Parameter Estimation of Asymmetrical Six-Phase Induction Machines Using Modified Standard Tests

Stator Fault Detection in Induction Machines by Parameter Estimation Using Adaptive Kalman Filter

This paper presents a parametric low differential order model, suitable for mathematically analysis for Induction Machines with faulty stator. An adaptive Kalman filter is proposed for recursively estimating the states and parameters of continuous–time model with discrete measurements for fault detection ends. Typical motor faults as interturn short circuit and increased winding resistance ...

Investigating Direct Torque Control of Six-Phase Induction Machines Under Open Phase Fault Conditions

This paper presents analysis and evaluation of classical direct torque control(DTC), for controlling a symmetrical six phase induction motor (SPIM) under open phasefault conditions. The machine has two three-phase windings spatially shifted by 60 electricaldegrees. The strategy of the proposed method consists of choosing the switching modesaccording to the configuration of living phases in such...

Diagnosis of induction machines by parameter estimation

Online State Space Model Parameter Estimation in Synchronous Machines

The purpose of this paper is to present a new approach based on the Least Squares Error method for estimating the unknown parameters of the nonlinear 3rd order synchronous generator model. The proposed method uses the mathematical relationships between the machine parameters and on-line input/output measurements to estimate the parameters of the nonlinear state space model. The field voltage is...

Kinetic Parameter Estimation Using Modified Differential Evolution

For the development of mathematical models in chemical engineering, the parameter estimation methods are very important as design, optimization and advanced control of chemical processes depend on values of model parameters obtained from experimental data. Nonlinearity in models makes the estimation of parameter more difficult and more challenging. This paper presents an evolutionary computatio...