Transients detection using Artificial Neural Network based HS-transform

P. Hariramakrishnan, S. Kumar
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引用次数: 1

Abstract

This paper proposes HS-transform technique and Artificial Neural Network (ANN) algorithm for the detection and classification of voltage transients. Transients due to energization of capacitor banks has been considered for the analysis. In the proposed methodology, the HS-transform is employed to generate the instantaneous frequency vectors for S-matrix of the simulated transient signals. Using the statistical features of S-matrix vectors, the occurrence of transient is easily distinguished from the normal condition and from the other disturbance conditions by employing ANN algorithm. The advantage of the proposed algorithm is its accuracy in identifying the presence of transients and its classification accuracy. The performance of the proposed algorithm is manifested by the simulation of oscillatory transient event using MATLAB/SIMULINK software. The test results proved that the proposed algorithm is simple, fast and more accurate in identifying the event.
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基于hs变换的人工神经网络瞬态检测
本文提出了基于hs变换技术和人工神经网络(ANN)算法的电压暂态检测与分类方法。在分析中考虑了电容器组通电引起的瞬变。在该方法中,利用hs变换对模拟瞬态信号的s矩阵产生瞬时频率向量。利用s矩阵向量的统计特征,利用人工神经网络算法可以很容易地将瞬态的发生与正常状态和其他干扰状态区分开来。该算法的优点在于其识别瞬态的准确性和分类精度。利用MATLAB/SIMULINK软件对振荡瞬态事件进行仿真,验证了该算法的性能。实验结果表明,该算法简单、快速、准确地识别了事件。
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