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Abstract(s)
As linhas aéreas de transmissão e distribuição de energia elétrica encontram-se muitas vezes em zonas de elevada densidade de descargas atmosféricas, estando assim suscetíveis a desligamentos não programados causados por sobretensões. Os incidentes com origem em descargas atmosféricas representam atualmente, uma parte significativa do total de incidentes da rede. Existem ainda outras três grandes causas como os incêndios, a poluição dos isoladores e as cegonhas, sendo que têm sido tomadas medidas no sentido de atenuar as consequências dos mesmos. Por serem medidas consideravelmente mais complexas e mais dispendiosas do que as anteriormente referidas, as que melhoram a performance das linhas aquando das descargas atmosféricas não são tão comumente aplicadas.
Face à consciência da necessidade de uma intervenção, mas por se conhecer a sua inviabilidade quando aplicada em toda a rede, foi necessário identificar e priorizar as linhas ou troços que merecem maior atenção. Desta forma, foi através dos dados históricos, e após o seu tratamento, que se georreferenciaram os incidentes, ou seja, foi possível conhecer a localização dos mesmos.
Com a construção de diversos mapas, obtidos do cruzamento de informação identificaram-se os principais fatores responsáveis pela suscetibilidade de uma linha aérea relativamente a descargas atmosféricas. Ou seja, foi possível identificar quais as causas estruturais, ambientais e contextuais que podem tornar uma linha ou troço mais sensível a sofrer incidentes por descargas atmosféricas.
Tendo como base os dados históricos desenvolveu-se uma metodologia que relaciona o nível de tensão, a altitude, a resistência de terra e o concelho do apoio ou linha em questão. Esta metodologia permite calcular a probabilidade de um dado apoio ou linha de sofrer um incidente devido a descargas atmosféricas. Esta probabilidade é designada índice de criticidade, sendo que através do solver do Excel foram atribuídos pesos distintos a cada parâmetro determinando a influência de cada um.
De forma a facilitar ao utilizador o cálculo do índice de criticidade, foram criadas duas interfaces distintas. Uma das interfaces, em Visual Basic for Applications, permite o cálculo por apoio reticulado. A outra, em Excel, possibilita o cálculo por linha e mesmo para a toda a rede.
Quando se trata da implementação de medidas, a análise de resultados reais obtidos é fundamental para se conhecer a eficácia do projeto. Por existir uma linha na qual foram tomadas algumas medidas, a Linha Sines-Tunes, procedeu-se à sua análise. Analisou-se o comportamento da linha, anterior e posterior à instalação de alguns descarregadores de sobretensão e da adição de um segundo cabo de guarda. Esta análise possibilitou percecionar a dimensão de uma intervenção parcial na linha, ou seja, que melhorias se podem obter com a adoção de medidas em alguns apoios ou troços das linhas.
É então neste ponto de vista que se mostra fundamental perceber quais as linhas que maiores implicações trazem á rede, de forma a combater esta problemática. Se de entre as linhas mais críticas se selecionarem os troços mais fustigados ou que apresentam características favoráveis a desencadear incidentes por descargas atmosféricas, e se forem estudadas medidas que maximizem resultados com menos recursos, então tal pode significar uma melhoria significativa da performance total da rede.
Overhead power lines are usually placed in the same location where there are highly dense lightning being vulnerable to unschedduled interruptions caused by overvoltage. Incidents by lightning cause represents, these days, a significant number of all the incidents registered on the power network. There are still three big causes such as fires, isolators pollution and storks, even with effective measures to reduce its effects. Because these measures are more complex and more expensive than the previously referred, the ones that improve line’s performance when lightning occurs are not usually applied. Given the awareness for an intervention, but because we know it is impracticable when applied to all the network, it was necessary to identify and prioritize the lines or line segments that are needed more focus on. So it was through historic data bases and after its analysis, that they geotag incidents so we could know its locations. With the new maps made by gathering, several information it was able to identify main factors responsible for the susceptiveness of a overhead power line associated to lightning discharges. In other words, it was possible to identify which structural, environmental and contextual causes that may influence the line or line segment to be more sensitive to suffer incidents by lightning. Since we have an historic database it was developed a method that relates the voltage level, spot elevation, ground resistance and the province located. This method allows us to calculate the probability of a single line studied to suffer an incident due a lightning discharge. This probability is designated as critical ratio, and through solver Excel were assigned different matter values for each parameter, knowing the previous records. So, it was made two in order to make it easy to calculate the critical ratio. One of the interfaces in Visual Basic for Applications allows the support calculation as the other allows the tower calculation. Lastly, in Excel, it was applied the method to lines or all the network to get the number of the critical and not critical lines. When it is about measure implementation, the analysis of the actual results obtained is very important to know its project’s efficiency. The Sines-Tunes line was reviewed because in this line were already running some of the referred measures. The line course was analyzed before and after the installation of some surge arrester and the addition of one earth wire. This review made it possible to understand the dimension of a partial intervention of the line or what improvements could obtain by adopting some measures on some supports or line segments. This side point that tells us how important it is to understand which lines causes more number of issues to the network, so we can solve this main problem. If we select the line segments more attacked or vulnerable to cause incidents by lightning discharge among the critical lines, and if there are studied measures that maximize the results with less resources, so it may lead to a significant improvement of the overall network’s performance.
Overhead power lines are usually placed in the same location where there are highly dense lightning being vulnerable to unschedduled interruptions caused by overvoltage. Incidents by lightning cause represents, these days, a significant number of all the incidents registered on the power network. There are still three big causes such as fires, isolators pollution and storks, even with effective measures to reduce its effects. Because these measures are more complex and more expensive than the previously referred, the ones that improve line’s performance when lightning occurs are not usually applied. Given the awareness for an intervention, but because we know it is impracticable when applied to all the network, it was necessary to identify and prioritize the lines or line segments that are needed more focus on. So it was through historic data bases and after its analysis, that they geotag incidents so we could know its locations. With the new maps made by gathering, several information it was able to identify main factors responsible for the susceptiveness of a overhead power line associated to lightning discharges. In other words, it was possible to identify which structural, environmental and contextual causes that may influence the line or line segment to be more sensitive to suffer incidents by lightning. Since we have an historic database it was developed a method that relates the voltage level, spot elevation, ground resistance and the province located. This method allows us to calculate the probability of a single line studied to suffer an incident due a lightning discharge. This probability is designated as critical ratio, and through solver Excel were assigned different matter values for each parameter, knowing the previous records. So, it was made two in order to make it easy to calculate the critical ratio. One of the interfaces in Visual Basic for Applications allows the support calculation as the other allows the tower calculation. Lastly, in Excel, it was applied the method to lines or all the network to get the number of the critical and not critical lines. When it is about measure implementation, the analysis of the actual results obtained is very important to know its project’s efficiency. The Sines-Tunes line was reviewed because in this line were already running some of the referred measures. The line course was analyzed before and after the installation of some surge arrester and the addition of one earth wire. This review made it possible to understand the dimension of a partial intervention of the line or what improvements could obtain by adopting some measures on some supports or line segments. This side point that tells us how important it is to understand which lines causes more number of issues to the network, so we can solve this main problem. If we select the line segments more attacked or vulnerable to cause incidents by lightning discharge among the critical lines, and if there are studied measures that maximize the results with less resources, so it may lead to a significant improvement of the overall network’s performance.
Description
Keywords
Descargas atmosférica Linhas aéreas Linhas de Transmissão Muito Alta Tensão Incidentes Lightning Overhead Power Lines Transmission Lines Very High Voltage Incident