# Optimization Procedure for Computing Sampling Time for Induction Machine Parameter Estimation  
[PERSON]  
1 Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, J.J.Strossmayer University Osijek, Tropinirova 2B, 3100 Osijek, Croatia; [EMAIL_ADDRESS] (T.V. ); [EMAIL_ADDRESS] (M.B. ); [EMAIL_ADDRESS] (V.J.S.) 1 Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, J.J.Strossmayer University Osijek, Tropinirova 2B, 3100 Osijek, Croatia; [EMAIL_ADDRESS] (T.V. ); [EMAIL_ADDRESS] (M.B. ); [EMAIL_ADDRESS] (V.J.S.) 2  
[PERSON]  
1 Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, J.J.Strossmayer University Osijek, Tropinirova 2B, 3100 Osijek, Croatia; [EMAIL_ADDRESS] (T.V. ); [EMAIL_ADDRESS] (M.B. ); [EMAIL_ADDRESS] (V.J.S.) 2  
[PERSON]  
1 Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, J.J.Strossmayer University Osijek, Tropinirova 2B, 3100 Osijek, Croatia; [EMAIL_ADDRESS] (T.V. ); [EMAIL_ADDRESS] (M.B. ); [EMAIL_ADDRESS] (V.  
The term optimal sampling time found in literature is quite rare. Paper [11] deals with selecting the optimal sampling time to improve the accuracy of the mean and RMS (root-mean-square) value computation for multisine signals and the paper [12] goes further and determines the optimal sampling for Gauss-Markov scalar processes. The latter paper is interesting in sense of this research since it shows that the optimal time sampling is also related to the window size of the collected data, a problem that this paper also takes into account. Also, the paper [12] discusses the optimal sampling time as the smallest sampling time, based on the variance of the estimated parameters, while it also notes that reducing sampling time beyond a certain point does not give significant improvement to the estimation results. A disscusion more suited to this research field is from [13]. There the authors analyse the optimal sampling time with respect to the optimal control law for linear systems. That paper also mentions the term quantization in the time domain, which is mainly viewed as a negative influence on controllers. The work presented in paper by [PERSON] in chapter 32 of [14] utilizes the time and amplitude quantization to stabilize unstable systems and refers to optimal sampling time from that point of view. From these references, the term optimal sampling time is hard to define without a specific purpose that the optimal sampling time refers to. Over 30 years ago, in [15] the authors dealt with the problem of optimal sampling time in general system parameter estimation itself. In this paper authors have derived a criteria for sampling time selection for linear deterministic systems. Few years later, in [16] the authors present the optimal sampling time for stochastic systems, mainly giving the Kalman filter optimal sampling time as a result. In this paper the authors follow the criteria for optimal sampling time given by [PERSON] and [PERSON] in [15]. This criteria is given for linear systems, whereas IM model is nonlinear. In order to apply the criteria by [PERSON], the linear system poles must be known in advance. Naturally they are not, nor are the parameters of the linearized system from which they can be computed. Therefore we propose using the Prony method to estimate the poles from the line start experiment of the IM, that can be used for Sinha method of optimal sampling time. Application of the Prony method to the approximation of induction machine nonlinear transient is a challenge that is addressed in this paper by optimal segmenting and decimation of the collected data. This is done to obtain intervals of data where the Prony approximation remains accurate. The paper is structured as follows: the second section introduces the parameter estimation problem this paper refers to and introduces the mathematical background for fitting the data by Prony exponential series to determine the linear system poles.