ABSTRACT
The paper proposes a data-driven fault-tolerant control (FTC) strategy to construct and accommodate the bias on ambient temperature measurements in supermarket refrigeration systems. The bias, which is caused by direct or indirect exposure of the sensor to the sun, can have a significant impact on the refrigeration system's energy consumption. Based on analysis of the real data a comprehensive model of the bias is developed and then used to generate realistic scenarios for testing the proposed FTC method. The FTC method uses a feed forward Neural Network (NN) as a black box model. The model is trained by active injection of perturbation signals during the night operations. During the Monte-Carlo tests, the strategy was implemented in a Plug & Play manner, demonstrating that substantial energy savings can be achieved during summer periods.
ABSTRACT
In this paper, a new method for equivalent circuit modeling of a traveling wave ultrasonic motor is presented. The free stator of the motor is modeled by an equivalent circuit containing complex circuit elements. A systematic approach for identifying the elements of the equivalent circuit is suggested. The Levenberg-Marquardt parameter estimation algorithm is used to model the alteration of the admittance after placing the rotor on the stator. Thereafter, theoretical assessments and experimental measurements are used to account for the speed reduction that is caused by placing the rotor on the stator and applying the load torque. Finally, the effects of temperature changes and the resultant response of the motor are computed. Results of the experiments and measurements are used to verify and validate the precision of the new modeling method.
