Abstract
The ice coating on the transmission line is extremely destructive to the safe operation of the power grid. Under natural conditions, the thickness of ice coating on the transmission line shows a nonlinear growth trend and many influencing factors increase the difficulty of forecasting. Therefore, a hybrid model was proposed in this paper, which mixed Ensemble Empirical Mode Decomposition (EEMD), Random Forest (RF) and Chaotic Grey Wolf Optimization-Extreme Learning Machine (CGWO-ELM) algorithms to predict short-term ice thickness. Firstly, the Ensemble Profit Mode Decomposition model was introduced to decompose the original ice thickness data into components representing different wave characteristics and to eliminate irregular components. In order to verify the accuracy of the model, two transmission lines in ‘hunan’ province were selected for case study. Then the reserved components were modeled one by one, building the random forest feature selection algorithm and Partial Autocorrelation Function (PACF) to extract the feature input of the model. At last, a component prediction model of ice thickness based on feature selection and CGWO-ELM was established for prediction. Simulation results show that the model proposed in this paper not only has good prediction performance, but also can greatly improve the accuracy of ice thickness prediction by selecting input terminal according to RF characteristics.
Highlights
Since the 21st century, due to the increasing demand for electricity caused by the rapid development of the national economy, power grid construction of China has entered a period of rapid development.The natural environment of different regions in China is quite different, which increases the probability of transmission lines suffering meteorological disasters and affects the safe and stable operation of transmission lines [1]
Xiao-min ma proposed a forecasting model based on grey Support Vector Machine (SVM) short-term ice thickness of transmission line [6], and the results showed that the SVM method can accurately predict the short-term ice thickness
The results show that the Grey Wolf algorithm is reasonable and efficient to predict the density of Chinese fir with SVM and near infrared spectroscopy (NIR) [14]
Summary
Since the 21st century, due to the increasing demand for electricity caused by the rapid development of the national economy, power grid construction of China has entered a period of rapid development. The natural environment of different regions in China is quite different, which increases the probability of transmission lines suffering meteorological disasters and affects the safe and stable operation of transmission lines [1]. It is convenient to predict the thickness of line icing to carry out timely response to the damage caused by icing, so as to ensure the safe and stable operation of power grid. For the model of line icing prediction, domestic scholars have done a lot of research, mainly divided into two methods. One is to use the physical model to predict the growth of the shape, density, and weight of the icing on the wire. The analysis and research models are noted as the Makkonen
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
