本論文探討利用類神經網路之預測能力與靜態無效率補償器(SVC)結合，同時達成改善電力系統三相負載不平衡與功率因數修正。考慮到當負載變動較大，使SVC的補償能力受到時間延遲的影響，故本論文應用類神經網路的預測能力，先預測出往後數個週期的有效功率與無效功率，再對電流進行補償，此方法的平均絕對值誤差率(MAPE)只有10%以下，符合高預測準確度，藉以改善延遲的情形。本論文以實際鋼鐵廠爐變一次側三相電壓、電流、有效功率、無效功率資料模擬。為得到SVC所需要的基頻數值以及準確的預測結果與加快運算速度，在訓練類神經網路之前，對這些電力資料進行快速傅立葉轉換(FFT)以取得基頻值，之後使用自定義函數L2範數法規一化法。因為資料型態的關係，此法比Matlab內建規一化指令更適合使用在本文。

This thesis examines the combination of artifical neural network(ANN) forecasting ability and the static var compensator(SVC). It can improve three-phase unbalance of load on the power system and correct the power factor at the same time. The compensation ability of an SVC is limited by delays in reactive power measurements and thysitor ignition. In order to improve the SVC performance, this thesis presents a technique based on the predicting ability of ANN. At first, it is to predict three phase active power and reactive power several cycles ahead, then it is to compensate current components. The MAPE is only 10% or less and has high accuracy of prediction to improve delay situations. This thesis used huge field data, collected from the primary side of an electric arc furnace transformer in a real steel plant. The data included three phase voltage, line current, active power, and reactive power. It wants to obtain the basedband value of SVC, get accurate results of prediction, and accelerate program speed of operation. Before traning the ANN, the program needs to evaluate the baseband of electrical data by the fast fourier transform(FFT). Then it is to employ L2 normalization to get normalized data. Because of data type, this is more suitable than the normalized instruction of Matlab. This thesis finds a better way in reactive power of compensation. It can also improve reactive power variation of the electric arc furnace load and power quality problem.

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