Said Drid

Implementation of a New MRAS Speed Sensorless Vector Control of Induction Machine

In this paper, a novel rotor speed estimation method using model reference adaptive system (MRAS) is proposed to improve the performance of a sensorless vector control in the very low and zero speed regions. In the classical MRAS method, the rotor flux of the adaptive model is compared with that of the reference model. The rotor speed is estimated from the fluxes difference of the two models using adequate adaptive mechanism. However, the performance of this technique at low speed remains uncertain and the MRAS loses its efficiency, but in the new MRAS method, two differences are used at the same time. The first is between rotor fluxes and the second between electromagnetic torques. The adaptive mechanism used in this new structure contains two parallel loops having Proportional-integral controller and low-pass filter. The first and the second loops are used to adjust the rotor flux and electromagnetic torque. To ensure good performance, a robust vector control using sliding mode control is proposed. The controllers are designed using the Lyapunov approach. Simulation and experimental results show the effectiveness of the proposed speed estimation method at low and zero speed regions, and good robustness with respect to parameter variations, measurement errors, and noise is obtained.

Neural classification method in fault detection and diagnosis for voltage source inverter in variable speed drive with induction motor

These days, electrical drives generally associate inverter and induction machine. Thus, these two elements must be taken into account in order to provide a relevant diagnosis of these electrical systems. The aim of this paper is to study the feasibility of fault detection and diagnosis in a three-phase inverter feeding an induction motor. The proposed approach is a neural network classification applied to the fault diagnosis of a field oriented drive of induction motor. Multilayer perception (MLP) networks are used to identify the type and location of occurring fault using the stator Concordia mean current vector. In the case of a single fault occurrence, a localization domain made with seven patterns is built. With the possibility of occurrence of two faults simultaneously, there are twenty-two different patterns. Simulated experimental results on 1.5-kW induction motor drives show the effectiveness of the proposed approach with a classification performance over than 95%.

Control of induction motor using polytopic LPV models

A Gain scheduled control design for the stator current is presented, the approach is novel in that the gain scheduled design does not involve linearization about operating points. Instead the motor dynamics are brought to linear parameter varying form via state transformation. A linear parameter varying system is defined as a linear system whose dynamics depends on unknown but measurable exogeneous parameter. The current equations in the (α,β) frame have a particular structure, allowing to be written as an LPV system because of affine dependence of rotational speed which is taken as time varying parameter. This varying parameter values can be measured on line during control operations. The LMI based gain scheduled controller using H-infinity synthesis and polytopic representation is designed such that the robust quadratic stability and robust quadratic performance can be assured along the reference trajectory of the varying parameter.

Sensorless speed backstepping control of induction motor based on sliding mode observer: Experimental results

This paper deals with a Sensorless backstepping controller. In order to improve the robustness of the proposed approach, we estimate the rotor flux components and rotor speed of the induction motor using a sliding mode observer. The backstepping technique is appropriate for such as non linear system. The overall system stability is proved through appropriate Lyapunov functions. Experimental result is presented to prove the effectiveness and validity of the proposed Sensorless control.

Advanced strategy of Demand-side management for photovoltaic-wind energy system

Demand side management is considered as the next evolution of smart grid technology, it can adjust the time and the quantity of the electricity usage either by shifting the demand during the peak hours or by moving the time of energy use to off-peak periods. The objective of this paper is to develop an efficient load side management strategy for photovoltaic-wind hybrid energy system for an isolated house. The strategy aims to control the non-critical loads of the house based on an efficient analysis of the daily load profile of the house and on the climate data. The simulation result shows the effectiveness of the proposed strategy, as it improves the energy balance of the system and eliminates the energy waste.

Robust control of the Photovoltaic system with improved maximum power point tracking

A robust voltage control intended for a photovoltaic system is considered. An improved maximum power points tracking (MPPT) is presented without Photovoltaic array characteristics known. In order to cope with large parameter variations and load disturbances, second order sliding mode controller, based on super twisting algorithm for DC-DC Boost converter is proposed. The designed control shows fast MPPT and good robustness with respect to parameter variations, measurement errors and unmodeled dynamics. The experimental results confirm the effectiveness of the proposed control.

High Performance of Self Scheduled Linear Parameter Varying Control with Flux Observer of Induction Motor

This paper deals with a robust controller for an induction motor (IM) which is represented as a linear parameter varying systems. To do so linear matrix inequality (LMI) based approach and robust Lyapunov feedback are associated. This approach is related to the fact that the synthesis of a linear parameter varying (LPV) feedback controller for the inner loop take into account rotor resistance and mechanical speed as varying parameter. An LPV flux observer is also synthesized to estimate rotor flux providing reference to cited above regulator. The induction motor is described as a polytopic LPV system because of speed and rotor resistance affine dependence. Their values can be estimated on line during systems operations. The simulation and experimental results largely confirm the effectiveness of the proposed control.

Robust flux and load torque estimation in Induction Machine

We propose in this paper an adaptive robust fuzzy observer to estimate flux and to identify the load torque for Induction Machines (IM) applications. Indeed, using a fuzzy IM model, we develop the structure of an observer where we consider the load torque as an unknown input. Sufficient conditions are presented to ensure the asymptotic convergence of the flux and the load torque errors estimation. In addition, a robustness criteria is considered, using the L2 technique, to take into consideration parameters uncertainties. Design conditions are founded on LMIs terms (Linear Matrix Inequalities) and an experimentation (using DSP 1104 real time controller) is carried out to show the effectiveness of the proposed results.

A comparative study of rotor flux estimation in induction motor with a linear parameter varying observer and Kalman Filter

The paper presents a comparison of performances and characteristics of a Self Scheduled linear parameter varying (LPV) Flux Observer and observation algorithms based on Kalman Filter for induction motor rotor flux estimation, inserted in a field oriented control scheme. The construction of the considered algorithms is described in detail, and the different design issues are explained. For true comparison the same measurements are assumed available to both deterministic and stochastic estimators, and the same controller parameters are used in simulation. The performances of the estimators are compared either in terms of observation errors during transient and steady state operations either in terms of robustness face to variations of motor parameters.

Voltage control of DC-DC buck converter using second order sliding mode control

This paper presents a robust voltage control intended for a photovoltaic system, the main objectives of the proposed control is to adapt with the parameter variations and load disturbances. Second order sliding mode controller, based on super twisting algorithm for DC-DC Buck converter is proposed. Applying SOSMC can improve the transient response of the system for a wide range of reference voltage or under large parameter variations and load disturbances. The robustness of the proposed control is tested by simulation in deferent operation condition such as parameters variation and load change. The experimental results confirm the effectiveness of the proposed control.