Induction motors have good efficiency at rated conditions, but at partial loads if operated with rated flux, they show poor efficiency, Motors in such conditions waste a lot of electricity, results in increased operational cost, hence significant loss of revenue, if run for long duration, Because of robustness, good power/mass relation, low cost and easy maintenance throughout its life cycle, induction motors, particularly squirrelcage induction motors are vastly used in industries. Because of the huge number of operational units worldwide, they consume a considerable amount of electrical energy, so even a minute efficiency improvement may lead to significant contributions in global electricity consumption, revenue saving and other environmental aspects. This paper uses key concepts of loss model control (LMC) and search control (SC) together for efficient operation of induction motors used in various industrial applications, where aforesaid load conditions may occur for prolonged durations. Based on the induction motor loss model in d-q frame, and using classical optimization techniques, done earlier, an optimal Ids expression in terms of machine parameters and load parameters is used to estimate optimal Ids values for various load conditions, offline, and finally tabulated. Based on which, an artificial neural network (ANN) controller is designed, taking torque and speed as input and Ids as output. The ANN controller reproduces the optimal Ids * value, as per load conditions, in feed-forward manner, and thus eliminates run-time computations and perturbations for optimal flux. The ANN training is performed in MA TLAB and the results have shown the superb accuracy of the model. Dynamic and steady-state performances are compared for conventional vector control (constant Ids) and proposed optimal control (optimal Ids) operations. Excellent dynamic as well as superior efficiency performance (1-18%) at steady- state, is observed in optimal flux operation, for load torque above 60% of rated, in simulation, for a wide range of speed, by the proposed method. Also, the method is easy to implement for real – time industrial facilities, fast response, ripple free operations, and offers higher energy savings ratio as compared to useful output power, in comparison with similar works done earlier.
- Induction motor drive
- Vector control
- Optimal control
- Efficiency optimization
Fig. 1(a). MATLAB model for efficiency validation, (b) Id,* values at different speeds
EXPECTED SIMULATION RESULTS:
Fig. 2(a) Efficiency- vs- Load-Torque at 120 rad/s, (b) %age Power saving – vs- Speed
Fig. 3. Speed, Torque, Input power and Efficiency performance at for a load cycle of (150 N -m, 120 radls) for 4 sec, (200 N -m, 150 rad/s) for 3 sec, and (150 Nm, 90 rad/s) for 3 sec.
Fig. 4. Switching state performances at sample 120 rad/sec speed at a load cycle of 16 seconds.
In this work, it is verified that the optimal flux operation is superior to that of vector control method under steady – state condition, in terms of efficiency enhancement and hence energy-saving. In general 1 – 18% efficiency improvement is observed on 50 HP, 60 Hz motor, at different load-torques (above 60%) and speeds, in Simulink environment. Efficiency improvement margin is seen degraded below 60% of rated load, and conventional vector control performs better. This can be seen as shortcoming of proposed method. The dynamic performance is seen satisfactory, similar to vector control or even better, in terms of overshoot, undershoot and settling time, speed and torque tracking accuracy is little bit deviated, but still the proposed approach is extremely suitable for such an application where maintaining speed and torque very precisely is not a critical issue, such as an induction motor drive used in an industrial HV AC applications [7, Bose 2004]. A lot of electricity can be saved with this minute compromise in speed and torque accuracy, since it offers higher amount of energy savings as compared to existing methods, hence a great contribution towards social and environmental aspects. The proposed method can be easily implemented on other induction motor drive systems also, for which the steady-state speed-vs.torque load characteristics are already known or can be predicted. The offline optimization as done here, is a limitation, as the optimal flux trajectories are only valid for one specific application, can also be considered as drawback. But, the proposed hybrid approach eliminates the need of run-time computation complexity in traditional loss model controller (LMC), so less hardware installations required in implementation, hence cost-effective. Also, since no run-time perturbations happening as it usually happen in conventional search control (SC), so no torque ripples, hence less wear and tear of induction motor drive.
 A. H. M. Yatim and W. M. Utomo, “To develop an efficient variable speed compressor motor system,” Universiti Teknologi Malaysia (UTM), Skudai, Malaysia, 2007.
 R. Hanitsch, “Energy efficient electric motors,” University of Technology, Berlin, Germany, 2000.
 Y. Yakhelef, “Energy efficiency optimization of induction motors,” Boumerdes University, Boumerdes, Algeria, 2007.
 M. W. Turner, V. E. McCormick and J. G. Cleland, Efficiency optimization control of AC induction motors: Initial laboratory results, United States Environmental Protection Agency, Research and Development, National Risk Management Research Laboratory, 1996.
 T. Fletier, W. Eichhammer and 1. Schleich, “Energy efficiency in electric motor systems: Technical potentials and policy approaches for developing countries,” United Nations Industrial Development, Vienna, 2011.