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Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.

This study provides the theoretical basis for the use of wavelet packet transform (WPT) approach for root mean square (rms) and power/energy measurements. The proposed approach can simultaneously measure the distribution of the rms and power with respect to individual frequency bands directly from the wavelet transform coefficients (WTCs) associated with concurrent voltage current pair. Their dependent quantities such as power factor and total harmonic band distortion can be calculated as well. Uniform frequency bands are yielded from the WPT decomposition process of power system waveforms and can be used for identifi cation of harmonic components. The frequency bands also retain both the time and frequency relationship of the original waveforms, which is one of the major benefits provided by this approach. The approach is evaluated by its application to both analytical and actual power system waveforms.

Power Engineering Letters T his section of the magazine offers a vehicle that speeds publication of new results, discoveries, and developments. The section affords authors the opportunity to publish contributions within a few months of submission to ensure rapid dissemination of ideas and timely archiving of developments in our rapidly changing field. Original and significant contributions in applications, case studies, and research in all fields of power engineering are invited. Of specific interest are contributions defining emerging problems and special needs in specific areas. Brief notes may also comment on published areas of established power topics.

This paper proposes an approach based on wavelet packet transform (WPT) for root mean square (rms) and power measurements. The algorithm can simultaneously measure the distribution of the rms and power with respect to individual frequency bands from the wavelet coefficients associated with each voltage current pair. The advantage of the WPT is that it can decompose a waveform into uniform frequency bands, which are important for identification of harmonic components and measurement of harmonic parameters. The algorithm is validated using simulated waveforms.

This paper introduces ai compression technique for power disturbance data via discrete wavelet transform (DWT) and wavelet packet transform, (WPT'). The data compression leads to a potential application for remote power protection and power quality monitoring. The compression technique is performed through signal decomposition up to a certain level, thresholding of wavelet coefficients, and signal reconstruction.The choice of which wavelet to use for the compression i~jof critical importance, because the wavelet affects reconstructed signal quality and the design of the system as a whole.The Minimum Description Length (MDL) criterion is proposed for the selection of an appropriate wavelet filter, This criterion permits to select not only the suitable wavelet filter but also the best number of wavelet retained cclefficients for signal reconstruction.The experimental study has been carried out for a single-phase to ground fault event, and the data compression results of using the suitable wavelet filter show that the compression ratios are less than 1:1.70 and are reduced to more than a half of that value by implementing an additional lossless coding, Index Terms -Data compression, power disturbances, wave lets, wavelet packets.