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Scientific Reports volume 14, Article number: 19832 (2024 ) Cite this article capacitors in series current
This research introduces an advanced finite control set model predictive current control (FCS-MPCC) specifically tailored for three-phase grid-connected inverters, with a primary focus on the suppression of common mode voltage (CMV). CMV is known for causing a range of issues, including leakage currents, electromagnetic interference (EMI), and accelerated system degradation. The proposed control strategy employs a system model that predicts the inverter’s future states, enabling the selection of optimal switching states from a finite set to achieve dual objectives: precise current control and effective CMV reduction, a meticulously designed cost function evaluates the potential switching states, balancing the accuracy of current tracking against the necessity to minimize CMV. The approach is grounded in a comprehensive mathematical model that captures the dynamics of CMV within the system, and it utilizes an optimization process that functions in real-time to determine the most suitable control action at each interval, Experimental validations of the proposed FCS-MPCC scheme have demonstrated its effectiveness in significantly improving the performance and durability of three-phase grid-connected inverters, Experimental validations of the proposed (MPC with CMV) scheme have demonstrated its effectiveness in significantly improving the performance and durability of three-phase grid-connected inverters. The proposed method achieved substantial reductions in CMV, notable improvements in current tracking accuracy, and extended system lifespan compared to conventional control methods.
Solar energy has emerged as a preeminent renewable energy solution to mitigate the energy crisis and environmental pollution1. This is attributed to its notable advancements, driven by heightened global consciousness regarding climate change and reduced reliance on fossil fuels. Governments across the globe have enacted incentivization measures to promote the utilization of sustainable energy sources, resulting in the widespread adoption of grid-connected photovoltaic (PV) systems2,3. These PV systems facilitate the seamless integration of solar power into the prevailing electrical grid infrastructure, thereby furnishing a dependable and environmentally benign electricity generation paradigm4.
Research on grid-connected inverter control technologies has become increasingly important. Conventional control techniques encompass linear approaches, like PI-based feed forward decoupling control5. However; Steady-state current errors arise on the alternating current (AC) side due to the PI controller’s exposure to low-frequency fundamental components. In addition to employing for eradicating steady-state errors, several alternative approaches, including hysteresis control, have been suggested to diminish current errors6,7.
The inverter establishes the connection between the photovoltaic (PV) system and the electrical grid and holds paramount significance. These inverters incorporate transformers to regulate the direct current (DC) voltage supplied to the inverter and to provide isolation between the PV system and the grid8,9. An advanced adaptive control method for a distributed generation system that uses a 3-phase inverter. The design of this control method is complex and time-consuming, as it involves adjusting the Ts stands for the control period parameter. Additionally, the parameter tuning process needs to account for various operating conditions. To improve system performance and responsiveness, the proposed method incorporates predictive control techniques, such as multi-step testing, rolling optimization, and feedback correction10 and model predictive control (MPC)11. In Refs.2,12, MPC has been successfully implemented in comparable photovoltaic system configurations. Utilizing LC models for MPC offers a significant benefit by presenting a comprehensive description of the converter’s dynamic behavior across all operational states, eliminating the need to explicitly list or assume a predetermined sequence of modes or switching times. In Ref.13. MPC is a powerful approach that effectively addresses the challenges posed by uncertain and nonlinear processes. It is capable of handling multiple constraints associated with spatial state variables14. By establishing a predictive model of the system, optimal control can be achieved by selecting the control mode that minimizes the evaluation function in Ref.15. Omitting zero switching states reduces CMV but increases output voltage pulsation, leading to higher harmonic distortion and potential performance issues. Balancing CMV reduction with the drawbacks of increased pulsation is essential, requiring optimized control strategies16,17.
The phenomenon of dead time in power electronic inverters leads to substantial spikes in common mode voltage (CMV) as a result of all switches being in the off state, thereby creating unplanned zero voltage vectors. Theoretically, these spikes can attain values up to ± udc/2. However, by carefully avoiding specific voltage vector switching combinations during the dead time, it is possible to confine the CMV within the range of ± udc/6. This approach not only mitigates the magnitude of the spikes but also decreases the total harmonic distortions (THDs) and current ripples18,19.
This paper investigates the performance of a power electronic converter using a (FCS-MPC) technique. Furthermore, the efficacy of the algorithm is evaluated by employing statistical model checking methodologies.
One of the unique characteristics of the conventional FCS-MPC is its ability to handle multiple goals20,21. As previously indicated22. Numerous endeavors have been made within scholarly publications to enhance switching sequences and enhance the cost function diminish output ripples23. However, for single-phase inverters, the limited availability of switching states presents a greater challenge from a technical and scientific standpoint24,25.
This paper presents a standard procedure for the widely used predictive control method. The technique eliminates the requirement for PWM blocks by using a set switching frequency for 3-phase voltage source inverters. Rather, the suggested approach chooses the best voltage states by taking the reference current’s slope into account alone. Using double-loop PI regulation, a popular control strategy for PWM inverters, as the foundation, this method entails creating a feed forward decoupling control scheme. The fundamental elements of this technique are the design of an outside loop with DC voltage, an inner loop with current, and PI regulators.
Figure 1 showcases system comprises a DC-AC inverter12. The load on the grid side is represented by R L e. The DC-AC inverter converts the augmented DC power into alternating current (AC) power, which is then transmitted to the grid via a filter; we examine a standard configuration for a grid-connected power system as depicted in Fig. 1, highlighting a DC–AC inverter’s critical role in bridging renewable energy sources with the electrical grid. The system under review converts DC power—potentially harvested from photovoltaic arrays, battery banks, or other DC-producing technologies—into AC power via the DC-AC inverter. The inverter, governed by (FCS-MPC), ensures the efficient transformation of power26,27.
The topological structure of the FCS-MPC Grid-connected PV power generating system.
The objective of this controller is to uphold a stable voltage on the DC link at a pre-established value (\({{v}_{dc}}^{*}\) ), thereby ensuring optimal system operation28,29. This is accomplished by controlling the process of creating a reference root mean square (RMS) value for the grid current (\({{I}_{d}}^{*}\) ) in order to transfer electricity from the photovoltaic (PV) side to the grid side, as shown in Fig. 2. Compared to the inverter controller, this controller runs at a narrower bandwidth to maintain system reliability.
DC Link’s voltage regulator.
The conversion of electrical energy from DC to AC is achieved through the power circuit of a three-phase inverter, employing the electrical arrangement depicted in Fig. 3. To prevent any potential short-circuit, the two switches in each phase of the inverter operate in a complementary manner, ensuring the proper functioning of the system30.
Voltage source inverter power circuit.
The DC source and the switching states of power switches S1 through S6 can be symbolized by the switching signals \({S}_{a}\) , \({S}_{b}\) , and \({S}_{c}\) , as specified in the following manner:
The load current dynamics for each phase can be expressed using the definitions of the variables in Fig. 10. For each phase can be formulated as follows.
where the load inductance, L, and load resistance, R, and e is the electromotive force (EMF) of the grid are represented.
With the use of historical and present states, a complex mathematical model of the system is used by Ts MPC technology to forecast the inverter current levels for the upcoming switching cycle. It finds the best switch combination to enable precise tracking of the current information by utilizing an optimal value function31,32. The following specifications are followed in the establishment of the value function for system optimization and the prediction model33. The algorithm flowchart for predictive current control is shown in Fig. 4.
The algorithm flowchart of predictive current control.
The MPCC (Model Predictive Control with Constraints) method relies heavily on the predictive model as its core building block. To acquire the forecasted value at the subsequent time step (k + 1).
The Forward-Euler method is a fundamental numerical technique used to approximate the derivative of a function within discrete-time modeling. It operates by applying straightforward arithmetic operations, making it an accessible and practical choice for various computational tasks.
Simplicity The Forward-Euler method is notably easy to implement. Its reliance on basic arithmetic operations simplifies its application, making it ideal for introductory learning in discrete-time modeling and for scenarios requiring quick computational solutions.
Computational efficiency The method demands minimal computational resources, enhancing its suitability for real-time applications where rapid estimations are critical.
Low memory requirements Due to its requirement of only the current and previous function values, the Forward-Euler method has low memory consumption compared to more complex numerical methods.
Accuracy The Forward-Euler method may exhibit lower accuracy, particularly in systems with rapid dynamics or nonlinear characteristics. Accumulation of approximation errors over time can lead to significant deviations from the actual solution.
Stability This method can become unstable when applied to stiff equations or when the step size (Ts) is not sufficiently small. In such scenarios, the Forward-Euler method may yield oscillatory or diverging solutions.
Error propagation The error in the Forward-Euler method is directly proportional to the step size (Ts). Achieving high accuracy often necessitates a very small step size, which can subsequently increase the computational burden and reduce efficiency34.
The discretization process utilizing the Forward Euler method involves estimating the derivative in the following manner:
Equation (4) is discretized using the Forward Euler method.
In an alternative technical and scientific formulation, using concise language while preserving the meaning, the symbol \(\widehat{e}\left(k\right)\) denotes the Estimated Counter-Femininity, where the superscript “p” signifies the predicted variables.
\(\widehat{e}\left(k-1\right)\) represent the estimated value of \(\left(k-1\right)\) . The current back excitation \(\left(k\right)\) , which is needed, can be approximated by extending previous values of the estimated back excitation. Alternatively, considering that the frequency of the back excitation is considerably lower than the sample rate, we will assume its negligible variation within a sample interval and accordingly establish its value.
Using the performance index of prediction error, the ideal value function minimizes the difference between the output current and the reference current at time k + 1 by selecting the most advantageous combination of switching variables from a range of prediction values. This value function is constructed based on the difference between the predicted current value and the reference current value at k + 1 time, as:
When a load with a given impedance is connected to a balanced three-phase power supply, the (CMV) is the voltage difference between the load’s neutral point and either the electrical ground or the midpoint of the DC link35. The ideal duration of the applied voltage vectors was ascertained using mathematical analyses, which also identified their order according to the definition of Total Harmonic Distortion (THD) in order to reduce its value36. The output voltage formulae for the inverter are provided by
Considering the symmetrical nature of the satirical three-phase voltage system:
From Eq. (16), we can deduce the following results.
where \({\text{v}}_{\text{AO}}\) , \({\text{v}}_{\text{BO}}\) and \({\text{v}}_{\text{CO}}\) are the voltages of the three phases. This average voltage represents the component of the phase voltages that is common to all phases, thereby quantifying the CMV.
A two-level, three-phase voltage source inverter (VSI) is part of the power converter design that is frequently utilized in variable speed drives is shown in Fig. 5. The VSI is fixed at Vdc/2, which represents half the voltage of the DC-link. This configuration enables the VSI to supply power to a star-connected grid.
Three-phase, two-level voltage source inverter with a load attached to a star.
The grid is characterized by phase impedances \({\text{Z}}_{\text{AN}}\) , \({\text{Z}}_{B\text{N}}\) , and \({\text{Z}}_{\text{CN}}\) , which represent the electrical properties of each phase. Additionally, there is a capacitive neutral-to-ground parasitic impedance (\({\text{Z}}_{\text{N}0}\) ), representing the parasitic capacitance between the grid’s neutral point and the ground37.
In accordance with the given information, the offset vector, which is the closest switching state to the zero (CMV) plane of the reference vector, is determined using vectors (Fig. 6). There are three possible selected vectors, each with their respective CMV values:
The CMV of the first vector is equal to − Vdc/6.
The CMV of the second vector is equal to + Vdc/6.
The CMV of the third vector is equal to zero.
Space voltage vector distribution of a two-level inverter.
The reference vector designates a two-level sub cube, whose vertices are also represented by these vectors. Vectors (8) are used to derive the other coordinates. These three coordinates add up to seven vectors; the other vertices are on the other side of the hexagon38, depending on which way the voltage change is going, the CMV changes by either Vdc/6 or − Vdc/6 when a voltage transition happens in each phase. Three of the four remaining vectors will have CMVs of − Vdc/6 or Vdc/6. It is crucial to disregard this vector in order to guarantee that the CMV stays within the designated range of ± Vdc/639.
Omitting the zero voltage vectors (V0, V7) from the switching sequence can mitigate (CMV) in power electronic systems. This omission precludes the concurrent activation or deactivation of the three high-side switches40, thereby eliminating their contribution to potential differences across the system’s input terminals and consequently reducing the CMV. Utilizing solely the non-zero voltage vectors (V1, V2, V3, V4, V5, V6) further aids in diminishing CMV by preventing the simultaneous switching of high-side switches. This approach directly contributes to a reduction in CMV. However, it’s crucial to acknowledge that depending exclusively on non-zero voltage vectors might not universally constitute the most effective or suitable strategy across different systems and applications. Alternative strategies, such as employing differential amplifiers or implementing shielding and grounding techniques, might be indispensable for the effective attenuation of CMV41. The technique suggested in Ref.42, which involves the selective exclusion of specific non-adjacent and non-opposing voltage vectors (V0, V7) during the switching cycle, has demonstrated efficacy in addressing the CMV challenge. Nonetheless, the issue of CMV persists, exacerbated by the dead time effect the interval between deactivating one switch and activating another. Recent advancements propose the pre-selection of (V1, V2, V3, V4, V5, V6) for application during the switching process via algorithmic approaches, significantly ameliorating both CMV and (THD) while introducing greater complexity compared to earlier methods43.
The algorithmic pre-selection of non-zero voltage vectors (V1, V2, V3, V4, V5, V6) necessitates a meticulous evaluation of various parameters, including switching frequency and load characteristics. Despite the increased complexity, this method offers the potential for superior performance in advanced electronic power systems, representing a promising avenue for enhancing system efficacy.
Implementation of (MPC) for minimizing (CMV) in power converter systems, alongside addressing additional control objectives, facilitates precise regulation of the converter system with reliability and efficiency. Through MPC, precise control actions are computed based on a predictive model, enabling real-time adjustments to minimize CMV while meeting desired performance criteria. This methodology ensures optimal operation of the power converter system by dynamically adapting control actions to changing operating conditions and disturbances, thereby enhancing overall system performance and reliability.
Figure 7 depicts the performance analysis of a 3-phase inverter utilizing the conventional Model Predictive Control (FCS-MPCC) technique. In Fig. 7a, the plot showcases the three-phase currents, which exhibit sinusoidal characteristics. The frequency alterations from 50 to 100 Hz, illustrated in the simulations (Fig. 7a), underscore the robustness of the proposed method. Empirical evidence from these simulations demonstrates that the method reliably sustains superior performance across the specified frequency spectrum. This adaptability to varying frequencies is critical for applications characterized by fluctuating operating conditions. Consequently, the proposed method’s practical utility and effectiveness in real-world scenarios are further validated. Figure 7b provides insight into (THD) of the currents, measuring approximately 0.26% and 0.35%. Additionally, Fig. 7c illustrates the Common Mode Voltage (CMV) of the 3-phase inverter.
Operation of the 3-phase inverter with conventional (FCS-MPCC). (a) Three-phase currents. (b) THD. (c) CMV.
Figure 8 illustrates the performance analysis of a three-phase inverter employing (CMV) technique. In Fig. 8a, the plot represents the three-phase currents, which exhibit sinusoidal properties, indicative of their expected behavior. Figure 8b presents the measurement of (THD) of the currents, yielding a value of approximately 0.35%. Furthermore, Fig. 8c demonstrates (CMV) of the three-phase inverter.
Operation of a three-phase inverter with (CMV) reduction. (a) Three-phase currents. (b) THD. (c) CMV.
The comparative analysis of CMV values achieved through different strategies, as depicted in Figs. 7c and 8c, highlights the efficacy of the proposed methods. Figure 7c demonstrates the CMV simulation using the FCS-MPCC method, which incorporates zero vectors, resulting in an inverter CMV peaking at Vdc/2. This elevated CMV contributes to additional power losses and poses a risk to the network’s safe and stable operation. Conversely,
Figure 8c showcases the CMV outcomes when employing the proposed MPC with CMV strategies. Here, the CMV is markedly diminished to Vdc/6, the lowest achievable value. This significant reduction underscores the effectiveness of the proposed strategy in mitigating CMV impacts, thereby enhancing performance and ensuring greater system stability compared to conventional methods.
When comparing the performance analysis results between the three-phase inverter utilizing the conventional (MPC) technique (Fig. 7) and the one employing (CMV) technique (Fig. 8), several differences can be observed.
In terms of the three-phase currents represented in Figs. 7a and 8a, both plots exhibit sinusoidal characteristics, indicating that the currents in both cases follow the expected behavior.
However, when examining (THD) of the currents, Fig. 7b shows a THD measurement of approximately 0.26% for the MPC-based inverter, while Fig. 8b presents a THD value of approximately 0.35% for the CMV-based inverter. This suggests that the CMV technique yields a lower THD value, indicating reduced harmonic content in the currents compared to the MPC technique.
The proposed method utilizing (MPC with CMV) technique for a three-phase inverter demonstrates superior performance compared to other state-of-the-art predictive control techniques. Empirical evidence shows that the proposed method maintains sinusoidal three-phase currents with robustness across a frequency range of 50 to 100 Hz, highlighting its adaptability to fluctuating operating conditions. Additionally, both methods achieve fully consistent distortion values, with approximately 0.26% for the (FCS-MPC) and 0.35% for the (MPC with CMV) technique. This convergence in lower distortion values indicates that more effectively minimizes harmonic content, resulting in cleaner power output. These advantages validate the practical utility and effectiveness of the proposed method in real-world applications.
The provided Fig. 9 compares the computational burden of two methods, MPC with CMV and FCS-MPCC, across four different sampling times (Ts1, Ts2, Ts3, and Ts4) for the predictive algorithm. Quantitative analysis reveals that the MPC with CMV method consistently exhibits a lower computational burden compared to the FCS-MPCC method. For instance, during Ts2, the computational burden for MPC with CMV is approximately 2e−7, whereas for FCS-MPCC, it reaches around 3e−7, indicating a significant reduction of 1e−7. Similar reductions are observed in other sampling times, such as Ts3 and Ts4, where MPC with CMV consistently outperforms FCS-MPCC. These results demonstrate that the MPC with CMV method offers a substantial reduction in computational burden, thereby enhancing computational efficiency and making it a more optimal choice for applications requiring lower computational resources in predictive algorithms.
Comparison of computational burden for MPC with CMV and FCS-MPCC methods across different sampling times (Ts) in predictive algorithms.
This section provides an empirical comparison of two suggested (MPC) approaches, FCS-MPCC and MPC with (CMV), and conventional methods applied to a three-phase, two-level inverter. A two-level inverter that is driven by a DC power supply powers an R-L load in the experimental arrangement. Six SiC MOSFETs are used in the building of the inverter. The MicroLabBox board, which is built around the TMS320F240 digital signal processor (DSP) and houses the rival MPC controllers, facilitates control implementation. Six digital outputs on the MicroLabBox provide the control signals to the semiconductor switching devices. The WT500 power analyzer (YOKOGAWA) is used for (THD) measurement and harmonic spectrum visualization. Three transducers, LEM LA-25P, keep an eye on the inverter the three analog-to-digital converter (ADC) inputs on the MicroLabBox are used to sample the sensor signals and the inverter’s sensors signals are sampled by three analog-to-digital converter (ADC) inputs on the MicroLabBox, and the output phase currents of the inverter are monitored. Figure 10 shows the whole experimental configuration including its measurement facilities. Table 1 lists all of the important experimental parameters. Every competing MPC algorithm is put through two tests to evaluate its dynamic and steady-state performance.
The performance evaluation setup is experimental.
To examine the ephemeral characteristics of two distinct control methodologies, namely, Predictive Current Control utilizing (FCS-MPCC) and (MPC) augmented with (CMV) mitigation techniques, a comprehensive investigation was undertaken. This exploration involved subjecting both the traditional and innovatively proposed algorithms to a variety of testing conditions. The examination of output current waveforms is detailed in Figs. 11 and 12. Specifically, Fig. 11a depicts the current outcomes (Ia, Ib) derived from the application of the FCS-MPCC approach, whereas Fig. 11b presents the results for the MPC with CMV methodology in terms of stream current (Ia, Ib). These findings corroborate the reliability and effectiveness of the methods put forward. In Fig. 11a,b, it is evident that the implementation of the conventional FCS-MPC strategy leads to an increment in the CMV up to the range of ± Vdc/2. Conversely, the novel approach employing four MPC with a CMV strategy significantly curtails the CMV to within ± Vdc/6, demonstrating a substantial improvement in mitigating common mode voltage fluctuations. Despite the deliberate avoidance of zero vectors (V0 and V7), the presence of CMV spikes approximating ± Vdc/2 in Fig. 11a is noteworthy, attributable to the oversight of dead time effects. Nonetheless, the newly proposed approach not only diminishes but also confines the CMV to a mere ± Vdc/6. In pursuit of understanding the transient behavior inherent to both the conventional FCS-MPC and the MPC with CMV strategies, an analytical review under various test conditions was conducted. Figure 12 showcases the output current waveforms (Ia) of the 2L-VSI, underscoring that the proposed predictive control strategies, particularly the MPC with CMV, exhibit superior dynamic performance across diverse conditions, including scenarios where the reference current undergoes abrupt changes. Furthermore, these predictive controllers demonstrate commendable dynamic performance, characterized by swift response times without detriment to the system’s stability. This comprehensive analysis not only validates the efficacy of the proposed methods but also highlights their potential in enhancing the operational efficiency and reliability of control systems subjected to variable conditions.
Experimental results of steady-state performance (50 Hz). (a) The conventional FCS-MPC; (b) MPC with CMV strategy.
Experimental results of dynamic performance with the current stepped from 2 to 3 A. (a) The conventional FCS-MPC; (b) MPC with CMV strategy.
Figure 13 presents the experimental results of percentage harmonic distortion (THD) in the current for two control strategies: (a) the conventional (FCS-MPC) and (b) model predictive control (MPC) with Common Mode Voltage (CMV) strategy. The comparison highlights the performance differences between the traditional FCS-MPC approach and the enhanced MPC that incorporates CMV considerations. The conventional FCS-MPC method is shown in Fig. 13a, which illustrates the level of THD in the current without any specific mitigation for common mode voltage issues. In contrast, Fig. 13b demonstrates the effectiveness of the CMV strategy in reducing THD, showcasing the improvements achieved by addressing common mode voltage, thereby enhancing the overall quality and performance of the current control system.
Experimental results of percentage harmonic distortion (THD) in the current. (a) The conventional FCS-MPC; (b) MPC with CMV strategy. (a) The conventional FCS-MPC; (b) MPC with CMV strategy.
The predictive controllers, FCS-MPCC and MPC with CMV, display similar dynamic behavior, characterized by rapid responses without sacrificing control order. The percentage of demonstrates the percentage harmonic distortion (THD) in the proposed case is quantified at approximately 2.2%, while in the traditional case, it stands at around 2.1%. This subtle difference underscores the efficiency of the proposed methods, showcasing their ability to consistently achieve high-quality synchronous output streams.
This research pioneers an advanced (FCS-MPCC) for three-phase grid-connected inverters, targeting (CMV) suppression. CMV, notorious for causing leakage currents, electromagnetic interference, and system degradation, necessitates efficient mitigation. The proposed strategy leverages a predictive model for optimal switching state selection, aiming for precise current control and CMV reduction. A balanced cost function evaluates switching states, harmonizing current tracking accuracy with CMV minimization. Rooted in a comprehensive mathematical model, this approach ensures real-time optimal control decisions. Experimental validation confirms the scheme’s efficacy in enhancing inverter performance and durability, mitigating CMV’s adverse effects, and ensuring system longevity and reliability. This work significantly advances power electronics, offering robust solutions for integrating renewable energy sources into the electrical grid, marking a substantial stride in the sustainable and efficient management of power systems, Experimental validation confirms the scheme’s efficacy in enhancing inverter performance and durability, mitigating CMV adverse effects, and ensuring system longevity and reliability. Specifically, the proposed (MPC with CMV) method significantly reduced CMV, improved current tracking accuracy, and extended system lifespan compared to traditional methods.
The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.
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Laboratory of Unite Renewable Energy Development Eloued, Department of Mechanical Engineering, University of El Oued, 39000, El Oued, Algeria
Ali Bebboukha, Redha Meneceur & Labiod Chouaib
Power Electronics and Renewable Energy Research Laboratory (PEARL), Department of Electrical Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia
Mohammad Anas Anees & Saad Mekhilef
Department of Electrical Engineering, Aligarh Muslim University, Aligarh, India
Department of Electrical Engineering, Port Said University, Port Said, 42526, Egypt
School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Melbourne, Australia
Electrical Engineering Department, Faculty of Engineering, Aswan University, Aswan, 81542, Egypt
Chair of High-Power Converter Systems (HLU), Technical University of Munich (TUM), Munich, Germany
Department of Theoretical Electrical Engineering and Diagnostics of Electrical Equipment, Institute of Electrodynamics, National Academy of Sciences of Ukraine, Peremogy, 56, Kyiv-57, 03680, Ukraine
Center for Information-Analytical and Technical Support of Nuclear Power Facilities Monitoring of the National Academy of Sciences of Ukraine, Akademika Palladina Avenue, 34-A, Kyiv, Ukraine
Department of Electrical Engineering, Graphic Era (Deemed to be University), Dehradun, 248002, India
Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, Jordan
Graphic Era Hill University, Dehradun, 248002, India
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Ali Bebboukha, Redha Meneceur, Labiod Chouaib, Mohammad Anas Anees: Conceptualization, Methodology, Software, Visualization, Investigation, Writing- Original draft preparation. Ahmed Elsanabary, Saad Mekhilef, Abualkasim Bakeer: Data curation, Validation, Supervision, Resources, Writing—Review & Editing. Ibrahim Harbi, Mohit Bajaj, Ievgen Zaitsev: Project administration, Supervision, Resources, Writing—Review & Editing.
Correspondence to Ievgen Zaitsev or Mohit Bajaj.
The authors declare no competing interests.
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Bebboukha, A., Meneceur, R., Chouaib, L. et al. Finite control set model predictive current control for three phase grid connected inverter with common mode voltage suppression. Sci Rep 14, 19832 (2024). https://doi.org/10.1038/s41598-024-71051-9
DOI: https://doi.org/10.1038/s41598-024-71051-9
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