Grid Voltages Estimation for Three-Phase PWM Rectifiers Control Without AC Voltage Sensors

This paper proposes a new ac voltage sensorless control scheme for the three-phase pulse-width modulation rectifier. A new startup process to ensure a smooth starting of the system is also proposed. The sensorless control scheme uses an adaptive neural (AN) estimator inserted in voltage-oriented control to eliminate the grid voltage sensors. The developed AN estimator combines an AN network in series with an AN filter. The AN estimator structure leads to simple, accurate, and fast grid voltages estimation, and makes it ideal for low-cost digital signal processor implementation. Lyapunov-based stability and parameters tuning of the AN estimator are performed. Simulation and experimental tests are carried out to verify the feasibility and effectiveness of the AN estimator. Obtained results show that the proposed AN estimator presented faster convergence and better accuracy than the second-order generalized integrator-based estimator; the new startup procedure avoided the overcurrent and reduced the settling time; and the AN estimator presented high performances even under distorted and unbalanced grid voltages

Adaptative neural control for maximum power extraction in photovoltaic systems

International audienceThis paper proposes a new adaptive neural control (ANC) based strategy for maximum power point tracking (MPPT) in a photovoltaic (PV) system. The proposed strategy exploits the online trained adaptive linear neuron (ADALINE) technique. This results in a simple, fast and accurate MPPT algorithm which is easy for implementation. The ANC method is based on the incremental conductance and implemented in direct control mode. Tracking performances of the proposal are experimentally assessed using the EN 50530 standard dynamic tests. A comparison with perturb and observe algorithm is achieved. Superiority of the suggested method in terms of tracking features, convergence speed and oscillatory behaviors reduction is proven. The originality of this work is the design of an efficient and simple ADALINE based MPPT algorithm that reaches very quickly the maximum power point. Moreover, the proposal is experimentally tested in a real PV system according to the EN 50530 standard

Adaptive AC Filter Parameters Identification for Voltage-Oriented Control of Three-Phase Voltage-Source Rectifiers, International Journal of Modelling

International audienceThis paper proposes an adaptive voltage-oriented control (VOC) with onlineac filter parameters identification for three-phase voltage-source rectifier (VSR). A new methodbased on adaptive linear neuron (ADALINE) is first designed to identify the ac filter parameters.For accurate identification, the VSR nonlinearity is included in the ADALINE structure.Thereafter, the developed ADALINE is inserted in the VOC to realise an adaptive VOC. Thus,the decoupled terms and the proportional-integral current controller gains are updated online.Finally, the ADALINE ability to track properly the ac filter parameters is investigated byexperimental analysis. It shows that the VSR nonlinearity consideration has significant influenceon the resistance identification. Compared to the VOC, the enhancement of the proposedadaptive VOC is experimentally proved. The originality of this paper is the building of a VSRmodel including VSR nonlinearity that is suitable for implementation with ADALINE. This leadsto ease of implementation and accurate identification

A Novel Method for Identifying Parameters of Induction Motors at Standstill Using ADALINE

International audienceIn this paper, we propose a new method for an online electric parameters identification of an induction motor (IM). This method is carried out in the IM standstill configuration and uses adaptive linear neuron (ADALINE) networks. In order to simplify the identification process, the IM model is approximated by two first-order subsystems: one is valid at low frequencies (LFs), called the slow system and the other is valid at high frequencies (HFs), called the fast system. By means of two ADALINE networks, the parameters of the slow system and the fast system are identified in LFs and HFs, respectively, and thus, the required IM parameters are derived. Finally, experimental results are presented in order to validate the proposed method and to check the accuracy of the obtained parameters. The originality of this paper is the building of a model representation that is suitable for implementation with ADALINE networks. This leads to a simple implementation and ease of parameters identification

ADALINE approach for induction motor mechanical parameters identification

International audienceTwo 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 optimized 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

Unity Efficiency and Zero-Oscillations Based MPPT for Photovoltaic Systems

Maximum power point tracking (MPPT) is essential for photovoltaic systems to ensure a maximum power extraction from PV panels. However, some issues such as oscillations, power loss and other technical aspects still unsolved. This paper presents and discusses a new MPPT algorithm with zero-oscillations and unity efficiency in transient and steady-states. This algorithm leads to track the maximum power point under extreme operating conditions. The proposed MPPT method is based on the simple adaptive linear neuron. In addition, its implementation is achieved without any additional control loop, which resulted in a simple control. In order to validate the proposal effectiveness, both simulation and experiment tests are carried out under variable irradiance and load. Comparison between the developed MPPT and the conventional perturb and observe algorithm is also performed. Obtained results show that with the proposed method, unity efficiency is reached and oscillations are fully removed in the transient and steady-states. The originality of this work is the design of a simple and efficient MPPT algorithm based on the ADALINE with unity efficiency and zero-oscillations. Moreover, the proposal is verified using a real PV system under irradiance and load changes

Adaptive Neural PLL for Grid-connected DFIG Synchronization

International audienceIn this paper, an adaptive neural phase-locked loop (AN-PLL) based on adaptive linear neuron is proposed for grid-connected doubly fed induction generator (DFIG) synchronization. The proposed AN-PLL architecture comprises three stages, namely, the frequency of polluted and distorted grid voltages is tracked online; the grid voltages are filtered, and the voltage vector amplitude is detected; the phase angle is estimated. First, the AN-PLL architecture is implemented and applied to a real three-phase power supply. Thereafter, the performances and robustness of the new AN-PLL under voltage sag and two-phase faults are compared with those of conventional PLL. Finally, an application of the suggested AN-PLL in the grid-connected DFIG-decoupled control strategy is conducted. Experimental results prove the good performances of the new AN-PLL in grid-connected DFIG synchronization