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首页> 外文期刊>International Journal of Knowledge-Based in Intelligent Engineering Systems >Time frequency analysis and power signal disturbance classification using support vector machine and differential evolution algorithm
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Time frequency analysis and power signal disturbance classification using support vector machine and differential evolution algorithm

机译：支持向量机和差分进化算法的时频分析和功率信号扰动分类

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摘要

The paper proposes a new approach for Time frequency analysis using modified time-time transform (TT-transform) for recognizing non-stationary power signal disturbance patterns. The TT-transform is derived from the well known S-transform (ST) and uses a new window function with its width inversely proportional to the frequency raised to a power 'c', varying between 0 and 1. The power disturbance signals after being processed by the TT-transform yields features, which are used for automatic recognition of disturbances; with the help of kernel based support vector machine (SVM) algorithm. Further to improve the classification performance of the TT-SVM based pattern recognizer, a differential evolution optimization algorithm (DEOA) is used. Several test cases are provided to prove the significant improvement in recognition, accuracy and drastic reduction of support vectors. Power signal classification; S-transform; Modified TT-transform; support vector machine (SVM); radial
机译：本文提出了一种新的时间频率分析方法，该方法使用改进的时间-时间变换（TT-transform）来识别非平稳功率信号扰动模式。 TT变换是从众所周知的S变换（ST）派生而来的，它使用新的窗口函数，其宽度与提高到功率'c'的频率成反比，在0到1之间变化。由TT变换处理后产生特征，用于自动识别干扰；借助基于内核的支持向量机（SVM）算法。为了进一步提高基于TT-SVM的模式识别器的分类性能，使用了差分进化优化算法（DEOA）。提供了几个测试用例，以证明支持向量的识别，准确性和大幅减少。功率信号分类； S变换改进的TT转换；支持向量机（SVM）；放射状的

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