應用模糊基因演算法於電力系統負載模型參數確認之研究

A Study of Power System Load Modeling by Using Fuzzy-GA Algorithm

蔡宏福(Hung-Fu Tsai)

指導教授 ： 曾國雄

國立臺北科技大學/機電學院/電機工程系所/碩士(2005年)

摘要

負載模型是進行電力系統規劃及運轉之重要參考依據，因此負載模型的建立對於電力公司來說極為重要。電力系統負載種類繁多，且其特性不一，如何將其表示為數學函式甚為困難，一般確認負載模型參數之方法可概分為兩種，一是元件聚集法，另一為監錄量測法，當使用元件聚集法確認負載模型參數時，必須耗費龐大的人力及時間，因此本論文利用台電公司近幾年於二次變電所量得的資料，採監錄量測法確認負載模型之參數，利用系統發生擾動時記錄的資料，以基因演算法尋找最佳參數值，再利用模糊基因演算法建立整年度之歸屬函數，進而求得各月份對應之最佳參數來表示負載功率與電壓之關係，經由模擬結果顯示，若單以夏季或冬季之參數模擬整年度之負載特性，在某些月份中，其模擬之誤差值明顯較大，若改採模糊基因模型則其誤差值較為平均，顯示使用模糊基因演算法於負載模型參數確認之可行性。

關鍵字

負載模型 ； 模糊理論 ； 基因演算法 ； 監錄量測法

並列摘要

Load model is a basis of power system planning and operating. Hence, load modeling is very important to power utility. There are many kinds of load components and each of them having various characteristics. Since it could hardly be expressed in mathematic form, the method to represent the load modeling using Component-based Approach and Measured-based Approach are employed. Using Component-based Approach need to spent a lot of time and money. This paper adopts the data of recent years of Taipower substation to estimate the parameters of load model by using Measured-based Approach. Using the genetic algorithm finds the optimal parameters. And using fuzzy-genetic algorithm constructs the membership function of all year. Then it can get the optimal parameters of each month for representation the relationship between power and voltage. The results show that using summer’s parameters or winter’s parameters to simulate load’s characteristics at each month will get larger mismatch at some month. And simulating by using the parameters of fuzzy-genetic model would get the generally mismatch. It shows the workable of load modeling by using fuzzy-genetic algorithm.

並列關鍵字

Load Models ； Fuzzy Theory ； Genetic Algorithm ； Measured-based Approach

參考文獻

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