Typical Partial Discharge Signal Acquisition and Feature Extraction in High Voltage Switchgear

Abstract

With the progress of power system technology, how to effectively evaluate the reliability of high-voltage switchgear equipment has become an important part of power grid power supply and industrial distribution. Switchgear is used in power system power generation, transmission, power distribution, power conversion and consumption play a role in switching, control or protection, voltage level in 3.6kV-550kV electrical products, usually contains a variety of switches, circuit breakers, contactors and other components. In the process of use, due to various reasons, partial discharge may occur. Partial discharge refers to a partial discharge phenomenon in insulating media, which is usually caused by defects in the medium or excessive local electric field intensity. In switchgear, PD may be caused by aging of equipment, deterioration of insulation materials, dust accumulation and other reasons. Pd generates discharge current and discharge heat, which may damage the insulation performance of the equipment or even cause a fire. Therefore, it is very important to detect and deal with the partial discharge phenomenon in the switchgear in time. Therefore, timely detection of partial discharge of equipment can deal with partial discharge problems by checking equipment, cleaning equipment, timely replacement of aging components, and other methods to avoid losses caused by equipment failure due to partial discharge. Due to the complex distribution site environment and the interference of wireless communication signals from other equipment, the real high-frequency signals of partial discharge will be interfered, which will affect the accuracy of insulation performance evaluation of electrical equipment in the switchgear. Therefore, how to correctly collect partial discharge signals and how to identify the characteristics of partial discharge signals will be an important part of partial discharge detection in switchgear. At present, the commonly used partial discharge signal feature recognition is mainly based on the typical signal simulated by Matlab and artificially added noise for processing. Due to the wide frequency band of local discharge and the complex field environment, the real collected waveform will be different from the typical discharge signal. The original signal is filtered and detected for processing and analysis.