Enhanced Control and Power Management for a Renewable Energy-Based Water Pumping System

Mossa, Mahmoud A.; Gam, Olfa; Quynh, Nguyen Vu

doi:10.1109/access.2022.3163530

Cited by 16 publications

(10 citation statements)

References 58 publications

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“…This component is connected to the generator via a gearbox, which amplifies the rotational speed from the wind turbine to the electric generator [43][44][45][46][47][48][49][50][51]. To transmit the generator's output to the electrical grid, a control system is utilized [52,53]. Eq (1) represents the mechanical power P m generated by the wind turbine [54]:…”

Section: Wind Turbine (Hawt) Modellingmentioning

confidence: 99%

“…Research in neural computing has extensively explored the application of Artificial Neural Networks (ANN) as a data processing technique [ 53 ]. This method extends the capabilities of traditional nonlinear automation techniques, providing enhanced effectiveness and reliability in various applications, including identification, control, and filtering.…”

Section: System Controlmentioning

confidence: 99%

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Enhancing the control of doubly fed induction generators using artificial neural networks in the presence of real wind profiles

Dardabi,

Djebli,

Chojaa

et al. 2024

PLoS ONE

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This study tackles the complex task of integrating wind energy systems into the electric grid, facing challenges such as power oscillations and unreliable energy generation due to fluctuating wind speeds. Focused on wind energy conversion systems, particularly those utilizing double-fed induction generators (DFIGs), the research introduces a novel approach to enhance Direct Power Control (DPC) effectiveness. Traditional DPC, while simple, encounters issues like torque ripples and reduced power quality due to a hysteresis controller. In response, the study proposes an innovative DPC method for DFIGs using artificial neural networks (ANNs). Experimental verification shows ANNs effectively addressing issues with the hysteresis controller and switching table. Additionally, the study addresses wind speed variability by employing an artificial neural network to directly control reactive and active power of DFIG, aiming to minimize challenges with varying wind speeds. Results highlight the effectiveness and reliability of the developed intelligent strategy, outperforming traditional methods by reducing current harmonics and improving dynamic response. This research contributes valuable insights into enhancing the performance and reliability of renewable energy systems, advancing solutions for wind energy integration complexities.

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Section: Wind Turbine (Hawt) Modellingmentioning

confidence: 99%

Section: System Controlmentioning

confidence: 99%

Enhancing the control of doubly fed induction generators using artificial neural networks in the presence of real wind profiles

Dardabi,

Djebli,

Chojaa

et al. 2024

PLoS ONE

Self Cite

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“…The electricity generation can be performed through either conventional energy resources or renewable energy resources. As the non-renewable energy sources began to decrease day by day remarkably, the current trend became towards the renewable energy sources, such as solar, wind, wave and geothermal energies [1][2][3][4].There are different types of electric generators that are utilized to generate electricity through renewable energy resources, like synchronous generator (SG), self-excited induction generator (SEIG), and doubly fed induction generator (DFIG) [5][6][7][8][9][10]. The DFIG has been frequently utilized with wind turbines over recent years and is still in use, this overall system is called as wind energy conversion system (WECS).…”

Section: Introductionmentioning

confidence: 99%

Optimizing the Dynamic Performance of a Wind Driven Standalone DFIG Using an Advanced Control Algorithm

Abdelhamid

Mossa

Hassan

2022

Journal of Robotics and Control

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The article seeks to improve the dynamic performance of a standalone doubly fed induction generator (DFIG) which driven by a wind turbine, with the help of an effective control approach. The superiority of the designed predictive controller can be confirmed through evaluating the performance of the DFIG under other control algorithm, which is the model predictive direct torque control (MPDTC), model predictive current control (MPCC) as classic types of control. Firstly, the operating principles of the two controllers are described in details. After that, a comprehensive comparison is performed among the dynamic performances of the designed MPDTC, MPCC techniques and the predictive control strategy, so we can easily present the merits and deficiencies of each control scheme to be able to easily select the most appropriate algorithm to be utilized with the DFIG. The comparison is carried out in terms of system simplicity, dynamic response, ripples’ content, number of performed commutations and total harmonic distortion (THD). The results of the comparison prove the effectiveness and validation of our proposed predictive controller; as it achieves the system simplicity, its dynamic response is faster than that of MPDTC and MPCC, it presents a lower content of ripples compared to MPDTC and MPCC. Moreover, it can minimize the computational burden, remarkably. Furthermore, the numerical results are showing a marked reduction in the THD with a percentage of 2.23 % compared to MPDTC and 1.8 % compared to MPCC. For these reasons, it can be said that the formulated controller is the most convenient to be used with the DFIG to achieve the best dynamic performance.

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“…For the wind turbine, a control strategy that implements the maximum power point tracking (MPPT) and pitch angle control must be involved to prevent optimally exploiting the wind energy, and to prevent the operation over the specified nominal wind speeds [24,25]. On the other side, for the generator unit, different schemes were used to manage its dynamics [26][27][28][29][30][31][32][33][34][35][36]. In [26,27], the vector control principle was used in which a good steady state dynamic was obtained but the system response delay was the most noticeable drawback.…”

Section: Introductionmentioning

confidence: 99%

“…In [26,27], the vector control principle was used in which a good steady state dynamic was obtained but the system response delay was the most noticeable drawback. To avoid vector control deficiencies, the direct torque control (DTC) and direct power control (DPC) controls were adopted in [28][29][30][31][32]. In these schemes, the internal current control loops were replaced with the hysteresis comparators in addition to using a look-up table.…”

Section: Introductionmentioning

confidence: 99%

Performance dynamics improvement of a hybrid wind/fuel cell/battery system for standalone operation

Mossa

Echeikh

Quynh

2022

IET Renewable Power Gen

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The present study is concerned with improving the dynamics of a hybrid generation system utilized for feeding an isolated load. The system under study consists of a wind-driven synchronous generator with permanent magnet type, a fuel cell stack and a storage battery layout used to enhance the system reliability. A detailed design for all system parts is introduced. A new formulated predictive controller is utilized to enhance the performance of synchronous generator in comparison with traditional controllers. The wind turbine power system is designed and adopted a maximum power point tracking (MPPT) strategy to optimally exploit the captured wind energy. An energy management procedure is also considered to balance the power-sharing between different system units. Extensive performance evaluation analysis is introduced in order to validate the capability of the designed controllers of the generator and fuel cell and check the feasibility of the energy management strategy (EMS) as well. The obtained results approve the capability of the proposed controller with the synchronous generator in achieving better dynamics compared with traditional schemes and confirm the validity of the fuel cell control system in managing the stack power. The results also approve the effectiveness of the designed EMS in preserving a balanced power flow.

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Enhanced Control and Power Management for a Renewable Energy-Based Water Pumping System

Cited by 16 publications

References 58 publications

Enhancing the control of doubly fed induction generators using artificial neural networks in the presence of real wind profiles

Enhancing the control of doubly fed induction generators using artificial neural networks in the presence of real wind profiles

Optimizing the Dynamic Performance of a Wind Driven Standalone DFIG Using an Advanced Control Algorithm

Performance dynamics improvement of a hybrid wind/fuel cell/battery system for standalone operation

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