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Abstract

Random nature of corona processes in UHV power lines and the accompanying noise is the reason that in practice the best determination of acoustic parameters, necessary for the noise evaluation, is obtained from the continuous monitoring procedure. However because of considerable fluctuations (both the useful signal part and the interfering components), careful selection of monitored parameters is necessary to enable a possibility of automatic determination of the parameters that are required for long-term evaluation of corona noise. In the present work a practical realization is shown for estimation of corona noise parameters, based on the data obtained from continuous monitoring stations, making use of the statistical spectra measurement and characteristic features of corona process acoustic signal. Selected results are presented from continuous monitoring of corona noise generated at a 400 kV power line, with special attention focused on definitions of the measured quantities, which enable automatic estimation of the basic factors required for noise evaluation. Accompanying monitoring of environmental conditions, including humidity, precipitation intensity and fog density, that are well correlated with the corona process intensity, which might definitely increase the filtration efficiency of environmental disturbances and on the other hand, it enables verification of calculation methods applied to corona noise. The paper also contains a description of practical approach to selection signal parameters of corona noise in continuous monitoring stations.
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Abstract

Low frequency noise is one of the most harmful factors occurring in human working and living environment. Low frequency noise components from 20 to 250 Hz are often the cause of employee complaints. Noise from power stations is an actual problem for large cities, including Cairo. The noise from equipments of station could be a serious problem for station and for environmental area. The development of power stations in Cairo leads to appearing a wide range of gas turbines which are strong source of noise. Two measurement techniques using C-weighted along side the A-weighted scale are explored. C-weighting is far more sensitive to detect low frequency sound. Spectrum analysis in the low frequency range is done in order to identify a significant tonal component. Field studies were supported by a questionnaire to determine whether sociological or other factors might influence the results by using annoyance rating mean value. Subjects included in the study were 153 (mean = 36.86, SD = 8.49) male employees at the three electrical power stations. The (C-A) level difference is an appropriate metric for indicating a potential low frequency noise problem. A-weighting characteristics seem to be able to predict quite accurately annoyance experienced from LFN at workplaces. The aim of the present study is to find simple and reliable method for assessing low frequency noise in occupational environment to prevent its effects on work performance for the workers. The proposed method has to be compared with European methods.
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