Contribution of DFIG wind turbines to Primary Frequency Control

In recent years, worldwide power systems are experiencing a steadily growth in wind power penetration. A common concern in the operation of such systems is related to the frequency stability. Modern variable speed wind turbines have a limited capacity in providing ancillary services, such as: fastfrequency response and primary frequency regulation. This thesis aims at developing a new methodology for the analysis of frequency dynamics in largescale power systems with high level of wind energy share. Firstly, a simplified electromechanical model of a doubly fed induction generator (DFIG) based wind turbine has been proposed. In addition, a virtual inertia controller version of the optimized power point tracking method (OPPT) has been adapted for this kind of wind turbines. In this method, the maximum power point tracking curve (MPPT) is shifted to drive variations in the active power injection as a function of the grid frequency deviation, by exploiting the available inertial resources. The proposed methodology integrates the model in a primary frequency control scheme to assess the interaction with the rest of the plants in the power system

A Practical Model and an Optimal Controller for Variable Speed Wind Turbine Permanent Magnet Synchronous Generator

The aim of this paper is the complete modeling and simulation of an optimal control system using practical setup parameters for a wind energy conversion system (WECS) through a direct driven permanent magnet synchronous generator (D-PMSG) feeding ac power to the utility grid. The generator is connected to the grid through a back-to-back PWM converter with a switching frequency of 10 KHz. A maximum power point tracking (MPPT) control is proposed to ensure the maximum power capture from wind turbine, and a PI controller designed for the wind turbine to generate optimum speed for the generator via an aerodynamic model. MATLAB/Simulink results demonstrate the accuracy of the developed control scheme

Sensorless control of a power converter for a cluster of small wind turbines

This study presents a new sensorless control for small wind turbine clusters with a single power converter with a direct torque control algorithm. The proposed system consists of a wind farm connected to a back-to-back power converter that interfaces the wind farm with the AC grid. The studied wind turbines are based on fixed-speed wind turbines equipped with squirrel cage induction generators with individual pitch control. The presented structure permits to reduce the number of converters and allows to accomplish the grid codes (fault ride through capability and reactive power support). Furthermore, the generated active power can be reduced according to grid operator requirements. The presented control scheme can be applied to wind turbine repowering projects, wind farms connected to a microgrid, even, new small onshore and offshore power plants. The system performance and stability is studied and validated by means of dynamic simulations.Postprint (author's final draft

A Review of Control Techniques for Wind Energy Conversion System

Wind energy is the most efficient and advanced form of renewable energy (RE) in recent decades, and an effective controller is required to regulate the power generated by wind energy. This study provides an overview of state-of-the-art control strategies for wind energy conversion systems (WECS). Studies on the pitch angle controller, the maximum power point tracking (MPPT) controller, the machine side controller (MSC), and the grid side controller (GSC) are reviewed and discussed. Related works are analyzed, including evolution, software used, input and output parameters, specifications, merits, and limitations of different control techniques. The analysis shows that better performance can be obtained by the adaptive and soft-computing based pitch angle controller and MPPT controller, the field-oriented control for MSC, and the voltage-oriented control for GSC. This study provides an appropriate benchmark for further wind energy research

Photovoltaic sample-and-hold circuit enabling MPPT indoors for low-power systems

Photovoltaic (PV) energy harvesting is commonly used to power autonomous devices, and maximum power point tracking (MPPT) is often used to optimize its efficiency. This paper describes an ultra low-power MPPT circuit with a novel sample-and-hold and cold-start arrangement, enabling MPPT across the range of light intensities found indoors, which has not been reported before. The circuit has been validated in practice and found to cold-start and operate from 100 lux (typical of dim indoor lighting) up to 5000 lux with a 55cm2 amorphous silicon PV module. It is more efficient than non-MPPT circuits, which are the state-of-the-art for indoor PV systems. The proposed circuit maximizes the active time of the PV module by carrying out samples only once per minute. The MPPT control arrangement draws a quiescent current draw of only 8uA, and does not require an additional light sensor as has been required by previously-reported low-power MPPT circuits

L1 Adaptive Speed Control of a Small Wind Energy Conversion System for Maximum Power Point Tracking

This paper presents the design of an L1 adaptive controller for maximum power point tracking (MPPT) of a small variable speed wind energy conversion system (WECS). The proposed controller generates the optimal torque command for the vector controlled generator-side converter based on the wind speed estimation. The proposed MPPT control algorithm has a generic structure and can be used for different generator types. In order to verify the efficacy of the proposed L1 adaptive controller for the MPPT of the WECS, a full converter wind turbine with a squirrel cage induction generator is used to carry out case studies using MATLAB/Simulink. The case study results show that the designed L1 adaptive controller has good tracking performance even with unmodelled dynamics and in the presence of parameter uncertainties and unknown disturbances

Hybrid MPPT Control: P&O and Neural Network for Wind Energy Conversion System

In the field of wind turbine performance optimization, many techniques are employed to track the maximum power point (MPPT), one of the most commonly used MPPT algorithms is the perturb and observe technique (PO) because of its ease of implementation. However, the main disadvantage of this method is the lack of accuracy due to fluctuations around the maximum power point. In contrast, MPPT control employing neural networks proved to be an effective solution, in terms of accuracy. The contribution of this work is to propose a hybrid maximum power point tracking control using two types of MPPT control: neural network control (NNC) and the perturbation and observe method (PO), thus the PO method can offer better performance. Furthermore, this study aims to provide a comparison of the hybrid method with each algorithm and NNC. At the resulting duty cycle of the 2 methods, we applied the combination operation. A DC-DC boost converter is subjected to the hybrid MPPT control.  This converter is part of a wind energy conversion system employing a permanent magnet synchronous generator (PMSG). The chain is modeled using MATLAB/Simulink software. The effectiveness of the controller is tested at varying wind speeds. In terms of the Integral time absolute error (ITAE), using the PO technique, the ITAE is 9.72. But, if we apply the suggested technique, it is smaller at 4.55. The corresponding simulation results show that the proposed hybrid method performs best compared to the PO method. Simulation results ensure the performance of the proposed hybrid MPPT control.