A Study on the Use of Machine Learning for Detecting Abnormalities in Gas Pressure Regulator

By the end of 2021, the average penetration rate of gas(LNG:Liquefied Natural Gas) nationwide reached 85% in Korea. Being extensively utilized in various fields including households and industries, ensuring a consistent supply of gas is crucial. Additionally, detecting pressure anomalies is important as it allows for timely adjustments when required. This paper presents a model for anomaly detection in the secondary pressure of gas regulators located in the Gyeryong city Korea.In addition, weather data that may affect the gas pressure was collected, and the analysis was carried out considering temperature and wind speed. Given the interconnected nature of most gas regulators, this study models four adjacent gas regulators in Gyeryong city simultaneously to identify abnormal pressure values. The models used in this analysis are VAR, a traditional multivariate time series model, Long Short-Term Memory Auto Encoder (LSTM-AE), a machine learning technique for time series anomaly detection, and Multivariate Time Series Anomaly Detection via Graph Attention Network (MTAD-GAT), which directly incorporates variable correlations into the model.The study develops proper models that analyzes four gas regulators in Gyeryong City using three algorithms to identify abnormal pressure points. The performance evaluation of each model utilized an F1 score, and the MTAD-GAT model demonstrated the highest F1 score. Furthermore, the model not only identifies abnormal values at a specific time but also considers the value immediately preceding the anomaly. When implemented on-site, this model will be able to enhance the efficiency of anomaly detection.