Recently, many signal-processing techniques, such as fast Fourier transform, short-time Fourier transform, wavelet transform (WT), and wavelet packet transform (WPT), have been applied to detect, identify, and classify power-quality (PQ) disturbances. For research on PQ analysis, it is critical to apply the appropriate signal-processing techniques to solve PQ problems. In this paper, a new time-frequency analysis method, namely, the Gabor-Wigner transform (GWT), is introduced and applied to detect and identify PQ disturbances. Since GWT is an operational combination of the Gabor transform (GT) and the Wigner distribution function (WDF), it can overcome the disadvantages of both. GWT has two advantages which are that it has fewer cross-term problems than the WDF and higher clarity than the GT. Studies are presented which verify that the merits of GWT make it adequate for PQ analysis. In the case studies considered here, the various PQ disturbances, including voltage swell, voltage sag, harmonics, interharmonics, transients, voltage changes with multiple frequencies and voltage fluctuation, or flicker, will be thoroughly investigated by using this new time-frequency analysis method.

The purpose of this study is to design a novel custom power demand forecasting algorithm based on the LSTM Deep-Learning method regarding the recent power demand patterns. We performed tests to verify the error rates of the forecasting module, and to confirm the sudden change of power patterns in the actual power demand monitoring system. We collected the power usage data in every five-minute resolution in a day from some groups of the residential, public offices, hospitals, and industrial factories buildings in one year. In order to grasp the external factors and to predict the power demand of each facility, a comparative experiment was conducted in three ways; short-term, long-term, seasonal forecasting exp[eriments. The seasonal patterns of power demand usages were analyzed regarding the residential building. The overall error rates of power demand forecasting using the proposed LSTM module were reduced in terms of each facility. The predicted power demand data shows a certain pattern according to each facility. Especially, the forecasting difference of the residential seasonal forecasting pattern in summer and winter was very different from other seasons. It is possible to reduce unnecessary demand management costs by the designed accurate forecasting method.

This paper presents a simple and efficient power-quality (PQ) evaluation method based on the half-cycle root-mean-square (H-RMS) calculation, which can be easily applied to waveform distortions processed from instantaneous values. PQ disturbances, especially related to voltage magnitude variations, can be generally divided into two categories by the measuring method: One is the rms-based PQ events, which include voltage sag, swell, undervoltage, overvoltage, and interruption, which are defined by the magnitude variations in terms of rms values. The other is the instantaneous value-based PQ events, which include transient, noise, harmonics, interharmonics, and flicker (or voltage fluctuation), some of which are assessed by the raw data or processed through frequency analysis and filtering. According to the fundamental characteristic and definition of each PQ event, the acquired signal data should be processed through the different routes. This study shows that the H-RMS-based measuring process can be usefully applied to quantify the instantaneous value-based PQ events, especially in regards to waveform distortions, such as harmonic distortion and flicker, which can be assessed by the IEC flicker severity index (Pst) and the equivalent 10-Hz flicker index (ΔV₁₀).

Advanced Railway Power Quality Detecting Algorithm Using a Combined TEO and STFT Method Railway system;Power Quality(PQ);Detection;Teager Energy Operator(TEO);Short-Time Fourier Transform(STFT); Because an electric railway vehicle is a large scale moving load, it can cause various kinds of power quality problems in the railroad power system. The power quality impacts are considered as the strong instantaneous stresses to the related power systems and can cause an accelerating aging and a malfunction of the power supplying components. Therefore, it is necessary to detect the small and intermittent symptoms mixed in the voltage waveform. However, they cannot be detected by the triggering functions of the existing power analyzers installed in the railway systems. This paper will examine the drawback of some fast detection tools and propose an advanced detecting and analyzing method based on a combined TEO and STFT algorithm.