本文提出利用結合基因演算法與滑動模式控制來設計電力系統穩定器。電力系統 穩定器可利用最佳線性調整來設計，但利用此方法會造成設計上的耗費及減少可 靠度。因此，我們提出只利用需要的狀態變數之控制設計，如角頻率及轉矩角。 為了解決這些設計上的問題，我們使用最佳降階法將發電機降階成兩狀態變數矩 陣，利用基因演算法尋找切換平面向量與回授增益向量，提出模糊化適應函數的 觀念，將性能指標與適應度的高低模糊化，並將系統所需的適應函數特性以模糊 語意變數及規則庫表示，如此即可定義出一符合系統要求的模糊適應函數，解決 適應函數選取的問題，提升搜尋效率。再利用滑動模式控制尋找發電機的控制信 號，並配合最佳降階式設計，應用此方法於雙機無限匯流排電力系統，模擬此結 果並舉出其優點。 This thesis proposes a new approach for combining genetic algorithm and sliding mode control to design the power system stabilizers (PSS). The design of a PSS can be formulated as an optimal linear regulator control problem. However, implementing this technique requires the design of estimators. This increases the implementation and reduces the reliability of control system. These reasons, therefore, favor a control scheme that uses only some desired state variables, such as torque angle and speed. To deal with this problem, we use the optimal reduced models to reduce the power system model into two state variables system by each generator. We use the genetic algorithm to find the switching surface vector and switching control signals, propose a approach "fuzzifier fitness function" to improve the search effect of genetic algorithms, and use sliding mode control to find control signal of the generator. The advantages of the proposed method are illustrated by numerical simulation of the two machines-infinite-bus power systems.
