Lassaâd Sbita

FDI based on an adaptive observer for current and speed sensors of PMSM drives

Abstract This paper deals with a new method of current and speed sensors faults detection isolation (FDI) and identification for a permanent magnet synchronous motor (PMSM) drives. A new state variable is introduced so that an augmented system can be constructed to treat PMSM sensor faults as actuator faults. This method uses the PMSM model and a bank of adaptive observers to generate residuals. The residuals results are used for sensor fault detection. A logic algorithm is built in such a way to isolate and identify the faulty sensor for a stator phase current fault after detecting the fault occurrence. Simulation results are presented to illustrate the functionality of theoretical developments. Experimental results with 1.1-kW PMSM have validated the effectiveness of the proposed FDI method. The experimental implementation is carried out on powerful dSpace DS1103 controller board based on the DSP TMS320F240.

FDI based on Artificial Neural Network for Low-Voltage-Ride-Through in DFIG-based Wind Turbine

As per modern electrical grid rules, Wind Turbine needs to operate continually even in presence severe grid faults as Low Voltage Ride Through (LVRT). Hence, a new LVRT Fault Detection and Identification (FDI) procedure has been developed to take the appropriate decision in order to develop the convenient control strategy. To obtain much better decision and enhanced FDI during grid fault, the proposed procedure is based on voltage indicators analysis using a new Artificial Neural Network architecture (ANN). In fact, two features are extracted (the amplitude and the angle phase). It is divided into two steps. The first is fault indicators generation and the second is indicators analysis for fault diagnosis. The first step is composed of six ANNs which are dedicated to describe the three phases of the grid (three amplitudes and three angle phases). Regarding to the second step, it is composed of a single ANN which analysis the indicators and generates a decision signal that describes the function mode (healthy or faulty). On other hand, the decision signal identifies the fault type. It allows distinguishing between the four faulty types. The diagnosis procedure is tested in simulation and experimental prototype. The obtained results confirm and approve its efficiency, rapidity, robustness and immunity to the noise and unknown inputs.

Current sensors gain faults detection and isolation based on an adaptive observer for PMSM drives

This paper deals with a new method current sensors gain faults detection and isolation (FDI) for permanent magnet synchronous motor (PMSM) drives. A new state variable is introduced so that an augmented system can be constructed to treat PMSM sensor faults as actuator faults. This method uses the PMSM model and a bank of adaptive observers to generate residuals. The resulting residuals are used for sensor gain fault detection. A logic algorithm is built in such a way to isolate and identify the faulty sensor for a stator phase current gain fault after detecting the fault occurrence. The validity of the proposed method is verified by simulation tests.

A novel IMC controller based on bacterial foraging optimization algorithm applied to a high speed range PMSM drive

This paper is a proposal of a modified internal model control based on an intelligent technique. The indirect field oriented control strategy (IFOC) is used as a permanent magnet synchronous motor (PMSM) drive platform. Neural network controller and estimator are respectively added to replace the conventional speed regulator and the speed encoder in the global drive scheme. A wide speed working range is considered and high speed mode is incorporated in the study testes. In the IFOC inner control loops, the commonly used synchronous frame conventional proportional plus integral (PI) controllers are replaced by two modified internal model control (IMC) regulators. Therefore, a method based on the bacterial foraging optimization (BFO) algorithm is performed to optimize and adjust the IMC low pass filter parameters. The robustness of the proposed PMSM sensorless drive scheme is confirmed by simulation tests in the MATLAB/SIMULINK. Moreover, a comparative evaluation results are illustrated to prove the effectiveness of the proposed control algorithm according to different controllers combinations.

Particle swarm optimization based fault diagnosis for non-salient PMSM with multi-phase inter-turn short circuit

This paper deals with a new scheme for the automatic diagnosis of multiphase inter-turn short circuit faults in a non-salient permanent magnet synchronous motor (PMSM) stator windings. Base on a general dynamic model of a non-salient PMSM subjected to the multiphase inter-turn short circuit fault, a particle swarm optimization (PSO) algorithm is developed which can provide a diagnosis of the fault position and level by identifying the amount of the shorted turns for each phase. The advantages of the proposed algorithm, is the capability of single, multiple and simultaneous phase ITSC fault diagnosis. The proposed method is simulated in MATLAB/ Simulink environment. Extensive simulation studies provide preliminary verification of the fault diagnosis scheme.

Current sensors faults detection, isolation and control reconfiguration for PMSM drives

This paper deals with a new method current sensors faults detection isolation (FDI) and reconfiguration of the control loops of a permanent magnet synchronous motor (PMSM) drives. The stator currents are measured as well as observed. During fault free operation, the measured signals are used for the PMSM control. In the case of current sensors faults, the faulty measurements are detected and isolated using the new FDI algorithm. This algorithm uses an augmented PMSM model and a bank of adaptive observers to generate residuals. The resulting residuals are used for sensor fault detection. A logic algorithm is built in such a way to isolate and identify the faulty sensor for a stator phase current fault after detecting the fault occurrence. After sensor fault detection and isolation, the control is reconfigured using the healthy observers outputs. The validity of the proposed method is verified by simulation tests.

Efficiency boosting for PV systems using new MPPT method

In this paper a PV system topology incorporating a new maximum power point tracking controller (MPPT) method using a PI controller is studied. The controller is formulated based on the bijectivity between the Voltage and Power in the PV generator (PVG) characteristic; therefore if the optimal voltage is reached, this means that the maximum of power is obtained. The proposed MPPT algorithm is implemented on a dSpace DS1104 controller board. In order to demonstrate the efficiency of the proposed algorithm in real time, an experimental setup using a boost converter connected to a resistive load is successfully implemented and studied. The obtained experimental results prove the validity of the proposed MPPT algorithm.

Modelling for non-salient PMSM with multi-phase inter-turn short circuit

In this paper, a general dynamic model of a nonsalient PMSM subjected to the multiphase inter-turn short circuit fault is proposed. This model can quantitatively describe the faults occurring in multiple phases of the stator. This fault model is simulated in MATLAB/Simulink environment. Extensive simulation studies provide preliminary verification of the validity and the precision of the proposed dynamic model for different levels of fault severity.

Multimodel Modeling of Doubly Fed Induction Motor

As the electric drives are highly complex and nonlinear systems subject of several disturbances, it is a great problem to represent them via a unique model with sufficient precision and simple structure. Thus, this paper presents a new doubly fed induction motor (DFIM) model based on multimodel approach as a robust modeling method able to replace the complex system by a set of simpler local models. This approach consists of four steps which are clusters estimation, structure identification, parametric identification and local models combination. The collected data on DFIM are, firstly, clustered into several groups through a Chui’s clustering algorithm. Then, the structure identification is performed on each group using the instrumental ratio (RDI) method. Parameters of each sub model are identified using recursive least square (RLS) method. Finally, obtained sub models are combined using the validity concept. A detailed dynamic model of a DFIM with grid-connected stator and PWM inverter connected rotor, side is presented in the dq-synchronous reference frame in order to generate the input/output data. Simulation results are presented and analyzed up under Matlab/Simulink environment

Single, multiple and simultaneous current sensors FDI based on an adaptive observer for PMSM drives

This paper deals with a new method single, multiple and simultaneous current sensors faults detection isolation (FDI) and identification for permanent magnet synchronous motor (PMSM) drives. A new state variable is introduced so that an augmented system can be constructed to treat PMSM sensor faults as actuator faults. This method uses the PMSM model and a bank of adaptive observers to generate residuals. The resulting residuals are used for sensor fault detection. A logic algorithm is built in such a way to isolate and identify the faulty sensor for a stator phase current fault after detecting the fault occurrence. The validity of the proposed method is verified by simulation tests.