ADALINE approach for induction motor mechanical parameters identification

Abstract

Two new methods to identify the mechanical parameters in induction motor based field oriented drives are presented in this paper. The identified parameters are: the moment of inertia and the viscous damping coefficient. The proposed methods are based on the adaptive linear neuron (ADALINE) networks. The two parameters are derived and optimised during the online training process. During the identification phase, the motor torque is controlled by the well-known field oriented control strategy. This torque is subjected to variations in order to obtain mechanical speed transients. The two proposed methods are simple to implement compared to the previous techniques. They require only the stator current and mechanical speed measurements. Finally, the effectiveness of the two methods and the accuracy of the derived parameters are proven experimentally by two direct starting tests. The originality of this work is the building of a model representation that it is suitable for implementation with ADALINE networks. This leads to a simple implementation and ease of mechanical parameters identification.

Adaptive Neural PLL for Grid-connected DFIG Synchronization

“…The least mean square algorithm with the learning rate η is used for weight training [20]- [24]. ADALINE weight vector W(k)=[w 1 (k) w 2 (k)] is recursively updated as follows:…”

“…To improve the robustness of the PLL architecture without increasing the computational cost, a new adaptive neural PLL (AN-PLL) for grid-connected DFIG synchronization is developed in this study. The proposed AN-PLL architecture is based on adaptive linear neuron (ADALINE) networks, which are successfully applied to several fields, such as network frequency tracking [20], [21], current harmonic estimation [22], and induction motor parameter identification [23], [24]. The main advantages of the proposed AN-PLL are its accuracy, robustness, and adaptive structure.…”

Hybrid artificial neural network for induction motor parameter estimation

“…In [2], there is a survey, where different ANN structures and training algorithms are described and analysed. In this structure compilation, two ANNs are selected and discussed for their effectiveness and simplicity to be implemented, one is an Adaptive Linear Neuron (ADALINE) structure presented in [3] and [4] and the other one is a Cerebellar Model Articulation Controller (CMAC) reported in [5].…”

Analytical approach to estimate mechanical parameters in induction machine using transient response parameters

“…Requirement of accurate induction machine parameters estimation attracts the researchers to involve them for the development of new and innovative approaches for the same. Induction machine parameters can be classified in two broad categories: (1) electrical parameters and (2) mechanical parameters . Although the machine dynamic performances are much affected by the mechanical parameters, in most of the available studies, electrical parameters have been evaluated while the researchers did not pay much attention towards the estimation of mechanical parameters.…”