Partial Discharge Detection Method Using Optical-fiber Current Sensor

Partial discharge (PD) on-line detection can timely reflect the degradation degree of power apparatus, thus becoming a useful technique for condition monitoring and failure diagnosis. Because of high level electromagnetic interference, the development of high quality sensor is the key of PD on-line detection. Optical detection method has many advantages, such as great ability of anti-interference, excellent insulation property, high sensitivity, and is helpful to PD on-line detection.On basis of Faraday Effect, optical-fiber current sensor (OCS) is used for PD detection in the first time. It will possibly set a foundation for optical method applied into PD detection. The characteristics of different types OCS were analyzed and research into polarized light system of OCS using Jones matrix method, then the mathematics model of OCS was set up at last.Full-fiber OCS with effective band of 300 MHz and correlative circuit has been developed. Double constructer was adopted to enhance the sensitivity of system. Then its frequency-response property, anti-interference property and loss property have been analyzed in detail, from which the conclusion can be got that when the closed light path of OCS meet Ampere's law, the sensor is supposed to have anti-interference feature.The cause of linear birefringence and its property was analyzed. The effects of all the factors on system error are related to the linear birefringence, and the larger the linear birefringence is, the larger the effects of the factors are. Methods to eliminate linear birefringence were discussed.PD test system and five models were set up. After picking classical PD signals with OCS, a comparison has been made between the optical method and other methods used in high level interference environment, and the result shows that PD detection using OCS has many merits such as great ability of anti-interference, high sensitivity and quick response.Suitable extraction of characteristic parameters is vital for PD pattern recognition. Single PD pulse acquired with OCS has been transformed into 3-D time-frequency image. The characteristics were represented with parameters of frequency, time and energy. It has been proposed that direct extraction of 3-D images may be used for PD pattern recognition. Compared with the methods for extraction of characteristic only in time domain or frequency domain, 3-D images can instantaneously reflect the magnitudes of different frequency components and their features in time domain, but there is no interference among different frequency components, thus it is more accurate than other methods.In order to extract the features of 3-D images, the fractal box-counting dimension and vacancy rate are adopted to characterize the complex time-frequency surface, which reflect the gradient at the magnitude and the variation trend at the trailing edge of time-frequency spectrum and can compress the dimensions of vector. The identification results using back propagation neural network (BPNN) and fractal theory based on time-frequency method demonstrates the effectiveness of OCS in PD detection.