電力系統模擬廣泛地應用於電力系統規劃與運轉控制，其中同步發電機模型的正確性更具關鍵性。若模型與實際機組間存在過大誤差，將無法正確反應系統的特性，其中尤以對系統暫態響應之評估影響最鉅，其中的誤差可能將導致系統設計與規劃時產生誤判，若情勢嚴重則可能造成系統故障，甚至引發區域性停電而致經濟損失。
模型的正確性在於1) 合適的數學模型及2) 正確的參數設定。許多商用的電力系統模擬軟體已包含了現成的模型供系統模擬使用，因此本文假設數學模型的架構合適無錯誤；然而因某些模型參數無法從製造商取得，或隨著設備老化與運轉環境的改變，模型中的參數需要一套參數辨識方法以準確的設定及校正。本文提出使用改良型電子飄移演算法的參數校正方法，僅需比較模型輸出與設備量測值，即可獲得最適的參數值。為了增進參數辨識的效能，本方法結合了參數選擇及參數分類兩個前置作業；參數選擇分析參數與輸出之間的關係，挑選出具影響力易於辨識的參數；參數分類依據參數對不同輸出的影響性加以分類，並使用四階層最佳化方法，有效的找到最佳解。由於改良型電子飄移演算法使用搜尋歷程資料庫的特性，有效地利用參數選擇過程中累積的資訊，成功地整合最佳化搜尋與參數分類，進而提升參數辨識方法的效能。
本文使用三個測試系統驗證所提出方法的可行性及效能，研究結果證實所提方法在模擬及實測數據都有更佳的表現。除此之外，所提方法易於實踐，可廣泛地應用在不同的領域中。

Nowadays, power system simulation is widely used for power system operation management and planning, and the accuracy of the synchronous generator model plays a critical role in power system simulation. Models with huge deficiencies may fail to reflect the system's behavior, especially the system's dynamic responses. It might lead to wrong predictions, causing incorrect decisions and operation, and end up with damage to the system or even a regional blackout that causes economical loss.
The accuracy of a model comprises 1) proper structure and 2) correct setting of the model parameters. Since many power system simulation tools have been developed, equipped with well-designed templates for model construction, we assume that there is no problem with the model structure in this thesis. However, due to device aging, changes in the operating conditions, or the inaccessibility of some parameters, there is a veritable need for a parameter identification method to set or calibrate the model parameters.
In this thesis, we propose a new parameter identification method based on a modified electron drifting algorithm (MeDA), which can obtain the best-fit parameter values by using only the comparison of the measurement data with the model outputs instead of the information of the model's inner equations. To enhance performance, parameter selection and classification are applied. The parameter selection analyzes the parameter-output relationship and selects parameters that are the most influential and identifiable. The parameter classification categorizes the selected parameters into groups according to their effects on individual outputs. Then, it applies the four-stage algorithm to effectively solve the optimization problem. In addition, MeDA has a unique feature of using a database that stores the search historical data. It can efficiently utilize the data collected from the parameter classification, and thus perfectly integrates the parameter classification with the optimization search.
The feasibility and performance of the proposed method are verified through three tests. They confirm that the proposed method performs better than other methods in both simulations and experimental tests. Last but not least, the proposed method is easy to implement, showing its great applicability to various areas.

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