Model-Free Predictive Control of Grid-Forming Inverters With $LCL$ Filters

Heydari, Rasool; Young, Héctor; Flores‐Bahamonde, Freddy; Vaez‐Zadeh, Sadegh; González-Castaño, Catalina; Sabzevari, Sanaz; Rodríguez, José

doi:10.1109/tpel.2022.3159730

Cited by 43 publications

(13 citation statements)

References 27 publications

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“…Model predictive control has been extensively used to control this converter. To reduce the dependency of the model predictive control on the model parameters, a model-free predictive control [34] has been proposed for voltage regulation of a three-phase inverter with an LCL filter. The proposed approach uses a two-stage model structure using auto-regression with exogenous input.…”

Section: Application Of Mfpc In Microgridsmentioning

confidence: 99%

“…The results have shown that the proposed approach significantly improved the performance of the inverter in response to variation in the input values. Figures 25-28 were taken from [34], which show the performance of model-based and model-free predictive control for the change in values of inductance and capacitance. Figure 25 shows that model-based predictive control fails to regulate the current for a change in the capacitance.…”

Section: Application Of Mfpc In Microgridsmentioning

confidence: 99%

“…Model-free predictive control for frequency synchronization of a microgrid [32] Model-free predictive control for fast frequency synchronization of a microgrid using a battery energy storage system [33] Model-free predictive control of grid-forming inverters with an LCL filter [34] Model-free neural-network-based predictive controller for frequency and voltage regulation of synchronverters [35]…”

Section: Mfpc For Microgrids Referencementioning

confidence: 99%

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Model-Free Predictive Control and Its Applications

2022

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Predictive control offers many advantages such as simple design and a systematic way to handle constraints. Model predictive control (MPC) belongs to predictive control, which uses a model of the system for predictions used in predictive control. A major drawback of MPC is the dependence of its performance on the model of the system. Any discrepancy between the system model and actual plant behavior will greatly affect the performance of the MPC. Recently, model-free approaches have been gaining attention because they are not dependent on the system model parameters. To obtain the advantages of both a model-free approach and predictive control, model-free predictive control (MFPC) is being explored and reported in the literature for different applications such as power electronics and electric drives. This paper presents an overview of model-free predictive control. A comprehensive review of the application of MFPC in power converters, electric drives, power systems, and microgrids is presented in this paper. Moreover, challenges, opportunities, and emerging trends in MFPC are also discussed in this paper.

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Section: Application Of Mfpc In Microgridsmentioning

confidence: 99%

Section: Application Of Mfpc In Microgridsmentioning

confidence: 99%

Section: Mfpc For Microgrids Referencementioning

confidence: 99%

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Model-Free Predictive Control and Its Applications

2022

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“…where i opt is the same i that minimises the cost function Equation (18) in the first stage, and n = 0, 1, …, 6. Note that while the existing methods in the literature utilise lookup tables and extra information to categorise VVs, the proposed method simply uses the above equation to select the VVs.…”

Section: Proposed Vector Selection Techniquementioning

confidence: 99%

“…To improve the robustness of the MPC, several methods have been proposed in the literature, such as disturbance rejection techniques [14,15], adaptive control systems [16] etc. On this subject, the most recent research trend is model-free predictive control (MFPC) methods [17,18]. In the MFPC, the conventional model of the plant is not utilised to attain the predictions of the control variables.…”

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confidence: 99%

Sampling error‐based model‐free predictive current control of open‐end winding induction motor with simplified vector selection

Mousavi

Davari

Flores‐Bahamonde

et al. 2022

IET Electric Power Appl

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A sampling error‐based finite‐set predictive current control (FS‐PCC) is proposed in this article for the open‐end winding induction motor (OEWIM) drive. The proposed scheme controls the zero‐sequence current (ZSC) alongside the stator currents. In a model‐free approach, this method predicts the future of ZSC and stator current components by the stator current and voltage sampling errors. In this way, the parameters of the OEWIM are not utilised in the prediction algorithm of the FS‐PCC. So, the proposed method is robust against the variation of the parameter. Moreover, this article presents a simple vector selection technique for the FS‐PCC of the OEWIM. The proposed technique has two cost functions and a simple algebraic equation to put the voltage vectors (VVs) in the prediction algorithm. The first cost function uses VVs that do not have the zero‐sequence voltage component. Then, the algebraic equation determines VVs that must be utilised in the second cost function. Finally, the optimum VV is selected by the second cost function. In the proposed scheme, the prediction algorithm is iterated 14 times instead of 27 iterations of the conventional predictive algorithm. So, besides establishing a novel model‐free prediction algorithm, the proposed method has almost 50% fewer calculations. The validity of the proposed sampling error‐based FS‐PCC and the simplified vector selection technique has been verified through experimental tests.

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