Abstract
The article presents an approach to creating a system for estimating resource expenditure and predicting the induction motors windings insulation condition based on capacitive leakage currents. The approach is based on the measurement of capacitive leakage currents created by a continuous sequence of rectangular voltage pulses. A decrease in the magnitude of these currents indicates a decrease in the residual life of the winding insulation. Experiments show an exponential decrease in leakage currents due to the development of insulation degradation processes in the long term. It was proposed to estimate the residual resource value using a modeling exponent, whose parameters are determined in the current time mode using parameter identification methods such as the least squares method (OLS) or methods based on the Kalman algorithm. The advantage of the proposed method is the comparative simplicity of the technical means used and the ability to assess the residual life of the winding insulation relying only on the data experimentally obtained through measurement. The article describes the operation of the algorithm for the prediction of the insulation condition based on the parameters identification of the modeling exponent. The possibility of predicting the residual resource, expressed in units of time, as the difference between the predicted time of failure and the current point in time, where the current time here means the operating time is shown. The results of the proposed algorithm simulation with the identification of a modeling exponent based on OLS are given. The algorithm was simulated with a measurement interval of 100 hours with a Gaussian distribution law for the error of measured leakage currents with a standard deviation of 20%. It is shown that the values of the modeling exponent parameters agree quite well with the true values at this level of noise even without using prefiltration of the leakage current measured values sequence.
Keywords
Induction motor, stator winding, winding insulation, residual life, leakage currents, diagnostics, insulation condition monitoring, insulation condition prediction, parameter identification, least squares method.
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