A multi-objective model predictive current control with two-step horizon for double-stage grid-connected inverter PEMFC system

Kanouni, Badreddine; Badoud, Abd Essalam; Mekhilef, Saad

doi:10.1016/j.ijhydene.2021.10.182

Cited by 23 publications

(4 citation statements)

References 26 publications

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“…The construction of accurate models for PEMFCs has become a pivotal point for researchers [18] who seek to establish reliable models using software programs that closely correlate with experimental results [19]. The significance of PEMFC modeling lies in its impact on analyses, particularly in microgrids and smart grid applications [20].…”

Section: A Problem Under Studymentioning

confidence: 99%

Parameters Estimation of Proton Exchange Membrane Fuel Cell Model Based on an Improved Walrus Optimization Algorithm

Alqahtani,

Hasanien,

Alharbi

et al. 2024

IEEE Access

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Proton Exchange Membrane Fuel Cells (PEMFCs) play a crucial role in the advancement of clean hydrogen vehicles. Their ability to convert hydrogen into electricity makes them promising candidates to replace conventional engines. However, optimizing their performance and efficiency necessitates accurate modeling techniques capable of simulating their behavior. In this context, this paper proposes an advanced approach for precise parameter estimation in PEMFC models. Employing an Enhanced Walrus Optimization (EWO) algorithm integrated with Lévy flight exploration, the approach tackles the inherent nonlinearity of PEMFC systems. The technique aims to minimize the squared error between measured and simulated terminal voltage, thereby ensuring superior accuracy and robustness compared to established algorithms. The effectiveness of the proposed model is validated through comparisons between theoretical simulations and experimental measurements. The findings demonstrate the efficacy of the EWO algorithm, consistently outperforming previously published algorithms and achieving notably lower errors. Moreover, the incorporation of Lévy flights enhances the algorithm's capabilities, leading to expedited convergence and more accurate parameter estimations. Beyond facilitating precise parameter estimation, this enhanced modeling strategy opens avenues for refining design and optimization strategies in fuel cell research and development. The major contributions of this paper include the enhancement of the WO algorithm, evaluation of theoretical model accuracy, and robustness assessment of the EWO in optimizing the PEMFC model. By furnishing accurate models validated through experimental evidence, this enhanced modeling strategy paves the way for refining design and optimization strategies in fuel cell research and development.

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Section: A Problem Under Studymentioning

confidence: 99%

Parameters Estimation of Proton Exchange Membrane Fuel Cell Model Based on an Improved Walrus Optimization Algorithm

Alqahtani,

Hasanien,

Alharbi

et al. 2024

IEEE Access

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“…The nonlinear MPC-L 1 optimisation task is formulated with the use of the general form of the optimisation task in (18) and the MPC-L 1 cost function (27). It takes the following form min…”

Section: Computationally Efficient Nonlinear Mpc Using the L 1 Cost-f...mentioning

confidence: 99%

“…Hierarchical techniques can also be applied to develop a comprehensive energy management strategy consisting of high-level control, which is based on fuzzy logic technique as well as adaptive control loop and low-level control that generates a pulse-width-modulation signal so that appropriate power distribution is achieved and the operating voltage of the powertrain is stabilised [15]. In recent years, applications of MPC algorithms [16][17][18] to PEM fuel cells have been reported. A linear model can be used for MPC prediction [19].…”

Section: Introductionmentioning

confidence: 99%

Fast Model Predictive Control of PEM Fuel Cell System Using the L1 Norm

Nebeluk

Ławryńczuk

2022

Energies

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This work describes the development of a fast Model Predictive Control (MPC) algorithm for a Proton Exchange Membrane (PEM) fuel cell. The MPC cost-function used considers the sum of absolute values of predicted control errors (the L1 norm). Unlike previous approaches to nonlinear MPC-L1, in which quite complicated neural approximators have been used, two analytical approximators of the absolute value function are utilised. An advanced trajectory linearisation is performed on-line. As a result, an easy-to-solve quadratic optimisation task is derived. All implementation details of the discussed algorithm are detailed for two considered approximators. Furthermore, the algorithm is thoroughly compared with the classical MPC-L2 method in which the sum of squared predicted control errors is minimised. A multi-criteria control quality assessment is performed as the MPC-L1 and MPC-L2 algorithms are compared using four control quality indicators. It is shown that the presented MPC-L1 scheme gives better results for the PEM.

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“…Using a mathematical model of the inverter system, the MPC algorithm predicts how the output voltage and current will behave in the future. It determines the most effective control inputs that minimize a given cost function within a constrained amount of time using these forecasts 10 , 11 . In (MPC), the cost function typically includes penalties for deviations from the predefined set point and includes limitations on the inverter's functioning, like voltage and maximum current limits.…”

Section: Introductionmentioning

confidence: 99%

A reduced vector model predictive controller for a three-level neutral point clamped inverter with common-mode voltage suppression

Bebboukha,

Chouaib,

Meneceur

et al. 2024

Sci Rep

Self Cite

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This paper presents a novel, state-of-the-art predictive control architecture that addresses the computational complexity and limitations of conventional predictive control methodologies while enhancing the performance efficacy of predictive control techniques applied to three-level voltage source converters (NPC inverters). This framework's main goal is to decrease the number of filtered voltage lifespan vectors in each sector, which will increase the overall efficiency of the control system and allow for common mode voltage reduction in three-level voltage source converters. Two particular tactics are described in order to accomplish this. First, a statistical approach is presented for the proactive detection of potential voltage vectors, with an emphasis on selecting and including the vectors that are most frequently used. This method lowers the computational load by limiting the search space needed to find the best voltage vectors. Then, using statistical analysis, a plan is presented to split the sectors into two separate parts, so greatly limiting the number of voltage vectors. The goal of this improved predictive control methodology is to reduce computing demands and mitigate common mode voltage. The suggested strategy's resilience is confirmed in a range of operational scenarios using simulations and empirical evaluation. The findings indicate a pronounced enhancement in computational efficiency and a notable diminution in common mode voltage, thereby underscoring the efficacy of the proposed methodology. This increases their ability to incorporate renewable energy sources into the electrical grid.

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A multi-objective model predictive current control with two-step horizon for double-stage grid-connected inverter PEMFC system

Cited by 23 publications

References 26 publications

Parameters Estimation of Proton Exchange Membrane Fuel Cell Model Based on an Improved Walrus Optimization Algorithm

Parameters Estimation of Proton Exchange Membrane Fuel Cell Model Based on an Improved Walrus Optimization Algorithm

Fast Model Predictive Control of PEM Fuel Cell System Using the L1 Norm

A reduced vector model predictive controller for a three-level neutral point clamped inverter with common-mode voltage suppression

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