Energetic Macroscopic Representation of a Marine Current Turbine System with Loss Minimization Control

This paper presents the modeling and energy management system (EMS) of a hybrid marine‐hydrogen power generation system. The proposed system aims to convert the static nature of the tidal energy into an active system by using a hydrogen energy storage system. The system of the tidal energy converter (TEC) considers the fixed pitch direct drive technology while the hydrogen system consists of 1.0 MW (megawatt) proton exchange membrane electrolyzer. A MATLAB/Simulink based model has been developed for studying and evaluating the effectiveness of the proposed EMS. The developed model depends on scaling up a 50 W proton exchange membrane (PEM) electrolyzer model to 1 MW scale by adapting the model parameters for providing the same key performance indicators (KPIs). The EMS aims to convert all the TEC generated energy into hydrogen with considering the efficient and safe operation of the different system components. Thus, the loss minimization (efficiency maximization) of the tidal turbine generator is integrated as one of the EMS goals to evaluate its effect on hydrogen production. The generator of the TEC is controlled by two different strategies for estimating the surplus hydrogen that could be produced. The strategies are the maximum torque/ampere and the loss minimization.

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“…, which is an off‐line reformulation of Eq. considering the different generator parameters, as listed in .

true {normali}_{normaldrefLM} = ω_{e}^{2} {normalL}_{normals} (R_{s} + R_{C}) λ_{r} false/ {normalR}_{normals} {normalR}_{normalC}^{2} + ω_{e}^{2} rm L_{s}^{2} (R_{s} + R_{c})

true {normali}_{normaldrefLM} = 0.3727 ω_{e}^{2} false/ 129.33 + 1.82 * 10^{- 4} ω_{e}^{2}

true {normali}_{normaldsrefLMP normal. normalU} = 0.049 ω_{normaleP normal. normalU}^{3} - 0.25 ω_{normaleP normal. normalU}^{2} + 0.0063 ω_{normaleP normal. normalU} - 0.00035

…”

Section: System Modelingmentioning

confidence: 99%

Energy Management of a Hybrid Tidal Turbine‐Hydrogen Micro‐Grid: Losses Minimization Strategy

et al. 2020

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“…One of the main targets of this work is to make the PVG operate at its operating point MPPT. To do so, it is necessary to refer to the MCS of the PVG side [28]. The voltage V is chosen as an output and the control chain consists of two cascade controllers.…”

Section: Backstepping Based Cascade Control Sizing Of the Pvgmentioning

confidence: 99%

Nonlinear Robust Backstepping Control for Three-Phase Grid-Connected PV Systems

Boujmil

Badis

Mansouri

2018

Mathematical Problems in Engineering

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This paper proposes a cascade control structure for three-phase grid-connected Photovoltaic (PV) systems. The PV system consists of a PV Generator, DC/DC converter, a DC link, a DC/AC fully controlled inverter, and the main grid. For the control process, a new control strategy using nonlinear Backstepping technique is developed. This strategy comprises three targets, namely, DC/DC converter control; tight control of the DC link voltage; and delivering the desired output power to the active grid with unity power factor (PF). Moreover, the control process relies mainly on the formulation of stability based on Lyapunov functions. Maximizing the energy reproduced from a solar power generation system is investigated as well by using the Perturb and Observe (P&O) algorithm. The Energetic Macroscopic Representation (EMR) and its reverse Maximum Control Structure (MCS) are used to provide, respectively, an instantaneous average model and a cascade control structure. The robust proposed control strategy adapts well to the cascade control technique. Simulations have been conducted using Matlab/Simulink software in order to illustrate the validity and robustness of the proposed technique under different operating conditions, namely, abrupt changing weather condition, sudden parametric variations, and voltage dips, and when facing measurement uncertainties. The problem of controlling the grid-connected PV system is addressed and dealt by using the nonlinear Backstepping control.

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“…In recent years, with the increasing energy consumption around the world and the sharp increase of environmental pollution, marine energy has attracted more and more attention around the world due to its high energy density, predictability and relative stability [1]- [3]. Marine current power generation is becoming more attractive and competitive [4], [5], and its technology has made great progress [6], as proved by the number of projects using marine current turbines (MCTs) [7]. It is foreseeable that MCTs will become more and more important in power supply systems [8].…”

Section: Introductionmentioning

confidence: 99%

A Wavelet Threshold Denoising-Based Imbalance Fault Detection Method for Marine Current Turbines

Wang²,

Wang³

et al. 2020

IEEE Access

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Blade imbalance fault caused by the marine organisms is considered as the most important fault in marine current turbines. Therefore, it is important to detect the fault accurately and quickly to mitigate its effect, minimize the downtime, and maximize the productivity. Imbalance fault detection methods using generator stator current signals have attracted attentions due to their low cost, operability and stability compared to the ones using vibration analysis. However, it is difficult to extract the fault signature and automatically detect the imbalance fault under different flow velocity conditions. In this paper, a wavelet threshold denoising-based imbalance fault detection method using the stator current is proposed. The signal is analyzed through three consecutive steps: the parameters offline setting based on wavelet threshold denoising, the Hilbert transform method and the Principle Component Analysis-based detection method. With this approach, the imbalance fault can be detected automatically. The imbalance fault detection is assessed under different flow velocity conditions and validated using an experimental platform. The results are promising with false alarm and false negative rates less than 1% and 5% respectively when using Q statistic. Moreover, the experimental results show that the proposed method has good stability under different flow velocity conditions. INDEX TERMS Hilbert transform, imbalance fault, marine current turbine, principle component analysis, wavelet.

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Energetic Macroscopic Representation of a Marine Current Turbine System with Loss Minimization Control

Cited by 25 publications

References 45 publications

Energy Management of a Hybrid Tidal Turbine‐Hydrogen Micro‐Grid: Losses Minimization Strategy

Energy Management of a Hybrid Tidal Turbine‐Hydrogen Micro‐Grid: Losses Minimization Strategy

Nonlinear Robust Backstepping Control for Three-Phase Grid-Connected PV Systems

A Wavelet Threshold Denoising-Based Imbalance Fault Detection Method for Marine Current Turbines

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