Integrated DWT–FFT approach for detection and classification of power quality disturbances
Integrated DWT–FFT approach for detection and classification of power quality disturbances
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DOI：
10.1016/j.ijepes.2014.04.015
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发表时间：
2014-10
影响因子：
5.2
通讯作者：
S. Deokar;L. Waghmare
中科院分区：
文献类型：
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作者：
S. Deokar;L. Waghmare
电力系统中的信号中总是含有一定的电能质量干扰和噪声，这是检测和时间定位的最大障碍。提出了一种基于规则的离散小波变换-快速傅立叶变换集成方法。为了检测输入信号中存在的电能质量扰动，对输入波形进行离散小波变换处理。利用离散小波系数计算精细系数平方特征的平均能量熵。首先检测到各种电能质量干扰，然后使用该特性将其分为四大类，即与凹陷、膨胀、中断和谐波相关的干扰。使用快速傅里叶变换特征对每个主要类别进行进一步分类。考虑了12种电能质量扰动，其中包括7种基本扰动和5种非常接近实际情况的组合扰动，通过参数方程生成电能质量扰动进行分类。通过在凹陷、膨胀、谐波和闪烁四种基本扰动中加入噪声，还考虑了另外四种情况。使用Mathworks Matlab R2008b对这16种情况进行了仿真。对150个测试信号进行了不同持续时间的分类器性能测试，测试了不同的有噪声和无噪声干扰。所开发的分类器准确率达到99.043%。仿真结果表明，该方法对各种电能质量扰动的检测和分类是有效的。
The signals in the electrical power system always have some power quality disturbances and noise contents which is the biggest obstacle in detection and time localization. In this paper, an integrated rule based approach of discrete wavelet transform – fast Fourier transform is proposed. For the detection of power quality disturbance present in the input signal, the input waveform is processed by discrete wavelet transform. The discrete wavelet coefficients are used to calculate average energy entropy of squared detailed coefficients feature. The various power quality disturbances are initially detected and then classified into four main categories as disturbances related to sag, swell, interruption and harmonics using this feature. Further classification of each main category is done using fast Fourier transform features. The total twelve types of power quality disturbances including seven basic and five combinations which are very close to real situations, are considered for the classification which are generated by parametric equations. Also four another cases are considered by adding noise to four basic disturbances sag, swell, harmonics and flicker. All sixteen cases are simulated using Mathworks Matlab R2008b. The performance of classifier is tested for 150 test signals for various durations with different disturbances with and without noise. The developed classifier is able to achieve 99.043% accuracy. From the simulation results, it can be seen that the proposed approach is effective for the detection and classification of various power quality disturbances.