标题：Prediction of Audible Noise from UHV AC Transmission Lines Based on Relevance Vector Learning Mechanism
作者：Niu Lin; Liu Min; Zhao Jian-guo; Li Ke-jun
作者机构：[Niu Lin; Liu Min; Zhao Jian-guo] Shandong Elect Power Res Inst, Jinan, Shandong, Peoples R China.; [Niu Lin; Liu Min; Zhao Jian-guo] State Grid Chi 更多
会议名称：Asia-Pacific Power and Energy Engineering Conference (APPEEC)
会议日期：MAR 28-31, 2010
来源：2010 ASIA-PACIFIC POWER AND ENERGY ENGINEERING CONFERENCE (APPEEC)
关键词：AC; UHV transmission line; audible noise; relevance vector machine;; prediction
摘要：Audible noise produced by corona discharges is one of the more important considerations in the design of UHV AC transmission lines, which will greatly affect the electromagnetic environment and the technical economical index of transmission lines, etc. So it will be of very important practical significance that making scientific researches on AN prediction from UHV AC transmission lines. Based on the basic philosophy of sound propagation and attenuation, quantitative relationship of the model with sound pressure level and sound power level is deduced, which it will provide the theory basis for AN prediction. To overcome the limitation of current prediction formulas, a novel machine learning technique, i.e. relevance vector machine (RVM) for AN prediction is presented in this paper. The RVM has a probabilistic Bayesian learning framework and has good generalization capability, as a result it can yield higher prediction accuracy and more universal application arrange. The proposed method has been tested on the typical transmission lines in the World, and result indicates the effectiveness of such prediction model.