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Time-Frequency Analysis (TFA) Method for Load Identification on Non-Intrusive Load Monitoring

机译:非侵入式负荷监测中的时频分析(TFA)负荷识别方法

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Public awareness of energy conservation can be realized through the process of electricity monitoring so that information can be found related to electricity usage and potential savings that can be made by the community. With the information on the results of this monitoring, the community can manage electricity usage more optimally so that it will have a positive impact on reducing electricity usage costs. The diversity of both linear and non-linear electrical equipment is a challenge in this monitoring process. In this study, the monitoring process is carried out in the aggregate on several electrical equipment indirectly on the electrical panel using the Non-Intrusive Load Monitoring (NILM) approach. The NILM approach is carried out through time and frequency domain analysis (TFA) to identify electrical equipment. Current signals (I) obtained in aggregate will be identified based on the characteristics of the frequency spectrum and its harmonics. The development of the TFA method was carried out to obtain feature vectors used for the identification process. Besides that, the noise reduction method was developed using the hybrid filter method by combining Median Filter and Average FFT filter (MFAFFT). The use of hybrid filter is done to eliminate noise through the frequency domain that was previously still remaining when filtered using Median Filter in the time domain. Classification performance of the test shows better accuracy results on hybrid filtered and using time and frequency feature. In SNR scenarios >15, the accuracy results are quite good with values greater than 0.9 and relatively stable at values above 0.9 at SNR >25.
机译:可以通过电力监控过程来实现公众的节能意识,从而可以找到与电力使用和社区可能节省的成本有关的信息。借助有关监视结果的信息,社区可以更优化地管理用电,从而对降低用电成本产生积极影响。线性和非线性电气设备的多样性在此监视过程中是一个挑战。在这项研究中,使用非侵入式负载监控(NILM)方法在总的来说,间接在配电盘上的几台电气设备上进行监控过程。 NILM方法是通过时域和频域分析(TFA)进行的,以识别电气设备。将根据频谱及其谐波的特征来识别合计获得的电流信号(I)。进行了TFA方法的开发以获得用于识别过程的特征向量。除此之外,通过将中值滤波器和平均FFT滤波器(MFAFFT)相结合,使用混合滤波器方法开发了降噪方法。进行混合滤波器的使用是为了消除频域中的噪声,该噪声以前在时域中使用中值滤波器进行滤波时仍会保留。测试的分类性能在混合滤波以及使用时间和频率功能方面显示出更好的准确性结果。在SNR大于15的情况下,值大于0.9时,精度结果非常好;在SNR> 25时,在0.9以上时,精度相对稳定。

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