本論文擬研提被動式電力濾波器之最佳化設計，用於減少電力系統諧波汙染及改善功率因數。由於被動式電力濾波器設計問題為複雜的多目標優化問題，本文將藉由人工蜂群演算法與蜂群演算法，結合柏拉圖最佳化，提出兩種被動式電力濾波器之最佳化設計方案。在設計多目標最佳化問題中，考慮了單通濾波器、二階阻尼濾波器、三階阻尼濾波器與C-Type阻尼濾波器的特性。在多準則決策中，本文利用最小曼哈頓距離從柏拉圖最佳化的解集合中挑選出最終解決方案。一系列案例研究亦將被用於驗證所研提方法之有效性與優越性。

Passive power filters (PPFs) are most effective in mitigating harmonics pollutions from the power system but the design of PPFs involves many objectives which makes them a complex multiple objectives optimization problem. This thesis is about to propose two new methods to achieve an optimal design of PPFs based on the famous artificial bee colony (ABC) and bee swarm optimization (BSO) algorithms with the help of an external archive and Pareto optimality. Single tuned, second-order damped, third-order damped, and C-Type damped order filter are four different types of PPFs and their characteristics are considered for the design optimization problem.
In this multi-criteria decision making (MCDM) problem, a minimum Manhattan distance (MMD) approach is used to select the final solution among the Pareto-optimal set. A series of case studies have examined to prove the efficiency and better performance of the proposed method over previous famous algorithms.

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