Localização de faltas em linhas de transmissão utilizando ondas viajantes e transformada Wavelet sob influência de ruído branco

• Ano de defesa: 2015
• Autor(a) principal: Abreu, Francisco Carlos Moreira
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Palavras-chave em Português: Engenharia elétrica; Simulação por computador
• Link de acesso: http://www.repositorio.ufc.br/handle/riufc/13873

This work aims to implement a method of fault location in transmission lines making use of traveling waves and wavelet transform. To evaluate the performance of the developed system, considerations on the several families of wavelet and the influence of the white noise are investigated. To observe the problem of fault location with high frequency signals making use of the theory of traveling waves, a 500kV line from the Eletrobras System – CHESF that links Teresina II and Sobral III sub-stations is simulated through ATP (Alternative Transient Program) software. In that simulation, different kinds of contingent are used with sample signs in 400 kHz. The signals that came from the ATP simulations were processed through the algorithm fault location based on the traveling time definition of the tension waves from the fault point on the line terminals. To determine the moment of the wave travels, it was used the wavelet transform with the wavelet multiresolution analysis (WMA) in a decomposition level. Since the Wave Reflection Intervals were defined, the fault distance was estimated taking into account those intervals and speed of propagation of the wave on the line. The white noise is added to the tension signs aiming to bring forward the sign from the computer simulations form the signs found in actual oscilographies. The results have shown that that the localization algorithm had Total Relative Average Error (EMRt) acceptable for the Signal-to-Noise Ratio (SNR) from 60dB. Special highlight must be made to the wavelet rbio 3.5 because it showed the best results in all kinds of faults considered with SNR from 60dB with 0.15% EMRt, which represents a 500m absolute average error. Thus, the results show a performance optimization regarding the wavelet that suits best to the algorithm and guides practical application of the fault locator.