Research on Partial Discharge Characteristic Detection of Electrical Equipment Based on Machine Learning Algorithm

A large number of experimental data and longterm on-site operation experience show that partial discharge(DC) of Electrical equipment(EE) is the main cause of damage to the insulation system of EE and power failure, so strengthening the effective detection of partial DC is a necessary measure to ensure the safe and reliable operation of the power system. For the pulse current method, chemical detection method, radio frequency detection method, infrared thermal imaging method, optical measurement method, ultrasonic detection method and UHF detection method, the detection principle, technical means, characteristics and application range are discussed and analyzed in detail. It is believed that the ultrasonic detection method and UHF detection method can achieve complementary advantages and disadvantages, and can be applied to the partial DC detection and online monitoring of most EE. However, it is also pointed out that the problems existing in the ultrasonic and UHF joint detection technology are developing towards large-scale, long-distance, UHV, intelligent and AC DC hybrid transmission in China’s power system. The safe operation of EE is an important guarantee for the safe and reliable operation of the power grid, and the good insulation of EE directly affects the safe operation of EE. Partial DC detection is an effective means and evaluation method to judge the good insulation system, so the research on partial DC detection of EE is particularly important. So far, the main partial DC detection methods at home and abroad include pulse current method, chemical detection method, radio frequency detection method, infrared thermal imaging method, optical measurement method, ultrasonic detection method and UHF detection method. This paper introduces the detection principle, technical means and development at home and abroad of these methods, analyzes the advantages and disadvantages and application scope of these methods, and puts forward its own opinions on the focus of future research. Carry out classified data processing to eliminate noise interference. Compared with the traditional frequency modulation noise suppression method, the noise suppression effect is better and has been widely used by EE. The research on the detection of partial discharge characteristics of electrical equipment based on machine learning algorithms has achieved some research results, specifically as follows: Feature extraction: Using machine learning algorithms to extract features of partial discharge signals, including peak value, pulse width, frequency, etc., to effectively detect and identify partial discharges. Model optimization: Machine learning algorithms are used to optimize the model, improving the accuracy and robustness of partial discharge detection, while reducing the false detection rate. Data collection: Machine learning algorithms are used to collect and process partial discharge data for electrical equipment, improving the reliability and accuracy of the data. Multi pattern recognition: Machine learning algorithms are used to achieve multi pattern recognition, including time domain, frequency domain, and time frequency domain, improving the accuracy and reliability of partial discharge detection. Algorithm innovation: The research on partial discharge detection based on machine learning algorithms is continuously advancing, and some new algorithms and methods have emerged, such as partial discharge detection based on deep learning, partial discharge detection based on artificial intelligence, and so on. In summary, the research on partial discharge characteristics detection of electrical equipment based on machine learning algorithms has achieved some research results, and there is still great room for development in the future. These achievements have important significance in the safe operation and equipment maintenance of power systems.