Artificial Intelligent Systems and Machine Learning

K. Mohan Raj ¹, Sridhar Makkapati ¹, S. Paramasivam ²

Affiliations
1 Electrical and Electronics Engineering Department, SRM University, IN
2 ESAB, Chennai, IN

Abstract

Monitoring and fault diagnosis in induction motor has been a challenging task for the engineers in recent industrial applications. In general, current monitoring techniques are usually applied to detect various types of induction motor faults. The present work proceeds with the fault diagnosis in voltage source inverter fed induction motor using current parks vector approach. In this paper, different types of faults such as 3-∅ short circuit fault, L-L faults and open circuit faults are introduced and diagnosed using the current park spectrum where the spectrum differs from the ideal to the faulty condition.

Keywords

Diagnosis, Induction Motor, L-L Fault, Three Phase Short Circuit, Current Parks Vector.

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D. Susitra ¹, S. Paramasivam ²

Affiliations
1 Sathyabama University, Chennai-600119, IN
2 ESAB Group, Sriperumbudur Taluk, Kanchipuram District-602105, IN

Abstract

This paper presents two different methods of real time applicable modeling techniques for the Non-linear inductance model of a Switched reluctance Machine (SRM). These methods are based on Multivariate nonlinear regression technique (MVNLRT) and Adaptive Neuro fuzzy inference system (ANFIS). These techniques are applied for the nonlinear inductance calculation by using the magnetization characteristics of SRM. MVNLRT is an excellent solution for nonlinear modeling and its real time implementations. Similarly ANFIS also has a strong nonlinear approximation ability which could be used for nonlinear modeling and its real time implementations. In this paper, the best features of MVNLRT and ANFIS are utilized to develop the computationally efficient inductance model for SRM. Mathematical models for the phase Inductance L(I,θ) using MVNLRT and ANFIS have been successfully arrived, tested and presented for various values of phase currents(Iph) and rotor positions(θ) of a non linear SRM. It is observed that MVNLRT and ANFIS are highly suitable for Inductance L(I,θ) modeling of SRM which is found to be in good agreement with the training data used for modeling.

Keywords

Non-Linear Inductance Model, Multivariate Non-Linear Regression Technique (MVNLRT), Adaptive Neuro-Fuzzy Inference System (ANFIS), Switched Reluctance Machine (SRM).

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C. Kamalakannan ¹, S. R. Paranjothi ¹, S. Paramasivam ²

Affiliations
1 EEE Department, Rajalakshmi Engineering College, Chennai-602105, IN
2 ESAB Engineering Services Ltd., Irungattukotai, Chennai, IN

Abstract

The speed of a rotating machine is a highly critical parameter. Also, the speed is typically affected by loads and nonlinear variation of circuit elements placed on the machine (in addition to the type of control employed). The inductance of the set of poles shall exhibit a nonlinear behavior in most of the machines and makes the control difficult. For example, when the rotor is aligned with any given pole pair, it is clearly mid-way between the other two pole pairs. Thus rotation in one direction will increase the inductance of one set of poles and decrease the inductance of the remaining set. In this paper, an optimal control method for the best motoring operation of SRM drives is attempted. The high motoring torque, high operating efficiency, and low torque ripple was considered as best motoring operation. The multi objective function developed in this work is to obtain a compromise between maximum average torque, maximum average torque per rms current (minimum copper loss), and maximum torque smoothness factor (minimum torque ripple). The proposed multi-objective function meets the requirement and accordingly, the turn-off and turn-on angles were optimized. Two angular controllers were proposed to adjust the turn-on and turn-off angles automatically to obtain the objective function. The simulation and experimental results exhibits that this method can be used to improve the best motoring operation of SRM drives in practical applications.

Keywords

PI Controller, Speed Control and Switched Reluctance Motor.

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