TWI398742B - Method for optimizing generator parameters by taguchi method and fuzzy inference - Google Patents

Description

Method of determining the optimal parameters of generators by using Taguchi method and fuzzy inference

The invention relates to a method for optimizing the parameters of a generator, in particular to a method for applying the Taguchi method and the fuzzy inference to determine the optimal parameters of the generator.

According to, how to improve the generator's power generation efficiency has always been the research goal in this field, which means to achieve high power generation efficiency with low input torque. However, in order to meet these two repulsive design goals, designers must find a balance point. To improve the overall performance of the generator, and the performance of the generator is determined by the process parameters of the generator. Therefore, the optimization of the process parameters becomes the highest principle in the design of the generator. However, the process affecting the efficiency of the generator There are quite a lot of parameters, which makes it difficult to find the best combination of parameters. Most of the previous process parameters are determined by the empirical calculations accumulated by the predecessors, and they are completed by constantly trying to make mistakes and corrections. A large amount of manpower, cost and directly affect the delay of the production cycle, so that the production plant is in a very disadvantageous position in this highly competitive market.

In order to solve this problem, the predecessors introduced a one-factor method, a full factor method and a partial factor method to find the optimal parameters. However, the above various methods are not systematic enough, low reproducibility and too complicated. .. and other shortcomings, therefore, the predecessors had to find a more suitable parameter optimization method, in which the Taguchi method has been proved to be a very effective parameter optimization method, which introduces the concept of statistical introduction to the operator The optimal combination of parameters can be obtained with an appropriate number of experiments. Since it does not require a complicated calculation process and allows multiple parameter variations at the same time, the process of finding the optimal parameter combination is simplified and systematic.

However, although the Taguchi method is a very effective and suitable method, it must simultaneously consider the design goals of high power generation efficiency and low cogging torque, which shows the deficiency of the traditional Taguchi method, that is, the decision of multiple parameters. In the process, the Taguchi method may not be able to determine all the best parameters.

The main object of the present invention is to provide a method for applying the Taguchi method and fuzzy inference to determine the optimal parameters of the generator, and it is hoped that the design and the improvement of the conventional generator parameter optimization method are not systematic, reproducible and too complicated... And other issues.

To achieve the foregoing, the present invention comprises the steps of: defining a plurality of functional requirements, and selecting appropriate generator parameters as control factors, and each control factor includes at least two different levels; for the defined complex functional requirements, respectively The Taguchi method experiment obtains the functional quantified data of different levels of each control factor; the functional quantification data determines the optimal level of each control factor under each functional requirement, and then forms the optimal control factor combination under the functional demand; The optimal combination of control factors for each functional requirement, extracting the control factors that optimize the functional requirements that are simultaneously satisfied; determining whether there are control factors to be determined, and if all the control factors have been determined, the parameter design is completed; if it still has to be determined The control factor means that the Taguchi method cannot determine all the control factors that satisfy the requirements of each function at the same time, then use the fuzzy inference to find the optimal level of the undetermined control factor; perform the fuzzy inference, and take the control factor to be determined as the input variable, each function Demand as an output variable and defining the modulus of the input variable and the output variable a set, wherein the set of children in the fuzzy rule is the order of the input variables; defining the fuzzy rule, and substituting the subset of the input variables into the fuzzy rule, obtaining the permutation combination of all the input variable subsets, and deducing the combinations The obtained output variable result; the output variable result obtained from the fuzzy inference determines the input variable that satisfies the functional requirements at the same time, and obtains the optimal control factor to be determined; combined with the optimal control factor obtained by the Taguchi method and the fuzzy inference, it is multi-purpose. Optimized manufacturing parameters.

The invention utilizes the Taguchi method to find the optimal parameters of the generator, and utilizes the advantages of systemization and simple and clear process to complete the optimization with a proper number of experiments, and with fuzzy inference to overcome the Taguchi method cannot be aimed at two repels. The functional requirements are the shortcomings of the complete parameter optimization, complementing all the parameters that cannot be determined by the Taguchi method, and thus providing a parameter optimization method that is systematic, highly reproducible and can simultaneously satisfy multiple functional requirements.

The invention applies the Taguchi method and the fuzzy inference to determine the generator parameters, and uses the software simulation and the real machine experiment to prove that the generator applying the parameters determined by the invention has better performance, and the operation procedure is to use the computer simulation. After the software RMxprt is used to construct the model of the generator prototype, based on the model, the Taguchi method and fuzzy inference are used to discuss how to modify the parameters of the permanent magnet generator to minimize the cogging torque of the prototype generator and the power generation efficiency. Maximizing, wherein, referring to the second figure, the generator (2) comprises a stator (21) and a rotor (22) pivoted in the stator (21), and the inner edge of the stator (21) is distributed Stator slots (211) arranged side by side, the parameters and data of the prototype generator are shown in Table 1, Table 2, and the performance of the prototype generator The performance is shown in Table 3.

Please refer to the first figure, which is a schematic flow chart of the method for applying the Taguchi method and the fuzzy inference to determine the optimal parameters of the generator, which comprises the following steps:

1. Defining a plurality of functional requirements and selecting appropriate generator parameters as control factors (101), wherein each control factor includes at least two different levels, the actual operation is as follows: the preferred embodiment defines two functions The demand is I. Generator efficiency, II. cogging torque, and select the parameters that may affect the generator characteristics as the control factor. Please refer to Table 4, which are A. slot pole ratio (level: 03:01 and 08:09 respectively), B. chute width (level is no chute, oblique half slot width and oblique one respectively) Slot width), C. magnet material (magnets of different strengths of n30sh, n33sh and n35sh, respectively), D. coil turns (bits) The standard is 36匝, 38匝 and 40匝), E. stator slot type (as shown in the third to fifth figures, the levels are Type-1 (211a), Type-2 (211b) and Type- respectively. 3 (211c) groove type), F. wire diameter width (levels are 0.7mm, 0.8mm and 0.9mm respectively), G. gullet opening width (levels are 1.0mm, 1.1mm and 1.2mm respectively) and H. Air gap average width (levels are 0.8mm, 0.9mm and 1.0mm respectively), wherein each control factor is set to three levels except that the control factor A has only two levels.

2. For the defined complex function requirements, the Taguchi method experiment (102) is used to obtain functional quantized data of different levels of each control factor. The actual operation is as follows: The preferred embodiment selects L 18 (3 ⁸ ) The orthogonal table, respectively, for the functional requirements I. Generator efficiency, and functional requirements II. Cogging torque is done in two sets of Taguchi method experiments, and each group of Taguchi method experiments contains 18 trials, as shown in Table 5 and Table 6, respectively. As shown, the level of each control factor is represented by Arabic numerals 1 to 3. For example, the control factor A (slot ratio) of the first test is level 1, which means the first test site. The slot ratio used is 03:01; the control factor A (slot pole ratio) of the 10th test is level 2, which means that the slot ratio used in the 10th test is 08:09, and the rest depends on Such a push will not be repeated.

3. The functional quantized data determines the optimal level of each control factor under each functional requirement, and then constitutes the optimal combination of control factors under the functional requirements. The actual operation is as follows: using Table 5, each control factor is calculated separately. The average S/N of the efficiency of the level, such as the average S/N of the efficiency of A1 is obtained by averaging the S/N values of the first to the ninth test, and the average S/N of the efficiency of B1 is the first to the first The S/N values of the 3rd and 10th to the 12th tests are averaged, and the others are equivalent. The average S/N of the efficiency of each control factor is shown in Table 7, and will be based on Table 7. The efficiency S/N average value is drawn as shown in the sixth figure. According to the sixth figure, the most efficient control factor combination is selected (A1, B3, C1, D2, E2, F2, G1, H1);

Using Table 6, the average value of the cogging torque S/N of each control factor at different levels is calculated. The results are shown in Table 8, and the cogging torque S/N of each control factor is averaged according to Table 8. The value is plotted as in the seventh figure. According to the seventh figure, the combination of control factors (A1, B3, C2, D2, E2, F2, G2, H3) with the smallest cogging torque is selected.

4. Compare the optimal control factor combinations for each functional requirement, and extract the control factors (103) that optimize the functional requirements that are simultaneously satisfied. The actual operation is as follows: the combination of the most effective control factors (A1, B3, C1) , D2, E2, F2, G1, H1), and the combination of control factors (A1, B3, C2, D2, E2, F2, G2, H3) with the lowest cogging torque, can simultaneously achieve maximum efficiency and cogging Control factors for torque minimization: (A1, B3, D2, E2, F2) to achieve the lowest cogging torque and highest efficiency design goals.

5. Determine whether there is a control factor to be determined (104). If all the control factors have been determined, the parameter design is completed. If there is still a control factor to be determined, it means that the control factor that satisfies the functional requirements at the same time cannot be determined by the Taguchi method. Then, use fuzzy inference to find the optimal level of the undetermined control factor. The actual operation is as follows: Since the Taguchi method cannot determine the optimal level of the control factor C. magnet material, G. cogging opening width and H. air gap average width, it means that it is impossible to determine all the simultaneous maximization of efficiency and The cogging torque minimizes the control factor of the demand. Therefore, the above three control factors C, G, and H need to use fuzzy inference to find the best level.

6. Perform fuzzy inference (105), take the undetermined control factor as the input variable, each function requirement as the output variable, and define the fuzzy set of the input variable and the output variable. The actual operation is as follows: the average width of the air gap (mm) , the opening width of the magnetic groove (mm) and the strength of the magnet to count the variables, efficiency (%) and cogging torque (N * m) to output variables, please refer to the eighth to twelfth figures, define the input variables and The fuzzy set of output variables is: Magnet strength = {weak (n30sh), normal (n33sh), strong (n35sh)}; slot opening width (mm) = {small, medium, large}; air gap average width (mm) = {small, medium , large}; efficiency (%) = {inferior, slightly worse, fair, good, excellent}; cogging torque (N*m) = {excellent, good, ordinary, slightly larger, too large}.

7. Define the fuzzy rule, and substitute the sub-set of each input variable into the fuzzy rule to obtain the arrangement and combination of all the sub-sets, and the output variable result inferred by each combination. The actual operation is as follows: See Table 9, for The fuzzy rule of the preferred embodiment; referring to the thirteenth figure, after substituting the sub-sets of the input variables into the fuzzy rule, obtaining the permutation and combination of all the input variable sub-sets, and the output variable results inferred by each combination, It can be further drawn into a fuzzy rule decision diagram.

8. The result of the fuzzy inference is determined, and the input variables satisfying the requirements of each function are determined (106), and the optimized control factor to be determined is obtained. The actual operation is as follows: Observing the fuzzy rule decision map, extracting and simultaneously satisfying the efficiency maximization And the combination of input variables that minimizes cogging torque, resulting in an optimized control factor: the optimal combination of control factors is (C2, G1, H2).

9. Combine the best control factor (107) obtained by Taguchi method and fuzzy inference to obtain multi-purpose optimized manufacturing parameters: the best combination of parameters is (A1, B3, C2, D2, E2, F2, G1, H2) ), meaning that the optimal parameters of the generator are control factor A: slot ratio is 03:01; control factor B: chute width is oblique slot width; control factor C: magnet material is n33sh; control factor D: The number of turns of the coil is 38 匝; the control factor E: the stator slot type is Type-2; the control factor F: the wire diameter is 0.8 mm; the control factor G: the slot opening width is 1.0 mm; the control factor H: the average width of the air gap It is 0.9mm.

After modifying the generator prototype according to the optimized parameters determined in this embodiment, the simulation is performed by RMxprt software. The simulation results are shown in Table 10. Comparing Table 3 and Table 10, the output efficiency is not optimized before. 75.07% increased to 84.7528%, and the cogging torque was reduced from 0.3016 (N*m) before the original optimization to 0.2616 (N*m).

According to the above optimized parameters, the actual machine is produced, and the performance output data is shown in Table 11. The data at the speed of 1100 (RPM) is observed. Compared with the simulation results in Table 10, the actual machine test and simulation are found. The resulting cogging torque error is about 3.11%, the output voltage error is about 0.79%, the output current error is about 6.28%, and the efficiency error is about 3.3%. The errors are all within an acceptable range, thereby demonstrating the feasibility and accuracy of the present invention.

(2) ‧‧‧Generator

(21) ‧‧‧ Stator

(211) (211a) (211b) (211c) ‧ ‧ stator slots

(22) ‧‧‧Rotor

The first picture: the block diagram of the creation.

Figure 2: Schematic diagram of the generator.

The third picture is a schematic diagram of the Type-1 stator slot type of the generator.

Figure 4: Schematic diagram of the Type-2 stator slot for the generator.

Figure 5: Schematic diagram of the Type-3 stator slot for the generator.

Figure 6: Schematic diagram of the efficiency S/N average for each control factor.

Figure 7: Schematic diagram of the average S/N of the cogging torque for each control factor.

Figure 8: Schematic diagram of the fuzzy set of magnet strength.

Figure IX: Schematic diagram of the fuzzy set of the slot opening width.

Figure 10: Schematic diagram of the fuzzy set of the average width of the air gap.

Figure 11: Schematic diagram of the fuzzy set of power generation efficiency.

Figure 12: Schematic diagram of the fuzzy set of cogging torque.

Thirteenth figure: A fuzzy rule decision diagram.

Claims (2)

A method for applying a Taguchi method and a fuzzy inference to determine an optimal parameter of a generator, comprising the steps of: defining a plurality of functional requirements, and selecting an appropriate generator parameter as a control factor, and each control factor includes at least two different levels; According to the defined complex function requirements, the Taguchi method is used to obtain the functional quantized data of different levels of each control factor; the functional quantized data determines the optimal level of each control factor under each functional requirement, and then constitutes the functional requirement. The optimal combination of control factors; compare the optimal combination of control factors for each functional requirement, extract the control factors that optimize the functional requirements that are simultaneously met; determine whether there are control factors to be determined, and if all control factors have been determined, That is, the parameter design is completed; if there is still a control factor to be determined, that is, the Taguchi method cannot determine all the control factors satisfying the functional requirements at the same time, the fuzzy inference is used to find the optimal level of the undetermined control factor; the fuzzy inference is executed and will be determined. Control factor as input variable, each function demand as input a variable, and defining a fuzzy set of input variables and output variables, wherein the subset of the fuzzy rules is the order of the input variables; defining the fuzzy rules, and substituting the subset of the input variables into the fuzzy rules to obtain all the input variables The permutation and combination of sub-sets, and the output variable results deduced from each combination; the output variable results obtained from the fuzzy inference determine the input variables that satisfy the functional requirements at the same time, and obtain the optimal control factors to be determined; combined with the Taguchi method and fuzzy Infer the best control factor obtained, Manufacturing parameters optimized for multiple purposes. The method for applying the Taguchi method and the fuzzy inference to determine the optimal parameters of the generator, as described in claim 1, wherein the definition function requirement includes the efficiency of the generator and the cogging torque, and the control factor includes the slot ratio and the skew. Slot width, magnet material, number of turns of the coil, stator slot shape, wire diameter width, slot opening width, and air gap average width.