隨著節能及環保意識提高，用電量佔所有電氣負載首位的電動機(馬達)具絕對節能潛力，高效率馬達在工業現代化的各產業中成為節能對策的首選。磁阻馬達具有結構簡單、機械強度高、生產成本低等優點，其應用範圍廣，在可變調速應用中可取代感應馬達及永磁馬達。然而，相較於感應馬達與永磁馬達，磁阻馬達轉矩漣波大、功率因數低，以致大幅降低磁阻馬達性能及應用。
本文使用有限元素分析軟體ANSYS Maxwell探討30 kW同步磁阻馬達的分析與設計，針對轉子結構進行優化，以改善轉矩漣波及功率因數為目標，並將田口法應用於馬達設計中，經由各實驗因子水準組合，計算各品質特性之信號雜訊比，透過變異數分析評估各因子對目標反應之影響程度，以提升磁阻馬達性能。
本研究加入模糊推論解決多目標問題，使參數達到優化，利用灰關聯分析將信號雜訊比轉換成灰關聯係數，經由模糊推論系統求得多重品質特性衡量指標，並決定磁阻馬達最佳參數組合，以驗證馬達設計之正確性。最後將磁阻馬達最佳化分析與設計成果，製作實體馬達成品並進行實測，探討與比較模擬與實測間誤差，以提高模擬及設計方法之準確性，供作為未來磁阻馬達設計參考。

With the increasing awareness of energy conservation and environmental protection, electric motors (motors), which consume the most power in all electrical loads, have absolute energy-saving potential. High-efficiency motors have become the first choice for energy-saving measures in various industries in industrial modernization. Reluctance motor has the advantages of simple structure, high mechanical strength, low production cost, etc. It has a wide range of applications and can replace induction motors and permanent magnet motors in variable speed applications. However, compared with induction motors and permanent magnet motors, reluctance motors have large torque ripple and low power factor, which greatly reduces the performance and applications of reluctance motors.
This thesis uses the finite element analysis software ANSYS Maxwell to analyze and design a 30 kW synchronous reluctance motor, optimizes the rotor structure to improve torque ripple and power factor, and applies Taguchi’s method to motor design. Combination of experimental factor levels, calculate the signal-to-noise ratio of each quality characteristic, and evaluate the influence of each factor on the target response through variance analysis to improve the performance of the reluctance motor.
In this study, fuzzy inference is added to solve the multi-objective problem and the parameters are optimized. The signal-to-noise ratio is converted into grey relational coefficient using grey relational analysis, and the multiple performance characteristics index is obtained through the fuzzy inference system, and the best parameters of the reluctance motor are determined, which verified the correctness of the motor design. Finally, the optimization analysis and design results of the reluctance motor are executed and implemented, and the practical product of the physical motor is produced and measured in machine factory. The error and deviation from measurement compared with simulations were studied in order to improve the accuracy of the simulation and design methodologies, as a reference for future design of reluctance motor.

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