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\title{FPGA Controlled Stator Resistance Estimation In IVC of IM using FLC}
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\begin{document}

             \author[1]{B. Mouli  Chandra}

             \author[2]{Dr. S.Tara  Kalyani}

             \affil[1]{  JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY, HYDERABAD, INDIA}

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\date{\small \em Received: 14 December 2012 Accepted: 3 January 2013 Published: 15 January 2013}

\maketitle


\begin{abstract}
        


In this paper online estimation of stator resistance in indirect vector control (IVC) of Induction motor (IM) is proposed using fuzzy logic controller (FLC). It is renowned that stator resistance of Induction motor which is sensitive to temperature rise in machine leads to performance deterioration as resistance used in controller is dissimilar from actual stator resistance. Here the effect of change in stator resistance is examined in terms of stator current, rotor flux and torque and corrected using fuzzy logic controller algorithm. The proposed algorithm was tested using MATLAB/SIMULINK software and practically implemented in Field programming Gate array (FPGA) Controller.

\end{abstract}


\keywords{fuzzy logic controller, indirect vector control, induction motor stator resistance estimation, FPGA controller.}

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\let\tabcellsep& 	 	 		 
\section[{Introduction}]{Introduction}\par
he superior performance of separately excited D.C motor in terms of dynamic control had become the work horses for an Industry. Later after development of power electronic converters Induction motors which are rugged in construction replaces the D.C motors even for Adjustable speed applications, i.e., D.C machine like performance was obtained by Induction motor by using vector control, where the three phase stator quantities has been resolved into d-q axes, one resembles torque producing quantities and other flux producing quantity. Therefore the independent control of torque and flux is possible in vector control. However flux position in vector control is essential. There are basically two types of vector control based on how the rotor flux is determined. In direct vector control it is found by direct flux sensors where as in indirect vector control it is found in feed forward manner. However rotor flux which is majorly affected by stator resistance variation which is dependent on temperature rises [40-50\%] in machine. In \hyperref[b0]{[1]} BEMF (Back Electromotive force Detector was proposed, in \hyperref[b1]{[2]} calculation of flux including stator resistance was accounted, in \hyperref[b2]{[3]} by treating the rotor speed and stator voltages and currents are the inputs to flux estimator was proposed. In \hyperref[b3]{[4]} Stator resistance tuning based on error between actual and measured values proposed using full order observer. In \hyperref[b4]{[5]} Model reference adaptive system (MRAS) based estimation of Rs. In this paper Flux estimated from the voltage model is compared with the flux estimated from the current model and error and change in error is given as inputs to the fuzzy controller and the output is taken as change in stator resistance and estimated stator resistance is found by adding the actual resistance. 
\section[{II.}]{II.} 
\section[{Induction Motor Model with Vector Control}]{Induction Motor Model with Vector Control}\par
Considering the modeling equation of Induction motor in stationary reference frame, the condition for ensuring vector control is\par
The decoupled stator voltages and are given by Year = = 0 = + + ? = + + + = = + , = , , = 1 ? (6) (7)\par
From equations (  {\ref 6}), \hyperref[b6]{(7)} undoubtedly shows that decoupled rotor fluxes dependent on stator resistance which is assorted during running conditions of motor. 
\section[{III.}]{III.} 
\section[{Effect of Stator Resistance}]{Effect of Stator Resistance}\par
When the Stator resistance is deviated from its actual value during the running conditions of motor, primarily it effect on rotor flux calculation, and motor toque, and stator currents by treating the inductance variation is zero in steady state condition.\par
IV. 
\section[{Stator Resistance Estimation using Fuzzy Controller}]{Stator Resistance Estimation using Fuzzy Controller}\par
It is evident from equations (  {\ref 6}) and (  {\ref 7}), the rotor flux estimation is affected by stator resistance variation. In order to minimize the error introduced because of stator resistance online stator resistance estimator must be integrated which is implemented by fuzzy logic controller. From the flux estimated by the voltage model equations, the current model equations are given by \hyperref[b7]{(8)} (9)\par
Adapting (  {\ref 6}) and (  {\ref 7}) equations in (  {\ref 8}) and (  {\ref 9}) we get reference values of decoupled stator quantities namely ids, ref and iqs, ref from which we can establish reference stator current as shown in Fig. \hyperref[fig_1]{1}. ,and this reference current is compared with actual currents in motor and error in currents as well as change in error are inputs to fuzzy controller and output is taken as change in stator resistance. This change in stator resistance is added to the actual value of stator resistance which gives new estimated stator resistance which is further used in voltage model equations. F + = ? + + = ? + = ? ? = ? ?\par
Examining the equation (  {\ref 8}) and (9) which also dependent on Rotor resistance which also simultaneously varies with the temperature rise but in this case it with an effort to determine stator resistance variations by assuming rotor resistance effect is constant. An algorithm was developed for stator resistance estimation shown in Fig.  {\ref 2}. In the present controller Mandani Fuzzy controller method was used for the estimation of stator resistance. It employs two inputs one is error produced by reference current generated by current model and actual feedback motor currents and the other is change in error produced by same. The rule base acts upon the inputs to produce the given outputs. The linguistic labels are divided into seven groups. They are Negative big, Negative medium, Negative small, Zero, Positive small, Positive medium, Positive big, which are generally expressed as NB,NM,NS,ZE,PS,PM,PB respectively. The rule base mapping of fuzzy inputs to derive require output is shown in table.1. Normally the output obtained produced is fuzzy in nature and has to be transformed into crisp value by using defuzzification method. Here Mean of Maximum method is used at the defuzzification stage.\par
Table  {\ref 1} : Computation of outputs using fuzzy rules Figure  {\ref 2} : Stator Resistance Algorithm V. 
\section[{Simulation and Experimental Analysis}]{Simulation and Experimental Analysis}\par
The performance of 1H.P 3-? Slip ring Induction motor with Indirect Vector control is simulated using MATLAB/Simulink software. After obtaining the satisfactory results, with an effort to analyze the performance of Induction motor with stator resistance variation an additional resistance is added in terms of step manner the response of rotor flux, torque, and steady state stator currents were analyzed. Initial stator resistance 10.6 ?, an additional resistance of 5? is added in step manner, the response in torque is decreased to be 32 N-m from 33.075 N-m, and similarly the rotor flux deviates to 1.08T from its actual value of 0.9T, correspondingly the stator current deviated to 2.1A from its rated value of 2.4A. Next the same results were analyzed experimentally using SPATRAN 3A FPGA Controller with the full rated torque of 33.075N-m. In this case to examine the effect of stator resistance variation an additional resistance of 5? is added abruptly in series with the star connected stator winding and abrupt changes in rotor flux, motor torque, and stator currents were experimentally verified and found to be similar to simulated results. Next by implementing Stator resistance algorithm using Fuzzy logic control stator resistance was estimated and adapted to current modeling equations, so estimated stator resistance was found to similar to actual resistance value and thus performance of machine was improved.\par
Table  {\ref 2} : Induction motor parameters     \begin{figure}[htbp]
\noindent\textbf{1}\includegraphics[]{image-2.png}
\caption{\label{fig_1}Figure 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{345}\includegraphics[]{image-3.png}
\caption{\label{fig_2}Figure 3 :ResultsFigure 4 :Figure 5 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{678}\includegraphics[]{image-4.png}
\caption{\label{fig_3}Figure 6 :Figure 7 :Figure 8 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{912}\includegraphics[]{image-5.png}
\caption{\label{fig_4}Figure 9 :Figure 12 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{13}\includegraphics[]{image-6.png}
\caption{\label{fig_5}Figure 13 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{17}\includegraphics[]{image-7.png}
\caption{\label{fig_6}Figure 17 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-8.png}
\caption{\label{figure8}}\end{figure}
 			\footnote{F © 2013 Global Journals Inc. (US)} 			\footnote{F © 2013 Global Journals Inc. (US)} 			\footnote{F © 2013 Global Journals Inc. (US)} 			\footnote{© 2013 Global Journals Inc. (US) ( ) F This page is intentionally left blank} 		 		\backmatter  			 \par
The main drawback of Indirect Vector control technique is stator and rotor resistance variations. In the paper the effect of stator resistance is investigated using MATLAB/Simulink Software as well as experimentally by using FPGA SPATRON 3A controller and also compensated by developing Fuzzy Algorithm.			 			  				\begin{bibitemlist}{1}
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\end{document}
