三相鼠籠式感應電動機之模式化與最佳化設計

MODELLING AND OPTIMIZATION DESIGN FOR THREE-PHASE SQUIRREL CAGE INDUCTION MOTOR

邱機平(Ji-Pyng Chiou)

鼠籠式感應電動機 ； 模式化 ； 實測資料 ； 特性分析 ； 混合式CODEQ演算法 ； squirrel cage induction motor ； system modelling ； measurement results ； characteristics analysis ； hybrid CODEQ method

技術學刊

37卷4期（2022 / 12 / 01）

297 - 309

繁體中文

本論文的目在於針對3相、4級、220伏特、60赫茲以及5馬力的鼠籠式感應電動機系統進行模式化，並使用已模式化之系統進行感應電動機之特性分析，最後應用混合式CODEQ演算法進行最佳化分析，分析的目標在於希望可以達到體積最小化以及效率最大化，為了讓分析的結果更加符合實際之感應電動機系統，電流、滑差、磁通密度以及功率因數等相關限制條件均納入考慮；在模式化部分，本論文使用東元公司之AEEF系列單鼠籠感應電動機作為模式化之對象，並且依此一感應電動機各個結構之大小、規格以及材料特性建立模式化系統，且與實際量測之資料進行比對，以便確認模式化之正確性；在確認模式化系統之正確性後，再以最佳化演算法針對此一模式化系統進行最佳化分析。為了達到快速且準確分析的目的，本論文使用混合式CODEQ演算法作為分析的工具，因為CODEQ演算法雖然使用雜亂搜尋（chaotic search）、反向學習（opposition-based learning）以及量化機制（quantum mechanics）等運算元克服差分進化法（Differential Evolution, DE）中有關交配因數（crossover factor）、比例參數（scaling factor）以及突變運算元（mutation operator）選擇上的缺點，但大量使用亂數亦會造成CODEQ演算法產生不穩定搜尋情況發生，因此，混合式CODEQ演算法係在CODEQ演算法中加入移居（Migrating）以及加速（Acceleration）兩個運算元以提升演算法的搜尋穩定性，並使用較小的族群個體數達到快速搜尋到解的目的，所以，本論文應用此一具備友善（Friendly）、容易使用（Ease to use）以及強韌（Robust）等特性的混合式CODEQ演算法於三相鼠籠式感應電動機以進行模式化與特性分析。

The modelling of the 3-phase, 4-pole, 220-volt, 60-hz, and 5-hp squirrel cage induction motor is presented in this paper, and the characteristics analysis of the system using hybrid CODEQ method is also discussed. This system has two objectives: minimizing the motor's volume and maximizing the system's efficiency. Various constraints including the current, torque, slip, magnetic flux density, power factor, and so on are considered to realize the induction motor design. The single squirrel cage induction motor of TECO's AEEF series is used in the work. To verify the accuracy of the mathematical model, the induction motor's dimensions, specifications, and materials characteristics are fixed during the simulation process. All simulation results are compared with the measurement results to prove the mathematical model of the squirrel cage induction motor is matched with the practical one. The hybrid CODEQ method is used in this paper to increase the analysis ability. The concepts of chaotic search, opposition-based learning, and quantum mechanics are used in the CODEQ algorithm to overcome the drawback of selection of the crossover factor, scaling factor, and mutation operator used in the differential evolution (DE) method. However, although the wide use of random numbers used in the CODEQ algorithm leads to a higher probability of obtaining a local optimum, much computation time is expended to evaluate the fitness function. So, the acceleration operation and migration operation are used in the CODEQ algorithm (called the hybrid CODEQ algorithm) to increase the search capacity. That is, a smaller population size can be used in the hybrid CODEQ method to achieve the aim of increasing convergence speed. So, the hybrid CODEQ method has friendly, easy to use and robust characteristics and is used to the system modelling and characteristics analysis for the 3-phase squirrel cage induction motor.

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