The monitoring of insulated gas leakage in the power industry mainly relies on infrared imaging means, which realizes gas diffusion imaging display by measuring the regional temperature difference caused by the selective radiation absorption of insulated gas. Aiming at the difficulty of identifying trace insulated gas leakage under complex background interference, this paper proposes the research of insulated gas leakage imaging enhancement technology based on Gauss Mixed Model (GMM), which enhances gas diffusion display by deducting complex dynamic background. Through the simulation experiment of insulated gas leakage, the infrared multi-frame images of gas leakage were collected and processed to realize the diffusion marking of insulated gas leakage, which preliminarily verifies the feasibility of the proposed method, and has certain application potential in intelligent imaging monitoring of insulated gas leakage in power industry.
Large transformer is the core equipment in power system, the diagnosis and prevention of equipment fault is very important to the safe operation of power system. At high temperature and high voltage, dissolved gas produced by the decomposition of transformer oil is an important indicator of transformer operation status. However, due to the low content of dissolved gas and the complexity of the measurement processes, it is easy to produce errors, which brings great challenges to the accurate detection of dissolved gas. In addition, how to establish the correct relationship between the content of dissolved gas components with the types and degrees of transformer fault also needs to be studied. Therefore, this paper first clarified the measurement processes of dissolved gas in transformer oil, then analysed the possible error sources of each link, then introduced common error assessment methods and proposed feasible methods to reduce dissolved gas test errors, and finally introduced the application of artificial intelligence to fault diagnosis of transformers based on dissolved gas content. This paper will provide some feasible theoretical support for reducing the measurement error of dissolved gas in transformer oil and accurately diagnosing transformer faults.
The accurate measurement of dissolved gas concentration in transformer oil is directly related to the safe operation state of transformer. In order to realize real-time and automatic monitoring of dissolved gas in transformer oil, online monitoring method has been developed and applied, but the accuracy of online monitoring method remains to be investigated. Therefore, this paper evaluates the accuracy of two online monitoring methods, i.e., gas chromatography and photoacoustic spectroscopy, that have been used in the market. The results show that the test results of two online monitoring methods can meet the requirements of Class A standard. Among them, the online monitoring method using gas chromatography has less error and higher accuracy, but its operation is relatively complicated and requires carrier gas. The online monitoring method using photoacoustic spectroscopy does not need carrier gas and the test speed is fast, but its test error is relatively large, and the accuracy is relatively low, indicating further research is still needed to improve the test accuracy.
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