Differential protective relay has been used as the primary protection of most power transformers for many years. 差動保護繼電器在大多數電力變壓器中作為主保護已經采用好多年了。Inrush can be generated when a transformer is switched on the transmission line or an external line fault is cleared. 湧流可能產生在變壓器切換到輸電線上或外部線路故障被清除時。 It may result in mal-operation of differential protection if blocking scheme is unavailable. Therefore, discrimination between an internal and a magnetizing inrush current has long been recognized as a challenging power transformer protection problem. 如果未能利用閉鎖方案，它會導致差動保護的誤動作。因此，在內部湧流和勵磁湧流之間做出鑒別壹直被認為是電力變壓器保護的具有挑戰性的問題。Since a magnetizing inrush current generally contains a large second harmonic component [2], [3] in comparison to an internal fault, conventional transformer protection systems are designed to restrain during inrush transient phenomenon by sensing this large second harmonic. 因為勵磁湧流與內部湧流相比，壹般包含大的二次諧波分量2，3，所以常規的變壓器保護系統都通過傳感這壹大的二次諧波而被設計成在湧流瞬態現象過程中抑制。However, the second harmonic component may also be generated during internal fault due to CT saturation or the existence of a shunt capacitor or the distributive capacitance in a long EHV transmission line to which the transformer may be connected. 然而，二次諧波分量在內部故障過程中也可能因CT（電流互感器）飽和，或並聯電容器的存在，或可能與變壓器連接的大長度超高壓輸電線上分布電容的存在而產生。In certain cases, the magnitude of the second harmonic in an internal fault current can be close to or greater than that present in the magnetizing. Moreover, the second harmonic components in the magnetizing inrush currents tend to be relatively small in modern large power transformer because of improvements in the power transformer core material. 在壹定情況下，在內部故障電流中二次諧波的幅值可能接近或大於存在於勵磁作用中的幅值。而且，在現代化的大型電力變壓器中，勵磁湧流中的二次諧波分量往往相對較小，因為電力變壓器鐵芯的材料改進了。Recently, other techniques have been developed to carry out current classification applied in transformer protection, including pattern recognition using trained artificial neural network (ANN), transformer inductance during saturation, fuzzy logic and flux and voltage restraints [3], [4]. 最近，其他技術已經開發出來，以進行電流的分類而用於變壓器保護，這些技術包括：采用經過訓練的人工神經網絡（ANN），在飽和過程中的變壓器電感，模糊邏輯，以及磁通和電壓制動來識別圖形3，4。These techniques require a large computational burden, large memory toaccommodate the required algorithms, complex experimental data or large dependence on the transformer parameters. 這些技術需要大的計算負荷，大的存儲器來容納所需的算法，還需要復雜的實驗數據或對變壓器參數大的依存性。
問題補充：This paper proposes a new principle to discriminate between an internal fault and a magnetizing inrush current by correlation function principle in DSP and the selfcorrelation
function of the sampled data is calculated and compared with the standard self-correlation function formed with sinusoidal current. The results of theoretical analysis and dynamic simulation verify the feasibility of the proposed method.
本文提出了壹種新的原理，以便用數據信號處理（DSP）中的相關函數原理鑒別內部故障湧流和勵磁湧流，並對采樣數據的自相關函數進行了計算，並與由正弦電流形成的標準自相關函數做了比較。理論分析和動態仿真的結果驗證了所提出方法的可行性。