Abstract
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.
Highlights
Renewable energy technologies are being increasingly exploited worldwide
From the benefits of both advanced approaches, which are the Artificial Neural Networks (ANN) and Fuzzy Gain Scheduling (FGS), this study focuses on the power output improvement of the Tidal Stream Generator (TSG) system by implementing a hybrid neural fuzzy design
The configuration of the Doubly Fed Induction Generator (DFIG)-based Tidal Stream Turbine (TST) with the back-to-back power converters allows for decoupling the control for both Grid Side Converter (GSC) and Rotor Side Converter (RSC) components [44,45]
Summary
Renewable energy technologies are being increasingly exploited worldwide. Countries around the world are resorting to integrating renewable energy resources into their energy policy to reduce fossil fuel usage and carbon emissions [1,2,3,4]. Concerning the tidal stream converters, the swell effect is supposed to be the most disturbing phenomenon for the tidal current speed input [18] This fluctuation will affect the harnessed output power. From the benefits of both advanced approaches, which are the ANN and FGS, this study focuses on the power output improvement of the TSG system by implementing a hybrid neural fuzzy design. In the operation in variable speed, the FGS-PI-based control is applied to the RSC This enables the TSG to track the MPPT strategy. The MPPT approach uses a multilayer feed-forward ANN that enhances a fuzzy rotational speed controller The aim of this command is to control the TSG plant, which, at each tidal velocity, must follow the optimal rotational speed where the maximum generated power is satisfied.
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