Simulation and Experimental Analysis of Common Mode Voltage and Bearing Voltage in Multiple Motors Fed by Single PWM Inverter for Long Cable Applications
Sharana Reddy1, Banakara Basavaraja2
1Sharana Reddy, Department of Electrical and Electronics Engineering, Ballari Institute of Technology and Management, Ballari, Karnataka, India.
2Banakara Basavaraja, Department of Electrical and Electronics Engineering University BDT College of Engineering Davengere, Karnataka, India.

Manuscript received on 04 April 2019 | Revised Manuscript received on 08 May 2019 | Manuscript published on 30 May 2019 | PP: 2380-2388 | Volume-8 Issue-1, May 2019 | Retrieval Number: A1916058119/19©BEIESP
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Abstract: This paper presents the influence of the motor cable length on the occurrence of over voltage at the motor terminals in multiple induction motors fed by single PWM inverter for long cable applications. Further, paper also discusses the effect of overvoltage at the motor terminals on the magnitude of the common mode voltage and the bearing voltage. Two 1/2-HP, 3-Ø induction motors (M1 and M2) and two 3HP induction motors (M3 and M4). These motors are installed at distance of 30m, 60m, 90m and 120 m respectively from inverter terminals in daisy chain configuration. As the motor cable length increases, the motor terminal voltage , common mode voltage and bearing voltage increases due to voltage reflection phenomenon and interaction between the individual motors. To suppress the over-voltage at all the motor terminals, thereby reducing the common mode voltage and bearing voltage, the RC passive filter have been installed at the motor terminals M4 to match with the cable surge impedance. The simulation results are compared with the experimental results. According to these results, the over-voltages at the motor terminal, common mode voltage and bearing voltage reduced more than 70 % in all the motors with long cables.
Index Terms: Bearing Voltage, Multiple Motors, PWM Inverter, Voltage Reflection Phenomenon
Scope of the Article: Simulation Optimization and Risk Management