基于Stockwell变换和Hilbert变换混合特征的电能质量扰动识别

Vishakha Pandya, S. Agarwal, Om Prakash Mahela, Sunita Choudhary
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引用次数: 4

摘要

本文主要研究单级电能质量(PQ)扰动的检测与分类。这是通过结合Stockwell变换、Hilbert变换和决策支持规则的特征来实现的。该算法可在在线PQ监测设备中实现。对不同PQ干扰的检测和分类性能进行了评价,包括电压跌落、电压膨胀、瞬间中断、振荡暂态、脉冲暂态、陷波、尖峰和谐波。结果表明,该算法的性能优于Stockwell变换和规则决策树支持算法。采用MATLAB软件进行研究
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Recognition of Power Quality Disturbances Using Hybrid Algorithm Based on Combined Features of Stockwell Transform and Hilbert Transform
Research activity under taken in this article is concentrated on detection and classification of single stage power quality (PQ) disturbances. This has been achieved with the help of combined features of Stockwell transform, Hilbert Transform and decision supported rules. Proposed algorithm can be implemented in online PQ monitoring equipments. Performance of algorithm is evaluated for detection and classification of different PQ disturbances which include sag in voltage, swell in voltagel, momentary interruption (MI), oscillatory transient (OT), impulsive transient (IT), notch, spike and harmonics. This is established that performance of algorithm is better compared to Stockwell transform and ruled decision tree supported algorithm. Study is carried out using MATLAB software
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