電力品質的穩定一直是各國電力公司所致力的目標之一，加上能源短缺，燃料成本逐年提高，如何在各方面撙節開支，需要電力公司各單位的共同努力。台電公司針對每個一次變電所之二次側電壓均有設定一組標準電壓。所謂標準電壓簡單的來說，就是一個電壓期望值的排程。一組好的標準電壓可使一次變電所轄區內各二次變電所的電壓變動降低，這可使各二次變電所主變壓器之有載分接頭切換器動作次數減少，可增加主變壓器的使用壽命並延長主變壓器的維護週期，無形之中可節省電力公司許多開銷。
本文提出一種計算標準電壓的方式，改善以往只能用經驗法則訂定之缺點。首先以實際負載與電壓資料建置類神經網路模型，並使用改良式粒子群演算法尋找徑向基底類神經網路的參數，建置出最佳網路模型。接著再套用前述之最佳網路，同樣使用改良式粒子群演算法進行標準電壓的時間設定及訂定值排程最佳化，最後以台電台南一次變電所轄區系統作為測試實例，分別訂定冬季及夏季標準電壓，驗證本文所提方法之可行性。

Stability of the power quality is one of the objectives that power companies always try to assure. With energy shortage and the increases of fuel cost over years, reduction of expenses in all areas is another effort of the power company. Dealing with the above problems, Taiwan Power Company sets up a standard voltage for secondary side of each primary substation. Standard voltage is a commitment of expected 69kV primary substation bus voltage. A proper setting of the standard voltage can reduce voltage variation, in the secondary substation, and reduce the operation frequencies of the on load tap changer. Besides, it can prolong the service life and the maintenance cycle, and it can also reduce maintenance cost of each main transformer.
This study proposes a method to calculate the standard voltage to improve the shortcomings that the voltage used to be set up with experience rule. The load and voltage data were used to build a neural network model. Improved particle swarm optimizer was used to find the parameters of the radial basis function neural network in order to build an efficient network. This network uses improved particle swarm optimizer again to the standard voltage. The proposed approach has been verified by the comparison of winter and summer standard voltages on the Tainan primary substation of taipower with accurate results.

中文摘要…………………………………I
英文摘要…………………………………II
目錄………………………………………III
圖目錄……………………………………VI
表目錄…………………………………VIII
第一章 緒論……………………………1
1-1 研究動機與目的………………………1
1-2 研究背景與方法………………………2
1-3 論文架構及概要………………………3
第二章 電力系統調度控制及電壓控制原理…………5
2-1 電力系統調度控制………………………………5
2-1-1 電力系統架構概述……………………………5
2-1-2 電力系統調度控制……………………………8
2-1-3 電力調度的基本要求…………………………11
2-2 電壓控制與變電所運轉方式…………………16
2-2-1 電壓控制…………………………………16
2-2-2 變電所的運轉方式…………………………18
第三章 類神經網路之理論基礎……………………25
3-1 簡介……………………………………………25
3-2 神經網路的模型………………………………25
3-3 倒傳遞類神經網路原理及架構………………29
3-3-1 倒傳遞類神經網路原理……………………29
3-3-2 倒傳遞類神經網路之架構…………………29
3-4 徑向基底類神經網路原理及架構……………33
3-4-1 徑向基底類神經網路原理…………………33
3-4-2 徑向基底類神經網路之架構………………33
第四章 設計粒子群演算法結合類神經網路之標準電壓設定……36
4-1 簡介……………………………………………36
4-2 傳統粒子群演算法……………………………37
4-3 改良式粒子群演算法…………………………40
4-4 結合粒子群演算法之類神經網路設計………42
4-5 使用粒子群演算法之標準電壓設定…………46
第五章 模擬結果與討論…………………………49
5-1 最佳類神經網路建構…………………………49
5-1-1 使用PSO 與改良式PSO 之BPNN建模比較……………50
5-1-2 使用PSO 與改良式PSO 之RBFNN 建模比較…………52
5-1-3 結論……………………………………54
5-2 標準電壓設定值模擬………………………56
5-2-1 使用PSO與改良式PSO 之冬季標準電壓設定比較………56
5-2-2 使用PSO 與改良式PSO 之夏季標準電壓設定比較………63
5-2-3 結論………………………………………70
第六章 結論與未來研究方向……………………71
6-1 結論……………………………………………71
6-2 未來研究方向…………………………………72
參考文獻…………………………………………73

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