This paper proposes a finite control set model predictive control (FCS-MPC) with a reduced computational burden for a single-phase grid-connected modified packed U-cell multilevel inverter (MPUC-MLI) with two control objectives: reference current tracking and switching frequency minimization. The considered competitive topology consists of two units with six active switches and two DC sources in each unit, allowing the generation of 49 levels in the output voltage, which is considered a significant reduction in the active and passive components compared to the conventional and recently developed topologies of multilevel inverters (MLIs). This topology has 49 different switching states, which means that 49 predictions of the future current and 49 calculations of the cost function are required for each evaluation of the conventional FCS-MPC. Accordingly, the computational load is heavy. Thus, this paper presents two reduced-complexity FCS-MPC methods to reduce the calculation burden. The first technique reduces the computational load almost to half by computing the reference voltage and dividing the states of the MLI into two sets. Based on the reference voltage polarity, one set is defined and evaluated to specify the optimal state, which has a minimal cost function. However, in the second proposed method, only three states of the 49 states are evaluated each iteration, achieving a significant reduction in the execution time and superior control performance compared to the conventional FCS-MPC. A mathematical analysis is conducted based on the reference voltage value to locate the three vectors under evaluation. In the second part of the paper, the sensitivity to parameter variations for the proposed simplified FCS-MPC is investigated and tackled by employing an extended Kalman filter (EKF). In addition, noise related to variable measurement is filtered in the proposed system with the EKF. The simulation investigation was performed using MATLAB/Simulink to validate the system under different operating conditions.

This paper proposes a finite control set model predictive control (FCS-MPC) with a reduced computational burden for a single-phase grid-connected modified packed U-cell multilevel inverter (MPUC-MLI) with two control objectives: reference current tracking and switching frequency minimization. The considered competitive topology consists of two units with six active switches and two DC sources in each unit, allowing the generation of 49 levels in the output voltage, which is considered a significant reduction in the active and passive components compared to the conventional and recently developed topologies of multilevel inverters (MLIs). This topology has 49 different switching states, which means that 49 predictions of the future current and 49 calculations of the cost function are required for each evaluation of the conventional FCS-MPC. Accordingly, the computational load is heavy. Thus, this paper presents two reduced-complexity FCS-MPC methods to reduce the calculation burden. The first technique reduces the computational load almost to half by computing the reference voltage and dividing the states of the MLI into two sets. Based on the reference voltage polarity, one set is defined and evaluated to specify the optimal state, which has a minimal cost function. However, in the second proposed method, only three states of the 49 states are evaluated each iteration, achieving a significant reduction in the execution time and superior control performance compared to the conventional FCS-MPC. A mathematical analysis is conducted based on the reference voltage value to locate the three vectors under evaluation. In the second part of the paper, the sensitivity to parameter variations for the proposed simplified FCS-MPC is investigated and tackled by employing an extended Kalman filter (EKF). In addition, noise related to variable measurement is filtered in the proposed system with the EKF. The simulation investigation was performed using MATLAB/Simulink to validate the system under different operating conditions.

Modeling solar photovoltaic (PV) cells/modules to estimate its parameters with the measured current-voltage (I–V ) values is a very important issue for the control, optimization, and effectiveness of PV systems. Therefore, in this research work, a robust approach based on Stochastic Fractal Search (SFS) optimization algorithm is introduced to estimate accurate and reliable values of solar PV parameters for its precise modeling. To assess the excellence of the proposed SFS algorithm, different solar PV equivalent circuit models, i.e. single-diode model (SDM), double-diode model (DDM), and PV module model are taken into consideration. The introduced algorithm is examined under three different case studies; (i) first case study: an experimental standard dataset of a commercial R.T.C. France silicon solar cell working at 33 ◦C, and solar radiance of 1000 W/m2; (ii) second case study: using a polycrystalline solar panel STP6 120/36 with 36 cells in series working at 22 ◦C, and (iii) third case study: an experimental dataset of ESP-160 PPW PV module working at 45 ◦C, this experimentation was carried out in the Laboratory of Renewable Energy at Assiut University, Egypt. The results obtained using the proposed method are compared with other recently published works, and hence, the achieved results show the superiority, perfectness, and effective modeling concerning various performance parameters. Thereby, the proposed SFS approach can be used for effective PV modeling to improve the efficiency of the PV system.

Fault location on one of two parallel lines is determined as a percentage of line length in the presence and absence of the mutual effect between lines. The methodology for fault location is based on the principle that at the fault E f1 +E f2 +E f0 = 0. This relation is utilized to determine the sequence voltages at the relay position, where the impedance to the fault location can be assessed. This makes it possible to devise new accurate algorithms for fault location, whatever the lines are fed from one end or both ends. The location of single line-to-ground (SLG) and double line-to-ground (DLG) faults at different points along the line is investigated. With the determination of the fault location, the impedance seen by the distance relay is defined for SLG and DLG faults. This impedance determines where the fault is within the reach of the relay.

New accurate algorithms based on mathematical modeling of two parallel transmissions lines system (TPTLS) as influenced by the mutual effect to determine the fault location is discussed in this work. The distance relay measures the impedance to the fault location which is the positive-sequence. The principle of summation of the positive-, negative-, and zero-sequence voltages which equal zero is used to determine the fault location on the TPTLS. Also, the impedance of the transmission line to the fault location is determined. These algorithms are applied to single-line-to-ground (SLG) and double-line-to-ground (DLG) faults. To detect the fault location along the transmission line, its impedance as seen by the distance relay is determined to indicate if the fault is within the relay’s reach area. TPTLS under study is fed from one- and both-ends. Schematic diagrams are obtained for the impedance relays to determine the fault location with high accuracy.

Channel-based microfluidics was proven to be a helpful platform for reproducible preparation of nanoparticles (NPs), where controlled mixing of fluids allows homogeneous and tuned process of NPs formation. Nanoprecipitation is a popular method for polymeric NPs formation based on controlled precipitation of a polymer upon mixing of two miscible solvents. Conventionally, flow rate, flow rate ratio and polymer concentration have been utilized to control NPs size and polydispersity. However, minimum attention has been given to the effect of channel geometry on nanoprecipitation process. In our study, we investigated the effect of channel geometry and design on the size and polydispersity index (PDI) of poly (lactic-co-glycolic) acid (PLGA) NPs. Ten different designs with varied channel length, aspect ratio, number of interfaces and channel curvature were fabricated and tested. These variations were introduced to modify the diffusion rate, the interface area or to introduce Dean flow, all of which will change the mixing time . The effects of these variations were compared to that of different flow parameters. Change in channel length did not have a significant effect on particle size. However, increasing the diffusion area and reducing significantly reduced NPs’ size. Moreover, when curvature was introduced into the channel, mixing was enhanced, and particle size was decreased in a manner dependent on the velocity of the generated Dean flow. While different flow parameters continue to be the main approach for adjusting NPs properties, we demonstrate that channel geometry modification enables tuning of NPs’ size using simple designs that can be easily adapted.

Channel-based microfluidics was proven to be a helpful platform for reproducible preparation of nanoparticles (NPs), where controlled mixing of fluids allows homogeneous and tuned process of NPs formation. Nanoprecipitation is a popular method for polymeric NPs formation based on controlled precipitation of a polymer upon mixing of two miscible solvents. Conventionally, flow rate, flow rate ratio and polymer concentration have been utilized to control NPs size and polydispersity. However, minimum attention has been given to the effect of channel geometry on nanoprecipitation process. In our study, we investigated the effect of channel geometry and design on the size and polydispersity index (PDI) of poly (lactic-co-glycolic) acid (PLGA) NPs. Ten different designs with varied channel length, aspect ratio, number of interfaces and channel curvature were fabricated and tested. These variations were introduced to modify the diffusion rate, the interface area or to introduce Dean flow, all of which will change the mixing time . The effects of these variations were compared to that of different flow parameters. Change in channel length did not have a significant effect on particle size. However, increasing the diffusion area and reducing significantly reduced NPs’ size. Moreover, when curvature was introduced into the channel, mixing was enhanced, and particle size was decreased in a manner dependent on the velocity of the generated Dean flow. While different flow parameters continue to be the main approach for adjusting NPs properties, we demonstrate that channel geometry modification enables tuning of NPs’ size using simple designs that can be easily adapted.

Channel-based microfluidics was proven to be a helpful platform for reproducible preparation of nanoparticles (NPs), where controlled mixing of fluids allows homogeneous and tuned process of NPs formation. Nanoprecipitation is a popular method for polymeric NPs formation based on controlled precipitation of a polymer upon mixing of two miscible solvents. Conventionally, flow rate, flow rate ratio and polymer concentration have been utilized to control NPs size and polydispersity. However, minimum attention has been given to the effect of channel geometry on nanoprecipitation process. In our study, we investigated the effect of channel geometry and design on the size and polydispersity index (PDI) of poly (lactic-co-glycolic) acid (PLGA) NPs. Ten different designs with varied channel length, aspect ratio, number of interfaces and channel curvature were fabricated and tested. These variations were introduced to modify the diffusion rate, the interface area or to introduce Dean flow, all of which will change the mixing time . The effects of these variations were compared to that of different flow parameters. Change in channel length did not have a significant effect on particle size. However, increasing the diffusion area and reducing significantly reduced NPs’ size. Moreover, when curvature was introduced into the channel, mixing was enhanced, and particle size was decreased in a manner dependent on the velocity of the generated Dean flow. While different flow parameters continue to be the main approach for adjusting NPs properties, we demonstrate that channel geometry modification enables tuning of NPs’ size using simple designs that can be easily adapted.

Channel-based microfluidics was proven to be a helpful platform for reproducible preparation of nanoparticles (NPs), where controlled mixing of fluids allows homogeneous and tuned process of NPs formation. Nanoprecipitation is a popular method for polymeric NPs formation based on controlled precipitation of a polymer upon mixing of two miscible solvents. Conventionally, flow rate, flow rate ratio and polymer concentration have been utilized to control NPs size and polydispersity. However, minimum attention has been given to the effect of channel geometry on nanoprecipitation process. In our study, we investigated the effect of channel geometry and design on the size and polydispersity index (PDI) of poly (lactic-co-glycolic) acid (PLGA) NPs. Ten different designs with varied channel length, aspect ratio, number of interfaces and channel curvature were fabricated and tested. These variations were introduced to modify the diffusion rate, the interface area or to introduce Dean flow, all of which will change the mixing time . The effects of these variations were compared to that of different flow parameters. Change in channel length did not have a significant effect on particle size. However, increasing the diffusion area and reducing significantly reduced NPs’ size. Moreover, when curvature was introduced into the channel, mixing was enhanced, and particle size was decreased in a manner dependent on the velocity of the generated Dean flow. While different flow parameters continue to be the main approach for adjusting NPs properties, we demonstrate that channel geometry modification enables tuning of NPs’ size using simple designs that can be easily adapted.

Channel-based microfluidics was proven to be a helpful platform for reproducible preparation of nanoparticles (NPs), where controlled mixing of fluids allows homogeneous and tuned process of NPs formation. Nanoprecipitation is a popular method for polymeric NPs formation based on controlled precipitation of a polymer upon mixing of two miscible solvents. Conventionally, flow rate, flow rate ratio and polymer concentration have been utilized to control NPs size and polydispersity. However, minimum attention has been given to the effect of channel geometry on nanoprecipitation process. In our study, we investigated the effect of channel geometry and design on the size and polydispersity index (PDI) of poly (lactic-co-glycolic) acid (PLGA) NPs. Ten different designs with varied channel length, aspect ratio, number of interfaces and channel curvature were fabricated and tested. These variations were introduced to modify the diffusion rate, the interface area or to introduce Dean flow, all of which will change the mixing time . The effects of these variations were compared to that of different flow parameters. Change in channel length did not have a significant effect on particle size. However, increasing the diffusion area and reducing significantly reduced NPs’ size. Moreover, when curvature was introduced into the channel, mixing was enhanced, and particle size was decreased in a manner dependent on the velocity of the generated Dean flow. While different flow parameters continue to be the main approach for adjusting NPs properties, we demonstrate that channel geometry modification enables tuning of NPs’ size using simple designs that can be easily adapted.