Journal of Electrical Engineering : Volume 18 / 2018 - Edition : 3

ADAPTIVE PREDICTION MODEL FOR EFFECTIVE MAINTENANCE OF ELECTRICAL MACHINES

Authors:
D Ganga
Dr V Ramachandran
Domain:
electrical machines and drives
Abstract:
This work proposes a two stage prediction approach for the estimation of non-stationary machine variables through an optimum and generalized model imbibing real time data uncertainties. The prediction of machine speed and controller set point has been made using the proposed model for a three-phase induction motor operating on a single loop speed control with AC drive and PI controller. The trend of the machine variables has been extracted and added upon the Auto Regressive Moving Average (ARMA) time series prediction at stage one. ARMA prediction has been carried out using different combinations of Auto Regressive (AR) and Moving Average (MA) methods in order to obtain prediction results with less Mean Squared Error (MSE). The resulting prediction error indicates the inadequacy of the model to estimate the data characteristics which has been resolved at the subsequent stage by cascading an adaptive Least Mean Square (LMS) FIR filter to the time series model. The adaptive filter receives the predicted output including training data and iteratively adjusts its coefficients for zero error convergence. This has been tested for different parameter settings of step size and iterations at a specified filter length. The inclusion of adaptive filter in cascade also models the unknown real time factors influencing the system operation in an optimum and adaptive manner from the data available rather than the physical or fixed assumptions. The prediction accuracy of the model propos
Download Article:
 
This article is written in Adobe PDF format ( .pdf file ).To view this article you need to download the file. Please rightclick on the link below and then select "Save target as" to download the file to your harddrive. Download Article
Jee homepage | Jee Archive | Hard Copy | Publishers | Contact