Wyniki 1-4 spośród 4 dla zapytania: authorDesc:"Makmur SAINI"

Impact of SMES Unit on DC-Link Voltage of DFIG during Various Types and Level of Faults DOI:10.15199/48.2019.08.27

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energy sources become more popular since the last decade due to some efforts on mitigating global warming from the use of conventional energy sources for power plants. One of the popular renewable energy sources is wind energy, where it is reported in JRC Wind Energy Status Report 2016 Edition that there are about 430 GW wind turbine generators have been installed worldwide till 2016. Within all types of wind turbine generators, Doubly Fed Induction Generator (DFIG) become the most type installed worldwide which dominate about 64% of market share in 2015 [1]. This fact is based on the advantages of DFIG in terms of technical aspect where DFIG could supply some amount of reactive power to the grid as it is equipped with power electronics that connected directly to the grid and rotor side. With about 33% capacity of power electronics, the cost of the DFIG system becomes cheaper than its main rival in the same class, Full Converter Wind Turbine Generator (FCWTG) type [2]. A typical model of a DFIG can be seen in Fig 1. Fig.1. Typical Configuration of A DFIG When wind turbines generators (WTGs) connected to the grid, there are some parameters must be complied to avoid the disconnection of WTGs to prevent any damages on the WTGs. For instance, voltage profile at the point of common coupling (PCC), rotors and stators' current, DClink voltage (for DFIG and FCWTG), etc [3]. A DC link as shown in Fig. 1. is obligated to maintain the transfer energy between the rotor and grid [4]. DC link power electronic that links a grid side converter (GSC) and a rotor side converter (RSC) are very sensitive with any faults, most of the wind turbine generator manufacturers recommended the safety margin voltage level on DC link that allowed the converters standstill is between 0.25%-1.25% [3]. Therefore any voltage profiles of DC-link that violate the safety range, the internal protection of the converters should block the converters and lead to [...]

A Novel TVA-REPSO Technique in Solving Generators Sizing Problems for South Sulawesi Network

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This paper present a novel optimization method, Time Varying Acceleration - Rank Evolutionary Particle Swarm Optimization (TVAREPSO) in solving optimum generator sizing for minimising power losses in the transmission system of South Sulawesi, Indonesia. A comparison between the proposed method and three other methods was done in order to find the best method to optimize the generators’ output size. The results show that the TVA-REPSO algorithm can obtain the same performance as PSO but it only required shorter computing time and can converges faster than the original PSO. Streszczenie. W artykule przedstawiono matematyczną metodę rozwiązania zagadnienia znalezienia optymalnego rozmiaru generatora, w celu minimalizacji strat w elektroenergetycznym systemie przesyłowym Południowej Sulawesi w Indonezji. W algorytmie wykorzystano optymalizację roju cząstek ze zmiennym w czasie przyspieszeniem (ang. TVA-REPSO). Dokonano porównania z innymi metodami, pokazało, że opracowana metoda ma skuteczność podobną do klasycznej metody PSO, lecz krótszy czas obliczeń. (Nowoczesna technika TVA-REPSO w rozwiązaniu zagadnienia doboru rozmiarów generatora w sieci elektroenergetycznej Południowej Sulawesi). Keywords: Generators’ optimal Output, Optimization Method, Power Loss Reduction, Voltage Stability Index Słowa kluczowe: optymalna praca generator, metoda optymalizacji, redukcja strat mocy, współczynnik stabilność napięcia. Introduction Transmission line is an important part in the power system that connects the power utilities to the consumers. Since the transmission line is operated in high voltage and in mesh configuration, the system will allow the transmission line to transfer a bulk of power to the load’s sides [1]. However, with the huge amount of power transferred from one location to other locations, the power losses in the network will also increase, and at the same time, reduce the efficiency of the transmission system. Many[...]

An accurate fault detection and location on transmission line using wavelet based on Clarke's transformation DOI:10.12915/pe.2014.11.42

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This paper presents accurate fault detection and location using wavelet based on Clarke's transformation. This study was done using Clarke's transformation method to convert current phase (three phase) signal into a two-phase current alpha and beta (current mode). The proposed method introduced the mode current to transform the signal using discrete wavelet transform (DWT) and was utilized to obtain the wavelet transform coefficients. Analysis was also conducted for other mother wavelets. The most accurate parent was wavelet Db8, with the fastest time of detection and the smallest error, whereas the largest error was found in Coil4 parent wavelet. The result for proposed method was compared with Db4, Sym4, Coil4 and Db8 and found to be very accurate Streszczenie. W artykule opisano dokładną metodę wykrywania awarii w sieciach przesyłowych bazująca na falkowej transformacie Clarka. Sygnał trójfazowy jest przekształcany do postaci dwufazowej Za najbardziej się do tego celu nadająca uznano falkę Db8 z najszybszym czasem wykrywania i najlepszą dokładnością. Wyniki porównano z innymi typami falek. Dokładna metoda lokalizacji awarii w sieciach przesyłowych bazująca na wykorzystaniu transformaty falkowej Clarka Keywords: Wavelet Transformation; Fault location; Fault detection; Clarke's Transformation. Słowa kluczowe: wykrywanie i lokalizacja awarii, transformata falkowa, transformata Clarka. doi:10.12915/pe.2014.11.42 Introduction Fault detection and determination of the location of short circuit transmission lines have become a growing concern. There are two commonly used methods to determine the location of the fault in accordance with standard IEEE Std C37.114. 2004 [1]. The first method is based on a frequency component, and the second is based on signal interference at high frequencies where the wave theory is ignored and a shorter sampling window is used [2]. The determination of wave theory for intrusion detection wa[...]

Single line to ground-fault detection for unit generatortransformer based on wavelet transform and neutral networks DOI:10.15199/48.2018.12.07

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Small current Ground-Fault (GF) detection has been a major concern in protective relaying for a long time. Relaying engineers and researchers often face the challenge of developing the most suitable technique that can detect faults with reasonable reliability to secure the run of a power system [1]. In general, a step up transformer at an electric power station can be categorized either as a unit generator-transformer configuration, a unit generatortransformer configuration with generator breaker, a crosscompound generator or a generator involving a unit transformer [2,3]. A GF on the transmission line or busbar can affect the system configuration of the generator. Several methods have been reported for generator GF protection [4]. These methods have been developed based on conventional method, third harmonic method, sub-harmonic injection method and numerical protection method. Fault detection and classification algorithms based on Wavelet Transform (WT) and Artificial Neural Network (ANN) was proposed in [5, 6]. Various feature extraction methods based on WT have been used for the detection and classification of fault. Reference [6] descibe fault location techniques in power system based on traveling wave using wavelet analysis and GPS timing. Fault classification algorithm based on energy and wavelet entropy in transmission have been proposed in [7, 8]. Reference [9-11] describe the feature extraction method based on fast WT, a fault index and wavelet power for use to detect the stator faults in the synchronous generator. Extraction of a statistical parameter as fault detection has been used for fault detection in previous studies, but only used standard deviation, kurtosis and skewness [12]. Meanwhile, the statistical feature parameters include kurtosis, skewness, crest factor, clearance factor, shape factor, impulse factor, variance, square root amplitude value and absolute mean amplitude value to fault diagnosis in [...]

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