Building the Structure and the Neuroemulator Angular Velocity's Learning Algorithm Selection of the Electric Drive of TVR-IM Type

• Published in: Applied mechanics and materials Vol. 792; no. Energy Systems, Materials and Designing in Mechanical Engineering; pp. 44 - 50
• Main Authors: Kozlova, L.E., Bolovin, E.V.
• Format: Journal Article
• Language: English
• Published: Zurich Trans Tech Publications Ltd 01.09.2015
• Subjects: Algorithms; Angular velocity; Back propagation; Buildings; Computer simulation; Control systems; Delay; Descent; Electric drives; Electric potential; Exploitation; Feedback; Induction motors; Machine learning; Mathematical models; Matlab; Motors; Neural networks; Parameter robustness; Propagation; Properties (attributes); Robustness; Running; Sensors; Velocity; Neural Network; Thyristor Voltage Regulator; Gradient Descent with Momentum Backpropagation (GDM); Gradient Descent Backpropagation (GD); Scaled Conjugate Gradient Backpropagation (SCG); Algorithm of Levenberg - Marquardt (LM); Learning Algorithm
• ISBN: 3038355488; 9783038355489
• ISSN: 1660-9336; 1662-7482; 1662-7482
• DOI: 10.4028/www.scientific.net/AMM.792.44

Today, one of the most common ways to control smooth starting and stopping of the induction motors are soft-start system. To ensure such control method the use of closed-speed asynchronous electric drive of TVR-IM type is required. Using real speed sensors is undesirable due to a number of inconveniences exploitation of the drive. The use of the observer based on a neural network is more convenient than the use of the real sensors. Its advantages are robustness, high generalizing properties, lack of requirements to the motor parameters, the relative ease of creation. This article presents the research and selection of the best learning algorithm of the neuroemulator angular velocity of the electric drive of TVR-IM type. There were investigated such learning algorithms as gradient descent back propagation, gradient descent with momentum back propagation, algorithm of Levenberg – Marquardt, scaled conjugate gradient back propagation (SCG). The input parameters of the neuroemulator were the pre treatment signals from the real sensors the stator current and the stator voltage and their delay, as well as a feedback signal from the estimated speed with delay. A comparative analysis of learning algorithms was performed on a simulation model of asynchronous electric drive implemented in software MATLAB Simulink, when the electric drive was running in dynamic mode. The simulation results demonstrate that the best method of learning is algorithm of Levenberg – Marquardt.