal structural parameters under defined conditions. This method optimizes multiple objectives to achieve a comprehensive optimal solution under defined conditions. This optimizes the parameters of the electric heating device and improves its overall performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 This is a flow chart of a method for optimizing parameters of an electric heating device based on multi-objective hierarchical analysis and genetic algorithm provided by an embodiment of the present invention;

[0055] Figure 2 2 is a torque comparison curve diagram of the electric heating device before and after optimization provided by an embodiment of the present invention;

[0056] Figure 3 This is a comparison curve of the heating power of the electric heating device before and after optimization provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0057] To make the objectives, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0058] The electric heating device of the present invention can be divided into components based on its structure: a power-generating stator, a heat-generating stator, a rotor, and permanent magnets. The power-generating and heat-generating stators respectively realize the device's power generation and heat-generating functions. In particular, to increase heat generation efficiency and facilitate heat transfer, the heat-generating stator is often perforated on the side closest to the permanent magnets. The permanent magnets establish the device's operating magnetic flux, and the rotor drives the permanent magnets to rotate, generating an alternating magnetic field.

[0059] The structural parameters of the electric heating device mainly include: the inner and outer diameters D of the power generation stator o1 、Effective length of the device L ef , permanent magnet pole arc coefficient α P , thickness h in the magnetization direction m , stator slot size parameters, air gap length of the power generation stator part, air gap length of the heating stator part, heating stator thickness d is , the number of holes in the heating stator, the radius of the holes in the heating stator r i wait.

[0060] Among them, the inner and outer diameters of the power generation stator, the effective length of the device, the pole arc coefficient of the permanent magnet of the power generation part and the thickness in the magnetization direction, as well as the stator slot size parameters affect the performance indicators of the power generation part; the thickness of the heating stator, the radius of the hollow circular hole of the heating stator, the pole arc coefficient of the permanent magnet of the heating part and the thickness in the magnetization direction affect the performance indicators of the heating part.

[0061] During the design process of the electric heating device, the performance indicators of the power generation part should be met first. Therefore, in general, the key performance indicators of the electric heating device mainly include: device output line voltage, device output harmonic content, device output heating power, device efficiency, device torque pulsation and device overall power density.

[0062] Based on this, the present invention provides a method for optimizing parameters of electric heating devices based on multi-objective hierarchical analysis and genetic algorithm, such as Figure 1 As shown, specifically including:

[0063] Screening of key parameters of electric heating devices based on multi-objective analytic hierarchy process:

[0064] (1) First, a hierarchical evaluation model needs to be constructed. The model mainly includes a target layer, a criterion layer, and a measure layer. Among them, the target layer is the final reflection of the indicators of each layer. For the method of the present invention, the target layer is to achieve the comprehensive optimization of the device performance; the criterion layer mainly includes the main factors affecting the device performance. The present invention mainly includes the device output line voltage, device output harmonic content, device output heating power, device efficiency, device torque pulsation, and device overall power density; the measure layer mainly includes measures that can be taken to achieve the goal. The present invention mainly adjusts the structural parameters of the electric heating device. The hierarchical evaluation model serves as the basis for the final calculation of the weight of the impact of each topological structural parameter change on the device performance. The total weight is obtained based on the comprehensive calculation of the weights between different layers of the hierarchical evaluation model.

[0065] (2) Construct a judgment matrix. The judgment matrix is ​​to determine the weight of each element relative to an element in the upper layer by comparing each element with each other. The values ​​in the judgment matrix are often given by Santy's 1-9 scaling method. The 1-9 scale represents the importance of two elements compared with each other, where 1 element represents that the two elements have the same importance. The larger the scale, the higher the importance. The scaling method uses the reciprocal of 1-9 to represent the importance of two elements when the order is swapped.

[0066] Therefore, for the elements of any layer, the present invention can establish a judgment matrix as follows:

[0067]

[0068] Where a ij Represents the importance of the i-th element compared to the j-th element. The elements in A satisfy:

[0069] a ij >0

[0070]

[0071] a ii =1

[0072] (3) The weight of each indicator is solved through the judgment matrix. This process is also called hierarchical single sorting. The specific steps include:

[0073] 1) Calculate the mth power of the product of each row of the judgment matrix to obtain an m-dimensional vector. This m-dimensional vector is the weight of each indicator before normalization. m is the row and column information of the judgment matrix. The number of rows and columns of the judgment matrix is ​​usually equal. Its calculation formula is as follows:

[0074]

[0075] 2) The obtained data is normalized to form a weight vector. At this time, the weight is obtained. The calculation formula is as follows:

[0076]

[0077] (4) Perform consistency check on the weight calculation results. The specific steps include:

[0078] 1) First, the maximum eigenvalue and CI value need to be calculated. The calculation formula of the maximum eigenvalue satisfies:

[0079]

[0080] Where λ max is the maximum eigenvalue of the judgment matrix, and AW is the cumulative value of the judgment matrix multiplied by the standardized weights and then row by row.

[0081] After solving the maximum eigenvalue, the CI value can be obtained. The calculation formula is as follows:

[0082]

[0083] 2) After obtaining the CI value, the present invention can perform a consistency test on the results in combination with the RI value table. The test method is to obtain the CR value. When CR < 0.1, it can be considered that it has passed the consistency test. The calculation formula is as follows:

[0084] RI is a statistical parameter that can be directly obtained by looking up the RI value table.

[0085] (5) After completing the above process, the hierarchical total ranking process can be carried out. The purpose of the hierarchical total ranking is to reflect the weight of the lowest level to the highest level target. For the present invention, the purpose of the hierarchical total ranking is to screen out a group of topological structure parameters that have the greatest impact on the key performance indicators of the device, so as to facilitate the subsequent use of the NSGA-II algorithm to set the optimization parameters. The final hierarchical total ranking weight calculation formula is:

[0086]

[0087] Where w is the total weight, which is obtained by comprehensive calculation of the weights between different layers of the hierarchical evaluation model. The total weight of the present invention characterizes the degree of influence of each topological structure parameter on the key performance indicators of the device. Through the comprehensive ranking and comparison of the total weight, it is possible to screen a group of topological structure parameters that have the greatest impact on the key performance parameters of the device. bai Represents the bottom layer b method to the middle layer a i The impact weight of the target, w aiz Represents the middle layer a i The weight of the target's impact on the total target z.

[0088] The total hierarchical ranking also needs to be checked for consistency. The judgment index is consistent with the single hierarchical ranking. The verification method is to calculate the CR value. When the CR value is less than the set threshold, it is judged to have passed the consistency test. The CR value calculation formula is as follows:

[0089]

[0090] Based on the key topological parameters selected by the multi-objective hierarchical analysis method, the performance indicators of the electric heating device are optimized using the NSGA-II genetic algorithm. The specific steps are as follows:

[0091] (1) Initializing the population, i.e., determining the decision variables and objective function values ​​of each individual. Here, the individual is the device topology parameter selected by the multi-objective hierarchical analysis method, and the objective function value is the set key performance indicator of the device. The range of the individual decision variables is set by the constraints based on the topology structure and the expected range;

[0092] (2) Perform non-dominated sorting on the individuals in the population and divide the individuals into different levels according to the dominance relationship. Here, the main method is to calculate the dominance number n p and the dominated set S p To judge, the steps include:

[0093] 1) If the dominance number is 0, then the solution p is on the first level non-dominated frontier. Through the above operations, find the dominance number and dominated set of each solution as well as the first level non-dominated frontier;

[0094] 2) Then, for each solution p with a dominance number of 0, traverse each member q in its dominance set, and reduce the dominance number of q by 1. If any q has a dominance number of 0, put it into a separate list Q. The members of Q are the second-level non-dominated frontier;

[0095] 3) Repeat 2) for each member of Q to find the third layer, then repeat 1) and 2) until all solutions are found.

[0096] (3) Calculate the crowding degree of each individual to maintain the diversity of the population. Crowding degree refers to the distance between an individual and its adjacent individuals in the target space.

[0097] (4) Based on the non-dominated sorting and crowding distribution, the next generation of population is selected. The principle of selection is to retain as many individuals as possible in the Pareto optimal solution set and maintain the diversity of the population.

[0098] (5) Repeat steps 2 to 4 until the termination condition is met.

[0099] In order to verify the feasibility of this optimization scheme, an electric-thermal multi-energy flexible magnetic control power generation device with a dual-stator configuration was built in finite element simulation. The optimization goals of this paper were to keep the output line voltage, harmonic content, heating power, and loss ratio within a reasonable range, as well as minimize torque pulsation and maximize power density. It was optimized based on this scheme. The comparison of key structural parameters of the device before and after optimization is shown in Table 1. The device in the embodiment adopts a trapezoidal slot structure.

[0100] Table 1 Comparison of key structural parameters of the device before and after optimization

[0101] parameter Before optimization After optimization Generator stator inner diameter / mm 170 167.1 Motor length / mm 65 70.938 External permanent magnet pole arc coefficient 0.6 0.674 External permanent magnet thickness / mm 6 3.925 <![CDATA[Stator slot h b > 3.6 3.535 <![CDATA[Dimension parameter b t > 0.5 0.505 <![CDATA[Number / mm h t1 > 3.5 3.373 Heating stator thickness / mm 10 13.397 Hole radius / mm 3.6 2.444 Internal permanent magnet pole arc coefficient 0.9 0.951 Inner permanent magnet thickness / mm 9 8.122

[0102] The changes of device torque and heating power before and after optimization are as follows Figure 2 、 Figure 3 After optimization, the effective value of the line voltage is 380.08V, the THD value of the harmonic content is 0.499%, the heat power is 3321.2W, the torque ripple is 0.768%, the motor efficiency is 97.41%, and the power density is 2.5513×10 6 W / m 2 The heating power increased by 20.50% compared with the initial optimization, the overall efficiency of the motor increased slightly by 0.13%, and the power density increased by 13.60%, which verified the effectiveness of the optimization method. The comparison of parameters before and after optimization is shown in Table 2.

[0103] Table 2 Comparison of parameters before and after optimization

[0104]

[0105]

[0106] In summary, the present invention optimizes the key performance indicators of the selected device through the above scheme, and based on the selected important topological structure parameters, effectively improves the performance of the device and achieves the comprehensive optimization under limited conditions.

[0107] The foregoing description is merely a preferred embodiment of the present invention. It should be understood that the present invention is not limited to the form disclosed herein and should not be construed as excluding other embodiments. Rather, the present invention can be used in various other combinations, modifications, and environments and can be modified within the scope of the concept described herein through the above teachings or techniques or knowledge in the relevant field. Modifications and variations made by those skilled in the art that do not depart from the spirit and scope of the present invention are intended to be protected by the appended claims.

Claims

1. The electric heating device parameter optimization method based on multi-objective hierarchical analysis and genetic algorithm is characterized by: include: S1. Construct a hierarchical evaluation model; The hierarchical evaluation model includes a target layer, a criterion layer, and a measure layer. The target layer is the final reflection of the indicators of each layer, that is, the performance indicators of the electric heating device reach the comprehensive optimization. The criterion layer includes factors that affect the performance of the device, such as the output line voltage of the electric heating device, the output harmonic content of the electric heating device, the output heating power of the electric heating device, the efficiency of the electric heating device, the torque ripple of the electric heating device, and the overall power density of the electric heating device. The measure layer includes measures taken to adjust the parameters of the electric heating device. S2, construct judgment matrix; The judgment matrix determines the weight of each element relative to an element in the upper layer by comparing each element with each other. The values ​​in the judgment matrix are given by Santy's 1-9 scaling method. The 1-9 scale represents the importance of two elements compared with each other, where 1 represents that the two elements have the same importance. The larger the scale, the higher the importance. The scaling method uses the reciprocal of 1-9 to represent the importance of two elements when the order is swapped. For the elements of any layer, a judgment matrix is ​​established as follows: Where a ij Represents the importance of the i-th element compared to the j-th element. The elements in A satisfy: a ij >0 a ii ＝1 S3. Calculate the weight of each indicator through the judgment matrix; Calculate the mth power of the product of each row of the judgment matrix to obtain an m-dimensional vector. The m-dimensional vector is the weight of each indicator before normalization. m is the row and column information of the judgment matrix. The number of rows and columns of the judgment matrix is ​​equal. Its calculation formula is as follows: The obtained data is standardized to obtain the weight vector, and the calculation formula is as follows: S4. performing consistency check on the calculation results of the weight vector; Calculate the maximum eigenvalue and CI value, where the calculation formula of the maximum eigenvalue satisfies: Where λ max is the maximum eigenvalue of the judgment matrix, and AW is the cumulative value of the judgment matrix multiplied by the standardized weights and then row by row; The calculation formula of CI value is as follows: After obtaining the CI value, the results are checked for consistency in combination with the RI value table. The check method is to calculate the CR value. When the CR value is less than the set threshold, it is judged to have passed the consistency test. The CR value calculation formula is as follows: RI is a statistical parameter and can be directly obtained by querying the RI value table; S5. Calculate the total hierarchical ranking weight using the following formula: In the formula, w is the total weight, w bai Represents the bottom layer b method to the middle layer a i The impact weight of the target, w aiz Represents the middle layer a i The impact weight of the target on the total target z; S6. Filter out the parameters of the electric heating device that have the greatest impact on the performance indicators of the electric heating device according to the total weight; S7. Based on the screened parameters of the electric heating device, the performance indicators of the electric heating device are optimized according to the NSGA-II genetic algorithm.

2. The method for optimizing parameters of an electric heating device based on multi-objective hierarchical analysis and genetic algorithm according to claim 1, characterized in that: Step S7 specifically includes: S71, initializing the population, that is, determining the decision variables and objective function values ​​of each individual, wherein the individual is the screened electric heating device parameters, and the objective function value is the set performance index value; S72, sort the individuals in the population by non-dominance, and divide the individuals into different levels according to the dominance relationship. p and the dominated set S p to judge; S73. Calculate the crowding degree of each individual to maintain the diversity of the population. The crowding degree refers to the distance between the individual and its adjacent individuals in the target space. S74. Select the next generation population based on non-dominated sorting and crowding distribution; S75, steps S72 to S71, until the termination condition is met.

3. The method for optimizing parameters of an electric heating device based on multi-objective hierarchical analysis and genetic algorithm according to claim 2, characterized in that: Step S72 specifically includes: S701. If the dominance number is 0, then the solution p is on the first non-dominated frontier. Find the dominance number and dominated set of each solution as well as the first non-dominated frontier. S702. For each solution p with a dominance number of 0, traverse each member q in its dominance set, and reduce the dominance number of q by 1. If any q has a dominance number of 0, put it into a separate list Q. The members of Q are the second-level non-dominated frontier. S703. Repeat step S702 for each member in Q to find the third layer, and then repeat the process from steps S701 to S702 until all solutions are sorted.

4. The method for optimizing parameters of an electric heating device based on multi-objective hierarchical analysis and genetic algorithm according to claim 1, characterized in that: Step S5 further includes: The consistency test of the total hierarchical ranking is performed. Combined with the RI value table, the results are tested for consistency. The test method is to calculate the CR value. When the CR value is less than the set threshold, it is judged that it has passed the consistency test. The CR value calculation formula is as follows:

5. The method for optimizing parameters of an electric heating device based on multi-objective hierarchical analysis and genetic algorithm according to claim 1, characterized in that: The parameters of the electric heating device include: The inner diameter of the generating stator, the effective length of the device, the pole arc coefficient of the permanent magnet, the thickness in the magnetizing direction, the size parameters of the stator slots, the air gap length of the generating stator part, the air gap length of the heating stator part, the thickness of the heating stator, the number of holes in the heating stator, and the radius of the holes in the heating stator; The inner diameter of the generator stator, the effective length of the device, the pole arc coefficient and thickness of the permanent magnet in the magnetizing direction of the generator part, and the size parameters of the stator slots affect the performance indicators of the generator part; The thickness of the heating stator, the radius of the heating stator hole, the pole arc coefficient of the permanent magnet of the heating part and the thickness in the magnetizing direction affect the performance indicators of the heating part.

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