This paper proposes an adaptive voltage-oriented control (VOC) with online ac filter parameters identification for three-phase voltage-source rectifier (VSR). A new method based 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 by experimental analysis. It shows that the VSR nonlinearity consideration has significant influence on the resistance identification. Compared to the VOC, the enhancement of the proposed adaptive VOC is experimentally proved. The originality of this paper is the building of a VSR model including VSR nonlinearity that is suitable for implementation with ADALINE. This leads to ease of implementation and accurate identification.

This paper presents the design and performance evaluation of an experimental platform that emulates the static and dynamic behavior of a 3 kW Wind Energy Conversion System (WECS). The platform includes a wind turbine emulator (WTE) using a separately excited DC motor (SEDCM) as the prime mover, coupled with a grid-connected doubly-fed induction generator (DFIG). This setup enables comprehensive laboratory studies of a WECS without the need for large-scale field installations. A novel inertia compensation strategy is implemented to ensure the SEDCM accurately replicates the power and torque characteristics of a real wind turbine across various wind profiles. The DFIG was chosen for its high efficiency at variable wind speeds and its reduced power converter requirements compared to other generators. The control strategy for the DFIG is detailed, highlighting its performance and seamless integration within the system. Unlike most studies focusing on generators connected to simple loads, this research considers a grid-connected system, which introduces additional challenges and requirements. This study thoroughly investigates the grid-connected converter, addressing specific demands for grid connection and ensuring compliance with grid standards. Experimental results validate the effectiveness of the emulator, demonstrating its potential as a key tool for optimizing wind turbine control strategies and real-time algorithm validation, and enhancing the performance and reliability of renewable energy systems.

In this article, a frequency-adaptive neural network-based virtual flux (FANN-VF) estimator is developed for sensorless control of a pulsewidth modulation converter under unbalanced and distorted grid conditions. This method exploits two parallel FANNs configured as quadrature signal generators. The VF fundamental positive and negative sequence components` are inherently separated in the estimator without employing any cascaded filters. A frequency-locked loop is introduced to accurately track the frequency variations. The estimated VF PNSCs are directly exploited to compute the power references in VF-based predictive direct power control scheme. The developed strategy ensures constant active power and sinusoidal current waveforms under nonideal grid conditions. Simulation and experimental tests are performed to verify the effectiveness of the proposed method.

A new method for adaptive ac filter parameters identification of three phase pulse-width modulated rectifiers is presented in this paper. The proposed method is based on three adaptive linear neurons to identify online the ac filter resistances and inductances. The main advantage of this method is its simplicity and it requires low computational cost. The identification algorithm is implemented and three experimental tests have been realized in order to identify in real time the ac filter resistances and inductances. First test is performed in steady state operation. Two other tests are conducted to testing the track ability of the proposed method relative to parameters variations. Experimental results demonstrate that the proposed method provides accurate ac filter parameter values and tracks well the parameters variations. The obtained parameter values can be exploited for diagnosis purposes of ac filter status or in a control strategies.

The rapid evolution of modern power systems, driven by the large-scale integration of renewable energy sources and the emergence of smart grids, presents new challenges in maintaining grid stability, power quality, and control reliability. As critical interfacing elements, three-phase pulse width modulation (PWM) converters must now ensure resilient and efficient operation under increasingly adverse and dynamic grid conditions. This paper proposes an adaptive neural network-based virtual flux (VF) estimator for sensorless predictive direct power control (PDPC) of PWM converters under nonideal grid voltage conditions. The proposed estimator is realized using an adaptive linear neuron (ADALINE) configured as a quadrature signal generator, offering robustness against grid voltage disturbances such as voltage unbalance, DC offset and harmonic distortion. In parallel, a PDPC scheme based on the extended pq theory is developed to reject active-power oscillations and to maintain near-sinusoidal grid currents under unbalanced conditions. The resulting VF-based PDPC (VF-PDPC) strategy is validated via real-time simulations on the OPAL-RT platform. Comparative analysis confirms that the ADALINE-based estimator surpasses conventional VF estimation techniques. Moreover, the VF-PDPC achieves superior performance over conventional PDPC and extended pq theory-based PDPC strategies, both of which rely on physical voltage sensors, confirming its robustness and effectiveness under non-ideal grid conditions.