Early warning of the abnormal response of the generator set electrical control based on SIP concept

• Published in: Advanced control for applications Vol. 6; no. 2
• Main Author: Li, Guannan
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.06.2024; Wiley Subscription Services, Inc
• Subjects: abnormal response warning; Algorithms; Autoregressive models; Back propagation networks; electrical control; False alarms; Generalized linear models; generator set; SIP concept; Statistical models; Supervisory control and data acquisition
• ISSN: 2578-0727; 2578-0727
• DOI: 10.1002/adc2.134

An early warning method based on Session Initiation Protocol (SIP) concept for generator set electrical control abnormal response is proposed. The first is to clean the electrical data of the generator set by SIP. Using Supervisory Control and Data Acquisition (SCADA) and Generalized Linear Models (GLM) algorithm, a linear model is constructed to analyze the electrical control of generating units. Using the Autoregressive Integrated Moving Average Model (ARIMA), an abnormal response early warning model for electrical control of generating units is established. BP neural network is used to train the abnormal response data of the generator set electrical control. According to the current response data, the model prediction is realized, and the early warning of the abnormal response of the generator set electrical control is effectively realized. The simulation results show that the proposed method can effectively reduce the early‐warning error, false alarm rate, and early‐warning delay of generator electrical control abnormal response. Research on the construction of a warning method for abnormal electrical control response of generator units based on the concept of SIP. Build an early warning model for abnormal electrical control reactions of generator sets through SIP based electrical data cleaning and electricity analysis. Simulation experiments show that the error‐warning rate of the proposed method is the lowest among the three methods; as the number of sample data increases, the accuracy of the three methods for abnormal response warning of generator unit electrical control decreases. However, the accuracy of the research method is still the highest, all above 90%. This indicates that the proposed method has good performance and can play a good role in the early warning of abnormal response in electrical control of generator sets.