Design Of Condition Monitoring System For Conventional Transformers

The conventional transformers are indispensable and important power equipments in normal operation of power network, which are widely used in power system. On-line monitoring of the running state of transformers can effectively monitor and identify the running state of transformers, reduce the probability of transformers faults, the time of maintenance and power-off maintenance, fully prolong its operational life span, improve power system security, reliability and economy.This paper studies the on-line condition monitoring of conventional transformers. Using micro-controller technology, sensor technology, signal processing technology, fiber grating temperature detecting technology, artificial intelligence algorithms, communication technology and virtual instrument technology to design a set of conventional transformers condition monitoring system. The condition monitoring system uses the conventional transformers temperature, two secondary side voltage of voltage transformers, two secondary side current of current transformers and humidity of operating environment for transformers as state characters to characterize the running state of the transformers. These state characters data are collected, comprehensively analyzed and horizontally compared by the condition monitoring system. The probabilistic neural network is used to identify the running state of the transformers. LabVIEW is used to develop the host computer software. The condition monitoring system realizes the real-time monitoring and recognition of the condition and development trend of the conventional transformers, which provide the basis for condition maintenance of the conventional transformers.Designing temperature measurement system based on fiber Bragg grating through fiber grating sensor, wavelength division multiplexing, space division multiplexing and wavelength demodulation technology to realize temperature acquisition of multiple positions for the transformers in different electrical gaps at the same time. The two secondary side voltage of voltage transformers and two secondary side current of current transformers are converted into voltage signal, which can be collected by the built-in ADC of TMS320F28235 digital signal controller, through the voltage and current converter, low pass filter and polarity conversion. HDC1050 sensors are used to acquire transformers operating environment humidity data, which are transmitted through the I2 C interface to communicate with TMS320F28235 digital signal controller.The state identification model is established by the probabilistic neural network for the temperature of the transformers, so as to realize the intelligent identification and classification of the state of the conventional transformers. The recognition results of the probabilistic neural network model are verified by experimental simulations. The recognition results are proved to be accurate and the verification method is feasible. The horizontal comparison and comprehensive analysis of the electrical characteristic state data of the transformers at different electrical gaps are carried out to judge the abnormal state of transformers.Lab VIEW is used to develop the host computer software for condition monitoring system, which is used to receive the data of condition monitoring uploaded from fiber Bragg grating sensor analyzer and TMS320F28235 through the serial communication. The data of condition monitoring are comprehensively analyzed, horizontally compared and intelligently analyzed by the host computer software, so as to realize the identification and classification of the conventional transformers condition and the functions of the on-line condition monitoring system. Through the performance test of the host computer software, the software is stable and the functions are running normally and accurately.