Monitoring of power factor for induction machines using estimation techniques

Power factor is a significant element in power systems which is defined as the angle difference between voltages and currents that produces power fluctuation between sources and loads. Since, 40-50% of consumption of electrical power in industry is induction machines which are inductive loads, monitoring of the power factor is necessary in order to protect systems. To monitor the power factor on induction machines, it would require both voltage and current waveforms measurement in order to apply the displacement method which require equipments. In this paper, we present a mathematical method using Kriging to determine the operating power factor for an induction machine. Estimation of the operating power factor would be effectively implemented for under load detection and compensation for improving the power quality. Experimental results will be indicated to substantiate the feasibility of the proposed methods

Estimating power factor of induction motors using regression technique

Induction motors are one of the largest power consumption in electrical systems. Since induction motors are inductive loads, they produce a lot of power quality issue in the electrical systems. Solving the power quality problem, monitoring power factor of induction motors is important because at no load or light load condition power factor is low and consequently low power factor not only provide a penalty charge, but also generates a huge current and losses in the grid systems. To measure the power factor, zero crossing and instantaneous power methods can be used. Both methods require motor voltage and current waveforms at operating times. Those methods may have a huge cost in terms of requiring the motor to be out of service for connecting devices. In this research, regression analysis will be applied to estimate the power factor of induction motor at any loading condition. The results of the proposed method will be compared with the measured power factor of induction motor in order to substantiate the feasibility of the proposed method

Estimating power factor of induction motors at any loading conditions using support vector regression (SVR)

Three-phase induction motors are industrial work horses known as inductive loads. Inductive loads always create low power factor due to consuming more reactive power. Low power factor not only makes a penalty charge for costumers, but also produces energy losses in electrical systems. To prevent these problems, the power factor which is the ratio of active and reactive power must be maintained toward unity. The power factor can be controlled by manipulating either of active or reactive power. In induction motors, active power is proportional to the motor load that variation of motor load results in increase or decrease in power factor. However, adding reactive power by capacitors would be a substantial solution to improve and control the power factor in unity. Many researches expressed that injecting improper reactive power to improve the power factor creates under- and over-correction. To prevent such problems, equation of power factor correction can be employed in order to obtain the optimal value of reactive power. In this equation, the presence of power factor at every single loading point is required. Estimation techniques can be a significant key to determine the power factor at every loading point. In this paper, several statistical methods including, kriging, regression, artificial neural network and support vector regression, are tested in three induction motors. A comparison is presented to verify the great performance of support vector regression method