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.

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.

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.

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. Önerilen yöntem ile tahmin edilen toplam elektrik tüketimi değerlerinin farklı çalışma sonuçları ile tutarlı olduğu belirlenmiştir.