Sürdürülebilir Kalkınma Amaçları

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• MakaleAçık Erişim2026Scopus SJRQ2/r/munzur/1337
  Single-Ended Fault Detection and Fault Location in Transmission Lines Using Approximate Derivative

  Akdağ, Mustafa; Mamiş, Mehmet Salih; Akmaz, Düzgün

  Fault location in power transmission lines (PTLs) relies on impedance or traveling wave (TW) principles. TW approaches offer superior accuracy and high robustness against fault resistance. While multi-ended methods require precise terminal synchronization, single-ended TW (SETW) methods utilize measurements from one terminal, requiring accurate distinction of reflected waves. This study employs the computationally efficient approximate derivative (AD)—the difference between consecutive samples—for SETW fault detection and location. Normally near zero, the AD of modal signals produces sharp transitions during faults. Comparing AD output to a threshold achieves fault detection. The AD then identifies arrival times of the incident and reflected TWs. When using TW theory to distinguish reflections from the fault point and remote end, the fault distance is calculated from their arrival time difference. Validated through 293 diverse ATP simulated fault scenarios, the approach delivered highly accurate results despite using a lower sampling rate than established methods, utilizing an exceptionally short data window—only 2.03 ms for a 300 km line. Finally, operational boundaries for the signal-to-noise ratio (SNR) in noisy conditions are established.

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  İncele
• MakaleAçık Erişim2024Scopus SJRQ2/r/munzur/1647
  Enhancing Fault Location Accuracy in Transmission Lines Using Transient Frequency Spectrum Analysis: An Investigation into Key Factors and Improvement Strategies

  Akdağ, Mustafa; Mamiş, Mehmet Salih; Akmaz, Düzgün

  Fault location estimation in transmission lines is critical for power system reliability. Various methods have been developed for this purpose, among which transient frequency spectrum analysis (TFSA) stands out as a recent method based on travelling wave (TW) theory. TFSA determines the fault location by analyzing the frequency spectrum of transient currents and/or voltages at the instant of the fault, offering advantages such as independence from fault impedance and the ability to locate faults with one-side measurements. Despite its success in fault location, TFSA has several considerations that warrant detailed investigation. This study explores the effects of source inductance, series compensation, fault arc, and current transformer (CT) characteristics on transient frequencies. Additionally, the impact of noise on TFSA results is examined. The new proposed source inductance compensation method can reduce the error of 6.55% to 0.88%, where the same error can be reduced to 3.45% with the compensation method given in previous study. Strategies to enhance accuracy are discussed and compared to previous studies, including a proposed detection approach providing appropriate data size and precise wave propagation speed calculations. These findings contribute to a deeper understanding of TFSA’s limitations and inform practical improvements for fault location accuracy in power transmission systems.

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  AtıfScopus6·WoS5·OpenAlex3

  İncele
• MakaleAçık Erişim2022/r/munzur/6549
  Çok Katmanlı Algılayıcı Algoritması, Korelasyon Tabanlı Özellik Seçme Yöntemi ve Eğri Uydurma Tekniği ile Türkiye’ de Toplam Elektrik Tüketiminin Tahmin Edilmesi

  Akmaz, Düzgün

  Elektrik tüketiminin tahmin edilmesi, güç sistemlerinin planlanması ve işletilmesinde önemli bir rol oynar. Bu yüzden bu çalışmada Çok Katmanlı Algılayıcı Algoritması (ÇKAA), Korelasyon Tabanlı Özellik Seçme Yöntemi (KTÖSY) ve Eğri Uydurma (EU) teknikleri ile Türkiye’ de toplam elektrik tüketimini tahmin eden bir model geliştirilmiştir. 2013-2018 yılları arası veriler ÇKAA, KTÖSY ve EU modelini oluşturmak için kullanılırken 2019-2020 yıllarındaki veriler ise modelin başarısını test etmek için kullanılmıştır. Yazılım sonuçları yöntemin geçmiş yıllardaki toplam elektrik tüketimini tahmin etmede başarılı olduğunu göstermiştir. Aynı zamanda oluşturulan model ile 2024 yılına kadar Türkiye’ deki toplam elektrik tüketimi de tahmin edilmiştir.

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  AtıfOpenAlex2·TR Dizin1

  İncele
• MakaleAçık Erişim2022/r/munzur/6508
  Identification of Power Quality Disturbance Signals with Stockwell Transform, ONE-R Feature Selection Method and Random Forest Algorithm

  Akmaz, Düzgün

  In this study, a method was applied to classify the Power Quality (PQ) distortion signals. In the method, a 2- dimensional Time-Frequency (TF) matrix was obtained by applying the Stockwell transform (S-transform-ST) to different PQ signals. Then, classification features were obtained with different entropy values of the obtained matrix and PQ signals. However, using all of the obtained classification features causes the performing method to become complicated. Therefore, in this study, the ONE-R feature selection method was used to determine the effective features in classification, to ensure high classification success with fewer features and a simpler structure. PQ disturbances were classified with the determined features and Random Forest (RF) algorithm. The simulation results showed that the method was successful. At the same time, the simulation results showed that the applied method, once trained, is also effective in the classification of different PQ disturbances with noise levels of 35 dB and above.

  AtıfOpenAlex1

  İncele
• MakaleAçık Erişim2022/r/munzur/6493
  Recognition of Power Quality Events Using Wavelet Transform, K-Nearest Neighbor Algorithm and Gain Ratio Feature Selection Method

  Akmaz, Düzgün

  Bu çalışmada Güç Kalitesi (GK) bozulumu sinyallerinin sınıflandırılması için bir yöntem uygulanmıştır. Ayrık Dalgacık Dönüşümü (ADD), gürültü içeren GK sinyallerine uygulanarak sinyaller ayrıştırılmıştır. Daha sonra sinyallerin farklı entropi değerleri kullanılarak 80 farklı sınıflandırma özelliği elde edilmiştir.Bu 80 özelliğin hepsinin sınıflandırma için kullanılması yöntemin eğitim/test sürelerinin uzamasına yol açmaktadır. Bu yüzden bütün sınıflandırma özellikleri arasında etkili özelliklerin belirlenmesi gereklidir. Bu çalışmada Kazanç Oranı (KO) özellik seçme yöntemi ve K-En Yakın Komşu Algoritması (K-EYK) ile etkili 10 sınıflandırma özelliği belirlenmiştir.Belirlenen 10 sınıflandırma özelliği ile K-EYK eğitilmiş ve test edilmiştir. Benzetim sonuçları gerçekleştirilen yöntemin sınıflandırma için etkili olduğunu göstermiştir. Aynı zamanda benzetim sonuçları seçilen parametreler için KO ile belirlenen 10 sınıflandırma özelliğinin, 80 sınıflandırma özelliğine göre daha kısa sürede daha yüksek bir sınıflandırma başarısı sağladığını göstermiştir.

  AtıfOpenAlex2·TR Dizin2

  İncele
• MakaleYalnız künye2022Scopus SJRQ2/r/munzur/2634
  A 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.

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  AtıfScopus10·WoS8·OpenAlex9

  İncele
• MakaleYalnız künye2022Scopus SJRQ2/r/munzur/2415
  Fault 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.

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  AtıfScopus9·WoS8·OpenAlex7

  İncele
• MakaleYalnız künye2020Scopus SJRQ1/r/munzur/3232
  Approximate-derivative-based signalprocessing method to segment power-quality disturbances

  Akmaz, Düzgün

  The increasing complexity of power systems and the increase in power quality (PQ) data have made it necessary to develop different and simple signal-processing tools. In this study, an approximate-derivative (AD) signal-processing tool based on a simple mathematical processing approach was developed for the segmentation of PQ disturbances. Although the developed method was highly effective for the segmentation of noise-free signals, the method was unable to properly handle the segmentation of noisy signals. Thus, to mitigate this situation, a denoising method based on the Sqtwolog threshold was applied to the noisy signals. After denoising, the proposed AD method effectively performed the segmentation. Subsequently, AD and a single-level discrete wavelet transform (DWT) with Daubechies 4 mother wavelets were compared through simulations, which showed that successful results can be obtained using the proposed method. Furthermore, all simulations showed that the application of AD and single-level DWT to PQ signals under different conditions resulted in similar patterns with different amplitudes. Therefore, this study provides a different approach for analysing signal using single-level DWT.

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  AtıfScopus5·WoS4·OpenAlex5

  İncele
• MakaleYalnız künye2019Scopus SJRQ2/r/munzur/3393
  Design and development of travelling-wave-frequency-based transmission line fault locator using TMS320 DSP

  Arkan, Müslüm; Akmaz, Düzgün; Mamiş, Mehmet S.; Tağluk, Mehmet Emin

  The authors use a TMS320 digital signal processor (TMS320-DSP) to determine fault instants and estimate their location in real time in a laboratory environment. The fault instant is determined via examining the instantaneous differential changes in the line currents. After the fault is detected, the fault location is determined by processing the time-domain transient current waves. First, the travelling-wave frequency is determined by application of the fast Fourier transform to the positive-sequence-component line current after the fault, and subsequently, the fault location is estimated by utilising this frequency. The alternative transients programme (ATP)–electromagnetic transient programme is used to simulate the line currents and create short-circuit fault conditions. Furthermore, LabVIEW software and a National Instruments data acquisition board are used to transform the line currents obtained through the ATP programme into analogue signals. The TMS320-DSP determines the fault in real time and estimates the fault location using the completed software and analogue input signals. Their results indicate that the prototype device designed with the use of the TMS320-DSP is suitable for real-time fault detection.

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  AtıfScopus16·WoS16·OpenAlex16

  İncele
• MakaleYalnız künye2018Scopus SJRQ1/r/munzur/3373
  Transmission line fault location using traveling wave frequencies and extreme learning machine

  Akmaz, Düzgün; Mamiş, Mehmet Salih; Arkan, Müslüm; Tağluk, Mehmet Emin

  In this research, a new approach was proposed for determining the fault location in transmission lines. Traveling wave frequencies and an extreme learning machine (ELM) were used to determine fault location. Transient signals in the time domain were transformed to the frequency domain using the fast Fourier transform (FFT) and the traveling wave frequencies were detected from the transient frequency spectrum. In order to detect the location of fault, traveling wave frequency was used initially to predict the fault location. The prediction of this fault location was tested for many different fault conditions and was found to be adversely affected by only the source inductance value. This is due to the negative effect of source inductance on wave velocity. Regression feature of ELM was used in order to improve the prediction of fault location and to minimize the negative effect of source inductance. For ELM regression training, values of the fault distance estimated from the traveling-wave frequencies and the source inductance values were used as ELM input data, and the actual distance values were used as ELM output data. After ELM regression training, ELM predicted a new fault location using the input data. The Alternative Transients Program (ATP/EMTP) was used to model J. Marti frequency dependent line model, and the MATLAB program was used to perform fault-detection algorithms. Simulation results show that the proposed method is very successful against many variables such as different fault resistances, source inductances, transmission line characteristics, transmission line lengths.

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  AtıfScopus104·WoS74·OpenAlex101

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