This dissertation deals with the on-line diagnostic method for oil-immersed power transformers by acoustic signal measurement, which is recently being accepted as a non-destructive and an effective dielectric diagnosis. Partial discharge (PD) detection can be divided into two methods: one, electrical, the other, non-electrical. The electrical method has high sensitivity, which enables precise measurement. However, some of the shortcomings of this method include the fact that it is likely to be affected by electromagnetic noise and its coupling network can not be installed during operation. The non-electrical method includes acoustic, optical, and chemical detection. The acoustic detection method is less sensitive than the electrical method. This method, however, is less likely to be affected by electromagnetic noise as it is electrically insulated. Additionally, the acoustic emission sensor can be installed easily during operation. Locating the defect is possible by measuring the time difference of arrival (TOA) of the acoustic signal using multiple sensors.
Partial discharges were generated by increasing the AC voltage from 0 to 50kV while immersing the electrode system in insulating oil.
To detect acoustic signals generated by PDs in insulating oil, a wideband acoustic emission (AE) sensor, with a frequency range of 100kHz～1MHz, and a narrowband AE sensor, with a frequency range of 50kHz～250kHz, were used. The two types of AE sensors that measured acoustic signals were installed on the outer surface of the metallic enclosure. As the AE sensor uses a single cable for transmitting both power and signal, therefore the acoustic signal was separated from the DC voltage by a circuit with high-pass filter characteristics. The decoupler was designed to separate the acoustic signals from the DC voltage. The prototype decoupler designed in this paper transmitted acoustic signals from the AE sensor to the DC source are attenuated by more than 200dB, but are transmitted to the input terminal of the amplifier without attenuation.
Electrode system have been fabricated to simulate the defects that can be generated inside the oil-immersed transformer. A plane electrode was made from a tungsten-copper alloy disc 15mm thickness and 60mm diameter to avoid electric field concentration the radius of curvature of the needle electrode was 10μm. A pressboard of thickness 1.6mm was inserted between the electrodes.
From the FFT results, the frequency ranges of the acoustic signals generated at the needle-plane, plane-plane, and particle electrodes were in the ranges 50～170kHz, 50～400kHz, and 50～400kHz, respectively. Although the signals vary depending on the type of defect, the frequency spectra of the acoustic signals lies in the range of 50～400kHz. Therefore, it should be noted that a narrow-band AE sensor with a resonant frequency of 140kHz is suitable for the diagnosis of oil-immersed transformers by acoustic detection.
Relationships between acoustic signals and distances were analyzed to determine the propagation characteristics of acoustic signals in the insulating oil. To determine the sensitivity of the measurement system, a calibration experiment was carried out with a standard PD calibrator (CAL 1A, Power Diagnostix Systems GmbH, 1pC～100pC).
The output voltage of the measurement system increased linearly in proportion to the injected charge. The sensitivity of the measurement system was measured as 23.65mV/pC.
Relationship between the magnitude of a PD pulse and the acoustic signal was analyzed while increasing the distance between the AE sensor and the electrode. When a PD of 23.3pC generated, the magnitude of the acoustic signal appeared to be 940mV, 795mV, 700mV, 570mV and 450mV at distances of 170mm, 300mm, 400mm, 590mm and 800mm, respectively. The acoustic signal was non-linearly attenuated with respect to distance.
To find the location of the PD occurrence in oil-immersed transformers by the acoustic method, three or more AE sensors are required. In this dissertation, five AE sensors were used to estimate the position in 3-D by the TOA of the acoustic signals and coordinates were marked on the enclosure to calculate the location of the PD occurrence by installation of AE sensors.
Five AE sensors were installed to estimate the position of the PD source in a 3-D by using the differences in the TOA of the acoustic signals. The experimental results show the position of the PD source with an error margin of 10%. The positioning error was due to the non-linear propagation characteristics of the acoustic signal.
The insulation diagnostic technique by acoustic signal analysis is expected to be widely used power facilities with oil insulation such as power transformers, metering out fits (MOFs).