An enhanced DC-link voltage response for wind-driven doubly fed induction generator using adaptive fuzzy extended state observer and sliding mode control

This paper presents an enhancement method to improve the performance of the DC-link voltage loop regulation in a Doubly-Fed Induction Generator (DFIG)- based wind energy converter. An intelligent, combined control approach based on a metaheuristics-tuned Second-Order Sliding Mode (SOSM) controller and an adaptive fuzzy-scheduled Extended State Observer (ESO) is proposed and successfully applied. The proposed fuzzy gains-scheduling mechanism is performed to adaptively tune and update the bandwidth of the ESO while disturbances occur. Besides common time-domain performance indexes, bounded limitations on the effective parameters of the designed Super Twisting (STA)-based SOSM controllers are set thanks to the Lyapunov theory and used as nonlinear constraints for the formulated hard optimization control problem. A set of advanced metaheuristics, such as Thermal Exchange Optimization (TEO), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Harmony Search Algorithm (HSA), Water Cycle Algorithm (WCA), and Grasshopper Optimization Algorithm (GOA), is considered to solve the constrained optimization problem. Demonstrative simulation results are carried out to show the superiority and effectiveness of the proposed control scheme in terms of grid disturbances rejection, closed-loop tracking performance, and robustness against the chattering phenomenon. Several comparisons to our related works, i.e., approaches based on TEO-tuned PI controller, TEO-tuned STA-SOSM controller, and STA-SOSM controller-based linear observer, are presented and discussed

Complementary Power Control for Doubly Fed Induction Generator-Based Tidal Stream Turbine Generation Plants

The latest forecasts on the upcoming effects of climate change are leading to a change in the worldwide power production model, with governments promoting clean and renewable energies, as is the case of tidal energy. Nevertheless, it is still necessary to improve the efficiency and lower the costs of the involved processes in order to achieve a Levelized Cost of Energy (LCoE) that allows these devices to be commercially competitive. In this context, this paper presents a novel complementary control strategy aimed to maximize the output power of a Tidal Stream Turbine (TST) composed of a hydrodynamic turbine, a Doubly-Fed Induction Generator (DFIG) and a back-to-back power converter. In particular, a global control scheme that supervises the switching between the two operation modes is developed and implemented. When the tidal speed is low enough, the plant operates in variable speed mode, where the system is regulated so that the turbo-generator module works in maximum power extraction mode for each given tidal velocity. For this purpose, the proposed back-to-back converter makes use of the field-oriented control in both the rotor side and grid side converters, so that a maximum power point tracking-based rotational speed control is applied in the Rotor Side Converter (RSC) to obtain the maximum power output. Analogously, when the system operates in power limitation mode, a pitch angle control is used to limit the power captured in the case of high tidal speeds. Both control schemes are then coordinated within a novel complementary control strategy. The results show an excellent performance of the system, affording maximum power extraction regardless of the tidal stream input.This work was supported in part by the University of the Basque Country (Universidad del Pais Vasco UPV/ Euskal Herriko Unibertsitatea EHU) through Project PPG17/33 and by the MINECO through the Research Project DPI2015-70075-R (MINECO/FEDER, EU). (Ministerio de Economa, Industria y Competitividad/Fondo Europeo de Desarrollo Regional, European Union). The authors would like also to thank the anonymous reviewers for the useful comments that have helped to improve the initial version of this manuscript

Hybrid Neural Fuzzy Design-Based Rotational Speed Control of a Tidal Stream Generator Plant

Artificial Intelligence techniques have shown outstanding results for solving many tasks in a wide variety of research areas. Its excellent capabilities for the purpose of robust pattern recognition which make them suitable for many complex renewable energy systems. In this context, the Simulation of Tidal Turbine in a Digital Environment seeks to make the tidal turbines competitive by driving up the extracted power associated with an adequate control. An increment in power extraction can only be archived by improved understanding of the behaviors of key components of the turbine power-train (blades, pitch-control, bearings, seals, gearboxes, generators and power-electronics). Whilst many of these components are used in wind turbines, the loading regime for a tidal turbine is quite different. This article presents a novel hybrid Neural Fuzzy design to control turbine power-trains with the objective of accurately deriving and improving the generated power. In addition, the proposed control scheme constitutes a basis for optimizing the turbine control approaches to maximize the output power production. Two study cases based on two realistic tidal sites are presented to test these control strategies. The simulation results prove the effectiveness of the investigated schemes, which present an improved power extraction capability and an effective reference tracking against disturbance.This work was supported by the MINECO through the Research Project DPI2015-70075-R (MINECO/FEDER, UE). The authors would like to thank the collaboration of the Basque Energy Agency (EVE) through Agreement UPV/EHUEVE23/6/2011, the Spanish National Fusion Laboratory (EURATOM-CIEMAT) through Agreement UPV/EHUCIEMAT08/190 and EUSKAMPUS-Campus of International Excellence

Fuzzy Supervision Based-Pitch Angle Control of a Tidal Stream Generator for a Disturbed Tidal Input

Energy originating in tidal and ocean currents appears to be more intense and predictable than other renewables. In this area of research, the Tidal Stream Generator (TSG) power plant is one of the most recent forms of renewable energy to be developed. The main feature of this energy converter is related to the input resource which is the tidal current speed. Since its behaviour is variable and with disturbances, these systems must be able to maintain performance despite the input variations. This article deals with the design and control of a tidal stream converter system. The Fuzzy Gain Scheduling (FGS) technique is used to control the blade pitch angle of the turbine, in order to protect the plant in the case of a strong tidal range. Rotational speed control is investigated by means of the back-to-back power converters. The optimal speed is provided using the Maximum Power Point Tracking (MPPT) strategy to harness maximum power from the tidal speed. To verify the robustness of the developed methods, two scenarios of a disturbed tidal resource with regular and irregular conditions are considered. The performed results prove the output power optimization and adaptive change of the pitch angle control to maintain the plant within the tolerable limits.This work was supported by the MINECO through the Research Project DPI2015-70075-R (MINECO/FEDER, UE) and in part by the University of the Basque Country (UPV/EHU) through PPG17/33. The authors would like to thank the collaboration of the Basque Energy Agency (EVE) through Agreement UPV/EHUEVE23/6/2011, the Spanish National Fusion Laboratory (EURATOM-CIEMAT) through Agreement UPV/EHUCIEMAT08/190 and EUSKAMPUS - Campus of international Excellence

Modeling and Performance Improvement of Direct Power Control of Doubly-Fed Induction Generator Based Wind Turbine through Second-Order Sliding Mode Control Approach

A second-order sliding mode (SOSM)-based direct power control (DPC) of a doubly-fed induction generator (DFIG) is introduced in this research paper. Firstly, the DFIG output powers are regulated with the developed SOSM controller-based DPC scheme. The Super Twisting Algorithm (STA) has been used to reduce the chattering phenomenon. The proposed strategy is a combination of the Lyapunov theory and metaheuristics algorithms, which has been considered to identify the optimal gains of the STA-SOSM controllers. The Lyapunov function method is employed to define the stability regions of the controller parameters. On the other hand, the metaheuristics algorithms are mainly employed to select the fine controllers’ parameters from the predefined ranges. A Thermal Exchange Optimization (TEO) method is used to compute the optimal gain parameters. To prove the superiority of the proposed TEO, its obtained results have been compared with those obtained by other algorithms, including particle swarm optimization, genetic algorithm, water cycle algorithm, grasshopper optimization algorithm and harmony search algorithm. Moreover, the results of the introduced TEO-based SOSM controller have been also compared with the Proportional-Integral (PI)-based vector control and the conventional sliding mode control-based DPC. Moreover, an empirical comparison is carried out to investigate the indication of every metaheuristics method by employing Friedman’s rank and Bonferroni tests. The main findings indicate the effectiveness of STA-SOSM control for system stability and power quality improvement. The ripples in the active and reactive powers are minimized and the harmonics’ distortions of stator and rotor currents are improved. Besides, the STA-SOSM controller shows a superior performance of control in terms of chattering phenomenon elimination

Advanced Metaheuristics-based Tuning of Effective Design Parameters for Model Predictive Control

International audienceThis paper presents a systematic tuning approach for Model Predictive Control (MPC) parameters' using an original LabVIEW-implementation of advanced metaheuristics algorithms. Perturbed Particle Swarm Optimization (pPSO), Gravitational Search Algorithm (GSA), Teaching-Learning Based Optimization (TLBO) and Grey Wolf Optimizer (GWO) metaheuristics are proposed to solve the formulated MPC tuning problem under operational constraints. The MPC tuning strategy is done offline for the selection of both prediction and control horizons as well as the weightings matrices. All proposed algorithms are firstly evaluated and validated on a benchmark of standard test functions. The same algorithms were then used to solve the formulated MPC tuning problem for two dynamical systems such as the magnetic levitation system MAGLEV 33-006, and the three-tank DTS200 process. Demonstrative results, in terms of statistical metrics and closed-loop systems responses, are presented and discussed in order to show the effectiveness and superiority of the proposed metaheuristics-tuned approach. The developed CAD interface for the LabVIEW implementation of the proposed metaheuristics is given and freely accessible for extended optimization puposes

LabVIEW Perturbed Particle Swarm Optimization Based Approach for Model Predictive Control Tuning

International audienceIn this paper, a new Model Predictive Controller (MPC) parameters tuning strategy is proposed using a LabVIEW-based perturbed Particle Swarm Algorithm (pPSA). This original LabVIEW implementation of this metaheuristic algorithm is rstly validated on some test functions in order to show its efficiency and validity. The optimization results are compared with the standard PSO approach. The parameters tuning problem, i.e. the weighting factors on the output error and input increments of the MPC algorithm, is then formulated and systematically solved, using the proposed LabVIEW pPSA algorithm. The case of a Magnetic Levitation (MAGLEV) system is investigated to illustrate the robustness and superiority of the proposed pPSA-based tuning MPC approach. All obtained simulation results, as well as the statistical analysis tests for the formulated control problem with and without constraints, are discussed and compared with the Genetic Algorithm Optimization (GAO)-based technique in order to improve the effectiveness of the proposed pPSA-based MPC tuning methodology

Rapid Model Predictive Control Prototyping with LabVIEW/CDSim and CompactRIO Target

International audienceIn this paper, an advanced Computer Aided Design (CAD) methodology for the Process-In-the-Loop (PIL) co-simulation and rapid prototyping of model predictive controllers is proposed and successfully implemented using the NI CompactRIO-9082 RT target and a host PC. The developed software (SW) and hardware (HW) co-design platform is based on the Control Design and Simulation (CDSim) module of LabVIEW environment and the Network Streams data communication protocol. The designed LabVIEW-based MPC algorithm as well as the dynamic model of the controlled plant are implemented as VI under the cRIO-9082 target and the host PC, respectively. This hardware model will be deployed on the CompactRIO Real-Time (RT) target within a PIL co-simulation framework. The proposed NI CompactRIO-9082 based CAD approach for prototyping and implementation of MPC algorithms is applied to the position control of a Magnetic Levitation system (MAGLEV). All obtained SW/HW co-simulation results are compared and discussed in order to improve the effectiveness of the proposed MPC co-design methodology