Reduction of truncation errors in planar, cylindrical and partial spherical near-field antenna measurements

A method to reduce truncation errors in near-field antenna measurements is presented. The method is based on the Gerchberg-Papoulis iterative algorithm used to extrapolate band-limited functions and it is able to extend the valid region of the calculated far-field pattern up to the whole forward hemisphere. The extension of the valid region is achieved by the iterative application of a transformation between two different domains. After each transformation, a filtering process that is based on known information at each domain is applied. The first domain is the spectral domain in which the plane wave spectrum (PWS) is reliable only within a known region. The second domain is the field distribution over the antenna under test (AUT) plane in which the desired field is assumed to be concentrated on the antenna aperture. The method can be applied to any scanning geometry, but in this paper, only the planar, cylindrical, and partial spherical near-field measurements are considered. Several simulation and measurement examples are presented to verify the effectiveness of the method.

A New Method to Reduce Truncation Errors in Partial Spherical Near-Field Measurements

A new and effective method for reduction of truncation errors in partial spherical near-field (SNF) measurements is proposed. The method is useful when measuring electrically large antennas, where the measurement time with the classical SNF technique is prohibitively long and an acquisition over the whole spherical surface is not practical. Therefore, to reduce the data acquisition time, partial sphere measurement is usually made, taking samples over a portion of the spherical surface in the direction of the main beam. But in this case, the radiation pattern is not known outside the measured angular sector as well as a truncation error is present in the calculated far-field pattern within this sector. The method is based on the Gerchberg-Papoulis algorithm used to extrapolate functions and it is able to extend the valid region of the calculated far-field pattern up to the whole forward hemisphere. To verify the effectiveness of the method, several examples are presented using both simulated and measured truncated near-field data.

A clustering technique for partial discharge and noise sources identification in power cables by means of waveform parameters

On-line partial discharge (PD) measurements have become a common technique for assessing the insulation condition of installed high voltage (HV) insulated cables. When on-line tests are performed in noisy environments, or when more than one source of pulse-shaped signals are present in a cable system, it is difficult to perform accurate diagnoses. In these cases, an adequate selection of the non-conventional measuring technique and the implementation of effective signal processing tools are essential for a correct evaluation of the insulation degradation. Once a specific noise rejection filter is applied, many signals can be identified as potential PD pulses, therefore, a classification tool to discriminate the PD sources involved is required. This paper proposes an efficient method for the classification of PD signals and pulse-type noise interferences measured in power cables with HFCT sensors. By using a signal feature generation algorithm, representative parameters associated to the waveform of each pulse acquired are calculated so that they can be separated in different clusters. The efficiency of the clustering technique proposed is demonstrated through an example with three different PD sources and several pulse-shaped interferences measured simultaneously in a cable system with a high frequency current transformer (HFCT).