Transmission Line Fault Detection, Classification and Location (original) (raw)

2015, International Journal of Advance Engineering and Research Development

The ability to detect, classify and locate the type of fault plays a great role in the protection of power system. This procedure is required to be precise with no time consumption. The disturbances of power systems are aperiodic, non-stationary, very short duration and impulsive in nature. The wavelet transform based approaches have been successfully detected, classify and locate the faults due to their ability to express the signal both in frequency and time domain. This paper describes the implementation of Wavelet Transform analysis on samples of simulated current waveforms obtained from MATLAB for each of the all possible fault scenarios in a modeled 400 kV transmission network. I. INTRODUCTION The quality and continuity of electrical power play an important role in economic activ ities. Transporting a clean electric power, without interruption, guaranteed a better performances and improved grid stability.. Many utilities around the world are currently in transition towards more co mpetit ive market strategies, increasing the quality of the offered service. In this way, the monitoring and analysis of the disturbances, such as transmission line fau lts, became v ital to power system operation. The transmission lines are part of the principal components of the electrical supply network. Existing transmission lines are forced to operate close to their operating limits. This issue implicates imp roving the fault clearance times in order to guarantee transient stability. They must be protected against any incident especially the electrical fault conditions. A fault protection system has become fundamentally important due to its ability to prevent economical losses. Protection features are performed by relays or mult ifunction devices. The three main protection functions are: detection, classification and localizat ion. The main purpose of a protection system is to process the voltage and/or current signals to determine whether a fault is present, to classify what kind of fault it may be, to estimate the fau lt location, and to take action to remove the fault from the power transmission-line system as fast and accurate as possible to de-energize the system fro m the harmful fau lts and restore the system after faults. The continuity service depends heavily on the possibility of detecting, classifying, locating, and isolating faults in the power transmission-line system. The time required to determine the fau lt location will affect the quality of the protection relay. Therefore, fast and accurate fault identification is an important requirement for the transmission line protection. Introduction of circuit breaker and protective relays. Protective relay are broadly classified into the following three categories depending on the technologies they use for their construction and operation, electromechanical relays, static relay and numerical relay. Over a past 30 year, many studies have been done on the transmission-line protection including fault detection, classification, and location and arcing fault discrimination for avoid ing reclosing on a permanent fault. Based on fault transients, several algorith ms have been reported for fault detection and classification. II. APPLICATION OF WAVEL ET TRANSFORM IN PROTECTION RELAYING For the last several years, Fourier t ransform has been extensively used by many researchers in the field o f power system protection. However, when a signal is transformed to the frequency domain, the time domain informat ion is lost, which is a serious drawback with Fourier transform. Fau lt signal contain numerous non-stationary or transitory characteristics. These characteristics are often very significant in the signal, and Fourier analysis is not suited for their detection. Wavelet transform are capable of revealing those aspects of data are usually missed by other signal analysis techniques. Furthermo re, as wavelet analysis provides info rmat ion in both frequency and time, it can co mp ress or denoise a signal without appreciab le degradation [ 1]. III. DISCRETE WAVEL ET TRANSFORM The discrete wavelet transform (DWT) uses filter banks for the construction of the time-frequency plane. A filter bank consists of filters which separate a signal into frequency bands. An example of a two channe l filter bank is shown in Fig. 1. A discrete time signal x(k) enters the analysis bank and is filtered by the filters L(z) and H(z) wh ich separate the frequency content of the input signal in frequency bands of equal width. The filters L(z) and H(z) are therefore respectively a low-pass and a high-pass filter. The outputs of the filters each contain half the frequency content, but an equal amount of samples as the input signal. The two outputs together contain the same frequency as the input signal. However the amount of data is doubled. The different output signals of the analysis filter bank are called subbands, the filter-bank technique is also called sub-band coding.