【摘要】：隨著電力電子裝置在源-網(wǎng)-荷側(cè)的大規(guī)模應(yīng)用,電力系統(tǒng)功率振蕩表現(xiàn)出較強(qiáng)的非平穩(wěn)特性和模式耦合性。本文提出一種基于多尺度線調(diào)頻基稀疏信號(hào)分解的電力系統(tǒng)非平穩(wěn)振蕩信號(hào)特征提取方法。多尺度線調(diào)頻基稀疏信號(hào)分解方法能夠在動(dòng)態(tài)的時(shí)間支撐區(qū)內(nèi)對(duì)信號(hào)進(jìn)行投影分解,逐次獲得能量最大的信號(hào)分量,具有良好的時(shí)頻聚集性,特別適用于非平穩(wěn)振蕩信號(hào)的分解。電力系統(tǒng)的功率振蕩信號(hào)本質(zhì)上是一種多模態(tài)時(shí)變振動(dòng)系統(tǒng)響應(yīng)信號(hào)。首先通過(guò)多尺度線調(diào)頻基稀疏信號(hào)分解方法得到多個(gè)單模態(tài)振動(dòng)響應(yīng)信號(hào),然后根據(jù)單模態(tài)振動(dòng)響應(yīng)特性采用最小二乘法進(jìn)行振蕩特征的提取。仿真算例和實(shí)例證明了該方法在電力系統(tǒng)非平穩(wěn)振蕩信號(hào)特征提取中的有效性和適應(yīng)性。
[Abstract]:With the large-scale application of power electronic devices in source-network-load side, the power oscillation of power system shows strong non-stationary characteristics and mode coupling. In this paper, a method for feature extraction of non-stationary oscillation signals in power system based on multi-scale linear frequency modulation basis sparse signal decomposition is proposed. The sparse signal decomposition method based on multi-scale linear frequency modulation (MFM) can be used to decompose the signal in the dynamic time support region and obtain the signal component with the largest energy one by one, which has good time-frequency aggregation. It is especially suitable for the decomposition of nonstationary oscillatory signals. The power oscillation signal of power system is essentially a multi-mode time-varying vibration system response signal. Firstly, multiple single mode vibration response signals are obtained by the sparse signal decomposition method of multi-scale linear frequency modulation basis, and then the oscillation characteristics are extracted by using the least square method according to the single mode vibration response characteristics. Simulation examples and examples show the effectiveness and adaptability of this method in the feature extraction of non-stationary oscillation signals in power system.
【作者單位】： 東北電力大學(xué)電氣工程學(xué)院;國(guó)網(wǎng)吉林省電力有限公司吉林供電公司;
【基金】：國(guó)家重點(diǎn)研發(fā)計(jì)劃(2016YFB0900100) 國(guó)家自然科學(xué)基金(51377017)資助項(xiàng)目
【分類號(hào)】：TM712

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