本論文旨在探討量子計算之支持向量機於電力系統暫態穩定度判別之應用效能，透過量子平行運算的能力，並結合滑動視窗演算法，以提升系統穩定度之判別準確性。本研究首先推導支持向量機原始問題之目標函數，並透過拉格朗日乘數法轉為對偶問題，接著將決策函數中的核函數運算透過數據映射至量子空間進行內積運算，然後調整分類模型之超平面位置，以降低不穩定數據誤判為穩定數據之風險，並結合滑動視窗演算法，設計參數調整機制，以有效強化模型的判別能力。而為驗證所提方法之可行性與判別效能，本文分別採用支持向量機與量子支持向量機於不同匯流排系統進行暫態穩定度判別，測試結果顯示，本文所提方法具備較高判別準確度，同時可提供量子計算應用於電力系統研究之應用參考。

This thesis investigates the application performance of quantum computing-based support vector machines for transient stability assessment in power systems. Through the utilization of quantum parallel computation combined with a sliding window algorithm, the proposed approach aims to enhance the accuracy of system stability classification. In this study, the optimization problem of the support vector machine is first transformed into its dual formulation through the method of Lagrange multipliers. Subsequently, the kernel function computations within the decision function are mapped into the quantum space to perform inner product calculations. This is followed by the adjustment of decision hyperplane so that the risk of misclassifying unstable data as stable can be better avoided. Furthermore, the method integrates the sliding window algorithm to tune the parameter in order to effectively strengthen the classification performance of the model. To verify the feasibility and effectiveness of the proposed method, both classical support vector machines as well as quantum support vector machines are both employed for transient stability classification across different bus systems. The test results demonstrate that the proposed method achieves higher classification accuracy and provides a valuable reference for applying quantum computing techniques in power system studies.

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