Sürdürülebilir Kalkınma Amaçları
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- MakaleYalnız künye2022Scopus SJRQ2A new signal processing approach/method for classification of power quality disturbances
Akmaz, Düzgün
The disadvantage of conventional signal processing methods used for Power Quality (PQ) classification is that their mathematical calculations are complex. Therefore, it is necessary to develop simple, effective, and fast signal processing tools to analyze the PQ signal. In this study, a new signal processing method with simple mathematical operations based on the Integer Factor (IF) down sampling/Approximation Derivatives (AD) was developed to analyze PQ signals. IF was employed to analyze the signals at various sampling frequencies, while the AD method was employed to obtain different degrees of detail and approximation coefficients for the signals. In order to prove that the IF/AD signal processing method can perform a fast and detailed signal processing analysis, the IF/AD method and the Discrete Wavelet Transforms (DWT)/Multiresolution method, which is one of the basic signal processing methods, were compared. It was determined that the developed signal processing method has analyzed PQ disturbances in a shorter time and in more detail than DWT/Multiresolution method. After it was determined that the IF/AD method was effective in the analysis of PQ signals, a new classification method based on the IF-AD signal processing approach, the Slime Mould feature selection algorithm, and Support Vector Machine was developed. The classification method was applied to synthetic signals with noise, real-time data generated in the laboratory and PQ online data. The results proved that the developed classification method was successful. Therefore, this study will provide a different approach to PQ classification methods and non-stationary signal analysis studies.
AtıfScopus10WoS8OpenAlex9
İncele - MakaleYalnız künye2022Scopus SJRQ2Fault location method on two-terminal transmission line using synchronized time information of traveling waves
Akmaz, Düzgün; Mamiş, Mehmet Salih
In this study, approximate derivative (AD) signal processing method using synchronous voltages data, based on the traveling wave theory, was developed to determine fault locations in transmission lines. The first aerial mode voltage signals were obtained by applying the Clarke transformation to the voltage signals that occur after a short circuit fault. Then, by taking the first AD of the aerial mode voltage signals, the traveling waves were obtained. Finally, the location of the fault was found by using the time difference information of the traveling waves, wave velocity value and transmission line length. The proposed signal processing method was found to be effective in determining the location of faults. It was tested and compared to the discrete wavelet transform (DWT) for various fault conditions. Based on the simulation results, the proposed method was found to have a slightly higher success rate than DWT, in general. Moreover, it can work with low sampling frequencies, and it provides a better resolution than DWT to display the traveling waves in noise conditions.
AtıfScopus9WoS8OpenAlex7İncele - BildiriYalnız künye2018Quality lignite coal detection with discrete wavelet transform, discrete fourier transform, and ANN based on k-means clustering method
Korkmaz, Sevcan Aytac; Esmeray, Furkan; Varol, A.; Karabatak, M.; Varol, C.
In this article, the lignite coal datas in the Kalburcayi area of the Sivas-Kangal Basin have been used. This original data obtained from Kalburcayi area of the Sivas-Kangal Basin consists of 66 observations in the lignite coal area, including lignite quality parameters such as moisture content, ash, sulfur content and calorific value. These lignite coal datas have been clustered in two group with k-means method according to calori values. This clustering lignite coal data is classified by the Artifical Neural Network (ANN) classifier. In addition, Discrete Fourier Transform (DFT) and Discrete Wavelet Transform (DWT) have been applied to coal data for ANN classifiers. DFT-ANN, DWT-ANN, and ANN classification success results are compared. The highest classification success rate was found by DWT-ANN method.
AtıfScopus8OpenAlex12İncele