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Frequency estimation of power system signals with chaotic oscillations using music and esprit algorithms

机译:用音乐和精神算法对具有混沌振荡的电力系统信号进行频率估计

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Chaotic ferroresonance is one of the disturbances of a power system, which may cause chaotic oscillations with over voltages and over currents. In order to protect system and keep it stable the frequency estimation should be fulfilled accurately. In this study first chaotic oscillations of ferroresonance are modeled with forced Duffing oscillator's dynamical equations. MUSIC (Multiple Signal Classification) and ESPRIT (Estimation of Parameters by Rotationally Invariant Technique) methods are proposed for frequency estimation of chaotically distorted power system signals. Frequency is estimated efficiently by using the MUSIC and ESPRIT methods. Finally, computer simulations have been carried out for the performance analysis of the proposed methods and the comparison results of the proposed methods based on the SNR (Signal to noise ratio) values are given.
机译:混沌铁磁谐振是电力系统的干扰之一,它可能会导致过电压和过电流的混沌振荡。为了保护系统并保持其稳定,应该准确地执行频率估计。在这项研究中,首先使用强制Duffing振荡器的动力学方程来模拟铁磁谐振的混沌振荡。提出了MUSIC(多信号分类)和ESPRIT(通过旋转不变技术进行参数估计)方法来估计混沌畸变的电力系统信号的频率。通过使用MUSIC和ESPRIT方法可以有效地估计频率。最后,对所提方法的性能进行了计算机仿真,并给出了基于SNR(信噪比)值的所提方法的比较结果。

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