Sürdürülebilir Kalkınma Amaçları
Yayınlar
- MakaleYalnız künye2019Scopus SJRQ2Design 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.
AtıfScopus16WoS16OpenAlex16
İncele - MakaleYalnız künye2018Scopus SJRQ1Transmission 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.
AtıfScopus104WoS74OpenAlex101İncele